Podcasts about outputs

The conversation begins with introductions from experts specializing in dairy genetics, reproductive management, transition cow health, and calf monitoring technologies, providing insight into how precision dairy tools are being applied across the dairy industry. 0:00-6:21 The panel then explores the evolution of computer vision and artificial intelligence, discussing how modern dairy technologies are moving beyond simple data collection to deliver actionable insights that help producers solve real-world challenges. They also examine technology adoption trends, workforce development, and the barriers farms continue to face, including connectivity and technical support. 9:32-24:08 A major focus of the discussion is precision health monitoring for transition cows and calves. The experts explain why the transition period remains one of the most critical opportunities for intervention and how emerging technologies can help identify animals needing attention while addressing the challenges of managing "yellow cows" that fall between healthy and sick. 24:08-36:11 Looking toward the future, the panel explores digital twin technology, genetic considerations, calf health monitoring, audio sensing, and innovative approaches such as Wi-Fi-based cattle monitoring systems. The discussion highlights how these emerging technologies may further advance precision dairy management in the coming years. 36:11-45:55 The episode concludes with a discussion on dairy welfare monitoring, AI model development, and reinforcement learning, emphasizing the importance of transforming large amounts of farm data into meaningful information that supports animal wellbeing and management decisions. 46:41-52:10 Please subscribe and share with your industry friends. Invite more people to join us at the Real Science Exchange virtual pub table. Please be sure to register for our upcoming Real Science Lecture Series webinars. Finally, if you want one of our Real Science Exchange t-shirts, screenshot your rating, review, or subscription. Then, email a picture to anh.marketing@balchem.com. Include your size and mailing address, and we'll mail you a shirt.

The perfect AI storm happened, and no one has noticed yet.

How did prompt engineering die so quickly? ☠️And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext Engineering Best Practices and ScalabilityTimestamps:00:00 "Access AI Community & Tools"03:08 "Mastering Context in AI"07:23 "Smart Models Require Less Precision"12:01 "Context Engineering Beats Prompt Engineering"15:49 "AI Context: Six Key Blocks"16:47 "Building Context for Better Results"19:53 "AI: Training, Not Easy Button"25:17 "Chain of Thought Prompting Decline"29:11 "Show, Don't Tell Techniques"32:13 "Context, Reuse, and Scalable Systems"33:19 "AI Chatbots: Memory and Skills"Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don't tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

OPEN HEAVENSMATALA LE LAGI MO LE ASO LULU 12 AOKUSO 2026(tusia e Pastor EA Adeboye) Manatu Autu: O mea e tuu iai e fausia mea e maua mai ai 2 (Inputs determine outputs 2) Tauloto Tusi Paia: Luka 12:48 “A o lē ‘ua lē iloa, ‘ua faia fo‘i e ia mea e tatau ‘ina sasaina ai, e māmā ‘ona sasaina o ia. Auā o lē ‘ua foa‘iina atu i ai o mea e tele, e su‘eina atu fo‘i ‘iā te ia o mea e tele; o lē fo‘i ‘ua tu‘uina atu i ai o mea e tele latou te fai atu ‘iā te ia, e ‘aumai mea e sili ‘ona tele.”Faitauga - Tusi Paia: Mataio 25:14-30A avatu e le Atua ia te oe se galuega, e na te foai atu mea uma e manaomia e faataunuuina ai. O nisi o punaoa manaomia o loo i lou siomaga ao isi, o loo i totonu ia te oe. E pei ona faaalia i le tauloto mai le Tusi Paia o le asō, o le tele o mea e avatu ia te oe, o le tele foi lea o mea e sueina ma aumai ia te oe. O tagata e manatu e lē lava punaoa ma mea o loo i o latou lima, e tatau ona faaeteete ia aua nei faapei o le auauna na inoino i le taleni na foai iai e lona matai, e pei ona faaalia i le faitauga o le Tusi Paia o le asō. O nisi tagata e manatu o mea ua latou maua e lē lava, ona taumafai lava lea e faatali i le Atua e foai atu nisi mea e sili atu, ona faatoa mafai lea ona faamaoni i sina mea o loo i o latou lima. Le au pele e, e sese lea ituaiga mafaufau lea. O se tagata e lē mafai ona faamaoni i sina mea itiiti ua ia maua, e lē tatau ona manatu e sili atu seisi mea na te maua. E foai e le Atua na o i latou ua faamaonia lo latou faamaoni i mea itiiti. Ou te sau mai se nuu laitiiti lava i luga o le faafanua. O le mativa ia o lou tamā e oo i tagata matitiva e latou te lauina lona mativa. E ui na matitiva ou matua ma e lē tele se punaoa na ou maua ao ou tuputupu ae, na foai mai e le Atua se taleni e tasi ia te a'u, o lo'u faiai. Na ou faaaogaina tatau le taleni na foai mai e le Atua e ala i lou toaga i le aoga ma suesue ma le punouai, ona amata lemu lava lea ona maua punaoa mai lea taleni. Na foai mai e le Atua ia te a'u le faaolataga i le 1973, e amata mai lea taimi, na avea au o se ipu e aoga i ona aao. A ou manatunatu i mea tetele ua faia e le Atua mo a'u ma faataga ou te ausia e ui e itiiti punaoa na ou maua i le amataga, ou te vivii faifaipea i le Alii i lona faamaoni. E moni, a faamaoni se tagata i mea itiiti, e faaopoopo mea ia te ia. Atalii poo le afafine o le Atua, tilotilo i lou siomaga ma vaavaai i totonu ia te oe, e te vaai atu ai i meaalofa e tele ua foai atu e le Atua ia te oe. Aua e te toilalo, faamolemole faaaoga meaalofa na, e manumalo ai agaga mo le malo o le Atua. Faaaoga lelei e siitia lou tulaga ma le pule, a oo ina e tautala, e faalogo tagata uma ma auauna i le Atua e tasi o loo e auauna iai. Ou te tatalo ia faaaoga e le Atua matautia lou tagata e faia mea tetele i lona malo ao e auauna ia te ia ma le faamaoni e faaaoga ai meaalofa ua ia foai atu ia te oe, i le suafa o Iesu, Amene. 

OPEN HEAVENSMATALA LE LAGI MO LE ASO LUA 11 AOKUSO 2026(tusia e Pastor EA Adeboye) Manatu Autu: O mea e tuu iai e fausia mea e maua mai ai 1 (Inputs determine outputs 1) Tauloto Tusi Paia: 1 Korinito 9:24 “Tou te lē iloa ‘ea, o ē tausiniō i le ala tanu, e tausiniō uma i latou, ‘ae maua le taui e le to‘atasi? ‘Ia fa‘apea ‘ona ‘outou tausiniō, ‘ina ‘ia ‘outou maua.”Faitauga - Tusi Paia: Faataoto 31:1-9O le mamao e te fia ausia i lau faigamalaga i le olaga nei, o le tele foi lena o le manaomia ona e galueaina lelei lou tagata. Mo se faataitaiga, afai e te fia malaga i se mea mamao ao loo tumu lelei le pinisini o le taavale, e te ono tu i se pamu i le ala e toe utu ina ia e ausia le nofoaga mamao o loo e malaga iai. Peitai, afai e latalata le nofoaga o loo e malaga atu iai, e te lē manaomia le tu i se pamu e toe utu le taavale. O soo se tagata e naunau e mamao le tulaga e ausia i le olaga nei, e tatau ona galueaina punaoa mo ia, e aofia ai le ola faapaiaina, galue punouai, aoaoga, lelei fesootaiga ma tagata i lona olaga, alo ese mai amio lē lelei o le ai ai soo, matamata tifaga, faaalu vale taimi i luga o upega tafailagi, pati faasoloatoa ma mea faapena. E manaomia le pulea lelei o lau amio ma lou tagata pe afai e mamao le tulaga e te fia ausia. Ou te manatua ao ou laitiiti, e faanofo lava a'u e lou tina i totonu o le fale e fai meaaoga ao taaalo soka au uo i fafo. E masani ona ou faasea i lou tina peitai na faaauau ona ia faia seia oo ina matua ma avea ma masani ia te au le faia o meaaoga. E iai se taimi na ou alu i le fale o le tuagane a lou tinā, o fai se solo tele o faafiafiaga i le auala, na o uma ai tagata o le matou aiga sei vagana a'u. Na sau seisi ma fesili mai pe aisea ou te le alu ai i fafo e matamata i faafiafiaga, na ou tali atu, ‘o le tagata o le a matamata uma ai tagata i le lumanai e lē matamata i ni faafiafiaga'. I le tele o tausaga mulimuli ane, ina ua siitia a'u e le Atua, na ia faamanatu mai ia te a'u lea aso, ma faapea mai, ‘o le asō, o loo matamata uma mai le lalolagi ia te oe'. Le au pele e, faaalu au tupe ma punaoa e galueaina lou tagata i mea uma e manaomia i le mamao ma le maualuga o le olaga e te fia ausia. Ao agai isi tagata na ave i le tafeaga i Papelonia e aai i meaai matagofie i le maota o le tupu, na taulai le vaai a Tanielu ma ana uo ia vavaeseina i latou mo le Alii (Tanielu 1:3-12). Ou te talitonu e lē na o i latou tagata Iutaia na aoaoina i le maota o le tupu i lea taimi, peitai e na o i latou na faia mea tetele aua foi na faaalu tatau a punaoa e galueaina i latou. Atalii poo le afafine o le Atua, afai e tetele ni au miti o iai ma o loo e naunau ia tino mai, e tatau ona e totogiina le tau e galueaina lou tagata. E tatau foi ona e tuuesea ni avega mamafa e pei o le paiē, leai o se pulea o lou tagata, ma tulaga eleelea e pei o le agasala e taofia oe mai le ausia o faamoemoega a le Atua mo lou olaga. Ou te tatalo ia e ausia faamoemoega a le Atua mo lou olaga, i le suafa o Iesu. Tatalo, Tamā, faamolemole fesoasoani mai ia te au ina ia galueaina punaoa talafeagai mo lo'u olaga ina ia ou ausia faamoemoega ua e saunia mo au, i le suafa o Iesu, Amene. 

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

Leadership success is usually measured through revenue, market share, promotions, productivity and team performance. Those indicators matter, but they do not tell us whether a leader is succeeding in life. A leader can deliver excellent corporate results while gradually damaging their marriage, weakening their relationship with their children, neglecting their health, mishandling their finances and losing touch with their friends. That is not genuine success. It is professional achievement purchased at an unnecessarily high personal price. This risk is especially relevant in Japan, where long working hours, loyalty to the organisation and the demands placed on player-managers can make work the dominant force in a leader's life. The Wheel of Life provides a useful way to examine whether leaders are achieving balanced and sustainable success. Why do Japanese leaders struggle with work-life balance? Many Japanese leaders still operate within a deeply established culture that rewards commitment, endurance and long working hours. Even when official working practices change, the expectation of total dedication can remain. Japan's post-war economic recovery was driven partly by extraordinary levels of personal sacrifice. During the rapid-growth era, many fathers spent most of their waking hours working, commuting or socialising with colleagues. Mothers frequently carried most of the responsibility for raising children and managing the household. Conditions have changed. Most schools and companies no longer operate every Saturday, dual-income households are increasingly common and younger employees often expect more control over their personal lives. However, the old attitudes have not completely disappeared. Middle-management positions have been reduced in many organisations, technology has transferred administrative work back to managers and leaders are often expected to manage teams while delivering their own individual results. These player-managers may supervise people, handle clients, prepare reports and complete routine administration themselves. Do now: Examine whether your working hours reflect genuine strategic necessity or simply an inherited organisational habit. What is the Wheel of Life for leaders? The Wheel of Life is a self-assessment tool that helps leaders evaluate several important areas of life rather than judging success through career achievement alone. The tool is normally presented as a circle divided into categories. The centre represents a score of zero and the outer edge represents ten. Leaders score their level of satisfaction in areas such as career, finances, family, health, friendships, community, personal interests and spirituality or personal meaning. When the points are connected, the resulting shape shows whether life is relatively balanced or heavily distorted. A leader may score nine in career but only three in health, two in family relationships and one in social life. That person may look successful in the office while experiencing a personal life that is increasingly difficult to sustain. The objective is not to achieve a perfect ten in every category. That is unrealistic. The purpose is to identify serious imbalances before they become crises. Do now: Score each area honestly from zero to ten and identify the two categories that require your immediate attention. Can career success damage a leader's family life? Yes. When work consistently receives the leader's best time, energy and attention, the family may be left with whatever is remaining. Over time, this can create distance, resentment and damaged relationships. Many leaders say they are working hard for their families. The intention may be genuine, but the outcome does not always match the explanation. A leader may provide financial security while rarely being emotionally or physically available. For male leaders in Japan, the traditional model of the absent salaryman father can still influence behaviour. He leaves early, returns late and assumes that providing income is his main family responsibility. However, spouses and children may need time, conversation, support and shared experiences more than another late-night meeting or client dinner. As more women develop independent careers and incomes, they may also be less willing to tolerate relationships in which responsibility and emotional connection are consistently one-sided. Working for the family while gradually losing the family does not make sense. Do now: Schedule protected family time with the same seriousness you apply to an executive meeting or major client appointment. Why should leaders take more responsibility for their finances? High income does not automatically create long-term financial security. Leaders still need to manage savings, investment, retirement planning, insurance and household risk. Many people in Japan have traditionally held a large proportion of their wealth in bank deposits. During long periods of deflation and low inflation, holding cash appeared relatively safe. In a more inflationary environment, however, cash can gradually lose purchasing power. Busy leaders often delay financial planning because it does not feel urgent. Retirement seems distant, investment appears complicated and the company pension may seem sufficient. The problem is that financial preparation benefits enormously from time. Delaying ten or twenty years can make the eventual task considerably harder. Japan's ageing population also places continuing pressure on public pension and social security systems. Leaders should not assume that future government benefits alone will provide the lifestyle they expect. This does not mean making reckless investments. It means becoming financially literate, seeking qualified advice where appropriate and preparing rather than hoping. Do now: Review your savings, investments, retirement plan, insurance and household obligations at least once each year. Why do busy leaders lose their friends? Friendships weaken when leaders repeatedly sacrifice social relationships to work. Connection requires time, effort and genuine interest, not occasional promises to catch up later. Social life is often one of the first casualties of overtime. Leaders postpone dinners, cancel weekend plans and stop calling people because the next deadline always seems more important. Remote and hybrid work have also blurred the boundary between professional and personal life. Without a physical commute to mark the end of the day, some leaders continue responding to emails, checking reports and attending online meetings well into the evening. Corporate entertaining should not be confused with friendship. Taking reluctant junior staff out for drinks or attending obligatory client functions may fill the calendar, but it does not necessarily create meaningful social support. Strong relationships are a form of wealth. Friends provide perspective, humour, honesty and support that cannot be replaced by job titles or business contacts. Do now: Contact one person you value but have neglected and arrange a specific time to meet rather than saying, "We should catch up sometime." Why do leaders need interests outside work? Hobbies and personal interests protect leaders from allowing their job to become their entire identity. They provide creativity, renewal and a sense of progress that is independent of corporate performance. Some leaders view personal interests as indulgent or unproductive. They believe every available hour should be used to advance the business. That approach may produce short-term output, but it can also make life increasingly narrow. Personal pursuits can include music, writing, painting, gardening, travel, sport, reading, cooking or learning a language. The activity does not need to generate income, improve a résumé or create a new business opportunity. For me, writing and recording articles on Saturdays can look like another form of work. In practice, writing also serves as a creative outlet. I cannot play a musical instrument, paint or draw particularly well, so writing gives me a way to create something and explore ideas beyond operational business tasks. Leaders need something they enjoy simply because it makes life richer. Do now: Protect regular time for one activity that has no connection to your targets, clients or corporate status. Why is health a leadership responsibility? Health is not separate from leadership performance. Energy, concentration, emotional control and decision-making all become harder when leaders neglect exercise, sleep, nutrition and medical care. Leaders often say they are too busy to exercise, but many of the same people can find time for long client dinners, alcohol and late-night work. The issue is usually not a complete absence of time. It is the priority assigned to health. Weight gain can happen gradually through business meals, entertaining and inactivity. I experienced this myself while working in Nagoya. After attending a work-related geisha party, someone gave me a commemorative photograph. The side-profile image revealed how much weight I had gained. It was an uncomfortable but useful moment of recognition. Losing weight and improving fitness require sustained lifestyle changes, not a few weeks of enthusiasm with a personal trainer. Leaders need systems they can maintain, including realistic exercise routines, better food choices and limits around alcohol. Do now: Choose one measurable health behaviour to improve for the next ninety days and track it consistently. Why should leaders contribute to their communities? Community involvement helps leaders develop perspective, strengthen relationships and contribute beyond the boundaries of their company. It also prevents professional status from becoming their only source of identity. Japan has a strong tradition of community responsibility shaped by cooperation, shared spaces and collective expectations. This could be seen during the pandemic, when many people voluntarily changed their behaviour to reduce risk to others. However, senior leaders can become disconnected from the communities around them. Work absorbs their attention and they spend most of their time with colleagues, clients and people from similar professional backgrounds. Communities need people who can organise, mentor, listen and solve problems. Leaders have useful experience that can support schools, local groups, professional associations, charities and younger generations. Community activity also benefits the leader. It exposes them to different experiences and reminds them that the world is larger than the company's latest spreadsheet, quarterly target or restructuring plan. Do now: Select one community, educational or professional group where your experience could make a practical contribution. What role does reflection or spirituality play in leadership? Leaders need time to consider who they are, what they value and what they intend to do with their lives. Without reflection, they may become highly efficient at pursuing goals they have never consciously chosen. Spirituality is deeply personal and does not need to refer to a particular religion. It can involve faith, philosophy, meditation, service, nature or simply quiet reflection. The underlying questions are universal. Why am I here? What kind of person am I becoming? What will matter when my career is over? What impact am I having on other people? Busy leaders often avoid these questions because financial reports, customer issues and operational problems feel more immediate. Yet a life filled only with targets, meetings and performance reviews can eventually feel empty, regardless of professional success. Reflection helps leaders reconnect daily actions with personal values. It can also expose uncomfortable contradictions between what they say matters and where they actually spend their time. Do now: Create a regular period without screens, meetings or work and use it to reflect on how you are investing your life. Conclusion Leaders have many roles. They are executives, managers, parents, spouses, friends, community members and individuals with physical, emotional and financial needs. Work is one important part of life, but it is not the whole of life. The Wheel of Life is useful because it reveals where professional ambition has created unhealthy imbalance. A leader who produces revenue while losing their family, health, friendships and financial security should not automatically be considered successful. Sustainable leadership means producing strong organisational outcomes without destroying the other areas that give life meaning. As a friend of mine says, "Time is life." The real question for every leader is simple: what are you doing with yours? Author Bio Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" in 2018 and 2021 and recipient of the Griffith University Business School Outstanding Alumnus Award in 2012. As a Dale Carnegie Master Trainer, Greg is certified to deliver leadership, communication, sales and presentation programmes globally, including Leadership Training for Results. He has written several books, including three bestsellers — Japan Business Mastery, Japan Sales Mastery and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have been translated into Japanese, including Za Eigyō（ザ営業）, Purezen no Tatsujin（プレゼンの達人）, Torēningu de Okane o Muda ni Suru no wa Yamemashō（トレーニングでお金を無駄にするのはやめましょう）and Gendaiban "Hito o Ugokasu" Rīdā（現代版「人を動かす」リーダー）. Greg also publishes daily business insights on LinkedIn, Facebook and X and hosts six weekly podcasts. On YouTube, he produces The Cutting Edge Japan Business Show, Japan Business Mastery and Japan's Top Business Interviews, which are widely followed by executives seeking practical strategies for succeeding in Japan.

Dein Ansprechpartner für Digitale Kompetenz und mehr Selbstbestimmtheit als Blinder Mensch!Hier sind wir: https://schulze-graben.de**Richtig prompten lernen: So holst du endlich professionelle Ergebnisse aus Grok, ChatGPT & Gemini – ohne teure Kurse!** Du nutzt KI täglich, aber die Antworten sind oft zu allgemein, unvollständig oder einfach nicht das, was du brauchst? In dieser Smütech-Episode zeigt dir Jockl Joachim Schulze (Schulze IT-Schulung) live und praxisnah, wie du mit einfachem Role Prompting und klaren Anweisungen aus jeder KI deutlich bessere, detailliertere und genau passende Ergebnisse holst. Du erfährst in dieser Folge: - Warum die meisten Prompt-Ergebnisse enttäuschen und wie du das sofort änderst - Der absolute „Magic Switch“: Eine einzige Rollenzuweisung („Du bist ein Meteorologe mit 20 Jahren Lokalradio-Erfahrung…“) verwandelt deine Outputs komplett - Live-Demo am PC: Wettervorhersage für Plauen & das Vogtland – einmal flach, einmal anschaulich wie vom Profi - Wie du dieselbe KI als melancholischen Schriftsteller oder präzisen C++-Programmierer einsetzt (und warum das so mächtig ist) - Die reale Gefahr von KI-generierten Inhalten und wie du sie sicher erkennst & prüfst - Praktische Anwendungen: Bessere Code-Reviews, Produktvergleiche, Recherche – und wie du damit Stunden pro Woche sparst Jockl demonstriert alles direkt mit Grok (DSGVO-konform und ohne Kundendaten), erklärt die wichtigsten Basics und warnt vor gängigen Fehlern. Perfekt für alle, die KI endlich richtig nutzen wollen – ob Einsteiger oder Fortgeschrittene. Nächste Woche: Grok Skills – die neue Funktion, die das Prompting nochmal auf ein neues Level hebt. Abonniere Smütech jetzt kostenlos und sichere dir jeden Montag neue, praxisnahe KI- und IT-Tipps! Mehr Infos, Beratung (ab 36 € / 30 Min) und alle Episoden: https://podcast.schulze-graben.de Tipp: Grok auch unterwegs nutzen iOS App: https://apps.apple.com/de/app/grok-ai-assistant/id6670324846 Android App: https://play.google.com/store/apps/details?id=ai.x.grok&hl=de&gl=DE #RichtigPrompten #PromptEngineering #Grok #ChatGPTTipps #KI lernen #Smütech #JocklJoachimSchulze (Transkript als WebVTT verfügbar – einfach in den Shownotes verlinken für maximale SEO-Wirkung)Schön, dass du dabei bist.Wenn du Heute was mitgenommen hast, dann gib doch etwas zurück.Das ist ganz einfach. Besuche https://danke.schulze-graben.de und zeig mir, ob dir die Show gefallen hat.Kleine Gesten machen den Unterschied.

In this week's episode, I am celebrating by sharing my favorite lessons from the Top 25 Most-listened episodes of The 20% Podcast which originally aired as Episode 217 of the show. Over the past few weeks, I have shared the Top 20 episodes (links to those episodes below), but wanted to compile all of these lessons into 1 episode. But first, let's start by counting down Episodes 25-21:25. Episode 106: “Figure 8s” with Landon Meyers24. Episode 112: “Embrace Nervousness” with Mike Wander 23. Episode 87: “Your Vibe Attracts Your Tribe” with Ariel Lee22. Episode 98: “Daniel's School of Business” with Daniel Ryan21. Episode 50: “Sales: An Underrated Profession” with Scott LeeseSee below for the rest of the Top 25 List:20. Episode 93: “Only Expose Yourself To Things You Have Space For” with Lindsay Boccardo19. Episode 80: “Proud of The Struggle in His Life” with Collin Mitchell18. Episode 5: “Great Ideas Unexecuted Are Bad Ideas” with Brian Bobeck17. Episode 72: “Managing The Course” with John Morris16. Episode 20: “Tough Times Are An Opportunity” with Larry Long Jr15. Episode 3: “Investing 101” with Tim Chubb14. Episode 75: “The Start of Gratitude” with Kevin Carpenter13. Episode 76: “Finding A Job That Fits Your Personality” with Joel Lalgee12. Episode 92: “Sellers Need To Be Mini-Marketers” with Jason Bay11. Episode 84: “The Law of Reciprocity” with Belal Batrawy10. Episode 97: “The 3.99 GPA” with Morgan Buchanan9. Episode 108: “Knowing Your Hourly Rate” with Ian Koniak8. Episode 66: “Showing Up Authentically” with Darren McKee7. Episode 1: “Finding The Angle That Motivates You” with Drew Cohen6. Episode 100: “Day in the Life of The Meckes Chief Residence Officer”  Dana Cohen5. Episode 58: “Get To The Truth” with Nick Cegelski4. Episode 96: “Building SaaSBros In Public” with Erik McKee3. Episode 79: “Creating The Evangelist Role” with Jen Allen-Knuth2. Episode 63: “Focus on Outputs, Not Outcomes” with Ian Koniak1. Episode 78: “How SaaS Saved His Life” with Anthony Natoli Check out the best of from the top 1-5 episodes:https://podcasts.apple.com/us/podcast/148-lessons-from-the-top-5-episodes-of-the/id1528398541?i=1000617537318 Check out the best of from the top 6-10 episodes:https://podcasts.apple.com/us/podcast/156-the-best-of-the-20-podcast-round-2/id1528398541?i=1000624379182Check out the best of the top 11-15 episodes:https://podcasts.apple.com/us/podcast/158-the-best-of-the-20-podcast-round-3-the-top/id1528398541?i=1000625921806 Check out the best of from the top 16-20 episodes:https://podcasts.apple.com/us/podcast/163-the-best-of-the-20-podcast-round-4-the-top/id1528398541?i=1000629890702 Thank you so much for your support. If there are any guests you'd like to hear me talk with on The 20% Podcast, send me a message on LinkedIn. Please enjoy this week's episode of The 20% Podcast.I am now in the early stages of writing my first book! It will cover my journey into sales, the lessons learned, and include stories and advice from top sales professionals around the world. I'm excited to share these interviews and bring you along on this journey!Like the show? Subscribe to the email: Subscribe HereI want your feedback! Reach out at 20percentpodcastquestions@gmail.com or connect with me on LinkedIn.If you know anyone who would benefit from this show, please share it! If you have suggestions for guests, let me know!Enjoy the show!

Episode 309   This week, your hosts Jay Gilbert & Mike Etchart break down these important music industry stories:   •  Spotify Slashes Streams of Hit Song after Suspicious Activity on Prediction Market Kalshi •  Viberate Opens Music Data to ChatGPT, Claude and Other AI bots Via Official MCP Server Launch •  Google Says AI Training Is Fair Use and Copyright Should Be Policed on Outputs, Not Inputs •  The Trends Defining Music, Culture & Brand Marketing in 2026   Subscribe to the newsletter! : YourMorning.Coffee

In this episode, Brent sits down with Tom Rodenhauser, Managing Partner at K2 Consulting Research, to discuss why AI is forcing consulting firms to rethink the way they create value.For decades, consulting firms have been paid for outputs — deliverables, implementations, and billable hours — with the assumption those outputs would drive meaningful business outcomes. But, that assumption is starting to break. Demand is still there. Clients are still buying. The model is holding, for now. But beneath the surface, AI is rapidly reshaping the economics of consulting. Firms that continue selling effort instead of impact risk competing on speed and price, while the firms that embrace measurable outcomes will define the next era of consulting.In this episode, you'll learn:Why AI is disrupting far more than productivityThe critical difference between outputs (deliverables) and outcomes (business results)Why outcome-based pricing remains difficult to implement, despite years of industry discussion.How consulting firms need to rethink utilization, compensation, client relationships, and commercial models for the AI era.Tom's predictions for the future of consulting Hosted on Acast. See acast.com/privacy for more information.

What You'll Learn in This Episode:In this episode, Catherine McDonald and Shayne Daughenbaugh welcome Michael Parent to discuss SIPOC, one of the most effective tools for understanding and improving business processes. Michael explains how SIPOC (Suppliers, Inputs, Process, Outputs, Customers) helps teams define process boundaries, identify stakeholders, and maintain focus on delivering customer value.The conversation explores different approaches to building a SIPOC, including why Michael prefers starting with the customer before mapping the process itself. They also discuss how SIPOC fits into Lean and Six Sigma problem-solving, how it helps prevent scope creep, and why it serves as a strong foundation for process mapping, stakeholder engagement, and continuous improvement efforts.Whether you're leading a process improvement project or trying to better understand how work flows through your organization, this episode provides practical guidance on using SIPOC to create clarity and alignment from the start.Key Takeaways:1. SIPOC provides a high-level view of a process and helps clearly define project scope and boundaries.2. Keeping the customer at the center of the discussion ensures improvement efforts remain focused on delivering value.3. SIPOC serves as a powerful starting point for process mapping, stakeholder analysis, and continuous improvement initiatives.4. The most effective SIPOCs involve the right mix of stakeholders and subject matter experts while avoiding unnecessary complexity and scope creep.Links:mparent@sixsigma-consulting.comhttps://www.linkedin.com/in/parent-michael/https://www.findleansolutions.com/summit-2026/https://www.findleansolutions.com/

In this episode, I discuss an important concept in Six Sigma:Y = f(x) or Y is a function of x.Y is your measured output, and X represents the inputs to your process, and the function represents the process and how the X factors relate or impact the Y.Another way to state it is Inputs (X), Processes (Function) and Outputs (Y) or IPO.I explain how this concept can be used when thinking through your own personal improvement journey. What are your inputs, processes and outputs.If you don't like how one of the outputs is performing (relationships, weight, health, income, etc), then what processes and inputs need to change.Listen to the episode to learn more. If you'd like the graphic I mentioned, send me an email at brion@biz-pi.comLearn more about BPIVisit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.leansixsigmaecosystem.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to access free courses and templates, or upgrade for premium content and coaching programsVisit ⁠⁠⁠⁠⁠⁠⁠https://www.biz-pi.com⁠⁠⁠⁠⁠⁠⁠ to learn more about me and my consulting firmVisit ⁠⁠⁠⁠⁠https://greenbeltcertification.com⁠⁠⁠⁠⁠ to learn how to get Lean, Green Belt or Black Belt training and certification for you or your organization

#870: The Fed holds rate steady in Kevin Warsh's first meeting, but the central bank teases a rate hike is more likely than a cut. Carvana introduces a new ‘playground' concept where shoppers can test-drive cars while purchases are still online. Qantas unveiled a new fly-direct route from Sydney to London, which would become the longest commercial passenger route in the world. Then, it's Neal's Numbers on World Cup teams, parents and kids looking at screens during meal times, and Toy Story 5. Finally, the US-Iran sign a Memorandum of Understanding to open the Strait of Hormuz To learn more visit https://www.servicenow.com Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠  Watch Morning Brew Daily Here:⁠ ⁠⁠https://www.youtube.com/@MorningBrewDailyShow⁠ Paid endorsement. Brokerage services provided by Open to the Public Investing Inc, member FINRA & SIPC. Advisory services by Public Advisors LLC, SEC-registered adviser.  Investing involves risk. Not investment advice. Agentic Brokerage is an AI-powered conversational tool that allows you to enter instructions for a set of self-directed, recurring transactions (your “Agent”) for your account. Outputs from Agentic Brokerage are provided for informational and illustrative purposes only, and should not be considered investment recommendations or advice. Complete disclosures available at public.com/disclosures. See terms of match program at https://public.com/disclosures/matchprogram. Matched funds must remain in your account for at least 5 years. Match rate and other terms are subject to change at any time. Learn more about your ad choices. Visit megaphone.fm/adchoices

Learn how the top Claude users in the world setup and optimize Projects that deliver time after time. Here is the link to the companion Substack blog post with the meta prompt that my team and my clients use everytime to setup a Project: https://tinyurl.com/Companion-Ep6-Claude-Projects

#868: Fox acquires Roku in a $22B deal to power its streaming aspirations. The UK is the latest major country that moves to ban social media use for kids under 16. Fans continue to loathe the mandatory hydration breaks during the World Cup because they believe it's less about player safety and more about commercial breaks. Then it's Toby's Trends that looks into why everybody is loving dates…the fruit, that is. Finally, the stock market cheers for US-Iran peace deal. To learn more visit https://www.servicenow.com Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠  Watch Morning Brew Daily Here:⁠ ⁠⁠https://www.youtube.com/@MorningBrewDailyShow⁠ Paid endorsement. Brokerage services provided by Open to the Public Investing Inc, member FINRA & SIPC. Advisory services by Public Advisors LLC, SEC-registered adviser.  Investing involves risk. Not investment advice. Agentic Brokerage is an AI-powered conversational tool that allows you to enter instructions for a set of self-directed, recurring transactions (your “Agent”) for your account. Outputs from Agentic Brokerage are provided for informational and illustrative purposes only, and should not be considered investment recommendations or advice. Complete disclosures available at public.com/disclosures. See terms of match program at https://public.com/disclosures/matchprogram. Matched funds must remain in your account for at least 5 years. Match rate and other terms are subject to change at any time. Learn more about your ad choices. Visit megaphone.fm/adchoices

#865: Neal and Toby talk about how inflation is heating up to the highest pace in three years. Plus, a whole bunch of FIFA World Cup news and how escorts are cashing in on the AI boom over in Silicon Valley. Hit TV shows are taking much longer in between seasons. Why Gen Z and Millennials looove waiting in lines for their trendy food spots. Finally, Rivian finally delivers its R2 model and the first trailer of the much-anticipated ‘The Social Reckoning' drops.  To learn more visit https://www.sage.com/morningbrew Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠  Watch Morning Brew Daily Here:⁠ ⁠⁠https://www.youtube.com/@MorningBrewDailyShow⁠ This is a paid advertisement. Today's episode of the Morning Brew Daily Show is brought to you by Sage — a trusted global provider and leader in accounting, financial, HR, and payroll technology for small and mid-sized businesses. The following commentary reflects general information about Sage and its products. Specific features, capabilities, and availability may vary by product, region, and customer requirements. To find out more, visit sage.com/morningbrew. Paid endorsement. Brokerage services provided by Open to the Public Investing Inc, member FINRA & SIPC. Advisory services by Public Advisors LLC, SEC-registered adviser.  Investing involves risk. Not investment advice. Agentic Brokerage is an AI-powered conversational tool that allows you to enter instructions for a set of self-directed, recurring transactions (your “Agent”) for your account. Outputs from Agentic Brokerage are provided for informational and illustrative purposes only, and should not be considered investment recommendations or advice. Complete disclosures available at public.com/disclosures. See terms of match program at https://public.com/disclosures/matchprogram. Matched funds must remain in your account for at least 5 years. Match rate and other terms are subject to change at any time. Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode gives you the simple tactics that 10x Claude Research ouputs. Here is the link to the companion Substack blog post with the copy and paste master prompt that will 10x the quality of your Claude AI Research outputs: https://tinyurl.com/Companion-Ep1 My full collection of growth hacks, playbooks, and meta prompts lives on my Substack at: https://ClaudeGenius.com

Producing more content faster is not the same as producing content that matters. In this episode of Content Amplified, Adam Haskew, Associate Director of Brand Experience at Redis, makes the case that AI accelerates your outputs but does nothing for your strategy, and that the gap between the two is where "AI slop" gets made. Adam argues the fix is the unglamorous, old-school stuff most teams skip when they are moving fast: kickoff calls, a genuinely complete brief, and human alignment at the very start of a project, before a single word is generated. He explains why a web page is really the same as an ebook when it comes to planning, why skipping alignment creates a "snowball effect" where small problems amplify downstream, and how about an hour and a half of upfront communication removes most of the noise. He also shares how he owns a brand voice review agent at Redis that every piece of content has to pass through before it ships, and why, quoting musician Nick Cave, AI that has never felt hunger or fear still cannot replace a human point of view. If you are shipping more content than ever but learning nothing from it, this conversation gives you the red flags to watch for and a starting point to fix it.About AdamAdam Haskew is the Associate Director of Brand Experience at Redis, where he leads a three-person team focused on brand voice consistency and accurate messaging across the website, print collateral, and trade show materials. He studied English literature and started his career in magazine publishing in Chattanooga, Tennessee, then worked at software companies, an insurance provider, and SaaS companies in the Bay Area before settling into a remote role at Redis. Adam sees AI as a tool in the toolbox, not a replacement for the human judgment that turns content into something worth reading. He believes the best content starts with a clear brief and human communication, then uses AI to execute against that strategy, never the other way around.Show Notes- Connect with Adam on LinkedIn: https://www.linkedin.com/in/adamhaskew/Text us what you think about this episode!

In this week's throwback episode that originally aired as Episode 222, I am breaking down my Top Lessons from the Top 5 All-Time Episodes of The 20% Podcast. In this week's episode, I took the top lesson from each guest, and will be sharing it on today's episode. Here are the Top 5 Episodes by Listens for The 20% Podcast:5. Nick Cegelski: Get To The Truth4. Erik McKee: Building SaaSBros In Public3. Jen Allen-Knuth: Creating The Evangelist Role2. Ian Koniak: Focus on Outputs, Not Outcomes1. Anthony Natoli - How SaaS Saved His LifeThe Top Lessons Include:Discipline Building in publicGiving more than you receiveDoing more than your job titleFocusing on your outputsControlling what you can controlThese are some of the hardest working people that I know. Many of which have overcome a significant amount of adversity to get to where they are today. These are all incredible humans who are all willing to go above and beyond for their clients, and all 5 guests give more than they receive.Thank you so much for your support If there are any guests you'd like to hear me talk with on The 20% Podcast, send me a message on LinkedIn. Please enjoy this week's episode of The 20% Podcast.____________________________________________________________________________I am now in the early stages of writing my first book! In this book, I will be telling my story of getting into sales and the lessons I have learned so far, and intertwine stories, tips, and advice from the Top Sales Professionals In The World! As a first time author, I want to share these interviews with you all, and take you on this book writing journey with me! Like the show? Subscribe to the email: https://mailchi.mp/a71e58dacffb/welcome-to-the-20-podcast-communityI want your feedback!Reach out to 20percentpodcastquestions@gmail.com, or find me on LinkedIn.

Jedes zweite Tool bekommt gerade einen KI-Chat. Und ausgerechnet der Head of ChatGPT findet das problematisch. Nick Turley stellte auf der OMR 2026 die provokante These auf, dass Chat-Interfaces grundsätzlich an ihre Grenzen stoßen. Sie erinnern ihn an MS-DOS. Damit trifft er, was viele in der Branche gerade denken: Die Welle von KI-Chats ist ein notwendiger Evolutionsschritt, aber noch nicht das Ziel für Amazon PPC.Yarin und Ines diskutieren, warum die nächste Generation von KI-Tools von reaktiv auf proaktiv umschalten muss, was das für Amazon Advertising und PPC-Manager bedeutet und worauf ihr bei der Tool-Auswahl jetzt achten solltet.Alle Themen der Episode im Überblick: Yarin & Ines stellen sich vor (00:00)Warum jeder Anbieter gerade KI-Chats baut (01:40)Nick Turleys These zu Chat-Interfaces (03:36)Die MS-DOS-Analogie: Warum Chat ein Rückschritt sein kann (04:06)Wo Chatbots bei 500 Produkten zusammenbrechen (10:11)Wenn KI ohne Kontrolle handelt: OpenClaw als Beispiel (14:06)Reaktiv vs. proaktiv: Der entscheidende Unterschied (15:09)Determinismus: Warum gleiche Inputs gleiche Outputs brauchen (21:18)Was die Aufgabe des PPC-Managers bleibt (22:38)Worauf ihr bei KI-Tools jetzt achten solltet (25:20)Links & Ressourcen:CORTUA Warteliste: Sichere dir deinen Platz – vor dem offiziellen StartOMR 2026 Talk: Nick Turley (Head of ChatGPT bei OpenAI) im InterviewYarin auf LinkedInInes Ehrhorn auf LinkedInFragen & Anregungen:Hintergründe sowie weiterführende Informationen zum Podcast findest du unter: https://www.adference.com/podcast-vitamin-aFür Fragen und Feedback schreib uns auf LinkedIn: https://www.linkedin.com/in/anna-waag/ oder hinterlasse einen Kommentar auf YouTube: https://www.youtube.com/@ADFERENCEMail: vitamin-a@adference.com

Fault Tolerance for Quantum Inputs and Outputs with Matthias ChristandlWhy This Episode MattersMost discussions of fault tolerance quietly assume a classical-in, classical-out picture: you feed in bits, the noisy quantum machine does its work, and a stable classical answer comes out the other side. Christandl — a mathematically trained quantum information theorist who also leads a Novo Nordisk Foundation–funded life sciences center — argues that this framing is too narrow for the era we are actually entering, where multi-core processors, networked QPUs, and quantum communication links all need to exchange quantum information between noisy machines.If you care about how quantum networks, distributed quantum computers, and quantum simulation workflows for chemistry and biology actually get built, this episode lays out a foundational way of thinking about the problem and connects it directly to current hardware and algorithm co-design.SponsorThis episode is brought to you by Outshift, Cisco's incubation engine. The need for computational power is rapidly increasing in every sector. From drug discovery to material innovation to complex financial modeling, classical systems are reaching their absolute limits. It's time for a paradigm shift. The answer is a scalable quantum network, built on open standards and vendor-agnostic architecture. By uniting distributed quantum devices, you unlock limitless computational power. Learn more about the Cisco Universal Quantum Switch at Outshift.com.Go deeper with the blog post.What We Get IntoWhy the fault tolerance theorem as usually stated leaves out the case that matters most for networking: quantum inputs and quantum outputs.How Christandl's group shows you can still prepare arbitrarily complex quantum states on a noisy machine, paying only one final layer of physical noise rather than collapsing the whole computation.What this means for restoring meaning to quantum channel capacity results in the presence of noisy encoders and decoders.Why distributed quantum computing — multi-core QPUs talking to each other in quantum, not classical, information — is the natural setting for this work.How recent quantum LDPC code work fits in, and why the team is now focused on making encoders and decoders more space-efficient.Christandl's debate with Gil Kalai: which skeptical assumptions are worth taking seriously, and which he thinks the fault tolerance machinery is robust against.The Quantum for Life workflow: zooming in on the quantum-relevant region of a protein–ligand interaction, running a small quantum simulation, and feeding the result into a classical machine-learning pipeline that needs many such small computations.Why "co-design" has replaced "bridging the gap" as the right metaphor for where quantum hardware and quantum software meet.How quantum sensing — for example, magnetic-field sensing with atomic clouds — could one day deliver genuine quantum inputs into a fault-tolerant quantum computer.Resources & LinksGuest LinksMatthias Christandl — University of Copenhagen Research Portal — Official institutional profile with publications and affiliations.Quantum for Life Center — University of Copenhagen — The Novo Nordisk Foundation–funded center Christandl leads, focused on quantum algorithms for the life sciences.UCPH Quantum Hub launch — The cross-faculty quantum community Christandl helped found at the University of Copenhagen.Christandl appointed 2024 Turing Chair — CWI/QuSoft — Background on his honorary visiting chair at QuSoft and CWI in Amsterdam.Papers & ArticlesFault-Tolerant Coding for Quantum Communication (arXiv:2009.07161) — The foundational paper (IEEE TIT 2024, with Müller-Hermes) that motivates the episode: channel coding when the encoder and decoder circuits themselves are noisy.Fundamental Limit on the Power of Entanglement Assistance in Quantum Communication (arXiv:2408.17290) — Christandl and collaborators settle a 2002 conjecture of Bennett et al. on entanglement-assisted capacity (PRL 2025).Asymptotic tensor rank is characterized by polynomials (arXiv:2411.15789) — STOC 2025 result connecting tensor theory to the matrix multiplication exponent.How to Use Quantum Computers for Biomolecular Free Energies (2026)More Quantum Chemistry with Fewer Qubits — Physical Review Research (2024) — The Quantum for Life paper underlying the protein–ligand workflow discussed in the episode.A Cornerstone of Entanglement Theory Restored — Nature Physics (2025) — Christandl's News & Views on the re-proof of the generalized quantum Stein's lemma.Quantum Duel: Matthias Christandl x Gil Kalai Key Quotes & InsightsOn reframing fault tolerance: Christandl argues that the fault tolerance theorem, as usually stated, assumes classical inputs and outputs — but the most important near-term use cases, from networked QPUs to multi-core processors, need quantum inputs and quantum outputs.On the unavoidable final layer of noise: "There will always be a final layer of noise being applied" when a noisy machine prepares a quantum state — and that single layer, not the whole computation, is the real price you pay.On the new metaphor: "A few years back, I would have told you the really important thing is bridging the gap between the hardware and the software. Now it's not anymore about bridging the gap. It's about working together."On Kalai's skepticism: Christandl finds the debate clarifying rather than threatening — the fault tolerance techniques look robust to the noise-model perturbations skeptics raise, and the engineering question is which code, not whether codes work at all.On what quantum advantage in life sciences might actually look like: Not one heroic simulation, but many small, exact quantum computations feeding training data into a much larger classical machine-learning workflow that predicts protein–ligand interactions.Related Episodes

Most AI conversations focus on models. The better conversation focuses on systems. In this episode, we continue our interview with Matt Levenhagen, exploring a practical challenge many developers are facing: integrating AI into business operations without creating costly chaos. The answer is not buying more AI tools. The answer is building an intentional AI Workflow Architecture. About Matt Levenhagen Matt is the founder and CEO of Unified Web Design, a web development agency focused on custom solutions, WordPress development, e-commerce, memberships, and business systems. His background as both a builder and agency owner gave him a unique perspective on where AI creates real leverage instead of superficial automation. Follow Matt on LinkedIn. AI Workflow Architecture Starts with Context Control One of the most important operational realities Matt discussed was token usage. Businesses rushing into AI often underestimate cost scaling. Every interaction with large models consumes resources, and poorly managed context windows dramatically increase operational expenses. Instead of treating AI like unlimited compute, Matt focused on controlling context intentionally. That included: Monitoring token usage Limiting unnecessary memory loading Structuring retrieval systems Using different models for different tasks Preventing oversized prompts This is a systems-thinking problem, not merely a coding problem. Developers who ignore architecture end up with bloated workflows that become financially unsustainable. The fastest way to make AI unprofitable is to send unnecessary context into every request. Why Retrieval Matters More Than Raw Memory A major breakthrough Matt discussed was implementing Retrieval-Augmented Generation (RAG). This matters because AI systems do not need all the information all the time. They need the right information at the right moment. That distinction completely changes system design. Without retrieval architecture: Costs increase Performance slows Outputs become less accurate Hallucinations increase Operational complexity grows RAG allows systems to retrieve semantically relevant information instead of dumping entire databases into prompts. This transforms AI from brute-force processing into intelligent retrieval. The future of AI operations will likely depend less on giant models and more on efficient information orchestration. AI Workflow Architecture Requires Layer Separation Another valuable concept from the conversation involved separating operational layers. Matt described balancing: Local storage Business memory External AI APIs Workflow automation SaaS integrations This layered architecture creates flexibility. Instead of locking the business into one AI provider, workflows remain adaptable. Different models can handle different workloads depending on cost, complexity, and accuracy requirements. This becomes increasingly important as pricing models fluctuate. Businesses relying entirely on one provider risk operational instability if pricing changes dramatically. Layer separation reduces that risk. The businesses that survive AI cost volatility will be the ones architected for flexibility instead of dependency. Why Embedded AI Features Often Disappoint Matt also discussed the growing wave of SaaS AI integrations. Every platform now markets AI capabilities: Project management tools Communication platforms CRM systems Design software Documentation systems Yet many users feel underwhelmed. The reason is architectural isolation. These tools only understand limited slices of operational context. They automate micro-tasks but rarely improve larger workflows. That creates a false impression that AI itself lacks value when the real issue is fragmented systems. AI becomes more useful as the organizational context becomes more connected. This is why developers building custom operational layers still maintain an enormous strategic advantage. AI Workflow Architecture Is an Operational Discipline The strongest insight from these episodes may be that AI implementation is becoming operational engineering. Success now depends on: Information structure Retrieval design Workflow sequencing Context prioritization Cost management Human oversight This moves AI away from novelty experimentation and toward infrastructure planning. Businesses that treat AI casually will likely accumulate technical debt quickly. Businesses that approach AI architecturally will build scalable operational leverage. AI is no longer just a development tool. It is becoming an operational systems discipline. Developers Must Learn Economic Thinking One overlooked topic in AI discussions is economics. Matt repeatedly referenced balancing capability with cost. This becomes critical because AI pricing models are still evolving rapidly. Businesses that ignore usage economics may accidentally build systems that become financially impossible to scale. Developers now need to think beyond: Can this be built? They also need to ask: Can this be sustained? Can this scale economically? Can context costs remain controlled? Can cheaper models handle simpler tasks? This represents a major evolution in modern software architecture. Review your current AI workflows and identify where unnecessary context or oversized prompts may be increasing costs. Conclusion AI Workflow Architecture is rapidly becoming one of the most important technical disciplines for modern developers. Matt Levenhagen's approach demonstrates that successful AI implementation is less about chasing the newest model and more about designing sustainable operational systems. The companies that gain long-term advantage from AI will not necessarily be the companies using the largest models. They will be the companies with the best architecture. Stay Connected: Join the Developreneur Community

Natalie Marcotullio & Mark Kilens invited me on their podcast GTM News Desk.We discuss:How do you build messaging that actually sticks across every team that touches it? How do you know when an "insight" is genuinely worth acting on or when it's just a pattern dressed up in fancy words? What does it take to teach AI to think like you, not just write like one version of you?Mojo Founder Eric Holland and I reveal what's really broken about how B2B companies develop and distribute their messaging. We dig into why most customer research is pulled from the most biased data available and how to document your decision-making, not just your knowledge, so AI can actually replicate your judgment. We also provide answers to why chasing every new AI workflow is making most marketers worse, not better. Eric and I challenge the idea that volume of insights equals quality of insights, arguing that a single finding that changes how your whole company goes to market is worth more than a hundred tagged Gong quotes.Jump in:(00:00) We need a higher standard for b2b research(00:35) What CMOs are actually talking about at conferences right now(04:40) How shame and fear became the default AI adoption strategy(08:41) The difference between AI-exhausted and AI-excited teams(12:44) Four Tendencies and what it means for AI adoption(16:31) Why B2B messaging is broken even at well-funded startups(19:14) What separates Mojo from just pointing Claude at your Notion(27:26) What good writing actually feels like and whether AI can get there(32:17) The solar plexus test and how to know when your copy is working(34:05) Why nuance and emotion are the real gap in AI writing(39:27) Why most insight tools are just pattern-matching machines(44:19) How to check if your researcher is actually any good(47:30) The danger of everyone using the same workflows to find the same "insights"(50:01) What B2B marketing gets wrong that B2C figured out long ago(53:21) Go deep on one thing for a quarter(55:22) Why you should only build what you already know in your sleepConnect with Eric: https://www.linkedin.com/in/eric-holland-not-a-marketer/ Connect with Anna: https://www.linkedin.com/in/annafurmanov/ Mojo: https://mojopmm.com/Mojo + Moxie: https://mojo-and-moxie.com/Natalie: https://www.linkedin.com/in/natalie-marcotullio/Mark: https://www.linkedin.com/in/markkilens/

The downside of powerful, autonomous models that can think and act?

Warren Buffett once said it's only when the tide goes out that you discover who's been swimming naked. This week, the tide went out on several fronts simultaneously, and what it revealed was uncomfortable, instructive, and in some cases, long overdue.France opened the week with a breach that should trouble every government running centralised identity infrastructure. Up to 19 million records tied to passports, ID cards, and driver's licenses are now circulating on criminal forums. What makes this worse than a typical data leak is the context: a similar dataset from the same agency surfaced in 2025. This wasn't a surprise attack on a hardened target. It was a recurring failure wearing the face of a solved problem.The Bitwarden supply chain story carried a similar energy. No vaults were cracked, no passwords were stolen, and most users never noticed a thing. But a malicious package briefly moved through npm as part of the Checkmarx campaign, targeting the developers who build the software everyone else depends on. The lesson isn't technical — it's structural. Your security posture now extends to every build pipeline, every dependency, and every automation script upstream of your product.Then came FAST16.SYS, and the week shifted into something darker. This rootkit, which appears to predate Stuxnet, didn't steal data or trigger alarms. It quietly altered precision calculations in memory while leaving every file on disk untouched. Systems looked healthy. Outputs looked reasonable. The only thing wrong was the answer. It is the most patient form of sabotage imaginable, and it reframes what advanced threats are actually capable of when detection, not damage, is the real objective.AI brought its own escalation this week. Researchers are now using AI systems to attack other AI systems at machine speed — probing, learning, and refining exploits far faster than any human team. At the same time, agent browsers like Interceptor are quietly repositioning the browser itself as an autonomous actor, raising legitimate questions about oversight when software is doing the clicking, typing, and deciding on your behalf.Anthropic's Mythos model access story tied several threads together neatly. Contractor credentials, open-source reconnaissance, and data exposed in a third-party breach combined to give a small group access to a restricted model. The intent was curiosity, not sabotage — but the mechanism was a textbook illustration of how third-party access chains create exposure that principal organisations rarely see coming.Apple closed out the privacy section with a rare win, patching a logging bug that had been silently retaining Signal message fragments for up to a month — long after deletion, long after the app was removed. The FBI had already used it in court. The patch is clean and the fix is automatic, but the episode is a pointed reminder that ephemeral and permanent are closer together than most people assume.The week closed on strategy. OpenAI and Microsoft have restructured their foundational partnership, removing exclusivity and capping revenue payments. The AI infrastructure layer is becoming contested ground, and this deal confirms that no single partnership, however dominant it once appeared, is permanent.This week's stories didn't shout. They accumulated. And that, more than anything, is the point.

HEARD IN THIS EPISODE ◼ Why the four-year decline in staffing revenue isn't a market problem — it's a sales behavior problem that leaders are actively making worse by obsessing over the wrong metrics ◼ How COVID didn't just change where salespeople work — it quietly made them lazy, and why most sales leaders haven't noticed yet ◼ What the best staffing firms are doing differently right now that almost nobody else is: pulling recruiters into the sales process before the deal is even close ◼ Why your ICP is probably wrong — and how getting more specific with a smaller target list will generate more revenue than casting a wider net ever could ◼ How one simple mental shift — from account management to pre-client acquisition — could completely rewire the way your sales team operates EXPECT TO LEARN This episode will fundamentally challenge how you think about sales activity and what it's actually worth. You'll walk away with a sharper framework for diagnosing what's broken in your sales organization, whether it's your people, your process, or the pressure you're putting on both, and a clear-eyed view of why doing less, more deliberately, consistently beats doing more with less intention. If you lead a staffing firm or a sales team, this conversation will make you uncomfortable in exactly the right way.   KEY MOMENTS [00:01] – The #1 mistake holding firms back [01:08] – Chasing shiny objects kills revenue [02:21] – What to do when business is down [04:08] – How to prioritize your fix-it list [05:28] – Did COVID break the staffing industry? [07:02] – How COVID made staffing sales reps lazy [08:21] – Outputs vs. outcomes in sales [09:18] – Rewarding the wrong KPIs [10:48] – What consultative selling looks like [12:49] – Where firms get their ICP wrong [14:45] – Messaging by ICP level [16:38] – The Under Armour sales analogy [18:58] – Research has never been easier [23:00] – What top firms do differently [24:45] – Speed closes deals, not relationships [27:52] – Fixing staffing's reputation problem [31:42] – Sales reps as pre-client managers [33:26] – Optimistic or pessimistic on 2026? [35:41] – Coach in the moment, not end of month [37:17] – About AFM Strategic Partners [42:20] – Rapid fire: book that changed her life [43:24] – Advice for new staffing professionals ABOUT THE GUEST Anna Frazzetto is the Founder and CEO of AFM Strategic Partners and one of the most recognized voices in staffing and sales leadership — named to Staffing Industry Analysts' Global Power 150 Women in Staffing for seven consecutive years. She built her career scaling businesses from the ground up, including growing a solutions practice from $3M to $100M, and has led global sales transformations across some of the most complex corners of the industry. Her perspective is rare because it spans both sides of the table: the strategic and the deeply human, shaped in part by her experience as a cancer survivor who chose to bet on herself and start something new. Her new book, *Sales Leadership in Action*, distills over 100 field-tested tips for sales leaders who want to stop firefighting and start building teams that close. ABOUT THE HOST Brad Bialy is a trusted voice and highly sought-after speaker in the staffing and recruiting industry, known for helping firms grow through integrated marketing, sales, and recruiting strategies. With over 13 years at Haley Marketing and a proven track record guiding hundreds of firms, Brad brings deep expertise and a fresh, actionable perspective to every engagement. He's the host of Take the Stage and InSights, two of the staffing industry's leading podcasts with more than 225,000 downloads. SPONSORS AND OFFERS Book a 30-minute marketing consultation with host Brad Bialy: https://bit.ly/Bialy30 Benefits in a Card helps staffing firms offer meaningful benefits to their entire workforce through flexible, unbundled plans designed for high-turnover environments—making it easier to control costs, improve retention, and stay competitive. https://www.BenefitsInACard.com TRICOM partners with staffing firms as an asset-based lender and full-service back-office provider, helping owners scale confidently by reducing risk and easing the operational strain of payroll, cash flow, and administration. https://www.tricom.com  

With a record corn crop to move, the corn industry is on the hunt for new and innovative uses for America's crop. And one solution may be found in one of the fastest-growing sectors of the clothing and textiles sector—athleisure wear. The popular clothing style—yoga pants, joggers, hoodies and more—combines high fashion with high-functionality and comfort, and has been gaining in popularity for years. But as with any product that is sourced from petrochemicals, there is an opportunity to replace the oil-based feedstock with one that is sourced from corn. And at Qore, a joint venture between Cargill and HELM, they're working on making this a possibility. So in this episode, we talk to Andrea Vanderhoff, Director of Technology and Sustainability at Qore, to learn more about how their QIRA technology is opening new avenues for corn-based products to penetrate the textiles market, including in athleisure wear. And, NCGA Director of Outputs and Measurements Harley Janssen joins us as well to talk about the potential impacts and benefits for the corn industry. To learn more about Qore and QIRA, visit www.myqira.com

The dominant structural shift highlighted is the movement of value from AI-driven features to the ownership and governance of the control plane—specifically, entities that set boundaries, maintain proof, and keep automated workflows within defined limits. This shift is evidenced by workforce polling from Quinnipiac University, business formation trends tracked by the Bank of America Institute and Census Bureau data, and product launches from vendors like TeamViewer and KnowBefore. These developments underscore a growing reliance on automation where traditional human oversight is minimized, and technology increasingly assumes direct control over work execution. The episode details workforce sentiment, citing a Quinnipiac University poll where only 15% of respondents expressed willingness to work for an AI boss, and 70% anticipated AI would reduce job opportunities. Bank of America Institute data notes a 15% year-over-year increase in high propensity businesses—those likely to launch—while businesses planning to hire have fallen by 4%. TeamViewer has introduced TIA Reporting, which generates dashboards via natural language prompts, reducing specialist requirements. KnowBefore's ADA Orchestration automates security awareness scheduling and execution, reportedly shortening setup times from hours to seconds. These examples show how vendors are deploying AI tools that replace specific manual oversight with algorithmic management. Supporting developments reinforce the governance gap. According to a CIO Dive report, 96% of C-suite leaders expect productivity gains from AI, yet 77% of employees report increased workloads, signaling misalignment between leadership intent and actual outcomes. Tech Bullion reveals 60% of organizations have AI integrated in at least one core function, with 65% using generative AI regularly, but fewer than a quarter have operationalized ethical AI frameworks. The Verge covers enhancements to Anthropics' tools that embed guardrails where organizational controls are lacking. Additional survey data from TechCrunch shows that usage of AI is growing while trust in its outputs remains weak; only 24% of respondents trust AI most of the time. Operationally, the implication is clear for MSPs and IT leaders: as organizations reduce human oversight and delegate more work to automation, the auditability, accountability, and control of automated workflows become direct contractual risk. Control layers—such as logging, exception handling, approval thresholds—must be productized and priced, not treated as informal advisory work. Liability for automation failures must be clearly assigned and managed through contractual terms, with automation incident response separated from standard support. Without enforceable governance and evidence of control, MSPs risk absorbing unpaid remediation work as clients expect both automation benefits and assurance of outcome. 00:00 Bossless Workforce 03:22 AI, No Guardrails 05:45 Govern or Absorb 08:41 Why Do We Care?  Supported by:  Nerdio HaloPSA  

Focus on Feedlots: Continued Heavy Cattle NASA STELLA at Ag Tech Day Reducing Corn Silage in Cow's Diet   00:01:05 – Focus on Feedlots: Continued Heavy Cattle: Justin Waggoner, K-State beef cattle specialist, starts today's show as he recaps the recent "Focus on Feedlots" report and where cattle are currently finishing in terms of weight. Focus on Feedlots KSUBeef.org jwaggon@ksu.edu    00:12:05 – NASA STELLA at Ag Tech Day: The show continues with Jacob Orser, program support specialist with NASA Acres, as he discusses NASA's STELLA and what he will be teaching kids at the upcoming Ag Tech Day. NASA - STELLA Ag Tech Day   00:23:05 – Reducing Corn Silage in Cow's Diet: K-State dairy specialist, Mike Brouk, ends the showing saying how recent studies show that a BMR male sterile sorghum hybrid can effectively replace about 25-30% of the corn silage in a lactating cow's diet.       Send comments, questions or requests for copies of past programs to ksrenews@ksu.edu.   Agriculture Today is a daily program featuring Kansas State University agricultural specialists and other experts examining ag issues facing Kansas and the nation. It is hosted by Shelby Varner and distributed to radio stations throughout Kansas and as a daily podcast.   K‑State Extension is a short name for the Kansas State University Cooperative Extension Service, a program designed to generate and distribute useful knowledge for the well‑being of Kansans. Supported by county, state, federal and private funds, the program has county Extension offices statewide. Its headquarters is on the K‑State campus in Manhattan. For more information, visit Extension.ksu.edu. K-State Extension is an equal opportunity provider and employer.

The Joint Readiness Training Center is pleased to present the one-hundredth-and-forty-third episode to air on ‘The Crucible - The JRTC Experience.' Hosted by MAJ David Pfaltzgraff, the BDE Executive Officer Observer-Coach-Trainer and MAJ Marc Howle, the Brigade Senior Engineer / Protection OCT for Brigade Command & Control (BDE HQ), on behalf of the Commander of Ops Group (COG). Today's guests are experts across JRTC: MSG Jared Cawthon as the BDE Fires Support NCOIC, MSG Randell Conway as the BDE Intelligence NCOIC OCT, both from BC2 (BDE HQ), and MAJ Lorenzo Evans is the Support Operations Plans Officer OCT for TF Sustainment (DSSB / LSB).   This episode focuses on the critical outputs of the military decision-making process (MDMP) and how their quality directly determines a unit's ability to execute in combat. Rather than viewing MDMP as a series of steps, the discussion emphasizes that its true value lies in the products it produces—clear commander's guidance, refined mission statements, synchronized warfighting function inputs, and shared fighting products that enable subordinate units to act. Key outputs such as planning guidance, initial and refined timelines, targeting products, and decision support tools are highlighted as essential for translating analysis into executable operations. When done correctly, these outputs create a common understanding across the formation and allow units to operate with speed, clarity, and purpose in a complex environment.   The conversation also underscores that poor or incomplete MDMP outputs are often the root cause of friction during execution. Vague guidance, inconsistent graphics, and lack of version control lead to desynchronized efforts and missed opportunities on the battlefield. Best practices focus on producing simple, clear, and timely outputs that are continuously refined through running estimates and rehearsals. The importance of early dissemination, shared digital and analog products, and enforcing standards across the staff is reinforced to ensure all echelons are aligned. Ultimately, the episode highlights that MDMP is only as effective as the outputs it delivers, and units that master these products gain a decisive advantage in large-scale combat operations.   Part of S13 “Hip Pocket Training” series.   For additional information and insights from this episode, please check-out our Instagram page @the_jrtc_crucible_podcast   Be sure to follow us on social media to keep up with the latest warfighting TTPs learned through the crucible that is the Joint Readiness Training Center.   Follow us by going to: https://linktr.ee/jrtc and then selecting your preferred podcast format.   Again, we'd like to thank our guests for participating. Don't forget to like, subscribe, and review us wherever you listen or watch your podcasts — and be sure to stay tuned for more in the near future.   “The Crucible – The JRTC Experience” is a product of the Joint Readiness Training Center.

In this episode of The Ross Simmonds Show, Ross sits down with Britney Muller, AI educator and founder of Orange Labs, to unpack what marketers are getting wrong about large language models, why reverse engineering ChatGPT is a dead end, and how to build real leverage in a probabilistic world. From practical AI workflows to the ethical risks shaping the future of the industry, this is a first-principles breakdown of what actually matters next. Key Takeaways and Insights: 1. AI is not search, it is a different machine entirely - LLMs are probabilistic word prediction systems, not ranking engines. There are no ranking factors inside ChatGPT and no URLs in its training data. - Most marketers are forcing AI into an outdated SEO mental model, and new technology requires a new framework. 2. Understanding RAG and how visibility actually works - LLMs are often paired with real-time search to stay current, but the core model and the retrieval layer are two separate systems. - Visibility in AI requires influence across both training data and search ecosystems, and SEO still matters even as the mechanics are shifting. 3. Brand mentions over backlinks - LLMs magnify what appears most frequently in training data, which means contextual brand mentions are becoming leverage. - One startup paid for brand mentions on commonly retrieved URLs rather than links and it worked. Distribution across relevant conversations increases the probability of surfacing. 4. Why you cannot reverse engineer LLMs - There is no deterministic ranking system to hack. Outputs vary across identical prompts because of probabilistic modeling. - Most AI tracking tools rely on synthetic prompts and crude metrics. Guarantees in GEO are dangerous and honesty builds trust. 5. Build your own AI tracking stack - Internal tools are now cheaper and more powerful than off-the-shelf platforms. Running prompts multiple times per day allows teams to measure probability ranges. - APIs allow thousands of queries at minimal cost. Control your data and do not outsource your intelligence. 6. Real AI workflows built by marketers - Competitive engagement scraping combined with AI-personalized outreach is producing 80 percent response rates. HARO filtering systems can now auto-draft responses inside Slack in real time. - The common thread across every workflow that works is the same: start with a clear problem, then layer in AI. 7. AI as personal leverage - Brittany used ChatGPT to win a home bidding war with a personalized letter and reframed a payment dispute email as a lawyer, which resulted in payment within 30 minutes. - AI is not just marketing leverage. It is life leverage. Literacy creates power. 8. Is SEO dead? Not quite. - Google patents suggest AI-first interfaces may replace traditional SERPs, and organic traffic levels will likely not return to pre-AI highs. - The pie may shrink but search will not disappear. Off-site distribution and social proof will matter more than ever. 9. The ethical risks of AI power - A small group of decision-makers controls foundational AI systems, and the incentives in place favor hype cycles and growth over accountability. - Reinforcement learning optimizes for pleasing users, not truth. AI literacy must include understanding bias and power structures. 10. The rise of AI agents - Early agents were mostly hype, but new iterations like Claude Chrome integrations can now visually interpret and act inside browsers using screenshot-based reasoning. -The future of marketing may involve AI transacting on behalf of users entirely, and execution changes workflows. Resources & Tools:

In this episode, Morgan sits down with serial entrepreneur, tech founder, and sales obsessive Darren Lee to talk about building multiple seven-figure businesses, why most AI is garbage, and what actually separates the people who scale from those who stay stuck. Darren shares how he lost over €100K hiring 60 people in a year, the brutal truth about A players vs B players, and why the best startups have no money. They also get into running away from pain vs chasing goals, decision-making frameworks for when your business gets bigger, working with your partner every single day, and why learning sales is the only thing that matters.Episode Timestamps0:00 Trailer0:48 Introduction: Meet Darren1:31 What is Aura?4:45 Starting a New Company9:24 Most AI is Garbage15:45 Systems Thinking Background18:23 Inputs, Outputs, Outcomes20:20 Disadvantaged Backgrounds Win24:07 The Economy Flight Story27:18 Someone Dumber is Winning32:51 Lost €100K Hiring 60 People33:47 A Players vs B Players36:17 Building a Hiring Engine48:50 One-Way vs Two-Way Doors53:24 Mentors and Category Kings57:15 Working With Your Partner1:01:02 Ten Year Vision1:03:00 Advice to His 18-Year-Old SelfAbout DarrenDarren Lee is a serial entrepreneur, tech founder, and sales systems expert. He runs multiple seven-figure businesses including a media company, education business, and Aura, an AI-powered sales management platform. With a background in engineering and startups, Darren is obsessed with process design, hiring A players, and building scalable systems. Based in Bali, he works alongside his wife Elise and has built a reputation for helping coaches and agency owners scale through systematic sales processes. This is his first time on the podcast.Connect with Darrenhttps://www.instagram.com/darrenlee.ks/?hl=en Connect with Mehttps://www.youtube.com/@morgantnelsonhttps://www.instagram.com/morgantnelson

Are numbers enough to tell the full story of your impact? In this episode of the Common Good Data podcast, Drew Reynolds sits down with Cheralynn Corsack, founder of Local Insight Studio, to explore how mixed methods evaluation can produce deeper, more actionable insight, especially in rural communities.Evaluation conversations often center on numbers. Outputs. Outcomes. KPIs. But data alone rarely captures the nuance of lived experience. Cheralynn explains how pairing quantitative data with qualitative insight, including interviews, focus groups, and participatory analysis, reveals dimensions of impact that surveys alone cannot surface.The conversation explores:• What mixed methods evaluation actually means in practice• Why participatory approaches are especially powerful in rural communities• How qualitative insight can reshape and deepen quantitative findings• The challenges of data access and representation in rural contexts• Moving from deficit based narratives to asset based framing• Translating evaluation findings into language communities can understand and useCheralynn also discusses the importance of relationship building, trust, and co-creation in evaluation work, and why sharing findings back to communities is not optional but essential.If you work in nonprofits, philanthropy, or community initiatives and want your evaluation work to be rigorous, human centered, and useful, this episode offers practical insight you can apply immediately.Learn more about Cheralynn and Local Insight Studio at localinsightstudio.comExplore Common Good Data's free course, Break the Starvation Cycle, at commongooddata.com/coursesSubscribe for more conversations on evaluation, strategy, and data for social impact.

Learn more about Refrigeration Mentor Customized Technical Training Programs at www.refrigerationmentor.com/courses Join the Refrigeration Mentor Hub here This conversation was from our latest Refrigeration Mentor Community Meetup, talking about refrigeration controls and electrical systems with Andrew Freeburg and Erik Holland. We cover control fundamentals such as transformers, multiplex board setup, communication basics, polarity, baud rate, cable practices, and fail-safe settings for loads. We also discuss how to build confidence through competence - studying, repetition, applying skills on real systems, asking questions, using community support, setting goals, and learning by teaching. Interested in joining the next Refrigeration Mentor Community Meetup? Click here. In this episode, we discuss: (00:30) Confidence and Competence (06:02) Learning How to Learn (09:58) Setting Goals and Support Groups (15:42) Dunning Kruger Effect (21:58) Electrical Basics and Safety (22:21) Center Tap Transformers (24:30) Multiplex Boards and Dip Switches (25:59) Binary Addressing Switches (26:37) Power and Comms Terminals (27:11) Comms Voltage and LEDs (29:40) Wiring Noise and Shielding (30:47) Fail Safe Dip Switches (33:46) Analog Inputs and Outputs (34:54) Software vs Hardware Logic (39:06) Panel Safety Basics (43:30) Meter Testing and Ratings (47:47) Electrical Safety Mindset  Helpful Links & Resources: Episode 371. A 6-Step Process for Faster Electrical Troubleshooting Episode 215. Understanding Refrigeration System Controls with Larry Herman of Redline Control Design

The perfect AI storm happened, and no one has noticed yet.

Nothing speaks more to a changing world than our own mind and body. We are designed for change; we change every second of every day—naturally. If we choose to resist change, we fight nature and interfere with the innate flow of life. This is why John avoids the term “anti-aging.” When we fight aging, we age faster, grow tired, and give power to the very things we do not want. In this episode, John offers tips on how to relax into the "flow" of life to improve your quality of life—physically, mentally, emotionally, and spiritually. Now in his mid-60s, John shares how scientific measures recently placed his “internal age” at just 33 years.

Nothing speaks more to a changing world than our own mind and body. We are designed for change; we change every second of every day—naturally. If we choose to resist change, we fight nature and interfere with the innate flow of life. This is why John avoids the term “anti-aging.” When we fight aging, we age faster, grow tired, and give power to the very things we do not want. In this episode, John offers tips on how to relax into the "flow" of life to improve your quality of life—physically, mentally, emotionally, and spiritually. Now in his mid-60s, John shares how scientific measures recently placed his “internal age” at just 33 years.

How did prompt engineering die so quickly? ☠️And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext Engineering Best Practices and ScalabilityTimestamps:00:00 "Access AI Community & Tools"03:08 "Mastering Context in AI"07:23 "Smart Models Require Less Precision"12:01 "Context Engineering Beats Prompt Engineering"15:49 "AI Context: Six Key Blocks"16:47 "Building Context for Better Results"19:53 "AI: Training, Not Easy Button"25:17 "Chain of Thought Prompting Decline"29:11 "Show, Don't Tell Techniques"32:13 "Context, Reuse, and Scalable Systems"33:19 "AI Chatbots: Memory and Skills"Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don't tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Outcomes, not Outputs We wonder why Agile is dying. Should we be so surprised? You’re probably watching your wallet these days. Prices are up, the economy is slow and uncertain. Your employers are no different. In 2026, story points, velocity, lead time and throughput might matter to YOU, but they don’t matter to THEM. Not to say that those measures aren’t important. Heck, you can’t tune your development, testing and deployment operations if you’re not looking at them. But if you were called to the mat tomorrow, if you were asked to prove the ROI of what you do, those metrics don’t tell a compelling story. It’s about Outcomes, not Outputs. Coaches and SM’s are now being called to defend their worth. They’re not interested in how well-tuned the development machine is if that doesn’t translate to real business results. The measures that the business cares about have to do with their financial outcomes, not outputs that don’t face the customer. Are your efforts helping the company create shareholder value? Do they impact earnings-per-share? If your boss spends a few million on a cadre of Agile Coaches this year, is that a net-positive investment for the company? More cashflow? More customers? Fewer abandoned carts? New subscribers? I know. I sound super-corporate right now. And maybe you hate that. But if you’re looking to accelerate your career in 2026, you can’t ask the people who fund it to trust you, sight unseen. They’re taking a closer look at the books, and these questions are long overdue. How are you contributing to our results? The Dev team might be considered a necessary expense, but if your Agile Shirpa talent aren’t making the team more impactful than they would be on their own, why are you even here? Remember, its Outcomes, not Outputs. This is the future of Agile practice in large enterprise. You have to collaborate with the business, and drive results for them. If you want to learn how, you should check out my brand-new Business Outcomes Partner Playbook. Get the edge so you can get your career back where it belongs, and say good-bye to to the upheaval and uncertainty that’s ripping through our industry. Did you enjoy this episode? You might also like these: The 2026 18th State Of Agile Report Episode 224 – Circulate Value – The Agile Survival Skill Episode 235 – Agile Is A Doorway **LEARN HOW TO DELIVER UNDENIABLE ROI THAT SAVES YOUR JOB AND ACCELERATES YOUR FUTURE** Get the Business Outcomes Partner Playbook Now! https://learning.fusechamber.com/offers/AFGm3tSy/checkout **FORGE GENESIS IS HERE** All the skills you need to stop relying on job postings and start enjoying the freedom of an Agile career on YOUR terms. First cohort starts in Jan 2026 https://learning.fusechamber.com/forge-genesis **THE ALL NEW FORGE LIGHTNING** 12 Weeks to elite leadership! https://learning.fusechamber.com/forge-lightning **JOIN MY BETA COMMUNITY FOR AGILE ENTREPRENEURS AND INTRAPRENEURS** The latest wave in professional Agile careers. Get the support you need to Forge Your Freedom! Join for FREE here: https://learning.fusechamber.com/offers/Sa3udEgz **CHECK OUT ALL MY PRODUCTS AND SERVICES HERE:** https://learning.fusechamber.com **ELEVATE YOUR PROFESSIONAL STORYTELLING – Now Live!** The most coveted communications skill – now at your fingertips! https://learning.fusechamber.com/storytelling **JOIN THE FORGE*** New cohorts for Fall 2025!  Email for more information: contact@badassagile.com **BREAK FREE OF CORPORATE AGILE!!*** Download my FREE Guide and learn how to shift from roles and process and use your agile skills in new and exciting ways! https://learning.fusechamber.com/future-of-agile-signup We’re also on YouTube! Follow the podcast, enjoy some panel/guest commentary, and get some quick tips and guidance from me: https://www.youtube.com/c/BadassAgile ****** Follow The LinkedIn Page: https://www.linkedin.com/showcase/badass-agile ****** Our mission is to create an elite tribe of leaders who focus on who they need to become in order to lead and inspire, and to be the best agile podcast and resource for effective mindset and leadership game. Contact us (contact@badassagile.com) for elite-level performance and agile coaching, speaking engagements, team-level and executive mindset/agile training, and licensing options for modern, high-impact, bite-sized learning and educational content.

Today's guest is Hayden Mitchell, Ph.D.  Hayden is a sports performance coach, educator, and researcher specializing in movement ecology and pedagogy, helping coaches design environments that support learning, resilience, self-actualization, and sustainable athletic performance through play and exploration. There is a great deal of conversation in sports performance around methods, including exercises, drills, systems, and models, but far less attention is given to coaching itself. Coaching methodology quietly shapes how athletes experience training, how they relate to challenge and failure, and ultimately how fully they are able to express themselves in performance. On the show today, Hayden speaks about exploring how coaching and physical education shape not just performance, but the whole human being. Hayden shares his path through sport, teaching, and doctoral work, including how life experiences changed his approach to leadership, control, and play. Together they discuss movement ecology, value orientations in coaching, such as mastery, learning process, self-actualization, social responsibility, and ecological integration, and why environment often matters as much as programming. The conversation highlights rhythm, joy, and exploration, along with practical ways coaches can use restraint, better questions, and playful constraints to help athletes own their development. Today's episode is brought to you by Hammer Strength. Use the code “justfly20” for 20% off any Lila Exogen wearable resistance training, including the popular Exogen Calf Sleeves. For this offer, head to Lilateam.com Use code “justfly10” for 10% off the Vert Trainer View more podcast episodes at the podcast homepage. (https://www.just-fly-sports.com/podcast-home/) Timestamps 0:00 – Hayden's coaching background 6:42 – Learning through experimentation 13:55 – Movement quality versus output 21:18 – Constraints based coaching 30:07 – Strength that transfers 39:50 – Variability and resilience 48:26 – Developing youth athletes 57:41 – Decision-making under fatigue 1:06:10 – Simplifying training programs 1:14:22 – Long term coaching philosophy Actionable Takeaways 6:42 – Learning through experimentation builds better coaches and athletes. Early coaching growth often comes from trying ideas, observing outcomes, and refining approaches. Allow room for trial and error in training rather than locking into rigid systems too early. Encourage athletes to feel and explore movement solutions instead of chasing perfect reps. Reflection after sessions helps clarify what actually transferred versus what just looked good. 13:55 – Movement quality creates the foundation for sustainable performance. Chasing outputs too early can hide inefficient movement strategies. Build positions, shapes, and rhythm before emphasizing max speed or max load. Use submaximal work to groove coordination and reduce compensation patterns. Improved movement quality often raises outputs without directly training them. 21:18 – Constraints guide learning better than constant verbal correction. Design drills that naturally guide athletes toward desired solutions. Reduce cue overload by letting the task do the teaching. Constraints promote adaptability instead of dependency on coaching feedback. This approach scales well in team settings with limited coaching bandwidth. 30:07 – Strength training should support movement, not replace it. Choose lifts that reinforce postures and force directions seen in sport. Avoid chasing strength numbers that disrupt rhythm or coordination. Use strength work to enhance confidence and robustness, not fatigue accumulation. Strong athletes still need to move well under dynamic conditions. 39:50 – Variability is a key driver of resilience. Expose athletes to multiple movement patterns and speeds. Avoid over standardizing drills to the point of robotic execution. Small variations build adaptability without sacrificing intent. Resilient athletes tolerate change better during competition. 48:26 – Youth athletes need exposure, not specialization. Prioritize broad skill development over early performance metrics. Multiple sports and movement environments improve long term ceilings. Avoid labeling young athletes too early based on temporary traits. Early diversity reduces burnout and overuse issues. 57:41 – Decision-making matters when athletes are tired. Fatigue reveals movement habits and decision quality. Train cognition alongside physical outputs when appropriate. Simple competitive games expose real world decision challenges. Performance under fatigue reflects true readiness. 1:06:10 – Simple programs executed well outperform complex plans done poorly. Clarity improves athlete buy in and consistency. Fewer exercises done with intent beat bloated sessions. Complexity should serve adaptation, not ego. Great programs are easy to repeat and sustain. 1:14:22 – Long term development requires patience and perspective. Short term gains should not compromise future potential. Progress is rarely linear, especially in young athletes. Coaching success is measured in years, not weeks. Build athletes you would want to train again in five years. Quotes from Hayden “Good movement solves a lot of problems before strength ever enters the conversation.” “When you design the environment well, you do not need to talk nearly as much.” “Outputs are easy to measure, but they are not always the most important thing.” “Variability is not chaos. It is preparation.” “Athletes who only know one solution struggle when conditions change.” “Young athletes do not need more specialization, they need more experiences.” “Strength should support expression, not restrict it.” “Simple does not mean easy. It means intentional.” “Fatigue exposes habits, not flaws.” “The goal is not just better athletes, but athletes who last.” About Hayden Mitchell Hayden Mitchell, PhD is a sports performance coach, educator, and researcher whose work sits at the intersection of movement ecology, pedagogy, and human development. He has coached and taught across a wide range of settings, from youth and collegiate sport to military, adaptive populations, and general fitness, working with ages 4 to 90. Hayden holds a doctorate in Human Performance and Sport Pedagogy and focuses on how environment, values, and teaching behaviors shape learning, resilience, and performance. His work emphasizes play, rhythm, and self-actualization, helping coaches and athletes move beyond rigid systems toward practices that develop both performance capacity and the whole human being.

In this episode of Tech Talks, Julian Dibbell is joined by partner Brian Nolan and associate Megan Fitzgerald to unpack how companies can protect AI assets and outputs using IP strategies. The conversation maps the key protectable components of AI—algorithms and code, trained models and parameters, proprietary datasets, and outputs—and evaluates the strengths and limits of trade secrets, copyrights, patents, and contracts. They highlight why trade secrets are particularly powerful for AI while probing emerging "improper means" issues like scraping, prompt injection, and ToS violations. They also survey evolving copyright law on human authorship and fair use in training, and discuss patent inventorship guidance and eligibility trends, before closing with practical contracting approaches to allocate data rights, output ownership, and IP strategy. Show Notes: 00:02 Introduction to Protecting AI Assets and Outputs 02:00 Protectable AI Assets: Algorithms, Models, Data, Outputs 06:07 IP Toolkit: Trade Secrets, Copyright, Patents, Contracts 09:55 "Improper means" in AI: Scraping, Prompts, ToS 12:44 Using Copyright to Protect AI 16:21 Copyright: Human Authorship, Code Protection, Fair Use 21:27 Patents: Inventorship Guidance, Eligibility, Open Issues 29:58 Contracts: Data Rights, Output Ownership, Strategy

Join the All In Mastermind: 100 Men, committed to their goals - https://www.muscleintelligence.com/apply/   Truth is, 98% of people set goals… and end the year in the same place. In this solo episode, Ben breaks down the 12 "Power Moves" framework he uses with elite founders, executives, and high performers to create repeatable wins in 2026. You'll learn why measuring inputs is a trap, how to build an outcomes-based scorecard for your body and life, and the real definition of success: intelligence + agency. Ben also explains the Mission–Map–Mentor model, the 4 resiliencies that determine follow-through (body, mind, stress, energy), and the 12 power moves that created exponential change in his own life. If you want a year that actually moves the needle, start here. 5 Bullet Points: Why "more information" can keep you stuck The scorecard that turns goals into outcomes Intelligence vs agency: the real success equation The 4 resiliencies that predict follow-through 12 Power Moves to build momentum fast   Whenever you're ready... here are 3 ways we can help you look, feel and perform at your best:   1. Grab a free copy of 1 of our BRAND NEW Peak Performance Protocols. This is for high performers looking to 10x their training and nutrition results by becoming 10x more effective. Click here - https://go.muscleintelligence.com/high-performance-executive-report/   2. Join the Muscle Intelligence Community and connect with other men like you who want to uplevel their health and fitness. It's our new Facebook group where I coach members live, share what's working with my private clients and announce tickets to my upcoming trainings and events. Click here - https://www.muscleintelligence.com/community   3. Read the Newsletter Join 200,000 men in their prime, reading our weekly newsletter: http://muscleintelligence.com/newsletter   Time Stamps: 00:00 Introduction to Power Moves 01:25 Curating Inputs and Outputs 02:17 Scorecards for Body, Health, and Wealth 06:13 Understanding Agency and Intelligence 20:12 The Importance of Strong Humans 22:17 Taking Personal Responsibility 22:36 Power Moves for Success 24:29 Creating a Scoreboard 27:33 Mastering Your Environment 28:35 Mastering Your Morning 30:32 Nurturing Family and Marriage 31:54 Connecting to a Higher Purpose 34:15 Becoming an A Player 37:48 Final Thoughts and Mentorship Invitation

This time Ned, Adam and Laura talk targets - and why the third Cycling and Walking Investment Strategy (CWIS3) needs outputs, not simply outcomes. They are joined by the CEO of the Walk, Wheel, Cycle Trust (formerly Sustrans), Xavier Brice, who knows all about strategies, and delivering active transport networks.The government recently ended a consultation on CWIS3 but, frustratingly, the proposals lacked any investment or much strategy. There were no SMART targets, or any outputs, i.e. routes; simply the unachievable outcome that by 2035 walking, wheeling and cycling will be "a safe, easy and accessible option for everyone". Road Investment Strategies, by contrast, focus heavily on routes and infrastructure, so why do we treat walking, wheeling and cycling differently?Xavier Brice has been CEO of the Walk Wheel Cycle Trust since 2016. In 2007 Brice led the development of a new walking and cycling strategy for London, with Transport for London.This month Adam, Laura and Xavier Brice coordinated an open letter to the Secretary of State supporting a better CWIS3. That letter was signed by more than 50 organisations across health, active travel and beyond. It asked that central government maps a true national network of routes by 2030, and sets targets to deliver that network to a proper, accessible standard by 2050.You can read the letter here: https://bsky.app/profile/adamtranter.bsky.social/post/3m7fv3vhyks2rThe letter was covered in the Guardian by Peter Walker: https://www.theguardian.com/politics/2025/dec/12/drivers-cyclists-transport-policy-conservatives-culture-wars-road-safety Shortly after that, Walker interviewed transport minister, Lilian Greenwood, about the importance of 'creating a system that works for everyone': https://www.theguardian.com/politics/2025/dec/12/drivers-cyclists-transport-policy-conservatives-culture-wars-road-safetyLaura's Freedom of Information requests to English local authorities found just 2 per cent had used legal powers to purchase land - something that's done routinely for roads https://substack.com/home/post/p-178788505And her article on CWIS3: https://lauralaker.substack.com/p/a-cycling-and-walking-strategy-walksThe Walk, Wheel Cycle Trust has been improving the National Cycle Network (NCN). In 2023/24 1.7km of an off-road muddy track connecting the residential area of Newton, in West Doncaster, to Danum retail park, was widened (on NCN62), with seven barriers removed or redesigned, along with improved wayfinding and signage. Estimated annual usage rose by 196% according to the Walk, Wheel Cycle Trust, from 150,000 trips in 2022 to 450,000 in 2024. Pedestrian and cycling trips increased by 191% and 192% respectively, while other users increased by 270%. Another path improvement project in Redcar and Cleveland saw ten barriers removed on NCN1 and NCN68. Wheelchair user trips increased four-fold, from 200 to 800, with 100% of disabled users saying they now use the route as the most convenient option.For ad-free listening, behind-the-scenes and bonus content and to help support the podcast - head to (https://www.patreon.com/StreetsAheadPodcast). We'll even send you some stickers! We're also on Bluesky and welcome your feedback on our episode: https://bsky.app/profile/podstreetsahead.bsky.social Hosted on Acast. See acast.com/privacy for more information.

How do volunteer leaders move from being seen as “extra hands” to strategic drivers of mission success? In this episode of the Volunteer Nation Podcast, Tobi Johnson is joined by Chris Wade and Matthew Cobble, co-hosts of the Time for Impact Podcast in the UK, for a practical and thought-provoking conversation about building influence through impact. Together, they explore why volunteering needs to be reframed as community participation and talent, not just unpaid labor and how leaders of volunteers can use data, stories, and strategic thinking to elevate their role inside organizations. This episode goes beyond counting hours or outputs and dives into how volunteer engagement directly contributes to outcomes, organizational strategy, and long-term change. Full show notes: 193. Building Influence with Impact with Chris Wade and Matthew Cobble Building Influence - Episode Highlights [00:31] - Introducing Special Guests: Chris Wade and Matthew Cobble [01:12] - Building Influence with Impact [01:57] - Meet Chris Wade: A Leader in Volunteerism [03:58] - Meet Matthew Cobble: A Journey in Volunteer Engagement [07:42] - The Importance of Volunteerism in Today's World [12:42] - Volunteers as a Strategic Asset [14:10] - Measuring Impact and Building Influence [24:12] - Challenges and Solutions in Volunteer Leadership [31:15] - Hypotheses and Program Design [32:18] - Vision Week and Volunteer Planning [33:06] - Shifting Mindsets on Volunteerism [34:12] - Strategic Planning and Data Utilization [36:13] - Design Thinking in Volunteer Management [37:39] - Collaborative Data Collection [40:32] - Practical How-Tos for Volunteer Impact [42:46] - Measuring Volunteer Impact [53:44] - Collecting Evidence and Surveys Helpful Links VolunteerPro Impact Lab  2025 Volunteer Management Progress Report – The Recruitment Edition  Time for Impact Podcast, Tobi Johnson on the Challenging, Brave Journey of Volunteer Leadership  Volunteer Nation Episode #175: Outputs vs Outcomes: Why Counting Hours Isn't Enough Info on Lewin's Force Field Analysis Info on Balanced Scorecard for Nonprofits  Info on the Double Diamond Design Process  Info on the Outcomes Star   Thanks for listening to this episode of the Volunteer Nation podcast. If you enjoyed it, please be sure to subscribe, rate, and review so we can reach more people like you who want to improve the impact of their good cause. For more tips and notes from the show, check us out at TobiJohnson.com. For any comments or questions, email us at WeCare@VolPro.net.

In this solo episode, Sam ties a bow on our ratios series — walking us through how to go from a rookie to an expert. Many recruiters are flying blind — not knowing what exactly is working, or why. Not knowing, definitively, what it takes to make a great placement. Sam breaks down the ratios of candidates needed for screening calls, interviews, and placements, contrasting the approaches of rookie and expert recruiters.Another takeaway? The importance of tracking recruitment metrics and creating a structured plan to achieve your goals effectively. You can't double-down on winning behaviors if you don't know what they are — so step one is to experiment, step two is to document, and step three is to create a feedback loop that ensures continued success. 

Work with a DDS coach: https://datadrivenstrength.com/coaching/0:02:14 - Zac's Work on Individual Response Variation (and his conclusion)0:08:35 - Key takeaway: programming around constraints, not different principles0:15:18 - New podcast approach: Building on their own work (Research & Coaching)0:19:03 - Experience vs weak Scientific Evidence0:29:17 - Coaching Systems Review: The 5 Core Values in the training process0:37:48 - The value of Training Skill for long-term success0:44:36 - Listener Question: How to interpret years of Training Data0:52:43 - Coaching as the Application of Principles within the athlete's Constraints0:59:11 - Key Inputs and Outputs to evaluate Strength & Hypertrophy programming1:17:50 - The 3 Big Unanswered Questions in Training Science (Fatigue, Creatine, Growth Limit)

Rob Wilson is a performance educator with over twenty years of experience helping people build durable, high-functioning bodies and minds. He joins us on The Ready State Podcast to unpack the uncomfortable truth about performance: it's not free. In this powerful conversation with Kelly and Juliet Starrett, Rob dives into the real price of pushing your limits, why sleep is non-negotiable, and how to reframe “selfish” self-care as the foundation for showing up better in every area of life. Together, they tackle burnout, aging, and what it takes to sustain health and high output in a world that rewards constant hustle.What You'll Learn in This EpisodeThe three waves of fitness, and why Rob Wilson's book represents the vanguard of the third wave.The problem with the democratization of health metrics like Heart Rate Variability (HRV) if you don't know how to interpret the data or take action.The story behind the "Check Engine Light" metaphor, which helps high performers prioritize what to address and what to ignore.Why the phrase "self-care" often fails with service-oriented and high-performing individuals and the analogy used instead.The "Cobra Effect" or Goodhart's Law, and how chasing a metric like a high HRV can lead to misleading and useless outcomes.How to stop the "medical cascade" and apply an experimental framework (test/retest) to chronic, nagging pain and everyday health issues.The true cost of high performance and the crucial need for a "cost mitigation strategy" to avoid burnout.Why context matters more than perfect protocols, and how to create a personal longevity dashboard for continuous adaptation.For more info, follow Rob on Instagram and definitely pick up a copy of his new book, Check Engine Light: Tuning Your Body and Mind to Achieve Performance Longevity.Key Highlights: (00:00) - Intro(00:48) - Check Engine Light Book Overview(06:49) - Check Engine Light Metaphor Explained(14:06) - Importance of Check Engine Light for Everyone(17:49) - Inputs and Outputs in Life(20:37) - One Size Fits All Approach: Myth or Reality?(25:25) - Identifying What Matters Most to You(27:28) - Performance Costs: Understanding Trade-offs(29:27) - Recommended Supplements for Health(32:55) - LMNT: Importance of Hydration Explained(37:10) - Resistance: Creativity's Universal Challenge(39:48) - Becoming Reasonable: A Personal Journey(45:18) - Heart Rate Variability (HRV): Benefits Explained(47:15) - Using Tracking Devices Mindfully(49:57) - The Cobra Effect: Understanding Consequences(57:55) - Setting Up Environments for Success(1:00:20) - Changes Since Writing the Book(1:03:14) - What's Next for Rob: Future Plans(1:04:30) - Finding Rob: Where to ConnectSponsorsThis episode of The Ready State Podcast is brought to you by LMNT and Momentous. 

What does it mean to focus on outcomes over outputs? In this podcast hosted by iDonate VP of Product Nacho Andrade, LucidLink Chief Product Officer Richard Yu will be speaking on driving product strategy through outcome-focused innovation. Richard shares his unique approach to product management, blending technical insight with commercial strategy to create transformative solutions that solve real-world problems.