Podcasts about managed

A chatty classroom is not necessarily out of control, and a quiet classroom is not automatically well managed. In this episode of Beginning Teacher Talk, Dr. Lori explains why quiet is not the same as managed, and how to create a classroom where students understand when talking supports learning, when quiet protects learning, and how to move successfully between the two. What You'll Learn: • Why a quiet classroom is not always a well-managed classroom • How to match the sound, movement, and participation to each activity • Why clear boundaries and expectations support a shared learning space • How to teach a chatty class when talking is invited and when it is time to listen • How fresh attention-getters can strengthen predictable classroom routines Resources Mentioned in This Episode: • Learn more about Dr. Lori's programs You do not need to silence your students all day to have a well-managed classroom. Listen to this episode to learn how purposeful talk, clear boundaries, and predictable attention routines can help you create a classroom where students use their voices responsibly and learning remains the focus, then follow and subscribe to Beginning Teacher Talk for more practical classroom management support. Need to regain control of your classroom? Save your seat at Dr. Lori's new, free class: Save your seat at the tropical island virtual teacher retreat, where we'll talk about how to prevent challenging behaviors from derailing your entire day, even if you feel like you've already tried everything, you don't have admin support, and parents don't seem to care. Click here to save your seat! https://www.drlorifriesen.com/need-this Stay connected with us! Follow us on Instagram @beginningteachertalk Looking for quick, actionable PD? Visit our YouTube channel! Be sure to follow, rate, and subscribe to the Beginning Teacher Talk podcast so you never miss an episode. Warmly, Dr. Lori

Stop Fighting AI. You Already Lost That Battle. Every major technology in human history has moved in one direction: forward. AI is no different. You can resist it, wait for someone to "regulate" it, or complain about it, but the wave is coming either way. You can get buried by it, or you can get on your surfboard and ride it. In this episode of the StopDoingNothing show, Patrick Allmond (25+ years in digital marketing, AI keynote speaker, and founder of StopDoingNothing Media) breaks down why fighting AI is a waste of time, where AI should never be making decisions, and the exact tools and maturity levels business owners should be using to get ahead.  In this episode: → Why success isn't what you should be chasing (and what to chase instead)  → The AI "self-regulation" executive order, and why it's a joke → Grok the caveman and why humans never go backwards on progress → The I, Robot problem: where AI should NEVER make the call → How my AI agents work overnight while I sleep → Going from idea to reality with AI, including AI-to-3D-printer  → Will AI kill creativity? A take for artists and musicians  → My 2.5 AI tools: ClickUp, HighLevel, and Claude → The 4 Levels of AI Maturity, and why most people are stuck at Level 1 → Why ClickUp is my second brain CHAPTERS  0:00 Intro: Dismissed from jury duty  1:50 Stop chasing success, find your mission  3:00 AI is taking over my world  4:10 The AI executive order and the "self-regulation" joke 5:05 Fighting progress is useless 6:00 Grok the caveman: humans never go backwards  6:55 New media vs old media 7:30 Why AI-savvy pros will run you over  8:55 Where AI should never decide: the I, Robot problem 10:40 My AI agents work while I sleep 11:15 From idea to reality with AI  12:25 How I repurpose this show with AI  13:15 Talk to AI, print it in 3D  14:55 Will AI kill creativity?  15:35 Not all "AI" is the same thing 16:40 AI isn't new, it's an evolution  17:35 My #1 AI tool: ClickUp  19:10 Tool #2: HighLevel business OS 20:00 Tool #2.5: Claude and the 4 Levels of AI Maturity  22:05 Level 3: Managed agents 22:25 Level 4: Proactive agent systems + knowledge base  23:40 ClickUp is my second brain 24:55 Surf the AI wave or get buried  25:35 Work with us, speaking and training 26:55 The I-35 tour and what's next Want help building AI systems, proactive agents, or a ClickUp/HighLevel setup for your business? StopDoingNothing Media is a verified, licensed ClickUp reseller, and we build this stuff every day.  Need an AI speaker or trainer for your team or event? Reach out.

Send us Fan MailWe break down what fault managed power systems are and why Class 4 “digital electricity” is becoming a real requirement for cabling pros, not a niche curiosity. With Ronna Davis from VoltServer and the FMP Alliance, we connect the code, the cabling, and the jobsite reality so techs and PMs can prepare instead of getting left behind. • fault managed power defined through NEC 2023 and UL 1400-1 and UL 1400-2 safety standards • why “limited energy” replaces “low voltage” as voltages rise but fault energy stays controlled • what actually runs over the pair and why FMPS data is circuit control, not network traffic • common cable gauges and what changes as FMPS moves toward data centers • leading applications like small cells, DAS, Wi‑Fi, security, powering IDFs and network enclosures • addressing fear about scope and focusing on labor shortages and getting more trained hands onsite • what changes in NEC 2026 and the push toward clearer code organization in future cycles • design best practices starting with load, power over distance, and centralized power planning • the transmitter cabling receiver model and why central UPS strategies can cut complexity • where to get trained through NECA, BICSI, the FMP Alliance, and vendors • why AI data centers care about power density, footprint, modular scaling, and rapid deployment • how to follow and join the FMP Alliance and where to learn more from VoltServer If you're watching this show on YouTube, would you mind hitting the subscribe button and the bell button to be notified when new content is being produced? If you're listening to us on one of the audio podcast platforms, would you mind leaving us a five-star rating? Wednesday night, 6 p.m. Eastern Standard Time, what are you doing? You should join us at TextGiving This Year. Email Chuck at advertising at letstalkcabling.com and let's connect your brand to the right audience today. When you click on that QR code right there, you can buy me a cup of coffee.Support the showKnowledge is power!  Visit our webpage to find out ways you can support the show.  Also if you would like to be a guest on the show, please complete the application.Chuck Bowser RCDD TECH#CBRCDD #RCDD

Luke Carignan runs this one solo and flips the format. No crisis, no warnings. Three healthcare workforce stories that went right, and three things healthcare HR can act on because of them.

Title: Episode 89: Can Managed Forests Help Canada Meet Its 30x30 Conservation Goal? Author(s): Worm, Loa Dalgaard Description: Conservation is often associated with national parks and protected areas. But what about places that are delivering benefits for biodiversity while being managed for other purposes? In this episode of Forest for the Future, we return to Canada to explore Other Effective Area-Based Conservation Measures, or OECMs, and their potential contribution to the country's 30x30 conservation goal. Host Loa Dalgaard Worm is joined by conservation expert Alison Woodley, Megan Lafferty, Director of Area-based Conservation at Nature Conservancy of Canada and Co-Chair of the IUCN World Commission on Protected Areas' OECM Specialist Group, and Vivian Peachey, Director of Climate and Landscape Solutions at FSC Canada. Together, they unpack what OECMs are, how they differ from protected areas, and where FSC-certified forests could play a role. Drawing on a project involving FSC Canada, Ontario Nature and Environment and Climate Change Canada, the conversation explores the opportunities within conservation area networks in certified forests. It also examines the practical challenges of competing land uses, shared responsibilities and ensuring that conservation lasts beyond a forest management licence. The guests explain why FSC certification alone does not establish OECM recognition, and why Indigenous rights, leadership and free, prior and informed consent are foundational to this work. The episode also looks at FSC's Verified Impact tool and its potential to connect measurable conservation outcomes with financial support for forest managers and their conservation partners, including Indigenous communities. Throughout the conversation, the focus is on what happens beyond the numbers: the quality of conservation, the people involved, and the arrangements needed to sustain biodiversity over time.

Funds liquidated some long positions after the trade summit with China failed to bring about grain specific headlines. Harvest progress is expected to lag because of recent wet weather and a soggy forecast for this week. Managed money continues to lead the grains headed into the Quarterly Stocks report later this week. Mike Castle with Stone X Financial recaps today's trade.

From the earliest days of open-weight models to becoming the neutral routing layer for more than 10 million developers, OpenRouter is one of the clearest bets that the future of AI will be multi-model. In this episode, OpenRouter co-founder & CEO Alex Atallah, with AMP's Anjney Midha returning with swyx to unpack how OpenRouter emerged from the first wave of Llama, Alpaca, Mistral, and Midjourney, why model diversity mattered before it was consensus, and how a company dismissed as “just a wrapper” became critical infrastructure for the AI ecosystem.We go deep on the product and distribution lessons behind OpenRouter: why model labs can spend billions training a checkpoint and still struggle to get it into developers' hands, how Mistral helped prove the value of a competitive inference marketplace, why OpenRouter chose focus over expanding into fine-tuning, memory, and other adjacent products, and how its rankings became a real-time map of how AI usage was changing. Alex also explains OpenRouter's early experiments with model fusion, why they deleted the first version and brought it back years later, and how the platform grew to more than 10 trillion tokens per day.Finally, Anjney explains why Stripe and OpenRouter fit together, why token fraud may become one of the defining security problems of the AI economy, and why the next wave of fraud won't just come from humans but from autonomous agents attacking increasingly valuable token flows.We discuss:* Why OpenRouter bet early that no single AI model would win everything* Alpaca, Llama, and open models becoming impossible to ignore* Why Discord's early AI deployments exposed the limitations of closed models* Why model labs can spend billions on training and still fail at distribution* How OpenRouter became a neutral distribution layer for model developers* Why VCs dismissed OpenRouter as “just a marketplace” or “just a wrapper”* The Mistral price war and the first real proof of an inference marketplace* How Midjourney scaled through Discord and what it taught the AI ecosystem* Why crypto infrastructure became a dress rehearsal for generative AI* OpenRouter vs. LM Arena and why their missions are fundamentally different* Why focus became one of OpenRouter's biggest strategic advantages* Anthropic's early focus on AI pair programming and coding* The OpenRouter products that were prototyped but never launched* MOM, OpenRouter's early Mixture of Models experiment* Why model fusion failed in 2024 — and why it works much better now* How OpenRouter's leaderboard became a live map of the AI industry* OpenClaw, auto-routing, and agents reshaping AI usage* How OpenRouter reached 10+ trillion tokens per day* Why inference gateways are increasingly becoming targets for fraud* Why Stripe's fraud infrastructure is strategically important to OpenRouter* The coming rise of agentic fraud and attacks on the token economy* What changes and what stays the same as OpenRouter joins StripeAlex Atallah* LinkedIn: https://www.linkedin.com/in/alexatallah/* X: https://x.com/alexatallah* Website: https://alexatallah.comAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney/* X: https://x.com/AnjneyMidha* AMP: https://www.amppublic.com/Timestamps00:00:00 Introduction00:02:12 Alpaca, Llama, and the Multi-Model Bet00:06:04 Discord, Open Models, and OpenRouter's Origins00:14:28 Why “One Model Wins” Was the Wrong Bet00:17:27 Why Model Labs Struggle With Distribution00:23:04 “Just a Wrapper”: Why VCs Misunderstood OpenRouter00:27:58 Bootstrapping OpenRouter Through Community00:36:16 Crypto, Midjourney, and the Early Generative AI Ecosystem00:43:38 Mistral and the Birth of the Inference Marketplace00:47:10 OpenRouter vs. LM Arena00:52:08 Focus, Anthropic, and Roads Not Taken00:59:34 Mixture of Models and Model Fusion01:02:44 Sonnet, OpenClaw, and OpenRouter's Explosive Growth01:09:03 Why Stripe Acquired OpenRouter01:12:45 Fraud and the Emerging Token Economy01:17:47 The Coming Wave of Agentic Fraud01:19:07 What's Next for OpenRouter at StripeTranscriptIntroduction: OpenRouter, Marketplaces, and Pub-Sub as a Product PrincipleSwyx [00:00:00]: Okay, we are here in Anja's house, which is where all big startups in San Francisco start.Anjney Midha [00:00:08]: Howdy.Swyx [00:00:08]: And, congrats on Cursor, Mistral. I don'- God knows what else. You got so much stuff going on.Anjney Midha [00:00:17]: There's, there's a lot going on. Well, OpenRouter is probably the - has been the most, I would say, like, one I'm excited about recently.Swyx [00:00:24]: Yeah. And we have Alex, first time on the pod, but,Anjney Midha [00:00:27]: Thanks for having me.Swyx [00:00:27]: You've been in the IE a few times. I appreciate every time you've shown up, for the community. Congrats. I just, like, what a journey. When I was looking back at your past posts, one of the earliest principles that I saw you write as a product person is sub as a product principle. And I wanted - you to maybe explain how you think about what should exist in the world.Anjney Midha [00:00:49]: Yeah. The sub piece, which was early 2023, I didn't think about it until we talked like 10 minutes ago, is about how there is like a way of thinking about products as an intersection between subscribing to data and publishing data. And marketplaces are an easy example of this. You have suppliers that are publishing some product to a SKU. And the SKU is like a sub topic that a consumer is subscribing to and just going to, like, consume whenever they want. And humans consume in a very, like, discreet, ad hoc way. It's not very scalable. all their attention is on the topic when they're buying the thing, and their attention is nowhere else when that happens. agents and consumers of inference don't act like that. They're consuming continuously, and they're changing the SKUs that they consume from all the time. So OpenRouter is like a blend between a normal API experience and a marketplace where we create model slug. We have the auto router. We have all kinds of, like, product SKUs that you can subscribe to. And then you can, like, continuously add, like, derive value and make decisions based on those consumers.Alpaca, Llama, and the Multi-Model BetSwyx [00:02:11]: Yeah. This is something that was more consensus now, but not consensus when you guys started, which was that there is such a demand for swapping models and changing things out and, that people would not use the native SDKs. I guess, for each of you, what was your realization moment that this would be it? I, - You've, you've given a talk at EIE about Alpaca as,Anjney Midha [00:02:33]: Yeah.Swyx [00:02:33]: One of your inspiring moments.Anjney Midha [00:02:35]: Alpaca, I can, like, rehash the Alpaca moment for a sec. Like, the very beginning, at the end of 2022, OpenAI was the only game in town. There was, like, OpenAI, Cohere,Swyx [00:02:47]: Yes.Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models.Swyx [00:02:54]: Yeah.Anjney Midha [00:02:54]: When Llama came out in January of 2023, it was like, “Wow, really exciting. This is really big.” It outperforms 3 on, one or two benchmarks. but you can't chat with it. It wasn't like - It wasn't an engaging model, but it seemed like someone just needed to fix a couple things and do some RLHF on it to get it all the way there. And Alpaca was the first model that I saw that did that. It only took $600 to do. A team at Stanford generated a bunch of synthetic data, tuned Llama, and made Alpaca, billion parameter model. Or was - Maybe it was thirteen billion parameters. And it was so good. Like, I was just, like, on an airplane using it. I, - in many cases, I, like, you could not discern a ChatGPT versus an Alpaca result. And I figured if it was this easy to make a model, one, we have a whole new way of monetizing data for the first time. you can just, like, take really valuable data and turn it into a service in $600. and that cost will probably go down over time.Swyx [00:04:03]: When you - So sorry. when you say monetizing your data as, what eventually will become an MCP endpoint or as a training data for a model?Anjney Midha [00:04:12]: Yeah, training data for a model.Swyx [00:04:13]: Awesome.Anjney Midha [00:04:13]: Like, an abstract way of saying like, “Hey, I have this data.”Swyx [00:04:15]: Compress it into a model.Anjney Midha [00:04:16]: Like, it makes sense for me in my product, but, like, I could repackage it in the form of a model and sell it. And so it's just a whole new business model for the economy. It also, of course, provides, like, a way of following what Frontier Labs are doing, but in a way that, like, a single developer or a small team of developers can roll on their own. And so - Whenever you have an example of that, like a breakout app that's doing really well, and then some framework for imitating it with - in your own flavor, you have an immediate ecosystem of, like an immediate ecosystem, like, should arise because there's just a huge gap between the, like, decisions that the single company is making and all of the variations in those decisions that, like, a wider ecosystem can create themselves. And so then, you need a marketplace to, like, discover all of those, services and all of those products. There wasn't any place on the internet that, like, was like a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.Swyx [00:05:29]: The closest would be Hugging Face.Anjney Midha [00:05:30]: Hugging Face was the closest at the time, yeah.Swyx [00:05:31]: They just started Hugging, like, a few years ago before that.Anjney Midha [00:05:34]: Yeah, and Hugging Face also didn't have the closed-source models.Swyx [00:05:37]: Yeah.Anjney Midha [00:05:38]: And they didn'- you couldn't use the models at the time. and there wasn't data about who was using them. There were, like, a bunch of differences between OpenRouter and Hugging Face, and those differences felt really critical to me, especially when I was just trying to learn about LLMs and, like, why people are choosing, like, Different little ones that are emerging over time.Discord, Open Models, and the Origins of OpenRouterSwyx [00:06:03]: Got it. And then, Ansh, no stranger to wanting more model diversity, at the time, you're a couple of years into your Anthropic journey, which we covered in the previous podcast as well. What was your introduction to Alex?Alex Atallah [00:06:16]: Well, the introduction was, I think, thirteen years before that.Swyx [00:06:20]: Oh.Alex Atallah [00:06:20]: But the OpenRouter handshake happened right over there, if you remember.Anjney Midha [00:06:23]: Yeah.Alex Atallah [00:06:24]: Which - So Alex and I, met, I believe as sophomores now, if I remember at the Stanford Review,Anjney Midha [00:06:32]: That's rightAlex Atallah [00:06:32]: Meeting for the first time.Anjney Midha [00:06:33]: I think so, yeah.Alex Atallah [00:06:35]: Yeah.Anjney Midha [00:06:35]: Yeah.Alex Atallah [00:06:35]: So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day. And, whatever-- for whatever reason, I, Alex and I both showed up to one of the meetings, and I remember, the editor-chief was a mutual friend of ours. Lisa was really a really great editor-chief, where, part of an editor-chief's job is to assign responsibilities to people and make sure the work gets done. and I, I may be misremembering the details, but I remember wanting to. It was surprising to me that at the time there was no dedicated technology section in the newspaper.Alex Atallah [00:07:11]: YouSwyx [00:07:13]: Because it's political, right?Alex Atallah [00:07:14]: It is primarilySwyx [00:07:14]: Like, it's talkingAlex Atallah [00:07:15]: It originally started as like aAnjney Midha [00:07:16]: Yes.Swyx [00:07:17]: Yeah, states and all those things.Alex Atallah [00:07:17]: Correct.Swyx [00:07:18]: Yeah.Alex Atallah [00:07:18]: But it, - To take us back in time, you may remember this, but, there was this technology, legislation that was being debated called, the Net Neutrality Act. And net neutrality is, like, inherently this political concept, right? It's, it's about the regulation of - internet broadband access. And so there was a community of us who were technologists, but also debating the politics of the technology. And I thought the Review would be a great place - to, like, write about that. And I was working on, I think, a net neutrality article, and I remember proposing, “Well, maybe we should start a technology section.” And Alex was one of the only people who said, “Yes, that would be cool.” And said. I forget whether we ended up writing stuff together, but - that's when we first met,Alex Atallah [00:08:03]: Was 2011 or twelve. I forget which year it was. It was one of those.Anjney Midha [00:08:09]: Yeah.Alex Atallah [00:08:09]: It was at Old Union, if I remember correctly.Alex Atallah [00:08:11]: That's where we used to meet. But, along the way, Alex and I have had a chance to, To hang out often. And probably the time when we had the most professional overlap was when I was running the platform at Discord, and it had become this explosive platform for cryptoSwyx [00:08:32]: YeahAlex Atallah [00:08:32]: And NFTs in the middle of the pandemic.Swyx [00:08:35]: Which also, by the way, you were in charge of safety and security as well, right?Alex Atallah [00:08:38]: I was the head of platform, which meant all of the crypto - the DAO and NFT launch security debugging fell onSwyx [00:08:45]: And their phishing and.Alex Atallah [00:08:47]: The phishing, the social engineering attacks, the katana DDoS that we were getting hit by. but it's around the time I first started teaching security at scale at Stanford, CS 153. And Alex was on the, - at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were. Like, and at peak, I forget, if you remember how much NFT volume was running throughSwyx [00:09:10]: DiscordAlex Atallah [00:09:10]: Discord, but it was, like, a meaningful amount of, like, it was, like, several billion dollars in NFT volume of GMV, so to speak, were running through the platform, and it was all coming from OpenSea. It was these, like, buy, sell,Swyx [00:09:20]: TheAlex Atallah [00:09:21]: ServersSwyx [00:09:21]: The D in DAO is Discord.Alex Atallah [00:09:25]: Yes. And so that's when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT. Sorry, three. No, it was five. Yeah, five, which is the RL version of three. And that's around the time we made a Discord bot with, OpenAI for internal deployment, and that's when I realized we would need. Like, since I was part of the deployment team.Anjney Midha [00:09:50]: What was the use case?Alex Atallah [00:09:51]: There were two that were. And there's, there's a post now called “Discord is Your Place for AI with Friends” that somebody sent me recently that I wrote, and published in twenty-three. But There were two use cases. One was Clyde, which was the - like, a party friend inside of Discord that could help you set up your Discord server and talk to you about onboarding and get your friends to hang out more. and then there was content moderation. And one of the realizations we had with content moderation was - it would refuse to moderate. Like, it would just refuse our prompts because the The training was. We were very early in the training era, and it would just. Our prompts would trigger it, its, like, guardrails. And we told OpenAI, “Hey, guys, we need access to the weights because if we're gonna be doing content moderation at scale, we had 250 million monthly active users, we need more reliability that the model will do what we need it to.” And they said, “Well, sorry, guys, that's not how this works. We're a closed-source company.” And so that was my first realization that we needed open models, and the enterprises would need more control over capabilities, and then ultimately would need some control plane or management system to orchestrate these open models. But there weren't no good - there were no good open alternatives until maybeAlex Atallah [00:11:10]: Six months later when Llama came out. And six months after that, I led the series A into Mistral, which was started by Guillaume and the Llama team. And - That, - Around that time is when I remember hearing about Alex launching OpenRouter and going, “These worlds are gonna collide, and I don't know when it'll make sense to team up.” But Alex was so early and could see. I think he was totally right about this ecosystem starting with Llama that then needed, like, a, an easy layer to manage for, especially for. I was approaching it from the enterprise perspective because I had been that, like, the. As the VP of platform at Discord, it was my job to ensure that when we deployed models to, like, 250 million users, they did what we wanted them to. And that was very hard, because if you outsourced it to the labs and they controlled the guardrails and their guardrails are their safety policies. Forbid the model from responding to your prompts. That was quite catastrophic.Swyx [00:12:05]: Yeah. But what, a moderation is the thing that they want to support. And obviously, beyond that, they would - OpenAI would work with you, presumably to give you a moderation endpoint, which they offer for free.Alex Atallah [00:12:16]: It was an interesting use case, that - So they did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini deployment of itself. And so the use case was instead of having human moderators that have to interpret the norms of the community, you just give the, - Often, like every, subreddit, Discord servers, public ones have their own rules that the user, the users create.Swyx [00:12:41]: Oh, yeah. We run the LinkedIn Discord in. Yeah.Alex Atallah [00:12:43]: And then humans used to read those norms and then enforce it every day manually, like observing each message in these communities. And these communities have like millions of users. So we had a 5,000+ person team globally in the, on the Discord content moderation team. These are outsourced contractors who had a really tough job. And so the idea was instead, if you could give the norms of that server To the LLM, then the LLM would do custom moderation for that server. It's almost like a, like context moderation for that server. And many of those servers' norms just violated OpenAI's rules. And so - It was like we had our own custom eval. So each server had its own custom eval. But Discord-- at the time, OpenAI's evals, we were all soAlex Atallah [00:13:28]: Primitive in our thinking about how to deploy these LLMs that often the training prompts were super handed. It said, “Oh, anything about Harry Potter, anything that has trademarked content, don'- refuse.” And if it was a fan - Harry Potter fan community, this is a real use case, that had content moderation, the LLM would just refuse.Swyx [00:13:48]: Yeah.Alex Atallah [00:13:49]: And that was just not precise enough.Anjney Midha [00:13:52]: Another one that we heard was like if someone was trying to write like a detective story, and there's one chapter with a lot of violence, like maybe someoneAlex Atallah [00:14:01]: RightAnjney Midha [00:14:01]: Like kills someone, the LLMs would just refuse to, like, help with that part of the story.Alex Atallah [00:14:07]: Yeah.Anjney Midha [00:14:07]: And then - like, we used to be like, okay, this is not like structurally inherent to LLMs. There must be, like, some choice out there so that I can, like, switch to another model, when I'm getting, like, a refusal or a bad result from the main one that I have. And that, like, tension also drove me for a marketplace.Why “One Model Wins” Was the Wrong BetSwyx [00:14:28]: Yeah. I think that is well accepted now. What was it like back then when you were raising or, starting this? did people get it? what was the, some of the struggles? I like getting stories out of him about how other VCs don't get it. So like anything you wanna, talk about, now - Let's, let's call it, that the early journey of OpenRouter is done, right? You can obviously talk about some of the early days stuff.Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of theSwyx [00:15:03]: Scaling laws.Anjney Midha [00:15:04]: Huh?Swyx [00:15:04]: Scaling laws.Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It'll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you'll just be fighting for scraps at the end. That was probably the biggest objection we got. it is interesting that Google won the search engine race with such a huge margin. I think, like, had there been more interesting benchmarks or had, like, search engines been, - had people, like, seen them a little bit more like LLMs where they're services that you can build companies on top of, that might not have been the case. but LLMs don't merely have a user interface. They're also, like, ways of building entirely new businesses. And, a Google-level monopoly would be like the Dutch East India Company times, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well. So it didn't seem like would be a really crazy outcome if that happened. And it's also less likely because the economics of, like, creating good competitors are much, like, much more decentralizable.Alex Atallah [00:16:25]: Everything Alex said is true, And I came at it from a completely different perspective, whichSwyx [00:16:31]: Yes, this is why we're here.Alex Atallah [00:16:32]: The scaling laws were never - In my mind, were always a feature, not a bug for why OpenRouter would be very valuable. Because, I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends - I went to grad school for machine learning, and I just had a lot of friends in the ML community who it was very obvious to us that the bitter lesson holds. And so I was like, “Oh, fantastic. Now we have at least two proof points that compute scaling works.” It was OpenAI and Anthropic. and by the time I think we decided to team up on OpenRouter, I had already invested in Mistral and Black Forest Labs and Luma. So there was multiple model companies and teams that I was, working with.Why Model Labs Struggle With DistributionSwyx [00:17:14]: But you did other modalities, whereas this is literallyAlex Atallah [00:17:16]: Across different modalities, yesSwyx [00:17:17]: Text.Alex Atallah [00:17:18]: Exactly. And it was so obvious to me that an ecosystem of different kinds of models were being created, and that this whole narrative of, like, Only one company will dominate like Google was, well, like maybe true, but one, I don't believe that. But two, there was so much extraordinary innovation happening across several different research teams. But the shared problem I was noticing across all of them was often, the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but never - like, are not developer mindset-oriented. Like, what happens after the training is done and the checkpoint comes out? Like, you'd be shocked how, like, similar the early training teams at OpenAI, sorry, Anthropic, BFL, Mistral, were in their, like, default approach to. Taking their research out of the, lab and scaling their impact, which is often, oh, the checkpoint is done, put it out as an API, done, and then there'd be crickets. in the case of Claude, the first Claude checkpoint was done a year before they released it internally. And then ChatGPT came out, and we decided, okay, yes, it's a good idea to release a Claude version externally.Alex Atallah [00:18:34]: And they had no plan, like no plan for how to get developers to try it out. And so if you go to the Claude one blog post, you'll notice there are, like, three developer examples for users of the API, and one is a Discord bot, and the second is Vivian, my wife's startup called Juny Learning, ‘- And then there was, like, Notion, because these were all friends of, like, the Anthropic Because that's how - like, last minute the planning was around, hey, once the model's done training, how do you get it out to the world? There was no distribution platform that understood what developers needed, all the key management, provisioning, like, simple, like, endpoint management, versioning control. Like, all these things that the scientists and researchers go, “ that's plumbing. I don't really think about it.”Swyx [00:19:15]: Implementation detail.Alex Atallah [00:19:16]: Right. And instead, Alex came at it from that perspective. And so, it was so obvious to me that, like, every single lab I was funding would spend - like, literally sometimes billions of dollars into training, and then a checkpoint would be done, and there'd be crickets, like, during early access because they're like, “Oh, that's right.”Alex Atallah [00:19:35]: It's hard to use a checkpoint to make anything. You need a whole bunch of plumbing around it to make it usable by a developer. And so by the - I think - it was so obvious to me that a distribution platform like OpenRouter was critical to have in the ecosystem if we wanted there to be competition to Google. Like, unless-- ‘cause with Google, DeepMind is done training a new checkpoint, and then they push a button, and it gets blasted out across all their surfaces from Google Docs to,Swyx [00:20:01]: Everywhere, even if I don't want it.Alex Atallah [00:20:02]: Everywhere. You wanna know about, like, on Android, like, overnight, they can deploy a new checkpoint to, like, a billion devices, right? And that invisible infra advantage, distribution advantage, most people don't realize, but until OpenRouter showed up, - you had to think about all of that yourself as a model lab. And it was very daunting. at Anthropic, I think it took, well, more than twelve months to get to our first 10 million in revenue. And in contrast with Black Forest Labs, I remember the early days, you guys had a conversation with the BFL team, and, it was so simple for OpenRouter to say, “Oh, no problem. Like, the day you launch, we can send 1 million developers to you.” that was crazy. That was like a step function change in, like, an hour.Swyx [00:20:46]: Is that a real number, a million?Alex Atallah [00:20:47]: I,Swyx [00:20:48]: Okay. All right.Alex Atallah [00:20:48]: I think today it's, like, 4 million. How many developers are on OpenRouter today?Anjney Midha [00:20:52]: Over ten,Alex Atallah [00:20:54]: Yeah.Anjney Midha [00:20:54]: Over 10 million, but, like, it's, it's hard to, youAlex Atallah [00:20:59]: I, yeah, I don't know how to. Yeah.Anjney Midha [00:21:00]: We do a lot of, like, account duping work, but, noAlex Atallah [00:21:04]: If you could get 1,000 developers, just to put in context If you get 1,000 developers who try the model on day one after you release it and just, like, do inference and give you feedback, that's a thousandAnjney Midha [00:21:15]: That's hugeAlex Atallah [00:21:16]: More developers than they knew how to get to on their own.Swyx [00:21:19]: Well, BFL had a reputation, but yes.Alex Atallah [00:21:21]: They had one in Stable Diffusion.Swyx [00:21:22]: Yeah.Alex Atallah [00:21:23]: And with Mistral, I don't know if you guys remember, but the first checkpoint they released was, like, torrents. It was, like, torrent weights.Swyx [00:21:31]: Yeah, they just put up a magnet link.Alex Atallah [00:21:33]: Yeah, there was no API.Anjney Midha [00:21:34]: Yeah.Alex Atallah [00:21:34]: Because they didn'- they weren't infra people.Alex Atallah [00:21:37]: ? Like, it's like, okay, download these weights, and you guys go figure out how to host it.Swyx [00:21:39]: Well, he has a story on his side, yeah.Anjney Midha [00:21:41]: Yeah, in addition to the, like, building a really good developer experience around it, the marketing that we do on, like, for different models is totally different and perceived totally differentlyAlex Atallah [00:21:54]: RightAnjney Midha [00:21:54]: From the marketing that a model lab does for itself.Alex Atallah [00:21:56]: Yes, 1,000%.Anjney Midha [00:21:57]: Right? We are like a, neutral layer looking at this market like it's a big dark room with all the corners completely obscure to users, and users are walking into the room and, like, feeling aroundAlex Atallah [00:22:09]: YeahAnjney Midha [00:22:09]: And trying to figure out what objects to grab off the tables and, like, build into, their companies. And it's just an insane way of working. Like, models are not products where you can just enumerate all their features onto a web page. They're all black boxes, including the open weight ones. So you need to, like, shine lights on all corners of this room, so that people can see what makes this model good, and you need the company shining that light to be a neutral third party, which is what we specialize in. So the, like. It'- In addition to developer experience, there's also, like, a very important, like, marketing and product packaging componentAlex Atallah [00:22:50]: YeahAnjney Midha [00:22:50]: And a way of, like, routing and discovering models becomes, like, critical to your market as a provider or a model lab or a server tool and more in the future.“Just a Wrapper”: Why VCs Misunderstood OpenRouterAlex Atallah [00:23:03]: And this value, to your earlier point about how many VCs, like, just don't. One of my biggest frustrations is that venture capitalists, many of them, like, just don't have any operating experience in the field. so unlike a traditional investor who's just maybe come up through the ranks as, like, a associate working on financial modeling or maybe hasn't been a real operator in the field for, like, more than ten years, which is a big part of the industry now, I had just arrived at a16z, like, a year after running the platform. And so I knew what the challenges were of, like, building a real - great developer experience and like, being able to create a working piece of software with a model. And there were a few, I won't name names, but there were investors who were looking at OpenRouter, and, felt at the time, like, when I would compare notes with people, that it was just, I quote unquote, “just a marketplace.”Swyx [00:23:59]: Yeah, just a thin layer, just aAlex Atallah [00:24:00]: CorrectSwyx [00:24:00]: JustAlex Atallah [00:24:01]: A wrapper or whatever on other people's APIs. And I was like, “You have no idea how strategic the value that OpenRouter has created by being able to orchestrate even three.” APIs in production. The amount of both engineering work and community design that goes into getting that live and running in production at the scale the OpenRouter team had started just doesn't happen by default. And that was one of the things that stood out to me about Alex from the earliest days. Like, he just understood, like, - from a systems perspective, like, how do you get these flywheels going? Like, that stood out to me with OpenSea when we were working together on the NFT integration at Discord. Like, Alex had a level of community-- like, systems thinking on how you get these flywheels going that most scientists and machine learning people just don'tAlex Atallah [00:24:48]: Think of. Like, we often think in terms of training.Swyx [00:24:52]: It's a linear stage.Alex Atallah [00:24:53]: It's this linear pipeline.Swyx [00:24:53]: There's no loop yet.Alex Atallah [00:24:54]: Yeah. It wasn't until much later that the modern context feedback loop cycle really got standardized in the industry. But at the time, if you remember, machine learning was like. Like, mostly we did a lot of ML, like, when I was in grad school on a laptop. So you just, like, download a dataset, ran some ablations, and you looked at the loss curves, and you're like, “Great, I made AI.” And the idea that you have to, like, deploy those capabilities, collect feedback trajectories, then, like, put those into a continuous loop, like, came much later. And it was very counterintuitive to the - like, the traditional AI mindset. I do remember doing the investment phase for, OpenRouter, I just didn't try and educate a bunch of other VCs on why it was not just a marketplace. I was like, “ what? I'm just gonna invest.”Anjney Midha [00:25:41]: Yeah.Alex Atallah [00:25:41]: And I'm going to, like, take the opportunity to partner with Alex, and if - no other VCs get it, that's totally fine. ‘Cause at the time, - it was not obvious, I think, to several of the investors that, like, OpenRouter was not more than just a wrapper around APIs. And - that infuriated me. And I was like, “ what? I don't have time to debate you. I'm - we're gonna, we're gonna invest.” And then I think, like, a month later, Matt Murphy marked it up by 10x. Like, - I think. I forget what the exact money was and so on, but, to his credit, Menlo Ventures realized, “Okay, there's much more strategic value here as well.” Maybe you didn't hear all these conversations behind the scenes But that frustrated me a lot. there's a lot of this, like, opining about wrappers. and if you're like, “Oh, an app is just a wrapper on a model,” then, like. And, OpenRouter is, like, this wrapper on top of other APIs, and this is the most stupid, reductive framework.Alex Atallah [00:26:31]: And so it's clearly somebody who has no experience deploying product at scale.Swyx [00:26:34]: It's the thing you dismiss other things with. Like, you're a - everyone's a wrapper on everything, right? Like, and there's, there's some Some wrappers have value.Alex Atallah [00:26:40]: Investors are wrappers and LPs, right?Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it's all wrappers down, all down to bare metal, I guess, and like energy.Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models.Anjney Midha [00:26:56]: Right.Swyx [00:26:57]: And, yeah, obviously this is, like. you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies.Alex Atallah [00:27:06]: It's so, hard to be. Like, the day a model launches, the fact that you have an OpenRouter, endpoint for that model frequently at the top of Hacker News on day one, people don't realize the amount of work that goes into accomplishing that. And OpenRouter used. Like, that would happen over and over again, and I remember going, “People have no idea how hard that is.”Alex Atallah [00:27:30]: That's not.Swyx [00:27:31]: Yeah, we've covered some of the inference engineering that goes behind,Alex Atallah [00:27:34]: YesSwyx [00:27:34]: Some of - with Base Ten and all those. Well, today you have, all those, like, cool code name things that people guess what Oxy Alpha is and all those things. But, like, I guess one of the things that you're teasing is, how do you get that initial flywheel going, right? Because today you have your scale and your reputation, all these things, so obviously you - you're driving immense distribution. But when you were early on, when it's mostlyBootstrapping OpenRouter Through CommunityAlex Atallah [00:27:55]: The bootstrap, yeah.Swyx [00:27:56]: Yeah.Alex Atallah [00:27:56]: What was the bootstrap like?Anjney Midha [00:27:58]: To bring it back to early Discord days, I think we, like, initially connected with. This is an OpenSea story, technically. But, and we initially connected when you were at Discord, and we talked about, like, - the Axie Infinity server.Alex Atallah [00:28:13]: Oh, yes. Yes.Anjney Midha [00:28:14]: This server was, like, the biggest server at theAlex Atallah [00:28:17]: YeahAnjney Midha [00:28:17]: At Discord.Alex Atallah [00:28:18]: That's right.Anjney Midha [00:28:19]: And you were like, constantly bumping up theAlex Atallah [00:28:22]: The limits on the server. Oh, my GodAnjney Midha [00:28:24]: Of how many people could be in the server.Swyx [00:28:24]: For those who don't know, like, 10% of Philippines was Axie.Alex Atallah [00:28:29]: Was on that server. That's a big hit.Swyx [00:28:31]: It was, like, a meaningful contributor to the GDP of the country.Alex Atallah [00:28:33]: It was an NFT, like, crypto game, but itSwyx [00:28:35]: It was like a Pokémon breeding thing.Anjney Midha [00:28:36]: Yeah.Alex Atallah [00:28:36]: Yeah. Similar. Yeah. There was battling, there was breeding, and then there was, like, a marketplace for trading.Swyx [00:28:43]: Earn as well.Alex Atallah [00:28:45]: Yeah, earn. And, like, the graphics were really cute and fun, and you like, you get emotional about your Axie that you make. So to, like, start a community like that, which we had to do many times at OpenSea with every early project, for us to create a marketplace for it, we need to make sure that the, like, the community wants it.Anjney Midha [00:29:09]: Right.Alex Atallah [00:29:09]: And it's like building something that people want and going and telling them about it. Like, you can do that on a one basis, but there's way higher leverage to do that in a community where everyone can talk to you at the same time. So we spent a lot of time, like, building things that the community really wanted. We did the same thing for OpenRouter. And, like, the Axie community was one of, like, a zillion communities we did that with. And Anj, like, saw us doing it and. ‘Cause you could just see people sharing OpenSea links constantly in that Discord. Like, users sharing links is a really clear indicator that, like, something important is going on. So we spent, a lot of time, like, first figuring out what the gap is in the technology that people care about. Like, what was the actual problem that needs to be solved? in early LLM days, it was, OpenAI refusing to finish the prompt or,Anjney Midha [00:30:09]: YeahAlex Atallah [00:30:10]: To, like, complete the task. It was also.Anjney Midha [00:30:13]: Inability to customize models. and so there are communities that, like are just completely blocked on that issue, and those are the communities that are most useful to learn about and dive into and explore.Alex Atallah [00:30:28]: Something that really struck me at that time, - as I was just hearing your talk, I remember noting - you may not remember this, but we - we had these, like working, Zoom calls that we were doing a sprint around for, like this OpenSea integration with Discord. and, we'd, we'd - it was myself, my engineering team. I think you were there. And I remember, Alex, in the middle of one of those calls, just like there was like silence. we were all like, “Oh, yeah, this totally makes sense. Let's do this.” And then there's - every, like everybody aligned. And Alex was like, “No, this makes no sense to me.” And everyone's - I remember going, “What? Like, it works. Like, you click on a link and this, then it bounces you out to, like, OpenSea.” And he was like, “It's not a good user experience. Yeah, we should not do this.” And I remember going, he was the only one person out of all of us to raise his hand and go, yes, it made sense from a technical implementation perspective. Like, we were bouncing the user out into the, into OpenSea. And so it kinda checked the box of the product manager's requirements on both sides. But Alex went one step further and was like, “ what would be better, guys? If we just embedded the experience right here inside of Discord so the link opened up as an embedded iframe, and you can just check out right there.”Alex Atallah [00:31:47]: And not one person on the call, and there's like seven of us who had met, like, week after week.Swyx [00:31:52]: And it's the guy who doesn't work for Discord.Alex Atallah [00:31:53]: And it's the guy who doesn't work for Discord.Swyx [00:31:55]: Like, technically, you benefit if they bounce.Alex Atallah [00:31:57]: Exactly. And that was, like, adversarial. To keep the user inside of Discord would be adversarial to OpenSea. And yet Alex put that user experience first. And I was like, “That's special.”Swyx [00:32:08]: Wow.Alex Atallah [00:32:08]: Because it's very hard to have somebody who's technical like Alex and understands the developer flow, but also understands the best user experience and wants to prioritize that. And that's two sides of the flywheel that if you can get spinning, like is often hard to stop. And you just reminded me, like that one was one of those moments where I go, I - I realized I gotta be better at user experience because I should have been the one who came up with that, and I didn't. And I learned from you. And, I think that went into one of our case studies for the PM training program at Discord.Swyx [00:32:34]: Whoa.Alex Atallah [00:32:36]: I don't know if it there is Because ofSwyx [00:32:38]: You need an Alex is the conclusion.Alex Atallah [00:32:40]: Yeah. You need an Alex. And this is why I'm not, nobody should be surprised why Stripe decided like they had to buy OpenRouter because it's a really rare combination of people who understand the machine learning community, the developer experience, and the user experience. And putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieveWindow AI, BYOM, and Finding the Right Form FactorSwyx [00:33:02]: Yeah.Alex Atallah [00:33:02]: Over the last, five years.Swyx [00:33:04]: Yeah. Well, we should talk about the other reasons for acquisitions, whichAlex Atallah [00:33:07]: Yes, we should.Swyx [00:33:07]: You've written about. I wanna proceed somewhat chronologically as well. So - there is a point that, one of the questions that, Dave from H of Zero sent in was, when did it - really started to work? And you brought up Mixtral. I don't know if you wanna bring up that story.Alex Atallah [00:33:22]: Oh, yeah.Swyx [00:33:23]: Which obviously you overlap with, so.Anjney Midha [00:33:26]: Yeah, the MoE was. I don't know when. there's no like one moment where I was like, “Oh, this is, officially starting to work.” It wasSwyx [00:33:36]: The moment where you had a Chrome extension, like, really super early on.Anjney Midha [00:33:39]: Oh, yeah. But, well, - yeah. So before OpenRouter, I wanted to, like, explore a bring-your-own-model experiment. And,Swyx [00:33:47]: Which anyone familiar with crypto is like, yeah, Phantom and all these things.Anjney Midha [00:33:50]: Yeah. So it felt like doing a MetaMask analogy for AI would be a fun way of exploring that. And at the time, there were no AI apps. There were probably as many AI apps that were, like, hitting AI - like, hitting an LLM via an API call as there were, like, games just doing it in JavaScript. like there was a, there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some desktopAlex Atallah [00:34:27]: Yes.Anjney Midha [00:34:27]: Managed app that is controlled by the user. and of course, there are like, I think, many reasons that did not happen. But back when the days were that primordial, I built a Chrome extension called Window AISwyx [00:34:43]: With Plasmo.Anjney Midha [00:34:44]: With Plasmo.Swyx [00:34:45]: I had come across early on, and I was like, “Who's gonna use this?” You did.Anjney Midha [00:34:49]: Plasmo had a couple, like, I think Phantom was using it. there were some other, like real companies using it.Alex Atallah [00:34:56]: It was like a shim.Swyx [00:34:57]: React for Chrome extension. It compiles to allAnjney Midha [00:35:00]: Yeah.Alex Atallah [00:35:00]: I see.Anjney Midha [00:35:00]: Like Next.js for Chrome extensions.Swyx [00:35:01]: Next.js, Next.js.Alex Atallah [00:35:02]: Okay.Anjney Midha [00:35:03]: And yeah, built Window AI on top of it. The creator of Plasmo, like started contributing code to Window AI, in GitHub, and that turned out to be Louis VicchiAlex Atallah [00:35:15]: Oh, you'Anjney Midha [00:35:15]: Who is the founder of OpenRouter.Alex Atallah [00:35:17]: That's right. You have told me this is how you met Louis. Yes.Anjney Midha [00:35:19]: Yeah.Alex Atallah [00:35:19]: Okay.Anjney Midha [00:35:20]: So, that allowed users to like configure which model they wanted to use for a web page in their browser, and then, like the app would just call out to that model when it needed to do things. not the right form factor for LLMs, but, it's like fun experiment. You learn a lot, and like I open sourced it. And the main learning is like, okay, this has to be an API, and it has to look a little bit - like, there has to be more of a developer experience here and more of a discovery experience as well. Like, I don't know where to use these models, and a little Chrome extension is not gonna help me discover. It's not enough real estate. I need more space. I need visuals. I need graphs. I need, examples. I need images. I need to, like, I need to be able to, like explore both as a human and as an agent.Crypto, Midjourney, and the Early Generative AI EcosystemAlex Atallah [00:36:10]: Yeah.Anjney Midha [00:36:10]: So that's how OpenRouter came to be.Alex Atallah [00:36:13]: A meta point that.Alex Atallah [00:36:16]: I think is underappreciated, but Alex is reminding me, is that we were quite lucky that we were so. we were, like, adjacent to the crypto community in those days. Because in hindsight, crypto ended up being like a dress rehearsal for generative models, right? If you think about the Axie experience, Alex is totally right, there were not that many AI apps at the time. And while I was dealing-- my job was to be the head of platform at Discord, which meant to be a general purpose place for communities and friends to create-- for developers to create apps and bots and, other services that could be deployed across Discord. And while 80% of the attention at the time was being spent on crypto, because that's where all the NFT volume was, there was, like, twenty percent of my time I was spending with a friend, who would get hotbot with me and ask me for. We would play Magic: The Gathering on weekends, and he was working on a little Discord bot that could take a text input and turn it into an image, and it was called Midjourney. YouSwyx [00:37:15]: Is that David?Alex Atallah [00:37:15]: It was David Holz.Alex Atallah [00:37:16]: He was a good friend. And David and I have both been failed ARVR founders, in the before that. And, I remember this. Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter off. And many of the, like, the abstractions and the infrastructure decisions we made to scale Axie happened just in time because they. Axie did this and then fell off a cliff. And then as Midjourney was taking off, we, like, explicitly decided to help David make the server, the Midjourney server, as the primary place for interaction with the model, because it was very hard for people to understand how to use the model if they couldn't see other people using it and copy them. And so the single-player Midjourney web app on its own, like midjourney.com, had, like, terrible retention because people would show up, they'd see this empty field. It's like E 2, and they would type in, like, cat or dog. And it was, like, paralyzing for them to have this blank canvas that they had to fill because they'd never used an AI model before. But instead, in a Discord server, you could see other people using it and riff off of their prompt, and the engagement was off the charts. And so scaling, Midjourney from zero to, like, 10 million monthly actives was a much smoother approach Axie Infinity. And so,Swyx [00:38:29]: Don't forget the best of four pictures, and you choose one.Alex Atallah [00:38:31]: The best, yeah, and then the other, weSwyx [00:38:32]: Which is the feedback loop.Alex Atallah [00:38:33]: The RLHF feedback loop, which, by the way, separately, like, Tom Brown, David and I used to play Magic: The Gathering on weekends. And so, like, it was one group of friends would hang out, and we'd. Like, these concepts were all being discussed all the time. But, there was.Alex Atallah [00:38:47]: I think there were few of us who bridged both the crypto worlds and the AI worlds. And compared to crypto, where it was - the question was always, what's the use case, for this technology? There was never any need to ask that for AI because it's, like, the use case was so visceral. It was like, I can create now anything at - I can imagine. I can write novels, I can code. And the infrastructure that those of us who believed in the distributed systems, like, value of crypto, like the censorship resistance part, found this use case that was explosive. And I think between Midjourney, the, Claude was a Discord bot launch, that we were using internally as an LLM. ElevenLabs had a TTS model that we had on Discord as well. Like, Discord became this petri dish for, like, early apps to innovate. And I don't think it's a coincidence that they found a home there before OpenRouter gave the world, like, a public home store or, like, a, storefront. Discord was this, like, almost petri dish storefront that - had, like, piggybacked on the infra we'd built for crypto communities. And then I think Alex was one of the first people to realize, wait a minute, like, these apps need their own home, on the internet. And then OpenRouter, to me, was a continuation of that community's needs. And of course, there was the crazy distribution that you enabled for a lot of these developers.Why OpenRouter Couldn't Just Live Inside DiscordSwyx [00:40:07]: So then my question is, how come you were. My perception is OpenRouter is not that Discord-centric, right? You have a Discord.Anjney Midha [00:40:14]: Yeah.Swyx [00:40:14]: And you use it to engage your community, but it's not like Midjourney where, like, no, that is like the primary way people experience OpenRouter.Anjney Midha [00:40:21]: Yeah, Midjourney, like, it really helps to see visually really quickly how people are using the model and how to prompt it.Swyx [00:40:29]: Yeah.Anjney Midha [00:40:29]: And I think that is partly why the server was so critical. It's like it is the user experience. It adds a ton.Swyx [00:40:36]: Yes.Anjney Midha [00:40:37]: And you can go the whole mile with just, like, prompting via Midjourney, like, the, via the Midjourney Discord server, getting your images and then sharing them and having fun. For OpenRouter, for LLMs, like, you need a lot of user experience around LLMs to make them, like, really usable.Swyx [00:40:54]: Charge point.Anjney Midha [00:40:55]: And yeah.Anjney Midha [00:40:57]: The, like, seeing the examples of other people is also not as useful because it's a lot of stuff to read. It takes a long time.Swyx [00:41:03]: Yeah.Anjney Midha [00:41:04]: You need, like, based integration. Not possible to do in a Discord server. You need, Or technic- it's possible. I shouldn't say that. It's just not a great developer experience. you need, like, - you need governance for. At the point where you got based integration, now you need governance for managing the LLMs that have access to it, the data policies, which teams. All that stuff needs a lot more than a Discord server can provide. So it's justSwyx [00:41:30]: YeahAnjney Midha [00:41:30]: It's not the right.Alex Atallah [00:41:32]: Well, in addition, you're not wrong, but also there's the very important distinction that, Midjourney was an end user application.Swyx [00:41:40]: Right.Alex Atallah [00:41:40]: And, that's why Discord, which has 250 million monthly end consumers, made, it made sense for Discord to be a host for that application experience. What I knew was gonna happen soon after Midjourney found explosive product-market fit, because we. I think when Midjourney launched, from launch to $100 million revenue run rate, it was less than eight months. And shortly thereafter, Stable Diffusion launched. And, all of us used to hang out in the Discord server. There, I think it was the,Swyx [00:42:13]: The Stability Discord?Alex Atallah [00:42:14]: It was theSwyx [00:42:16]: Yeah, LAION.Alex Atallah [00:42:16]: Yeah, the LAION Discord server.Swyx [00:42:17]: The image community that spawned Stable Diffusion.Alex Atallah [00:42:19]: The image community. Yeah. And so when Stable Diffusion came out, I realized- Oh, now other people can build their own Midjourney.Alex Atallah [00:42:27]: Because until then, Midjourney did not have an API, so they were a stack company, right? They were training their own models, and they were deploying them as an application. But if you wanted to build your own Midjourney, there was no API of that quality. and I think E two was still quite primitive. Like, Midjourney had great quality. And then when Stable Diffusion came out, suddenly there was this new person who - there was - this new capability in the world, which is a developer could create their own Midjourney. And that, I think, created the need for something like OpenRouter, because then you need an API to. If you - if you had the creativity of David Holz and you had Stable Diffusion as the model and you wanted to put these things together, how could you do that without having to figure out how to host the weights? And what OpenRouter, - the shape of OpenRouter enabled is that. Right? When you have open model alternatives to closed applications, OpenRouter's value in the world becomes extraordinary because now any developer can just show up and use theStable Diffusion and the Need for a Model API LayerSwyx [00:43:20]: You just love model diversity.Anjney Midha [00:43:21]: Did you just say the shape of OpenRouter?Alex Atallah [00:43:23]: Oh, no.Anjney Midha [00:43:25]: Were you in cloud? What is this the real Han?Alex Atallah [00:43:26]: I've been, I've been - I'm, I'm misaligned now. I've been overtrained. I've been using Cloud way too much, haven't I?Swyx [00:43:34]: Claude-ish is what people would say.Alex Atallah [00:43:35]: Claude-ish. Oh, God, I gotta untrain myself.Swyx [00:43:38]: Okay. - And I just wanna cap off the Mistral side. my TLDR is there was a Mistral price war, is what they called it, right? Like, round about NeurIPS is twenty-three or twenty-four.Mistral and the Birth of the Inference MarketplaceAnjney Midha [00:43:47]: Yes. DecemberSwyx [00:43:48]: They launched, the Mistral 8x7B, and like the price went down like 80%.Anjney Midha [00:43:54]: Yeah.Swyx [00:43:54]: To me, that's very positive because it's like the first, like, real competition to host Mistral. Is there more?Anjney Midha [00:44:01]: Yeah, that was. I'm, like, trying to remember it, all the things that happened. It. Like, we saw that model come out and immediately saw people say that it was the best model in the world.Alex Atallah [00:44:15]: Yes.Anjney Midha [00:44:15]: Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.Swyx [00:44:22]: It's hype, right? Is it?Anjney Midha [00:44:25]: It was hype. It was hype. It was also, like, hype from AI influencers at the time. And there were many examples where it was, like, outperforming four. So people really wanted to try it out and see, is this gonna be true for me too? And if so, at what price? And, the, like, inference landscape was really messy.Alex Atallah [00:44:49]: Yes.Anjney Midha [00:44:50]: We cleaned it up. - it allowed, like, providers to compete on price, so we could give you just the best price in one spot. And so it was, I think, the first clear example of, like, a provider marketplace working in a way that adds value to end developers.Alex Atallah [00:45:08]: Sean, you may not remember this, but I think we met for the first time a few days after Mistral came out at NeurIPSAnjney Midha [00:45:15]: Yeah.Alex Atallah [00:45:15]: At a luncheon.Swyx [00:45:16]: Yeah. That's where I also met BFL as well. Yeah.Alex Atallah [00:45:18]: And Guillaume was there.Swyx [00:45:19]: Yeah.Anjney Midha [00:45:19]: I was at NeurIPS at that time.Alex Atallah [00:45:20]: You were there too. And, we had just announced the Mistral investment, and I remember Guillaume was over there, and I remember turning to Guillaume and asking him, Like, “Is it is all the. Like, how are you feeling after the launch of Mistral and seven B?” And, him in his typical French fashion was like, “ it's a, it's an okay model. It's not that good.” And I was like. It was so, in contrast. But I remember him also saying that part of the reason he felt a lot of people Thought that it was better than four was because of the speed. - it was an MoE model that they had, like, absolutely figured out how to make super efficient. It was on the Pareto frontier. And this is an important thing about LLMs, right? Sometimes when they're faster, you think they're smarter, even though, like, if you did, N of, these common, like, evals that are - you do seven tries, and I don't remember. I think we should go back and figure out what the data says, but I wouldn't be surprised if it turns out, oh, on an N of seven attempts, four was smarter on evals, but the perception of on, like, or correctness would be smarter or more accurate. But, people, like, from a human preference perspective felt that it was faster because it - or smarter because it's so fast.Swyx [00:46:36]: Yeah. And most queries do not take that levelAlex Atallah [00:46:39]: Don't take that. That's true.Swyx [00:46:40]: Right? So this is the start of humans as routerAlex Atallah [00:46:42]: Yes.Swyx [00:46:42]: Which then eventually becomes OpenRouter as router of like theAlex Atallah [00:46:45]: Oh, that's interesting way to think about it. Yeah.Swyx [00:46:47]: Like, because humans are the routing mechanism. Like, I will ask the fast model first, and then if, like, oh, not good enough, I'm gonna upgrade manually.Alex Atallah [00:46:52]: Yes.Swyx [00:46:53]: But then he's gonna auto it.Alex Atallah [00:46:54]: I didn't, I hadn't thought of it that way, but that makes sense.Swyx [00:46:57]: Which then there's, there's a lot more techniques, like fusion. Fusion is the thing that we should talk about. Before I move on to those things, I just want to close off the early years. one thing that I observe, which you are also an investor in Arena.OpenRouter vs. LM ArenaAlex Atallah [00:47:10]: Right.Swyx [00:47:10]: And we talked about Midjourney having that feedback loop of, A, B, C, D, and choosing that very. being very important. And you understand the flywheel. So how come you didn't build Arena, and how come Arena didn't build OpenRouter?Anjney Midha [00:47:23]: Well, Arena started before OpenRouter, right?Swyx [00:47:27]: They had the school projectAnjney Midha [00:47:29]: Yeah, LMSwyx [00:47:29]: And then it became a company.Anjney Midha [00:47:31]: LM Arena, yeah.Swyx [00:47:32]: So, but, and I know you had some Arena experiences, like the up comparison type things.Anjney Midha [00:47:37]: Yeah.Swyx [00:47:37]: But you never really went as hard as Arena did.Swyx [00:47:40]: And,Anjney Midha [00:47:40]: In doing up experiences?Swyx [00:47:42]: Yes. And LM Arena did have a router project based on LM Arena ELOs, which they never commercialized.Anjney Midha [00:47:48]: It's hard to do a company that does both because one company is taking data and selling it, and the other company really can't by default. So, I think there is, like, a branding reason that there are two companies here. like, when you set up OpenRouter, there's no training, there are no prompts, right, aside from what your provider policy set. Like, OpenRou- like, OpenRouter can't see your prompts or completions. If you want to see that as an org, you have to opt into it and enable it. And so we're, like, pretty conservative and careful about data policy and security. And privacy. And LM Arena is like, their business model is like oriented around the labs and,Swyx [00:48:34]: Because they give it for free, right? You don't give it for free to give it for free.Anjney Midha [00:48:37]: Yeah.Anjney Midha [00:48:38]: But we do give some. We like have free endpoints too, but like those free endpoints, we, I think we're not collecting any prompts. We're not like monetizing the data unless you, opt into it for some reason.Alex Atallah [00:48:48]: This comparison. you're not the first person to ask me this, and Alex knows this, but I was the interim, like the founder, like first CEO of Arena for the first five months when, and we were helping Anastasios and Waylin spin out of Berkeley. And, I did invest in that before, OpenRouter, but it was very strange to me the comparisons that outside, folks would make between the two projects because the missions were completely different. The founding entity for Arena, we called it the AI Reliability Institute because it was there as an eval service. Like the data, so to speak, that they were originally, offering the labs was how do you make the evaluation of models more reliable than like the state of the art at the time, which was like really just finger in the wind.Alex Atallah [00:49:38]: That's what Anastasios and Waylin's PhD work was as scientists at Berkeley, was on statistical methodologies for correcting, eval estimates, based on like intrinsic biases and how you collected the data.Swyx [00:49:54]: Yes.Alex Atallah [00:49:54]: AndSwyx [00:49:54]: Style control.Alex Atallah [00:49:55]: Style control and stuff like that. And which is very much like a, hey, how. If you're a scientist and you're trying to. the highest expectation customer for Arena was always like a training and, like a researcher at a lab. Whereas the highest expectation customer from my perspective that Alex like really understood and was the mission was to serve was like a developer, right? Who then takes the result of the research and then produces an application that's deployed to the world. It was a completely different problem and person that these two teams were focused on. And so from the outside in. I don't know if you remember this, but I have a distinct memory of a few weeks before we did the term sheet, together for OpenRouter, I'd given you a call because we were trying to get a pooled data set together from OpenRouter and from Arena to, create like an open source repository of prompts. these projects were so different in their goals that it was totally normal to me to be like, “Oh, yeah, let's call Alex and see if he'd want to team up on pooling data,” because they're so different. We need. We don't have that data at all. We. Like, we didn't have API prompts. We didn't, we didn't have like what developers want to do with the models, which is very different from what researchers inside a model lab want to do before releasing the model.Swyx [00:51:15]: Yeah.Alex Atallah [00:51:15]: Does that make sense? And so to this day, I think you see that this difference, even though at a 30,000-foot level you could. I guess you could conclude that Arena and OpenRouter are adjacent, but, the roadmaps, the missions and so on at the time at least were like in very different directions.Swyx [00:51:36]: That ideal customer, I get. I totally get that.Alex Atallah [00:51:39]: Yes.Swyx [00:51:39]: As a founder, I want to own everything, right?Alex Atallah [00:51:41]: That's possible.Swyx [00:51:42]: Like this is clearly an adjacency that I'm like gonna explore that.Anjney Midha [00:51:45]: Own everything meaning like you don't know what to do yet, so you wanna like make sure you catch PMFocus, Anthropic, and Roads Not TakenAlex Atallah [00:51:51]: No, I think what heAnjney Midha [00:51:52]: As quickly as possible.Alex Atallah [00:51:53]: You want to own the entire infrastructure space, and so you expand to whatever demand you can capture.Swyx [00:51:58]: You want to have a play in each end.Alex Atallah [00:51:59]: Yeah, I think that's, that's hard, in reality, because serving multiple customers is difficult.Swyx [00:52:05]: Clearly, this is the one focus, right?Alex Atallah [00:52:08]: Yeah.Anjney Midha [00:52:08]: Yeah. I still think even in the age of AI, like focus is,Alex Atallah [00:52:12]: Is criticalAnjney Midha [00:52:13]: Underrated and critical, not just because you end up with a better product by focusing your humans on it, but also because the world knows what your focus is.Alex Atallah [00:52:22]: One thousand percent.Anjney Midha [00:52:23]: The world can map like, “Oh, I have this issue. Which brand out there is going to help me with that issue? This is the brand that's known for that focus.”Alex Atallah [00:52:31]: Yes.Anjney Midha [00:52:32]: So like if I want real attention on this issue, like this really matters to me, I should go with the brand that cares the most about it.Alex Atallah [00:52:39]: To underscore Alex's point about how important focus is, in the early days of Anthropic, it was not easy to. Like people think that the early days of Anthropic were like super easy because they were on their 3 guys who left, but it was very

Featuring Noe Arceo, Vice President of Talent Acquisition & Workforce Development, OhioHealthNoe Arceo calls the team he joined at OhioHealth a good team with good bones. His first year went into the parts of the hiring process they could control, and time to fill came down 42%. The method behind it fits on a single sheet of paper he has carried since his Baylor Scott & White days.

https://apticallc.com/services/on-premise/co-managed-it/60% of IT professionals report burnout from escalating demands. If your team is drowning in tickets and stalled projects, there's a partnership model gaining traction that promises relief without replacing anyone's job. Aptica, LLC City: Fort Wayne Address: 1690 Broadway, Suite 10, Website: https://apticallc.com/

Eight hundred hospitals, nursing homes, maternal wards, and psychiatric centers have already closed, cut services, or landed at risk. Not projected. Already happened. Three stories this week, and every one of them lands on an HR desk.

Lev. 9-10. 9/20/26. An expositional sermon from the book of Leviticus.

Featuring Kellie Woods, Manager of Talent Acquisition, Franciscan HealthMost people running talent acquisition at a large health system started at a large health system. Kellie Woods didn't. She spent years at two independent community hospitals in Northwest Indiana, watched both get absorbed, and now covers nursing, physician practices, home health and hospice across eleven hospitals with a team of seven. That path shows in how she reads what is coming.

This week's new music:  Mike D | Headie One | AirbourneAlso: 2026 exceptionallism, brutalist flats, equilibrium opprobrium, saving graces, why are you doing this to people who care about you, song title nominative determinism, MCAI, context-specific albums, doing 7 AFL's job for them, beer isn't a food group, raised on Hasselhoff and Scorpions, genre of origin stickers, Jason Status, ornithology, arcane bylaws, EP week,  Bills-Lions, NFL's greatest names, some surprisingly well aged AFL chat, the Clippers' remaining tools, don't Walter yourself, don't Hyrox yourself, healthmaxxbros ruin everything and getting croc'y in Rocky.  1:23  Headie One   4:22  Mike D 10:28  Airbourne15:06  Next week's albums 23:04  After Dark - NFL, AFL, NBA, Hyrox vs Crossfit, Olympic rowing venuesNext week's new albums:  Team Dresch | Ty Segall | Satan Takes A HolidaySpotify playlists: Album review playlist | Doc's and Beeso's 2026 mixtapes | All our playlistsThe list: Our previous review albums and year-end top fivesFind us on: Spotify Podcasts | Apple Podcasts | RSS feed for other appsSocials: Beeso on Bluesky | Doc on BlueSky | Pod Facebook | Pod email

This Spanish-language conversation is the first in a series on alternative approaches to security in Latin America, "Security Reimagined." Here, WOLA's Adam Isacson speaks with Ernesto López Portillo, coordinator of the Programa de Seguridad Ciudadana at the Universidad Iberoamericana (Ibero) in Mexico City. Housed within the university's advocacy directorate (Dirección de Incidencia), the program functions, in López Portillo's words, as a "nodo"—a node where academia, civil society, journalism, government, international human rights bodies, and embassies converge around questions many Mexican universities avoid. López Portillo describes a two-track strategy: a "structural" agenda of long-term research, and a "coyuntural" agenda responding to emerging crises. He recounts a recent trip to Sinaloa, the state with the sharpest homicide increase under President Claudia Sheinbaum, and notes that the program has introduced concepts that are still too unfamiliar in Mexico and much of Latin America: police accountability, police certification, and external civilian oversight of police. A central case study is Ciudad Nezahualcóyotl, in the Mexico City metropolitan area, where the program has worked for fifteen years. Improvements there, López Portillo argues, came from sustained leadership, continuity across administrations, neighborhood networks, and unusual measures like having officers read literature as a sensitization strategy. He distills the lesson into three requirements: political commitment (not merely "will"), technical commitment, and social commitment in the street. The conversation turns to why reform so rarely takes hold. Citing Argentine scholar Marcelo Saín's concept of the "political mismanagement of security," López Portillo argues that most elected officials delegate security to police and military commanders and face no political cost for failure. Mexico's political culture, he says, prizes loyalty over competence, producing "loyalty pyramids" and officials who neither know what to do nor whom to ask. He also challenges donors and cooperation agencies for steering technical assistance toward easy topics like "capacity-building" and away from internal affairs and accountability. Not all of the picture is bleak. The program's first call for "best practices in citizen security" drew 47 submissions, producing a public interactive map of violence-reduction projects across states from Chiapas to Chihuahua, like prison sports programs, urban design interventions, women-led violence reduction, and indigenous governance. Asked what a newly appointed security official should do, López Portillo emphasizes convening neighborhood leaders, business chambers, unions, cultural figures, and grassroots organizations; publicly accepting the problem; and building permanent consultation and evaluation mechanisms, neighborhood by neighborhood. He closes with a regional warning: the rise of the "mano dura" in Latin America, he predicts, will normalize states of exception and Bukele-style practices adapted to each country, with growing popular acceptance, including among young people. The region, he fears, risks forfeiting the consolidation of constitutional democracy in the name of security.

Nik and Michael are joined by Sai Srirampur to discuss ClickHouse's new managed Postgres service. Here are some links to things they mentioned: Sai Srirampur https://postgres.fm/people/sai-srirampurClickHouse Managed Postgres https://clickhouse.com/cloud/postgresClickPipes https://clickhouse.com/cloud/clickpipeswalshadow https://github.com/ClickHouse/walshadowpg_clickhouse https://github.com/clickHouse/pg_clickhouseClickHouse PostgreSQL powered by Ubicloud https://www.ubicloud.com/blog/clickhouse-postgresql-powered-by-ubicloudHacking Postgres logical decoding sessions https://hacking.postgres.tv/topics/logical-decoding/ wal-rus https://github.com/ClickHouse/wal-ruspg_createsubscriber https://www.postgresql.org/docs/current/app-pgcreatesubscriber.html~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Featuring Ivy Singley, Director of Employment Services and Workforce Development, Infirmary HealthReplacing one bedside nurse ran just over $61,000 last year. Every single percentage point of RN turnover swings the average hospital's bottom line by roughly $290,000 a year. Treating that as a pipeline problem misses what happens inside the walls every day, and Ivy Singley has spent almost 30 years in those walls.

Hey, digital CX trailblazers! Ever wondered how to move beyond basic AI tasks and build a true AI "team" for your business? In Episode 110 of the DCX Podcast, I dive headfirst into the world of building managed agents in Claude. This isn't your average chatbot; we're talking about sophisticated AI entities that can automate complex workflows, coordinate with each other, and even learn from their past actions.I share my journey of using Claude to audit my own business and identify 36 potential agents – from content scouts to student support – and discuss how these AI team members can transform efficiency. We'll break down the core components of managed agents (agents, environments, credentials vaults, memory stores, and sessions) and explore how you can use them to streamline everything from podcast production to customer success management. Imagine AI agents monitoring for churn, identifying upsell opportunities, or even triaging guest pitches! This episode is packed with practical insights to help you start thinking about your own AI team.The prompt:"I am wanting to automate my business via a series of Claude Managed Agents doing a variety tasks that are currently being done either manually or via scheduled tasks in Cowork or Routines in Claude Code.I want to be quite niche with who these agents are so that tasks can be delegated to a high degree of detail across those agents. That means I'll also need some agents that coordinate with each other to get broader tasks done by their sub teams, much like a team structure works in the workplace.I would like you to audit my business based on conversations I've had with Claude and tools you have access to. Then create a detailed list of agents you suggest be created to help automate and coordinate this work.Also, make suggestions for additional tools needed to execute this work, however try to manage within the tools we already have in our tech stack.Ask me any clarifying questions you have until you are 100% certain of the task you need to accomplish and that you'll execute it to a high degree of accuracy." If you're a new Digital CX leader looking to fast-track your onboarding, an individual contributor wanting to learn more about Digital or a senior leader looking for ways of enabling your team - look no further than the Digital CX Masterclass: visit https://digitalcustomersuccess.com/masterclass. Support the show+++++++++++++++++Like/Subscribe/Review:If you are getting value  from the show, please follow/subscribe so that you don't miss an episode and consider leaving us a review.  FREE Digital CX Maturity Assessment:Want to see how your digital cs program stacks up against The Four Pillars of Digital CX? Take a free, 15-minute assessment followed by tactical advice for advancing your program. Register for The Digital CX Masterclass:An 8-week, cohort based course that will get you fully operational in a few weeks instead of a few quarter! Fast-track your digital CS maturity and start shipping great work within the first week!Subscribe to the Newsletter:A weekly-ish companion newsletter in which I share the latest in digital cs, my own work and occasionally annoying opinions about the state of CX.Thank you for all of your support!The Digital Customer Success Podcast is hosted by Alex Turkovic

Join David Wolf, portfolio manager on Fidelity's Global Asset Allocation team, for an update on market dynamics and portfolio positioning. David will share insights into Fidelity Managed Portfolios, including recent over- and underweight exposures, and discuss how key macroeconomic themes are shaping the team's outlook for the remainder of the year. Recorded on September 15, 2026. At Fidelity, our mission is to build a better future for Canadian investors and help them stay ahead. We offer investors and institutions a range of innovative and trusted investment portfolios to help them reach their financial and life goals. Fidelity mutual funds and ETFs are available by working with a financial advisor or through an online brokerage account. Visit fidelity.ca/howtobuy for more information. For a fifth year in a row, FidelityConnects by Fidelity Investments Canada was ranked #1 podcast by Canadian financial advisors in the 2025 Environics' Advisor Digital Experience Study. -- La répartition mondiale en action dans les Portefeuilles gérés de Fidelity – David Wolf Pour une version avec des sous-titres français, veuillez consulter https://youtu.be/5Usvje_IvI0 Joignez-vous à David Wolf, gestionnaire de portefeuille au sein de l'équipe de répartition mondiale de l'actif de Fidelity, pour une mise à jour sur la dynamique du marché et la structure des portefeuilles. M. Wolf fera le point sur les Portefeuilles gérés de Fidelity, notamment sur les récentes surpondérations et sous-pondérations, et expliquera la façon dont les grands thèmes macroéconomiques façonnent les perspectives de l'équipe pour le reste de l'année. Date : 15 septembre 2026 Chez Fidelity, notre mission consiste à aider le public investisseur canadien à se bâtir un meilleur avenir et à rester à l'avant-garde. Nous offrons aux particuliers et aux institutions une gamme de portefeuilles de placement innovants et fiables pour les aider à atteindre leurs objectifs financiers et personnels. Les fonds communs de placement et les FNB de Fidelity sont offerts par l'intermédiaire des conseillers et conseillères en placements et de comptes de courtage en ligne. Pour de plus amples renseignements, visitez fidelity.ca/commentinvestir. Les baladodiffusions DialoguesFidelity se sont classées au premier rang pour une cinquième année consécutive lors du sondage 2025 d'Environics sur l'expérience numérique des conseillers et conseillères en placements au Canada.  

AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:From being Anthropic's first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won't be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.We discuss:* Why risk, liability, and trust may become the binding constraint on AI adoption* Rune's path from reading the Scaling Laws paper to joining Anthropic in its earliest days* What Anthropic understood about scaling, compute, and the future years before it became obvious* Why Waymo illustrates the gap between AI capability and real-world deployment* AIUC's $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies* AIUC-1: a standard for AI agent security, safety, and reliability* How agents are tested for jailbreaks, hallucinations, and data leakage* Why most AI companies optimize the happy path without seriously stress-testing adversarial cases* Why AI standards may need to update every quarter instead of every decade* The emerging trust gap between frontier AI labs and governments* Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface* Why standards and insurance may need to evolve together* How Lloyd's of London can insure AI systems and bring trust to enterprise deployment* What happens if a $20 Cursor subscription contributes to a $200M plane crash* The Air Canada chatbot case and how AI failures are beginning to clarify legal liability* Why copyright may be one of the hardest AI risks to insure* Evals, mechanistic interpretability, monitoring, and models becoming aware they're being tested* The impossible CISO mandate: adopt AI fast, but don't let anything go wrong* Why robotics will make AI liability dramatically more consequential* Whether AI engineers should have Level 1, 2, and 3 certifications* AIUC's roadmap across agents, frontier models, robotics, and universal red teaming* Why AGI could become a question of national sovereignty* Why the labs can never fully serve as their own watchdogs* The Big Short problem: how do you stop competing watchdogs from racing standards to the bottom?Rune Kvist* LinkedIn: https://www.linkedin.com/in/runekvist/* X: https://x.com/RuneKvistAIUC* https://aiuc.comTimestamps00:00:00 AIUC's $40M Round and the Risk Bottleneck for AI00:01:07 From Scaling Laws to Early Anthropic00:07:58 Why Trust, Not Capability, Could Limit AI Adoption00:12:19 Founding AIUC and Building AIUC-100:18:52 How AI Agents Are Audited and Stress-Tested00:25:26 Frontier Models, Government, and the AI Trust Gap00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk00:38:14 Why Standards and Insurance Belong Together00:41:45 What Does an AI Insurance Policy Actually Cover?00:50:44 The $20 Cursor Subscription and the $200M Plane Crash00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust00:56:21 From AI Agents to Models to Robotics00:58:29 Copyright, Adverse Selection, and AI Insurance01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness01:08:36 The Impossible Enterprise AI Mandate01:11:52 Prediction Markets vs. AI Audits01:14:43 Should AI Engineers Be Certified?01:19:10 AIUC's Roadmap, AGI, and Who Watches the Watchdogs?TranscriptIntroduction: AIUC, the $40M Series A, and Risk as the Adoption BottleneckSwyx [00:00:00]: Okay, we're in the studio with Rune from AIUC, the Artificial Intelligence Underwriting Company, with our trusty co-host, Vibhu. Welcome.Rune Kvist [00:00:10]: Thank you. Thanks for having me. Thank you.Swyx [00:00:11]: What are you announcing today?Rune Kvist [00:00:12]: We have raised $40 million, led by Ribbit Capital and First Harmonic.Swyx [00:00:17]: You first came to my attention when Nat and Daniel invested in you guys. Is the story, like, pretty much the same? Like, what are you today versus what you thought you were back then?Rune Kvist [00:00:26]: When we raised our seed round, we had a hypothesis that at some point risk was going to hold down adoption. At that point in time, that felt kind of hypothetical, and I think that is now over. Clearly, the moment is now with Mythos and Fable. It's pretty obvious that literally the binding constraint on adoption is risk. And so for us, it feels like this is a natural continuation of the same hypothesis, but where previously it was speculation, now it feels like fact.Swyx [00:00:54]: And let's get a list of the customers that you're highlighting as part of your Series A.Rune Kvist [00:00:58]: Totally. Yeah. So we are now working with folks like Cursor, Harvey, Lovable, ElevenLabs.Swyx [00:01:05]: Yeah. Amazing. Congrats.Rune Kvist [00:01:06]: Thank you.Swyx [00:01:07]: So you were famously one of the first hires involved in GTM and product. I'm just kind of curious: what was your path into AI? Just recap.Rune's Path Into AI: Scaling Laws, Capital, and AnthropicRune Kvist [00:01:18]: Yeah.Rune Kvist [00:01:19]: Late 2021, I sold a company, my first company, an edtech company. I had a bit of time to think about what was next. I came across the Scaling Laws paper, and that just struck me like lightning. I was just like, “This is a big idea.” In short, the Scaling Laws paper just says the bigger the model, the smarter the model.Swyx [00:01:38]: So this is the Kaplan one, not the Chinchilla one?Rune Kvist [00:01:40]: Exactly, the Kaplan one.Swyx [00:01:42]: Yeah.Rune Kvist [00:01:42]: And the important thing that clicked for me there was, oh, now capital will understand this. If you put in more money, you get more money out, and so that will kick off a hype cycle. And so you get a sense of predictable returns, which is, in fact, what's played out. And so I just packed my bags. I'd never been to San Francisco. I'd never been there. I just packed my bags, flew out here to find the people who had written it. And at the time, they had just started a small lab called Anthropic. There were around 40 people at the time or so. Drank a bunch of coffee until I eventually got introduced to Dario. And at the time, they were wrestling with some of these questions of, like, should we deploy our models? Should we make revenue? How should we engage with the rest of the world? They'd just broken off from OpenAI, and it's been publicly reported that they were kind of concerned with how they were dealing with deployment. So they were wrestling with some of those questions. At this point, this is early fog of war, like early 2022. The hottest product at the time was, like, Jasper. Like, there's nothing out there. So where value was going to accrue, and what the different parts of the stack were going to be, were all open questions.Swyx [00:02:48]: I want to highlight to people, you ask these questions because you have a PPE background.Rune Kvist [00:02:52]: Yes.Swyx [00:02:52]: I actually was in Singapore in one of the sort of feeder programs for prepping people for PPE. So I had a tutor. We learned, you know, philosophy and politics and economics. But, like, I think your kind of background matters. Machine learning people who read the neural, Scaling Laws paper would not necessarily draw the same conclusions that you did. Whereas any capitalist would read that and go, “Holy s**t.”Rune Kvist [00:03:19]: Correct.Swyx [00:03:20]: Right?Rune Kvist [00:03:21]: Yes.Swyx [00:03:21]: Who tipped you onto that paper? Because it's not a paper that you normally read, right, like, in your circles?Rune Kvist [00:03:26]: Yeah. I think I'd actually, ever since AlphaGo, had some appreciation that AI was a big deal.Swyx [00:03:36]: Yeah.Rune Kvist [00:03:36]: But it kind of felt like it raised all these kind of interesting philosophical questions, but it was kind of not clear from afar where exactly that would go. But it was obvious enough that it was like, this is going to be a big thing if we find the kind of right mechanism to kind of get the techno-capital machine to work on this. But it was just not clear. And so I think there was some way in which, like, that became obvious, and also it wasn't as obvious at the time than it is now, right? Like, it was just like, wow, this is so interesting. But it still felt, coming from kind of a philosophy and economics background, it felt like if this turns out to be true, you're going to be wrestling with all of the big questions in society. Everything you've learned about politics gets thrown out of the window. Everything you've learned about economics at least gets challenged. And so what felt interesting was to be at that frontier that has ramifications across everything. So that's why I sought it out.Swyx [00:04:32]: I mean, clearly really good insight. For people who don't know, the PPE program is, like, where prime ministers are born. So then you end up meeting Dario.Rune Kvist [00:04:41]: Yep. First Dario, yeah.Swyx [00:04:43]: Yeah. Well, I mean, like, so did you get extra insights from talking with them that you didn't get from your original hypothesis?Anthropic's Early Conviction and the Scaling Laws Crystal BallRune Kvist [00:04:50]: If you read the Scaling Laws paper, you get this, like, very vague sketch of like, wow, this seems kind of important. There are some lines on a chart. This seems kind of important. And what I think the team at Anthropic had thought more about than anyone was like, what are the implications of this if you really play this out? And back then they had, kind of vision documents for what the world would look like in 2026, and they were kind of in vivid detail playing out how much compute is going to be needed, what the CapEx was going to look like, what some of the societal concerns were going to be, but also what is the amount of economic value coming out here? And so it kind of felt like they held a crystal ball that in hindsight turned out to just be dramatically correct. And they weren't holding it like they were obviously correct. They were just like, “Take this hypothesis really seriously.”Swyx [00:05:38]: Think it through, yeah.Rune Kvist [00:05:38]: And think it through in the same way as the kind of situational awareness that isSwyx [00:05:43]: Across the street.Rune Kvist [00:05:44]: Across the street.Swyx [00:05:44]: Your office, yeah. Oh my God, we're all living across the street in the same one square mile.Rune Kvist [00:05:50]: Correct. And that's now a couple of years old, but also people keep referencing it these particular weeks with Fable and Mythos, and it's like, wow, if you take this one idea seriously- For the Scaling Laws, a lot of things fall into place.Vibhu [00:06:03]: And keep in mind, at this point, this is the same team that did GPT-1, GPT-2, and GPT-3.Rune Kvist [00:06:08]: Correct.Vibhu [00:06:08]: Which is also, like, it's not just some experimentation. Like, this is a real model that we just scaled up.Rune Kvist [00:06:14]: And they had deep conviction in this idea: if you take a big blob of compute and data, it just wants to learn, and out of that will come smarter and smarter models. And all the particulars were not clear.Vibhu [00:06:26]: Yeah.Rune Kvist [00:06:27]: And all the implications were not clear. But their deep conviction in this, like, core thesis, and that was kind of dizzying. It was both phenomenally interesting and exciting, and also very quickly you get to, like, the world we know today will no longer be if this hypothesis holds. So it also just felt, like, important in some kind of grand sense.Vibhu [00:06:48]: What kind of shaped you there? So that was early 2022. Not only had GPT-1, GPT-2, and GPT-3 come out, but, you know, the amazing founders of Anthropic that have never split up, the only ones, they actually had the conviction to leave OpenAI, start their lab. You said there were about 40 people there. What was the time like there?Inside Early Anthropic: Mission, Deployment, and RiskRune Kvist [00:07:06]: It was kind of remarkably like what it looks like on the outside today. Extremely cohesive, extremely mission-oriented, and living in this tension between their two ideas, which is AI could both go really well and really bad, and we want to be part of building it. That creates astounding amounts of tension. And they were wrestling with this incentive challenge where they know they're in a race that they're in where you might get forced to cut corners, but it also felt very important to them to be at the forefront of technology. And all of those ideas were just present at that time. It kind of feels like that line has been just very clear, and I think kind of love them or hate them, they have really stuck to their guns. There's a core set of beliefs that they hold more deeply than most companies hold any beliefs.Vibhu [00:07:58]: Yeah. Fast-forward to today.Rune Kvist [00:08:00]: Yeah.Vibhu [00:08:00]: What does that lead us to AI underwriting company? What are you up to? What motivated you to start this?From Waymo to AIUC: Confidence Infrastructure for AIRune Kvist [00:08:05]: Yeah. AIUC builds confidence infrastructure for frontier AI through standards and insurance. The link from Anthropic to building confidence infrastructure, looking out the windows at Anthropic offices and seeing Waymos driving by. Already back then, early 2022, Waymos were in some ways like AGI for cars. Like, they were superhuman drivers, but you couldn't take one to the airport. And now, four and a bit years later, you still can't take your Waymo to the airport, despite now everyone having kind of looked at the evidence and being like, “They're better drivers than humans.” So in that particular instance, what's clear is that the binding constraint on AI being useful is not capability, but is that liability or risk or trust. That problem is, general. The reason why right nowRune Kvist [00:08:52]: Fable is not open for access is not because it's not a good model, it's because it's a very good model. It's just hard to make promises about what it will or will not do. And this problem gets worse as AI gets better. Basically, more intelligent AI can be more autonomous. That's more valuable, but also the risk surface grows. And so - what Waymo illustrates is that unless you build the confidence infrastructure to make promises about AI, or at least bring light to the risks, you grind adoption to a halt. Governments, banks, hospitals, militaries need to have some sense of what AI will and will not do to be able to operate for them to incorporate it. And that's the problem that we're trying to solve. Now, why standards and insurance? If you trace this problem back through history, every technology wave has had some version of this problem. So if you go back to, like, year 1900, electricity comesVibhu [00:09:47]: Ben Franklin.Rune Kvist [00:09:48]: Cars burn down, sorry, houses burn down, lots of people die. 1930s, cars are a big deal, kill lots of people. 1950s, private nuclear energy is a big deal, poses big risks. In each of those instances, the market runs ahead of regulation to create confidence infrastructure because that's required to make go/go decisions. That is required for adoption, and the market fundamentally wants adoption. And in all of those instances, common blueprint emerges between standards and insurance. The reason these two components is standards kind of provide the rules of the road, and they also specify, like, what are the tests that need to be run so we can get a sense of how high the risk is. So take in the case of cars, that's like a car crash. Great, everyone, they inform your insurance pricing today, they inform your purchasing decisions, et cetera. That's basically the risk framework. The insurers are important because they pick up the bill. So they are the private institution that is most on the side of. That is best incentivized to quantify the risks truthfully and then figure out all the ways to reduce the risk ‘cause that increases their profit. So they're basically, they help shape the incentives. And these two work really well in unison. Now, how does that show up as a company? Well, one of the things that was obvious even - or starting to become obvious even a couple years ago was that frontier companies, some of our customers today, like Cursor, Sierra, ElevenLabs, Harvey, were going to have a very easy time selling a pilot to a bank. The, like, the demo just sells itself. It's magic. But bringing that through, if you want to do a wall-to-wall rollout at a bank or a hospital, you have to go through the risk process. These banks have no idea even which questions to ask, let alone which answers are sufficient, let alone, like, how do they go and test whether these agents actually work the way they're supposed to. And so they had this problem of, like, what can we say to earn the trust? And we think there's, like, a golden sentence that goes something like, “Hey, I hear you're really worried about hallucinations or jailbreaks or whatever it may be. We've had an independent third party test us against the gold standard. We passed with flying colors. And as a vote of confidence, the world's most conservative insurers have looked at the data.” And they're willing to take some of the risk onto their balance sheet.Swyx [00:12:06]: Yeah.Rune Kvist [00:12:07]: So if something does go wrongSwyx [00:12:07]: There's money behind it, yeah.Rune Kvist [00:12:09]: Exactly. So that's kind of like the link between all this. We can get into some of the hard parts related to the technical testing, which is, I think, the crux of the matter, but I'll pause there.Swyx [00:12:19]: How did you and Rajiv come together? This-- there's always, like, you come across very confident and, you know, and we're announcing your Series A and all these things, but I want to see, like, the early initial stages of, like, idea formation.Cofounding AIUC with Rajiv DattaniRune Kvist [00:12:31]: Yeah. Rajiv is actually my soon-to-be brother-in-law.Swyx [00:12:35]: Oh.Rune Kvist [00:12:36]: So I'm actually, in a week and a half getting married to Rajiv's sister.Swyx [00:12:42]: Okay, now you're tight.Rune Kvist [00:12:44]: Exactly.Swyx [00:12:44]: Now you know.Rune Kvist [00:12:45]: So - Rajiv and I have known each other for a decade. Funny story, I met both Rajiv and his sister, Hena, at the same time when Hena and I were interns at McKinsey in London, and Rajiv was assigned as my mentor. And so met them at the same time. For the longest time, it was not obvious that we were necessarily going to work together. I was in startups. He was, an insurance partner at McKinsey. Three or four years ago, I think Hena convinced him that AI was going to be a really big thing. And so he quit his job, cushy partner job at McKinsey in London, packed his bags, flew to San Francisco, and ended up joining METR. You guys are probably online enoughSwyx [00:13:24]: CEO.Rune Kvist [00:13:24]: Exactly.Swyx [00:13:24]: We've, we've, we've heard of METR.Rune Kvist [00:13:25]: You see the plot-- the chart of the horizons of the tasks that agents can take on is doubling extremely fast. So he was COO at METR, led their partnerships with Anthropic and OpenAI to test their models before release, but also working closely with the US and UK government, to figure out, like, how do you know whether a model can be released? And in some ways, that was, like, the perfect background. He's spent a lot of time in insurance, knows that world, spent a lot of time with frontier testing of models. And so when I was bumbling around this idea space, starting with some of the ideas we talked about related to Waymo, as soon as we got into the content, we were both like, “Oh, this would be an amazing business to build together.” This is wrestling with the problem that we both think is the most important in the world from a market angle, which is kind of our intuitions is that the market can do a lot, and the faster AI moves, the harder it is for government to solve some of these problems. And then it took a little bit of time to work through what is it like to work with family.Swyx [00:14:27]: Sure.Rune Kvist [00:14:27]: And,Swyx [00:14:30]: Because you were already dating at the timeRune Kvist [00:14:31]: Yeah. Yeah, exactly.Swyx [00:14:33]: Yeah.Rune Kvist [00:14:34]: Already back then, itSwyx [00:14:35]: Yeah.Rune Kvist [00:14:35]: We felt like we were a family.Swyx [00:14:36]: Nice.Rune Kvist [00:14:36]: And so starting a business together felt like kind of a big step. And, here we are with just immense amounts of trust.Vibhu [00:14:43]: Yeah. So now you're a company of how big? How big are you guys now?AIUC-1 Certification: Agent Security, Safety, and ReliabilityRune Kvist [00:14:46]: There are just 20 of us now.Vibhu [00:14:47]: 20 of you guys now, have Series A, and you have your first certification out, the AIUC-1. Let's bring up the certification. So this is the agent certification, right? What goes into the process? I have, like, two questions here. One is, walk us through the certification, and two is, what is the process for a company to get certified, you know?Rune Kvist [00:15:08]: Great. As it says right on the top, AIUC-1 is a standard for agent security, safety, and reliability. The fundamental design principle is take all of the concerns that slow down adoption, so all the questions, all the fears that keep, security leaders in the Fortune 1000 up at night, and put them into one comprehensive framework. That's what you'll see there. You can see the six categories. Two, you want to ground all of this in technical testing. So one of the concerns with security standards that often feel kind of like theater paperwork is that they're not actually ground out in, does any of this work? Does any of this matter? And so we had a conviction from early on that was going to be the kind of crux, was to pass this, you must get tested every quarter, basically run thousands of simulations to see, well, so can it actually be jailbroken? How hard is it to jailbreak? How often does it hallucinate? How often does it leak data? Et cetera. And then the last, core idea here, if you scroll up to the top here, is to refresh it quarterly.Rune Kvist [00:16:08]: So the core trait of AI is that it moves extremely fast. Whatever concerns we're discussing today were not the same ones three months ago, and this will keep changing. Typically, standards update on a, like, a decade cycle is obviously not going to work. But the question is kind of how do you update it? And the core thing here was to basically get the risk leaders of the Fortune 1000 around the table. So if you go over to the left hereVibhu [00:16:32]: YeahRune Kvist [00:16:32]: You'll see the AIUC-1 consortium. The consortium is a group of risk leaders who run real banks, real hospitals, real critical infrastructure, who are facing these challenges every day. And we meet with these folks twice a quarter and hear what's top of mind, what is keeping them up at night. There's tremendous amount of desire for that conversation. And then we operationalize that into a specific standard that gets into. And actually, we can go into and look at whatVibhu [00:16:55]: YeahRune Kvist [00:16:55]: What even is the standard. So if we go back to introduction, out there to the left, scroll up a little bit to the wheel, click into reliability. So if you take something like hallucinations sits in reliability. There is a number of requirements here. If you go into the top one, prevent hallucinated outputs, hallucinate outputs, this is one particular requirement. This is a technical control. Basically, we want some kind of ground in this filter. The first thing you see here is what's called a crosswalk. So everyone and their grandmother has put out a framework, very high-level framework for what are the AI risks.Swyx [00:17:27]: This is basically your competition,Rune Kvist [00:17:28]: In some ways our competitionSwyx [00:17:29]: Not seriously, yeah.Rune Kvist [00:17:30]: We're, in fact, friends with them. We'll come back to why.Swyx [00:17:31]: Yeah.Rune Kvist [00:17:32]: But mapping everything together so you have one superset. The claim you're trying to support here is, if you follow this framework, then you can also see how you follow the other frameworks. But the meat of it comes down here in control activities and evidence. So control activities is like, great, you have this high-level requirement. How do you turn that down to something operational? Here's what you must do, and then what is the evidence that we're looking for?Rune Kvist [00:17:57]: And the reason we go this deep is that there's actually not that much confusion about what are the big concerns in AI. Everyone agrees to these. The question, like, what are you actually supposed to do? And so. What we found a lot of demand for is getting down to the specific evidence, that people need to look for. Whether you are Cursor building something or, even JPMorgan building something, but also if you're just a risk leader at JPMorgan, like what exactly should you ask for? What can you ask for without sounding stupid? Like if you ask for some-- you won't believe the amount of time a risk leader has asked for the IP rights to the underlying model to Cursor or something, and you're just like “Sorry, what?” Like,Swyx [00:18:39]: You slip it in there and you seeRune Kvist [00:18:40]: SlipSwyx [00:18:40]: See if you notice.Rune Kvist [00:18:41]: See if they. Exactly.Swyx [00:18:42]: Yeah.Rune Kvist [00:18:42]: Put that in the questionnaire. All right, so that's kind of what our standard is, and we update this every quarter with these folks, to keep up with the latest concerns.Swyx [00:18:51]: Can I double-click on this one?Controls, Evidence, and Third-Party TestingRune Kvist [00:18:52]: Yeah.Swyx [00:18:52]: So first of all, the website's beautiful. Like, it's so confidence-inducing which is the whole point where, like, okay, I know exactly what I'm signing up for when I talk with you. Like, I don't even have to talk to you. I can just see your whole, certification, which is great. But, like, okay, so from here, like D001.1 configure a groundedness filter, how does that get applied? Like, you have a person thatRune Kvist [00:19:16]: Yeah,Swyx [00:19:16]: Goes through it?Rune Kvist [00:19:17]: If you, go backVibhu [00:19:19]: I did see somewhere there's like, you know, fifty-one requirements, a hundred thirty controls. There's like a wholeSwyx [00:19:25]: Right. I just want to. Like, to me, this doesn't translateVibhu [00:19:27]: Yeah.Swyx [00:19:27]: Into a test or an eval.Rune Kvist [00:19:28]: Yes. So if you go into, on the left-hand side. So actually, if - before we go in there are three types of requirements. The first is technical controls, like you must implement some guardrails.Rune Kvist [00:19:42]: Two, there are test controls. So you must have an independent third party go and run some tests against you. I'll show you one of those in a second. And then three, there are policy controls. For example, you must have a person whose name is on the line when you guys f**k up, and you must have a plan for how you tell your customers and how you engage with them. They're kind of more traditional, standard type stuff. So in this particular instance, we just check whether they in fact have a ground in filter. So we will partner with an auditor. So we partner with auditors like KPMG or like Schellman who go in and do the thing auditors do, which is to check the evidence. In this case, that might be a screenshot, it might be part of the code that they need to review to see that it actually. Just that it exists.Swyx [00:20:21]: Oh, okay.Rune Kvist [00:20:22]: And then the second thingSwyx [00:20:22]: So you're not testing the effectiveness of it.Rune Kvist [00:20:24]: That's the second thing. So if you go downSwyx [00:20:25]: Yeah.Rune Kvist [00:20:25]: To the third-party testing for hallucinations out on the left, that's basically the next requirement. This is where we test how well does it actually work.Swyx [00:20:32]: Okay, and is it you testing or the auditor?Rune Kvist [00:20:34]: We test them.Rune Kvist [00:20:35]: We test them.Swyx [00:20:36]: That's a lot of work.Vibhu [00:20:37]: How long does testing take? So if I want to get certified, justCertification Timelines, Remediation, and Quarterly UpdatesRune Kvist [00:20:40]: Yeah.Vibhu [00:20:40]: How long does the end roughly take?Rune Kvist [00:20:42]: Yeah, the end, almost always is dependent on, like, our customers needVibhu [00:20:47]: Yeah.Rune Kvist [00:20:47]: To look something for us. It takes somewhere between, like, 3 to 10 weeksSwyx [00:20:52]: Yeah.Rune Kvist [00:20:52]: Depending on how up to snuff they already are. So some people show up to us with, like, extremely rigorous security programs. When we test them, it works extremely well. We can get that done very quick. Some people come to us, and they're not that far along. We give them kind of the spec that they need to build towards, and then their security teams and engineers get to work and build to meet the standard. The testing itself typically takes a couple of weeks, including the time for them to remediate. Often, we'll find something that we cannot pass, where this is actually just not up to the standard. - you won't pass the standard. And then they will need to go and implement additional safeguards or additional remediation that makes them more robust so that they can actually kind of hand on heart look at their customers in the eyes and say, like, “Hey, we've done truly our very best.”Vibhu [00:21:35]: And they're certified for a year and have quarterly updates?Rune Kvist [00:21:38]: Correct, yeah.Vibhu [00:21:39]: And, yeah, it's pretty interesting. I think, you know, what's changed since. So this is certifying agents in production, right? Your customers, like you've had Lovable, ElevenLabs, Intercom, and they've all gone through this certification.Rune Kvist [00:21:50]: Yes.Vibhu [00:21:51]: What has changed? So I see you post, like, you know, Q2 added MCP agent,How Agent Risks Are Changing: Coding, MCP, and Agent-to-Agent InteractionsRune Kvist [00:21:56]: Yeah.Vibhu [00:21:56]: agent communication. Any other things that you want to kind of highlight since the first iteration? What comes in quarterly?Rune Kvist [00:22:03]: Yeah. So some of the changes have just been agents are not just one thing. So, like, if you take agents like Cursor and compare them to Sierra, they're really quite different. And compare them to Harvey again, compare them to you out of againSwyx [00:22:16]: ElevenLabs, yeah.Rune Kvist [00:22:17]: ElevenLabs, they're all quite different. And so we wanted to design a standard that works for all of the types of agents. And we started with one that was, like, pretty text-based, like, honestly, pretty customer support-focused. That's where there's a lot of existing demand. And then over time, we've picked, some of the frontier companies in each of these other domains that we could work with and build out the standard, so, such that we know that the same standard works for code, it works for customer support, works for automation, et cetera. So that's been one big thing. Yeah, then some of the things that have been top of mind recently, Mythos is bringing up a lot of concerns for security leaders. We're starting to get more and more questions around agent interactions. It's very nascent, at the moment, but it's starting to emerge. There've been a lot of, questions related to OpenClaw and MCP. Again, like agents starting to interact with each other, is really top of mind. Then as coding agents have really taken off, that's also where banks and hospitals, et cetera, are getting more and more precise on what it is they need. So really dialing in as that start to be, like, where most of the tokens flow through in the world, getting much sharper on that.Vibhu [00:23:26]: Can you share for people that are listening that don't really think about this? Like you mentioned, there's the obvious stuff, you know, hallucination, citations. What are best practices that people should do when building agents? Like, if they come to you pretty ready with certification like, you know, they'll probably pass certification. What are the things people don't think about that they should have?Best Practices for Agent Builders: Stress Tests and GuardrailsRune Kvist [00:23:46]: The most important thing is that a lot of companies have not done a serious stress test. They spend most of the time, perhaps rightly so, optimizing for how does it work in the good case, the average case, how high-quality is the output for the customer. And a lot of these companies are pretty new, so they haven't spent a lot of time stress testing the what is there as an adversary on the other side? What are some of the complicated corner cases that you've not really considered? So I think that's, like, a frame of mind. And you'll also see this in startups. It often takes a while until they hire their first security person. They- And that's a whole different kind of risk surface than just building a good product. So a lot of that applies. Most companies actually also have the right kind of architecture. Most of them will have some kind of guardrails in place, either some that come out of the box from their model provider or they'll have built their own filters that sit in between. They just don't work very well. The difference between putting a classifier in place that, like, maybe goes and checks whether you're giving medical advice when you shouldn't and says, “Hey, if this looks like medical advice, filter it out.” Lots of companies have that in place. The question is whether it works. And it's actually pretty fiddly to sit down and think about all the ways in which you could ask for medical advice, read the academic literature on what are the kinds ofRune Kvist [00:25:03]: Framings or tricks you might play to get an AI to give you medical advice when you really shouldn't. And so there's, like, an area of expertise that's just missing. So what we find is that most people have the right building blocks in place. They don'- It doesn'- It's not rocket science, but the finicky thing is, like, getting into the corners and testing whether it works such that you can look your customers in the eye, or maybe a bank or maybe a hospital and be like, “This is going to work for you.”Vibhu [00:25:26]: I see. So we talked a lot about the agent-level certification. Where do you guys go from here? So announcing series A camera, we talked about this a bit. There's the whole security risk of Fable, government stepping in. You guys are kind of announcing that you're also going into model certification?Toward Model Certification: The Government–Lab Trust GapRune Kvist [00:25:46]: When we do a bit of cutting afterwards,Vibhu [00:25:48]: YeahRune Kvist [00:25:48]: We will not yet be announcing this,Vibhu [00:25:49]: NiceRune Kvist [00:25:50]: The question that is top of everyone's minds now is at the model level. And Mythos, then Fable, has really brought this to the fore that in addition to the commercial risk and the kind of economic security risks that are happening at the agent layer, the models are going to present risk in the national security category. The shape of the problem is very similar. You have some people that are on the hook if something goes wrong. In the case of agents, it's often security leaders in the enterprise. In this case, it's the government. They don'- haven't necessarily spent their entire lives thinking about what are the new risks that come here, what is the kind of data you might be looking for, how might you test that? But they do have to make sure that their concerns are addressed. You have some frontier AI companies that are deeply technical. They know a lot about the risks, but they fundamentally have an incentive to not always be truthful. So you have a trust gap between the government and the labs. And in every other industry, you end up with some kind of body sitting between, a neutral third party sitting between those people. There's no other industry where you allow people to audit themselves. So there is going to be a need for a third party that can take the rigor of the labs to run frontier technical evals, but can also speak legible trust in the way that the government trusts PwC to go and run financial audits. And they know that they output audit reports in a way that's consistent, that's easy to read, that's factual, that's, trustworthy. Those two things need to be brought together. And what we've learned from our work with agents is that if you want those-- that communication between those two parties to be smooth, there has to be one common standard that is public, that people can go and inspect. What are the risks that matter? Within each of these risks, what are the kinds of threat models that you're really looking for? You need to specify for each of those risks, what are the guardrails that need to be in place, and what are the tests they need to run to see whether those guardrails are effective? And then you need to go and run audits that are - technical audits that are consistent. So if you're trying to bring trust, it's extremely important that you methodically work your way through the risks. You can't send one researcher in and say, like, “Come back with whatever you find.” You need to be able to explain exactly what you did, exactly what you tried, exactly what you did not try, and therefore the kinds of promises you can and cannot make at the end of it. I think ofNeutral Third Parties, CAISI, and Model Risk AuditsRune Kvist [00:28:13]: Fable as a direct symptom of this problem that the government was told that there's a risk. The government may struggle to assess just how big that risk is. They call Anthropic, and Anthropic is trying to tell them, “Hey, actually, every model can be jailbroken.”Swyx [00:28:28]: That's not what you want to hear, right?Rune Kvist [00:28:32]: As the government, that might be hard to trust.Rune Kvist [00:28:36]: And we think that a broker is the most natural solution. In other markets, you see something like, in financial markets, you see Moody's. Moody's goes in, and they look at a bond, and they output a rating. They say like, “Here's the evidence we found. Here's the rating.” We don't decide whether anyone should buy this bond or not buy this bond. Well, that depends on their risk appetite. But we do provide this common information layer that everyone can rely on. In the case of Moody's, the government, points to them and say, “Hey, pension funds, you should probably really take care. You shouldn't risk your pensioners' money, so you can only invest in triple-A rated bonds.” That means that now the government doesn't have to staff thousands of financial technical experts to rerun forecasts every week to see whether things are correctly rated. They get to point to some neutral third party. So my hypothesis is, my hunch is that you will see a third party that sits between the government and the labs, and it could either be the government builds it themselves. So something like CAISI was set up to do exactly this. And the questionSwyx [00:29:44]: Sorry, I'm not familiar with CAISI.Rune Kvist [00:29:45]: CAISI is the Center for AI Standards and Innovation.Swyx [00:29:49]: Okay.Rune Kvist [00:29:50]: I won't get into the details, but it's a body of NIST that typically sets standards. So it's basically a government body that has AI experts. Yeah, exactly. Exactly.Swyx [00:29:59]: Very key. Very key.Rune Kvist [00:30:00]: Very key.Vibhu [00:30:00]: I think, you know, it's one of those things where when you just sit back and listen-- look at it, like, is there enough technical expertise in the government to measure, test these things right now? Probably not, right? And Fable is a result of, okay, we've had to scale back and pause things,Rune Kvist [00:30:17]: Yeah. And they have excellent people, but they have an extraordinarily small budget compared to the scale of the challenge that's ahead of us. And I think they have a role to play. The question is kind of like, who does what? We have now outlined the jobs to be done, and they're quite extensive. Every model release, there is an astounding-- Given that they take in any input, their risk surface is astounding. And so the question is really: what can only the government do, and what can the market provide here that can keep up with the pace as AI risk changes? Our perspective is that also at the model layer, the risks that people care about today are not the same ones they cared about three months ago. So the pace of legislation is too slow to deal with pinpointing the risks here. And so we think there's a lot that the market can do to surface timely information. Ultimately, there is a bunch of policy decisions here. Is the national security risks of a model too high?Swyx [00:31:12]: Yeah.Rune Kvist [00:31:12]: That's a political answer. But what we want to make sure is that the process that produces this risk information is compatible with very fast innovation. So you don't want to. This is not a question of like, can you slow the things down? Can you keep, the models locked up until-- for months on end until everyone can make a guarantee? But it is this, can you, in the time it. Given that the US is competing with China on releasing models, can you insert risk information that allows the government to, like, make rapid decisions on some of these questions? Balancing that trade-off between failing to adopt AI is going to put us at risk, but also reckless adoption is going to put us at risk. And that's a very kind of fine balance that they're going to need, like, a lot of high-quality intelligence to make.Chinese Models, Data Flows, and National Security ConcernsSwyx [00:31:55]: Just a side mention, because you mentioned Chinese models, any specific concerns that you're hearing from your CISOs about that? ‘cause I guess it's free, but.Rune Kvist [00:32:05]: CISOs have a bunch of concerns around data flows in general that they're really concerned about. So there's a lot of questions like, if these models are Chinese, where does that, where does that data go? I think a lot of this can be addressed, but they come up often.Swyx [00:32:18]: I mean, they understand they're running on American GPUs.Rune Kvist [00:32:21]: Some of them, some of them understand that they're running on American GPUs.Swyx [00:32:23]: They're not, like, phoning home every time you, like, call home.Rune Kvist [00:32:26]: No. A year ago, there was not a lot of understanding of this. I actually think, you're seeing the security leaders becoming kind of AI literate at a blistering pace, and you're actually also seeing my Twitter timeline that's very pilled and my LinkedIn feed that used to not at all be pilled kind of converge. They're both talking about Fable.Swyx [00:32:45]: Right. Yeah, that's true.Rune Kvist [00:32:46]: They are both talking about whether you can prevent models from being jailbroken these days.Swyx [00:32:51]: Yeah.Rune Kvist [00:32:52]: Like national security national security risks are now the conversation that is actually emerging. Other than that, I think you mostly see a kind of general picture: there are no concerns with any particular model or any particular model output, but there is a general nervousness of having critical infrastructure run on models that are not produced in America by Americans where the American government has control.Swyx [00:33:14]: But it doesn't necessarily show up in your framework that directly, or it might, I don't know.Rune Kvist [00:33:18]: There's a bit of stuff in there actually on the, like, the provenance of the models and disclosing that. But I think there's a bunch of use cases where running a Chinese open-source model is just the best solution.Swyx [00:33:27]: Yeah.Rune Kvist [00:33:27]: And a concern is slightly more macro here, which is not best addressed at any particular certification level.Vibhu [00:33:32]: Is there anything interesting that you see at the. You know, if you're trying to fill that middle gap, that mediation gap, any interesting stuff that you guys forecast would be required other than, you know, what the average person might expect?Cyber, Child Safety, Bio Risk, and Expert CoordinationRune Kvist [00:33:47]: There's a bunch of interesting questions about what are the risks that matter here. So right now, the risk of the day is cyber, because it's very real, very tangible. And some of the risks that are also emerging as pretty real and pretty tangible are things like child safety is becoming both extremely important, but also politically important. And then there are some of the risks that are coming down the pipeline that today feel kind of speculative, but people who spend a lot of time with the models see them coming down is things like, risks that relate to biology.Rune Kvist [00:34:18]: And specifically whether models will help adversaries produce biological weapons and making that extremely cheap, extremely accessible, producing-- making the chance of another COVID or worse pandemic. COVID was not engineered to be bad, as if you were trying to do that. So I think those are some of the risks that are coming down the pipeline. I think one other thing to just note is that agents are kind of deliberately narrow. So, like, when a frontier agent company puts a chatbot that interacts with customers, they've really tried to narrow the topics it's interested in talking about. Such that if you ask it, like, “What do you think of the president?” it will just decline, which means that the kind of risk area is somewhat smaller. For models, it is infinite. And so there's not a single expert out there who can competently evaluate the risks of cyberattacks and fifteen-year-olds having month-long conversations with a chatbot and seeing whether it will in fact recommend suicide or something horrendous like that, and can evaluate the risks that terrorists can use AI to produce bioweapons. The risk surface is just too big. And so the central challenge actually becomes how do you get those subject matter experts to work within a one coherent framework that outputs one coherent report and rating that the world can go and inspect? ‘Cause that global perspective is central, but there's not a single organization today that could produce that.Swyx [00:35:47]: And you would be the presumptive one when you put out your model standards.Rune Kvist [00:35:51]: We think there can be one company that can, with a consortium of experts, build one coherent standard. I think we've shown that across all of the enterprise risks today. We think it could be one company that could, with a consortium, specify the audit rules, basically like the inputs and outputs that all these technical experts need. What access do they need? How should they treat infosec- info security? They can look at whether the eval- evals are well-produced without necessarily being able to say, “Hey, is this a threat or not a threat?” But overall, evaluating whether the evals are good, well-constructed, that set of audit rules that basically becomes the interface for all these experts, we think one clearinghouse could put together. To be clear. When I say one company, I think of it as one company coordinating lots of this in the same way that when we saw our consortium, it's not like we say we have all the answers on agent security. What we say is we are taking on the role of eliciting all of the concerns and being the secretary that puts it together and runs a tight house such that the standard updates lockstep every quarter, and that the audit reports that come out, in this case, 100-page audit reports, uniform and crisp and clear all to the level of detail that is required for executives that need to make a clear go/go decision. So that's kind of the role that we think we might play.OWASP, Frameworks, and the Operational Audit LayerSwyx [00:37:11]: I think in many ways you're performing the role that OWASP used to do there, and you said, like, you know, competition and partners.Rune Kvist [00:37:18]: Yeah.Swyx [00:37:19]: Can you go more into, like, how they partner?Rune Kvist [00:37:20]: Yeah. So first of all, OWASP is basically an open source community of security practitioners that are coming together to build frameworks for addressing the latest security concerns. We think they are phenomenal at creating frameworks. We'- In fact, we'- First of all, we're partners with them, so we have a joint article. Two, we've learned a lot from them. We think they're a tremendous source of intelligence. What OWASP does not do is building the machine that runs third-party audits such that a company like Cursor or a company like JPMorgan could get a third party to go and review them against this and say, “Hey, you've passed the standard, and here is the report that you can use to build trust and preempt your partners' or customers' questions.” So they fundamentally try to do something different. You - They are part of the information gathering and intelligence gathering and creating clarity, but the operational layer of turning this into promises is not the business they try to be in.Swyx [00:38:14]: The standard is emerging and is doing very well. Was it necessary to then also do underwriting? Obviously it's in the name, so please remember you thought about it first. I feel like if you just have enough consensus, you don't actually need the money angle, but it does help.Vibhu [00:38:30]: I did want to also note, you guys are a profit company too, right? It's not profit where there's a whole business side to it as well?Why For-Profit Standards and Insurers MatterRune Kvist [00:38:39]: Yeah. Yeah, so I'm just getting crazySwyx [00:38:41]: I think about the money part.Rune Kvist [00:38:42]: Yeah. Yeah, let's get into the money part. Let's start from actually your question, profit versus profit. In the security space today, cybersecurity, most of the standards are produced by nonprofits. I think that's an issue.Rune Kvist [00:39:00]: The question you have to ask yourself is, how do you create good incentives for these standards to be good and keep up?Rune Kvist [00:39:09]: Nonprofits tend to not have these adverse profit incentives where they, hollow out their standard and create a race to the bottom, but they're also not at all responsive by default to the communities that they serve. There's no process-- They don't have customers that they serve where they go and ask, “What do you want? What do you want? What do you want?” And when you look at the overall satisfaction with the security standards today, people tend to just not like them very much. You do see in other domains, that profit standards can serve the world quite well. So there are examples, like we talked about Moody's before. It's not without flaws, but, it is absolutely critical societal infrastructure that gets run at an astounding scale today. Your credit score, it's FICO. It's also a profit business. And when you go back even further in history, some of the crash testing standards came out of insurance companies.Rune Kvist [00:40:06]: The insurance companies together founded the Insurance Institute for Highway Safety because they were very interested in, like, how can we use standards to drive down mortality and save money? Go back, prior-- Our name actually pays homage to the Underwriters Laboratories, UL, which, was started right around when electricity came out. Houses started burning down. Insurers, again, were paying the bill, and they were maybe also good people, but their profit incentive was, let's prevent houses from burning down. Let's test all the electrical products, the light bulbs. All the light bulbs in here are probably tested, the toasters, et cetera. And they set up, an entity to create those standards. Today, UL has a profit entity and a profit entity. What they've recognized, they spun - They started profit. They spun out a profit because what they recognized was like, hey, actually to serve customers well, you need a profit entity. The lesson here is one of the ways that the market can align incentives so you're both responsive to customersRune Kvist [00:41:07]: And not hollowing out your standard over time is to align it with insurers because they fundamentally have good incentives. And so if you're a profit standard that works closely with insurers, you get the feedback loop in such that you're really tuned into your customers, but also have their interest at heart. So that's the model that we - the kind of inspirational model that we've learned a lot from, and that's also where the name comes from. In some ways, the term underwriting can both be associated with insurance, but it's also a broad term for, like, making decisions.Rune Kvist [00:41:40]: If you underwrite a decision, you're fundamentally kind of taking ownership for the consequences of it.AI Insurance Contracts, Lloyd's of London, and ElevenLabsSwyx [00:41:45]: Yeah, I mean, what does an insurance contract look like for AI?Rune Kvist [00:41:49]: Yeah. Most of the demand comes today for insurance contracts is, sitting between people who've built AI and people who are buying AI.Swyx [00:41:56]: Yes.Rune Kvist [00:41:57]: And what you want—the reason why people want insurers involved, both for the traditional reasons, hey, if something goes wrong, we want to be compensated, but it's in particular because insurers can bring trust to the equation. Because insurers will pay for the damages, if they're willing to write an insurance policy, that is them saying, “Hey, we think there is risk here, but that is manageable.” And that is kind of a. Their incentive aligns with the enterprises adopting it, so that's a really a good signal to the market. In the same way, actually, one of the things that Waymo tried to get their first permit to even operate in San Francisco was to get a lot of insurers to stack up a huge insurance policy. In the case if something went wrong, not because Google can't pay, but because it was very valuable to have a third party go and look at that dataRune Kvist [00:42:47]: That are trusted by governments, trusted by enterprises as conservative people and say, “Hey, we've looked at it. We're actually willing to take some of this on our balance sheet.” So that's, that's kind of the reason why people are interested in it. What it looks like is, in some ways like every other insurance contract. You specify what are the perils you want to cover, how much do you want to cover them, like up to what limits, and what does it cost to cover that. And in the case of, if we take a really concrete example, ElevenLabs, bought a first of its kind AI agent insurance policy. They work with some of the biggest, enterprises that work with governments. They're really interested in going above and beyond and making promises to their customers. So they wrote a policy that covers just some of the core concerns that their customers have been asking about. And, the crucial thing was really to get Lloyd's of London, the world's oldest insurer, one of our partners, to look at this data and be that third party alongside us to say, “Hey, we think there's something here that's worth underwriting.” and that's actually what it looks like. And so they will show that contract to their customers, and they can see how much they're covered for. They can see what exactly it covers, and that will also probably change next year. They will want to write an insurance policy that might cover more.Swyx [00:44:04]: When you say Lloyd's, is it reinsurance, or are they sharing somehow at the same level orRune Kvist [00:44:11]: Yeah. So typically, the way, new companies get into insurance is that they partner with insurers such that the insurers take the majority or all of the financial risks. Fundamentally, if insurance is useful, because it brings trust, you have to be able to pay the bill. Lloyd's of London is 400 years old. They've never not paid a claim. They're extremely trusted. What Lloyd's of London struggle to do on their own is to figure out which of the risks are real, what should we be looking for, what are the kinds of technical controls, and running the tests. So they use AIUC-1 as kind of the underwriting framework, and we produce a bunch of eval results that then directly feed in to inform the pricing. So this means that ElevenLabs customers know that payment will be there. They don't have to look to our series A and see, like, do we think they have enough cash on the balance sheet? They will look at Lloyd's.Swyx [00:45:05]: Yeah.Rune Kvist [00:45:05]: Yeah.Swyx [00:45:05]: And Lloyd's, like, famously very creative. I think I remember some headline like, they insured Jennifer Lopez's, butt or something.Rune Kvist [00:45:13]: Correct.Swyx [00:45:13]: Right?Rune Kvist [00:45:13]: And I think, was it, David Beckham's right foot?Swyx [00:45:16]: So, yeah. Right?Rune Kvist [00:45:17]: And stuff like this.Swyx [00:45:18]: So, like, clearly not a large data set.Rune Kvist [00:45:22]: Exactly. It's actually a remarkable institution that's both kind of has some of the truly school virtues of having been around for a long time. They, like, really. They really operate like a trusted entity, and they have appetite to figure out the future. And I think there's a lot of recognition that both there is, like, tremendous amount of risk in AI that is poorly understood today, so getting into this business carries real risks. But also this is where lots of the risk exposure will happen in the future. This is the one market where risk is truly growing. This is the one market that will also take out some of the existing markets. Take, like, auto insurance. When there are no human drivers, how's that market going to look? Well, it's clearly going to change. How are you going to assessSwyx [00:46:08]: You want to insure Waymo?Rune Kvist [00:46:10]: I. All I'll say is the principles for how you insure Waymo are very similar to how you insure other kinds of AI.Swyx [00:46:15]: Right.Rune Kvist [00:46:15]: So again, crash testing, that's what we do for customer share at Lovable. That will also need to happen for Waymo, which is not how you do it for human drivers. So there's this growing awareness that the world is changing very fast, and the only way to learn how to underwrite AI is to write some policies. You may incur some losses and think of that as R&D expense, really. But the question for them is, like, who are the trustedtechnical partners they can get into this business with that can help them navigate and make sure they don't make, kind of foolish mistakes? But also who is willing to hear the wisdom that they have? They've done this before. They've seen it was. They were there when cyber came out. So there are lots of ways in which AI feels completely new, but there's also lots of ways in which risks look the same. And so there's actually a tremendous amount of wisdom sitting in some folks that may have gray hair, but really have, like, a keen sense of, how to quantify risk.Swyx [00:47:08]: Yeah. And the number is. So it's basically like I want fifty million dollars worth of coverage against these perils, and Lloyd's will give you a quote on it, and then you have, like, a small markup or something, and then you turn it around and do that? Is that as simple as it is?Risk Capital, Premiums, and Working with InsurersRune Kvist [00:47:23]: You basically share some of that premium.Swyx [00:47:25]: Yeah.Rune Kvist [00:47:25]: X percent goes to the people who do the pricing of it.Swyx [00:47:28]: You're. It's kind of like a. It's kind of like a merchant bank for insurance type of thing.Rune Kvist [00:47:33]: Exactly. You basically split the fee, and you can think of the insurance supply chain as, like, there's bringing the capital, there is doing the pricing, and there is doing the distribution. And typically, you will pay out some X percent of premium here, Y percent of premium here, and the rest of it will go here.Swyx [00:47:46]: Does all the insurance world work like this, or is there some point at which, like. So if right now you have equity capitalRune Kvist [00:47:51]: Yeah.Swyx [00:47:52]: At some point, maybe you start raising, debt or whatever, and then you have enough of a bank account and enough history, let's say you've been in operation for ten yearsRune Kvist [00:48:00]: Correct.Swyx [00:48:00]: That you don't need Lloyd's anymore?Rune Kvist [00:48:02]: That's totally an option. And I could see some worlds where that makes sense, specifically if there are risks that we feel high confidence that we'd want to insure where the incumbent insurers are too slow to find appetiteSwyx [00:48:13]: Okay.Rune Kvist [00:48:13]: Or simply struggle to evaluate it such that they don't want to do it. But by and large, in general, you do not want to compete with insurers on, bringing risk capital to the game for two reasons. One is that's fundamentally a cost of capital game. They have extremely low cost of capital. Startups have high cost of capital, by and large. And two, you want to hedge your bets, and it's very helpful then to also have a portfolio of home insurance, of car insurance. And we're not about to become a car insurer nor a home insurer.Rune Kvist [00:48:43]: So they have some natural advantages, which makes it much more likely that we'll partner.Swyx [00:48:48]: Yeah.Rune Kvist [00:48:48]: And they bring that, the capital at scale, and we bring the technical expertise.Swyx [00:48:51]: You're, you're going to work with them for a long time.Vibhu [00:48:52]: How are the discussions with the insurers as well? So basically, they're going off of your certification, right? They're trusting the diligence on you that your certification is valid, you tested the right things, and they're backing the money that, you know, you have the right testing in place. So any interesting takeaways from working with insurers?Rune Kvist [00:49:12]: I think the maybe the first thing is they feed into the standard as well. So if there are things that they feel like they need that they're not seeing, we are also taking that as input into the standard, because fundamentally we think a good standard is one that creates a really healthy promise ecosystem, and we think insurers are a critical part of that. And again, they are the most well-incentivized to. They see all the lost data across every. Any particular CISO knows their particular concerns. Insurers see the concerns across the entire portfolio and often have direct access to, like, what exactly happened, who was at fault, et cetera, as they do part of their forensics. So they're actually, like, a great source of intelligence on this. One of the big takeaways from cyber insurance, which is a market that didn't work that well, was that the insurance and the technical expertise was not married up. What our conviction is that standards have to precede insurance. Fundamentally, what everyone first and foremost want, whether you're a CISO at JPMorgan or a CISO at Cursor or an underwriter at Lloyd's of London syndicate, is you want to not have an incidentRune Kvist [00:50:19]: In the first place. You want to know that the risk is well-managed, and only then does insurance start to make sense. So we'll see the standard ecosystem basically run ahead of the insurance. And the reason why we. You asked us kind of why I also do insurance, this is kind of proving what we think a whole promise confidence infrastructure ecosystem needs to look like, and we think it's very compelling to bring that to life, even if we think the standard is kind of the core linchpin that unlocks the rest.Claims, Liability, Air Canada, and Duty of CareSwyx [00:50:44]: There's been no claims yet, right?Rune Kvist [00:50:45]: Nope.Swyx [00:50:46]: This is one of those things where, you know, if people haven't really worked through what it means to cover things.Rune Kvist [00:50:52]: Yeah.Swyx [00:50:52]: So for example, I pay Cursor $20 a month.Rune Kvist [00:50:55]: Yep.Swyx [00:50:56]: And I write a vibe code something that makes, a plane crash, causing $200 million worth of damage.Rune Kvist [00:51:02]: Yes.Swyx [00:51:02]:

ChatGPT Is Inside Epic, 40 Mergers in Six Months, and 92% Bracing for StrainThree stories, one direction. Technology is accelerating, consolidation is accelerating, and the people running health systems have stopped waiting to see how it shakes out.

The BIG Show shares stories from listeners about their negative experiences with condo management & weigh in! Connect with us on Instagram: @kiss92fm @Glennn @angeliqueteo @officialtimoh Producers: @shalinisusan97 @shaistadinis - See omnystudio.com/listener for privacy information.

Launching a property management business might seem risky in a tougher market, but the current conditions could create an opening for new agencies willing to do things differently. On The Property Management Excellence (PMX) Podcast, Alex Whitlock is joined by Bairave Jeyasothy and Mignon Ahrns from Managed to explore why the current market could be an opportunity for property management start-ups despite the challenges facing the broader real estate sector. The conversation looks at what it takes to stand out, with a clear point of difference, strong relationships and transparency emerging as key ingredients for agencies trying to win and retain owners. Technology is another major focus, with the trio explaining how automation can help start-ups scale their rent rolls without simply adding more people, while the right training can ensure teams get the most from their tools. They also examine the opportunity created when sales and property management teams work together, with stronger collaboration potentially turning existing relationships into new managements and helping agencies grow their rent rolls organically.

70% of Engagement Comes Down to One Thing. Your Boss.Featuring Burl Stamp, FACHE, President, Stamp and Chase | Author, Becoming a Better BossBefore we get started, sit with this number: 70%. That is how much of employee engagement is determined not by your comp structure, your benefits package, or your culture initiative. It comes down to one thing. The person they report to.Bo sits down with Burl Stamp, former hospital CEO, fellow of the American College of Healthcare Executives, and author of the most timely leadership book in healthcare this year. Burl has spent more than two decades studying what separates the leaders people want to follow from the ones they simply tolerate.

L&G's James Giblin, Fund Manager, and Dorian Squires, Investment Director at Apollo Investment Management, go beyond performance numbers to explore the people, processes and partnerships that sit behind a managed portfolio service.Drawing on their own experiences, they share real-world examples of how investment decisions are made, how funds are selected and monitored, and how advisers and fund managers work together through changing market conditions.The discussion includes lessons from periods of market volatility, insights into the due diligence and governance that underpin portfolio construction, and real-world examples of how investment teams respond when markets come under pressure.This podcast is hosted by Sarka Halas, Content Manager, and was recorded on 2 September 2026. For professional investors only. Capital at risk. Risk management cannot fully eliminate the risk of investment loss. It should be noted that diversification is no guarantee against a loss in a declining market.For illustrative purposes only. Reference to a particular security is on a historic basis and does not mean that the security is currently held or will be held within an L&G portfolio. The above information does not constitute a recommendation to buy or sell any security.

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Managed money flows out of grains ahead of the USDA report on Friday, taking a cautious approach to what the yield projections may be. Corn and soybean yields are expected to be lower, but with a wide range of estimates. Wheat futures slid with pressure from row crops and the potential for higher winter wheat acreage. Arlan Suderman with Stone X Financial recaps today's trade.

In this episode of The IT Experts Podcast, I sit down with Adam Monks, group director for technical services at Academia Technology Group, to unpack the mindset shift that helped him build a ten million pound co-managed MSP. Adam Monks started his career on the help desk over twenty years ago and worked his way up through service delivery, customer success and professional services before deciding to build something of his own alongside his co-founder Chris. What makes Adam Monks' story so valuable for MSP owners is that his business, SmartDesc, grew almost entirely through a co-managed model, working with organisations of three hundred and five hundred staff from the very beginning, rather than starting small and scaling up gradually.     We talk about the early days, where trust and reputation carried everything. Adam Monks explains that in the beginning, clients are buying you, not your accreditations or vendor badges, because those simply take time to earn. Doing what you say you will do, delivering safely and quickly, and building a reputation for getting things done became the foundation for growth. That same principle stayed true even as the business scaled into the millions, though the accreditations and credibility markers mattered more as the deals got bigger.     One of the most interesting parts of the conversation covers the middle years, where Adam Monks describes the frustration of being seen as too small for the bigger contracts, even with strong references and a track record of results. He shares how sector focus, particularly within the charity and nonprofit space, gave SmartDesc an edge because those organisations were more open to sharing recommendations with each other. We also discuss the nervous moments, including a year when two founding customers, who made up around seventy percent of revenue, were both due for renewal at the same time. Adam Monks recalls standing in the street with his co-founder weighing up what could happen if neither renewed.     A turning point for the business came when growth stopped being reliable. Adam Monks talks candidly about the year the numbers flattened, and how that pushed him to bring in proper marketing support for the first time. Working with a specialist agency, SmartDesc introduced a monthly newsletter, quarterly round tables, webinars and events hosted through their Microsoft Elevate Partner status. Adam Monks is clear that round tables delivered the strongest results, not through instant leads but through consistent, senior level conversations that built trust over nine to twelve months. Alongside this, becoming a direct Microsoft CSP opened an entirely new revenue stream.     We also get into the leadership challenge that so many MSP owners will recognise. Adam Monks describes the awkward middle stage of twenty five to forty staff, where you are too big to stay hands on with everything, yet too small to have a fully structured leadership team. Bringing in a fractional finance director helped SmartDesc break through that stage, giving visibility into which parts of the business, MSP services, fractional leadership, cybersecurity and procurement, were genuinely profitable, and allowing accountable leaders to own each area.     Adam Monks also shares his honest view on running fully managed and co-managed services side by side, including the margin differences between the two models and why choosing fewer, bigger customers made sense for his business. The conversation closes with the story of SmartDesc's merger with academia technology group in October 2024, a deal that took the combined business to around two hundred and seventy five staff and gave Adam Monks a seat on the board.     If you are an MSP owner who has ever felt boxed in by size, or you are wrestling with the leadership gap that comes with growth, this conversation with Adam Monks will give you plenty to think about and apply straight away.    Connect with Adam Monks through LinkedIn and website.     Make sure to check out our Ultimate MSP Growth Guide, a free guide that walks you through a proven process to take your MSP from stuck to scalable, without working even more hours. It's 44 pages rammed with advice, insights and inspiration to help you decide what support is available to you now if you want to grow and scale your business. Click HERE to get your copy.    Connect on LinkedIn HERE with Ian and also with Stuart by clicking this LINK    And when you're ready to take the next step in growing your MSP, come and take the Scale with Confidence MSP Mastery Quiz. In just three minutes, you'll get a 360-degree scan of your MSP and identify the one or two tactics that could help you find more time, engage & align your people and generate more leads.    If you're serious about growth and want to explore what this could look like for your MSP, you can book a Right Fit Clarity Call with us HERE.  OR   To join our amazing Facebook Group of over 400 MSPs where we are helping you Scale Up with Confidence, then click HERE  Until next time, look after yourself and I'll catch up with you soon!

Why Healthcare HR Tech Fails: It's Never the SoftwareFeaturing Stephanie Wright, Founder, Wright Talent AdvisoryOnly 43% of HR professionals say their HR technology is effective. Fewer than half. These are organizations that ran vendor selection, sat through demos, built a business case, got budget approved, signed a contract, and went live. Then more than half of them looked at what they bought and said it was not working.Luke sits down with Stephanie Wright, a healthcare TA implementation specialist who has worked across Workday, Phenom, and other major platforms at health systems including Carilion Clinic. She launched Wright Talent Advisory in Dallas to do full-time what most HR leaders only do once in a career.

Hospital M&A Is Up 33%, CommonSpirit Has 250 AI Tools Running & UHS Just Bought a Mental Health AppAugust 31st, 2026. Bo Brabo and Luke Carignan. Three stories, one theme: health systems are making big, deliberate moves. Merging before they have to, deploying AI at scale, and buying digital companies to own the front door to care.

What happens behind the scenes at a managed IT company? Learn about the invisible work running 24/7, why response speed beats truck rolls, and when co-managed IT might be your best move. Read more at https://ergos.com/managed-it-company-services/ Ergos City: Houston Address: 6110 Clarkson Ln Website: https://ergos.com/

GEC 25 Year Impact Report: https://www.gec.ie/impact-report Niamh Collins has spent more than 15 years inside Ireland's startup ecosystem, and for the last three she has run the Guinness Enterprise Centre — one of Ireland's first enterprise hubs and now one of its largest, home to over 160 companies across five floors of a former Guinness hop store in the Liberties. Her vantage point is the connective tissue of the ecosystem: the introductions, signposting and supports that turn a founder with an idea into a company selling into the US, the UK and Europe.In this conversation, we get into:What "managed serendipity" looks like in practice — how one founder's presentation to a visiting Tampa Bay delegation turned into a new customer, a research partner and a US investor introductionThe supports most Irish founders don't know exist — monthly Local Enterprise Office clinics, Mentoring for Scale, and soft-landing agreements with hubs in the UK, US and EuropeWhat 25 years of the GEC adds up to — 1,300+ companies through the doors, and approximately €140M in turnover and €73M+ in exports from GEC companies last year aloneWhat "graduating" really means — Urban Fox's €8M raise, Mobility Mojo's €4.2M, and why the GEC doesn't say goodbye, just "see you for a while"The role of the Irish diaspora — mentoring, investing, opening doors, and Niamh's open invitation to the Digital Irish communityIf you're an Irish founder — or part of the diaspora wondering how to help from abroad — this episode is a map of the ecosystem behind Ireland's startup success.Niamh Collins has served as Centre Director of the Guinness Enterprise Centre since 2023. She brings over 15 years' experience within Ireland's startup ecosystem, with a strong track record of supporting early-stage companies to grow and scale through incubation, acceleration, and tailored business support programmes. Prior to joining the GEC, she was Director of the NovaUCD AgTech Innovation Centre and previously held the role of Chief Operating Officer at the DCU Ryan Academy.The Guinness Enterprise Centre is Ireland's largest enterprise campus, located in Dublin 8, and is home to 160 companies ranging from individual founders to established scaling teams. These companies operate across a diverse range of sectors, including digital health, medtech, technology, sustainability, aviation, professional services. The Centre provides a combination of co-working and private office space, alongside a comprehensive suite of business supports, programmes, and advisory clinics designed to enable growth, innovation, and international expansion.Want to get in contact with the Digital Irish team? Email us at podcast@digitalirish.com

Tell us what you think of the show! While some students enjoy summer break, their school buses are still hard at work. Rather than transporting pupils, they're hauling electrons and supporting the grid during critical periods of peak electricity demand.Such is one of the tantalizing promises of vehicle-to-grid, or V2G, technology, and it's far from fantasy. More than 30 utilities across 21 U.S. states currently have V2G school bus projects, from California to Connecticut, North Carolina up to Massachusetts. Since they can charge their beefy batteries of 200 kilowatt-hours or more when power prices are low, the peak-shavers-on-wheels are proving to be a valuable tool for utilities, especially as demand surges and affordability discussions grow louder. And applications go beyond buses. More broadly, vehicle-to-everything (V2X) technology enables batteries to communicate with and be discharged to external systems, whether that's an electric vehicle (EV) or something else. Such virtual power plant (VPP) programs are still in their infancy, and regulatory frameworks and standards to support them are far from established, but early studies are beginning to tease out the enticing possibilities for managing EV charging and smart battery dispatch.On this episode of the Factor This Policycast, the latest in a series presented in partnership with national business association Advanced Energy United, we dive into a world where smart charging plays a critical role in grid reliability and keeping electricity prices manageable. Host Paul Gerke leads a discussion between electrifying transportation policy principal Elizabeth Stears of Advanced Energy United and Leah Brams, market development manager at Highland Fleets, an electrification-as-a-service provider helping school districts, local governments, and commercial fleet owners affordably convert their traditional diesel vehicle fleets to EVs. They outline the policy reforms needed to unlock V2X's potential and identify the roadblocks to wider adoption. Brams and Stears identify how utilities can best leverage such technologies and share learnings from pilots- what works, what doesn't, and how we can refine policy to roll idling electric buses into peak-demand squashers.Energy policy on your mind? Check out all episodes of the Factor This Policycast

96% of Applicants Fall Into the Abyss. Take2 AI Is Pulling Them Back Out.Featuring Yaniv Shimoni, Co-Founder, Take2 AI | ASHHRA Podcast SponsorThe average hospital has 43 unfilled nursing positions. It takes up to 102 days to recruit an experienced RN. And your recruiting team is talking to three to four percent of the people who apply. The other 96%? They fall into the abyss. Yaniv Shimoni built a company to fix that — and healthcare was not the plan. The CHROs made it one.Bo sits down with Yaniv, co-founder of Take2 AI and Stanford GSB alum, to unpack what agentic AI actually means for healthcare talent acquisition and how health systems like CommonSpirit, Temple Health, and the VA are already using it.

Four Years Left: The 2030 Staffing Cliff, APRN Expansion & Why 76% of Leaders Can't ExecuteAugust 24th, 2026. Bo Brabo, Luke Carignan, and ASHHRA Executive Director Jeremy Sadlier. Four years to 2030. Three stories. One question underneath all of them: what are you actually doing about it?

Welcome back to Everymum the podcast, with me Aisling Keenan. What happens to a woman when the physical body, social identity and daily life she knew are all suddenly altered? That's what my guest this week and I discuss – Nicole Gaffney Hahesy, or Cole Gaffney as you might know her online, is an influencer and mother to a 7-month-old girl named Éala. We talk about her placenta previa, her tricky birth, Éala's stay in the NICU, and the fact that Éala still doesn't care much for sleeping. We talk about CMPA (Cow's milk protein allergy), reflex and colic and how Nicole faced all three in the early days of Éala's little life. Nicole has built an online presence around motherhood, style and everyday life, so I wanted to talk to her about the stuff underneath the nice pictures: postpartum body image, getting dressed when your body has changed, figuring out who you are when your priorities have completely shifted and the reality of trying to feel like yourself again when you're also becoming somebody completely new. Because I think there's a huge pressure on women to be grateful for motherhood every second of the day, while simultaneously looking like they've somehow emerged from it completely unscathed which we ALL know is not the case. Enjoy this episode with Nicole, and I'll be back next week with more. Hosted on Acast. See acast.com/privacy for more information.

Tom asks: "Sorry for this stupid question. I have read a lot of the red Loeb, but never understood how the Auxiliary forces were managed. By Roman officers, or were they independent?"   Join us on Patreon patreon.com/ancientwarfarepodcast  

Leadership Is a Verb, Not a Title — and New Hope Treatment Centers Is Proving ItFeaturing Tabitha Bramblett, Chief People Officer, New Hope Treatment CentersMost organizations promote their best performer into a leadership role and then wonder why everything falls apart. Tabitha decided to build the program that changes that — and she did it at a 500-person behavioral health organization serving kids and teens across four states.Luke and Bo sit down with Tabitha to talk about what leadership development actually looks like when you build it from scratch, why culture isn't a values poster, and what compassion has to do with your bottom line.

In "Eliminating EDI Headaches: Managed Integration vs. Ticket Queues", Joe Lynch speaks with Co-Founder and Leader of Atadex, Mitch Bernet, about how fully managed EDI and API integrations eliminate operational bottlenecks, speed up customer onboarding, and drive supply chain profitability. About Mitch Bernet Mitch Bernet is the Co-Founder and Leader of Atadex, bringing decades of supply chain expertise and executive leadership to the role. Raised in Cleveland, Ohio, he earned a degree from Providence College, an MBA in Finance from Creighton University, and launched his career at Union Pacific Railroad before taking senior management roles at Conrail, APL Logistics, and Hub Group. A seasoned entrepreneur, he went on to found Integra Logistics in 2003 and co-found Coyote Logistics in 2008—growing the Atlanta-based business through its eventual acquisition by UPS—before taking a brief hiatus and returning to the industry to co-found Atadex. Married to Rosanna for 35 years, he is the proud father of two children, Matthew and Katherine, who graduated from Georgia Tech and the University of Georgia, respectively. About Atadex Atadex is a full-service EDI (Electronic Data Interchange) and API integration provider exclusively focused on the supply chain and logistics industry. The company was founded to deliver fully managed, fast, low-cost data integration—handling everything end to end so clients don't need in-house EDI expertise and positioning itself as an alternative to legacy EDI vendors, self-serve platforms, and traditional professional services. Atadex connects any TMS or WMS (including Blue Yonder, Trimble, Manhattan, Turvo, Shipwell, TAI, and McLeod) to any trading partner, supporting all major formats (X12, EDIFACT, API, XML, CSV, JSON) and transaction types. The company processes over 50 million messages per month and has completed more than 5,000 go-lives, pairing each client with a dedicated, US-based account manager. Its goal is to fundamentally change how data integration is managed and serviced, helping clients improve productivity, performance, and profitability. Target customers include carriers and 3PLs with complex partner networks, warehouses and 4PLs seeking a fully outsourced EDI department, and TMS/WMS providers wanting to offer integration without building their own delivery teams. Atadex also offers AssetMaps (fleet consolidation) and Freight Board Central (freight board integration). Key Takeaways: Eliminating EDI Headaches: Managed Integration vs. Ticket Queues In "Eliminating EDI Headaches: Managed Integration vs. Ticket Queues", Joe Lynch speaks with Co-Founder and Leader of Atadex, Mitch Bernet, about how fully managed EDI and API integrations eliminate operational bottlenecks, speed up customer onboarding, and drive supply chain profitability. EDI as a Profitability Driver, Not Just an IT Task: Data integration directly impacts the bottom line by eliminating manual data entry, reducing human error, and allowing logistics staff to manage up to 25% more volume per person without adding head count. Overcoming System Incompatibility via "Universal Translation": Supply chain tech remains heavily fragmented between legacy systems (like mainframes or EDI X12) and modern RESTful APIs. Atadex acts as a universal translator, mapping and converting any data format into whatever spec a trading partner requires. Speed to Integration Equals Speed to Revenue: Onboarding delays can stall new customer relationships for months and destroy projected margins. Rapid implementation and proactive partner coordination directly protect customer retention and protect sales commissions. Fully Managed Service vs. Anonymous Ticket Queues: Logistics operates on extreme urgency where every minor issue feels like a major emergency. Relying on dedicated, domain-expert project managers who proactively catch failed transmissions beats putting in tickets with traditional tech providers. Decoupling Customer Support from Technical Engineering: Mirroring the operational structure pioneer Jeff Silver used at Coyote Logistics, separating client-facing project managers from back-end technical mappers ensures seamless communication and higher overall customer satisfaction. Transparent Pricing Over Complex VAN Fees: Traditional EDI value-added networks (VANs) often obscure costs behind per-kilo-character charges. Simplifying billing to clear, per-message rates provides total transparency so companies can calculate precise integration costs per trading partner. Practical AI Integration with a "People-First" Foundation: While AI tools (like Claude) are being developed internally to speed up complex mapping and protocol translation, technology will supplement—rather than replace—the critical human element required to support high-stakes logistics operations. Learn More About Eliminating EDI Headaches: Managed Integration vs. Ticket Queues Mitch Bernet | Linkedin Atadex | Linkedin Atadex USMMG Partners with Atadex for EDI Solutions | LinkedIn Trailer Bridge's Partnership With Atadex as Their EDI Provider Revolutionized Their Efficiency | LinkedIn The Game-Changer for Riverside Transport's EDI and Target Performance | LinkedIn Atadex| Facebook The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

Nurses Tripled AI Use Without You, 170K New Grads Can't Fix 40% Turnover & CHROs Think They've WonAugust 17th, 2026. All three: Bo Brabo, Luke Carignan, and ASHHRA Executive Director Jeremy Sadlier — plus a first for the Monday News Drop: a special field correspondent. Three stories, one thread: is your workforce moving faster than your strategy?

Grain markets remain supported as resilient demand and crop concerns push prices higher. On SF Daily, host Lorrie Boyer breaks down why China continues buying U.S. soybeans and why crop tours are raising questions about USDA production estimates.

Famous Birthdays is a classic open web publishing success story. Evan Britton bootstrapped the publisher to fill a simple need: People were seeking out information about the new crop of influencer and creator celebrities. Traffic peaked at 25 million t...

Niels Kaastrup-Larsen is joined by Andrew Beer and Tom Wrobel to examine a remarkable period for systematic investing. They discuss how CTAs have navigated volatile moves across equities, commodities, currencies and rates while preserving strong gains, and why diversification has been central to that resilience. The conversation explores leverage, the renewed interest in managed accounts and portable alpha, alongside the growing distinction between trend and non-trend strategies. They also challenge the traditional framing of CTAs as crisis alpha, debate whether greater complexity actually improves returns, and examine the difficulties investors face when selecting managers and benchmarking an industry where yesterday's winners may not remain tomorrow's leaders.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Andrew on Twitter.Follow Tom on LinkedIn.Episode TimeStamps: 00:00 - Introduction and eclipse mania02:57 - Volatility, valuations and a changing market environment06:22 - Trend Barometer falls as opportunities narrow08:21 - How CTAs have navigated 202610:10 - An extraordinary year for systematic investing14:07 - Yen intervention and CTA resilience17:11 - Trend versus non-trend performance22:20 - August performance and the systematic landscape24:48 - Active commodity strategies versus traditional CTAs29:39 - Situational Awareness and the risks of leverage32:14 - Managed accounts, capital efficiency and risk control37:49 - Are systematic strategies becoming over-engineered?42:45 - The growing divide between trend and non-trend CTAs50:34 - Reframing trend following as all-weather alpha55:07 - Why the crisis alpha narrative can be misleading01:04:06 - The hidden challenges of CTA benchmarks01:12:12 - Manager selection and diversification across CTAs01:14:15 - Does non-trend really improve a CTA portfolio?Copyright © 2025 – CMC AG – All Rights Reserved----PLUS: Whenever you're ready... here are 3 ways I can help you in your investment Journey:1. eBooks that cover key topics that you need to know about In my eBooks, I put together some key discoveries and things I have learnt during the more than 3 decades I have worked in the Trend Following industry, which I hope you will find useful. Click Here2. Daily Trend Barometer and Market Score One of the things I'm really proud of, is the fact that I have managed to published the Trend Barometer and Market Score each day for more than a decade...as these tools are really good at describing the environment for trend following managers as well as giving insights into the general positioning of a trend following strategy! Click Here3. Other Resources that can help youAnd if you are hungry for more useful resources from the trend following world...check out some precious resources that I have found over the years to be really valuable. Click HerePrivacy PolicyDisclaimer

If You're a FAN leave me a message :-) But more importantly, let us know what you think, suggestions, topics, constructive criticism... ALL WELCOME!!In this episode of The Executive Five, I challenge a contradiction many organisations create without realising it: they hire people for initiative, judgment, and independent thinking, then manage them through systems that reward caution and permission-seeking. This five-minute executive brief explores how unnecessary approvals, punished challenge, and low tolerance for intelligent mistakes quietly train strong people to become smaller.Key TakeawaysPotential disappears quickly when judgment is constantly overruled by hierarchy.Unnecessary approvals teach capable people to wait instead of lead.If challenge carries a social or career penalty, people stop challenging.Independent thinking has to be protected before it produces a visible win.When a strong hire becomes cautious, look at the system around them before blaming the person.Support the showSponsored by Doc Marty Mushrooms, with a 20% discount code mentioned in the episode. www.docmarty.comContact me:Daniel@the-success-blueprint.co.zawww.mindworx.bizdaniel@mindsworx.comInstagram: @Mindworx_Coaching

When regular Christian activity is coupled with habitual sexual sin, it's a telltale sign that a person is more devoted to a religious system than to Jesus Himself. Unless the Lord intervenes, this lack of a true saving relationship will send a person blindly into eternal damnation. But Pure Life Ministries was established because the Lord is not content to leave hypocrites and backsliders in this miserable condition, but desires to offer them mercy and grace in their place of need. This places on every man and woman the responsibility to respond to that offer. In this sermon, Luke Imperato takes us through what happens when we refuse to listen to God's warnings.   Scripture quotations taken from the (LSB®) Legacy Standard Bible®, Copyright © 2021 by The Lockman Foundation. Used by permission. All rights reserved. Managed in partnership with Three Sixteen Publishing Inc. LSBible.org and 316publishing.com

Do you pray and feel like no one is listening? Is faith a doctrine you believe in or an expectation you have? Let's break down the nuances of faith and prayer from the gospels and notice the stark differences. It changes everything. The managed self vs. the native self. If you appreciate my work please consider a donation to "paypal.me/newdayglobal". You can find me and my posts on Substack also. Thank you!

Too many clients panic when they are managed out of McKinsey, BCG or Bain. They assume the worst and imagine a tattered reputation. This could not be further from the truth. In this podcast, let's revisit a Case Interview & Management Consulting classic where we explain why it is in McKinsey's and BCG's best interest to never disclose you were managed out. It is part of their business models that the market never knows you may have been the world's most ridiculous consultant. So, if you are being managed out, relax.​ You will be ok. Here are some free gifts for you: Overall Approach Used in Well-Managed Strategy Studies free download: www.firmsconsulting.com/OverallApproach McKinsey & BCG winning resume free download: www.firmsconsulting.com/resumepdf Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo

This episode was sponsored by Cardiff & Sugartime Inc.    LightSpeed VT: https://www.lightspeedvt.com/ Dropping Bombs Podcast: https://www.droppingbombs.com/ Today's Dropping Bombs episode features David Sugarman, the former Wall Street vice president who talked his way into the NBA Players Association without a law degree and built a one-stop business empire for pro athletes under his "Sugar" persona.   David breaks down signing New Edition with $900 to his name, then the fallout years later: a federal grand jury testimony against his best friend, Fugees co-founder Pras Michel, tied to fugitive financier Jho Low, Leonardo DiCaprio, and Obama campaign money — plus the jail stint and divorce that cost him everything.   Wall Street, the Fugees, a fugitive financier, and a fall from grace — this episode has all of it, and somehow David is still standing. This one needs to be heard, not summarized.     

Dan Abramov joins Scott and Wes to explain AT Protocol, the open standard quietly rebuilding the social web. They get into how it actually works, why it's way bigger than just Bluesky, and why Dan calls it one of the most interesting ideas on the internet right now. Show Notes 00:00 Intro 00:45 Welcome to Syntax! 01:46 Introduction of Dan Abramov 02:55 Understanding AT Protocol and Its Importance 06:41 The Relationship Between AT Protocol and Bluesky 08:22 Identity and Hosting in AT Protocol 11:29 Brought to you by Sentry 11:54 Use Cases for AT Protocol 13:08 How Content is Managed in AT Protocol 19:12 Public Data and Future Extensions of AT Protocol pds.ls 20:33 Schema Flexibility in AT Protocol Standard.site 25:28 Exploring AT Protocol Patterns UFOs 28:27 Media and Data Integration Challenges Stream.place 31:15 User Experience and Accessibility in AT Protocols 32:13 AI Intersections with AT Protocol 36:54 Comparing Protocols: Bluesky vs. Mastodon 41:43 Decentralization and Crypto Connections 44:59 Addressing Abuse and Spam in Protocols 48:24 Community Trust and Content Quality 51:20 Innovations and Future of AT Protocol 55:55 Sick Picks + Shameless Plugs Sick Picks Scott: Wes: Dan: Solo Monk Shameless Plugs Scott: Wes: Dan: Next.js 16.3: Instant Navigations Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Danny Gal was born and raised in the UK, and now lives outside of London. He attended University in Nottingham... yep, the same one from Robin Hood. He LOVES challenges, and not just any challenges - the hard ones. He is done Iron Man competitions, ultra marathons, climbed Mount Kilimanjaro, and jumped out of a perfectly good plane, to name a few. He loves doing them once... and then never again. He's got 2 small kids, and believes in work hard, play hard.Danny has worked in many roles in the past, across enterprises and the like. What he found most difficult was scaling himself. He got to talking with his now co-founder about building something around the idea of scaling oneself, and took it to some businesses to validate it. Once he saw them get excited about it, he figured they were onto something.This is the creation story of Proteams.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://proteams.com/https://www.linkedin.com/in/dannygal/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Thomas Savidge proposes "Universal Savings Accounts" (USA) to replace complex government-managed savings vehicles. These accounts would grant individuals full ownership of their funds for unemployment or retirement. This reform aims to reduce fraud and address the long-term fiscal instability associated with the national "debt bomb." (16)1933 PERSIA