Podcasts about gpts

If you're a business owner who's built custom GPTs to share with your clients or sell as a product, listen in.On August 16th, 2026, OpenAI stopped letting personal accounts create or publish new custom GPTs. That covers the Free, Go, Plus and Pro tiers. Existing GPTs still work. Editing an existing one still works with an eligible subscription. Creating a new one to share is what's gone.There was no email about it. I found out from some buzz online and went and checked my own account.In this episode I walk through what actually changed, what I found when I tested it, and the bigger question underneath it all. What does this mean for you if your methodology is sitting inside a tool on somebody else's platform?What you'll learn:What OpenAI changed on August 16th, in their own words, and what still worksMy take on why this happened now, and what an 852 billion dollar valuation has to do with itThe backup habit to start this week for your GPT instructions, knowledge files and settingsWhy custom GPTs were never designed for what we've been using them forWhat multi-tenant access, data privacy and gated delivery mean for a non-technical builderHow a bot squad differs from a single GPT, using Nicole Pearl's Pearl Pitch Desk as an exampleCHAPTERS0:00 What OpenAI changed without telling you0:25 The exact wording of the new rule1:22 What still works, and what doesn't2:13 The IPO theory nobody's saying out loud4:01 Back up everything you've built5:49 You built something OpenAI never intended8:36 What a bot squad actually looks like10:55 The part vibe coders forget about12:21 Claude and wAIv, the wedding of the century15:47 No need to panic, but do decide--->Mentioned in this episodeThe AI Product Build Lab, Friday August 28, 11 a.m. to 1 p.m. MDT. Podcast listeners get in for $1 with the code PODCAST1, and the link below applies it automatically. Can't make it live? Register anyway and you'll get the replay, plus an invite to the next live session.https://graviastudio.com/build/?code=PODCAST1--->Introducing wAIvThis episode is brought to you by wAIv—our brand-new platform built for online experts who want to securely build and sell AI tools powered by YOUR thinking, YOUR frameworks and YOUR methodology.wAIv helps you create Bot Squads—a suite of AI tools that work together to help your clients implement your expertise faster and with better results than ever before.--->Your Next Steps:

Hey this is Alex, welcome to... the chillest week in AI, since ... a long time. Chill, if you consider Moderna and MERK announcing a cancer vaccine and surging 115% in a day, a chill week. This week, the only two model drops we really saw came from the excellent Z.ai folks, they announced GLM 5.3, API only for now, and an amazing tiny release of Qwen 3.89 27B. In other big AI news, OpenAI announced they are pausing RL efforts (Reinforcement Learning) to focus on security and alignment post the scary AI Swarms hacking incident, dedicating up to 20% of compute towards reviewing agent thinking processes, and Stripe buying OpenRouter for a reported $8B! Sometimes the chill weeks are actually good, we're able to chat about how we use AI, what changed for us, and give our guests a bit of breathing room. This week, I invited Francesco from CUA to talk about computer use in open source + their new history plugin, Bin from HeyGen to talk about HyperFrames, a way for your agents to create videos and a breaking news guest, Jeff Huber from Chroma jumped on to talk about their new Foundations release, a unified memory for your agents! This was a great episode, I hope you'll like it, it's up here on Substack and everywhere you get your pod (Spotify, Youtube, Apple Podcasts). ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Are we being fed slop again? (Is Claude dumb again?)Before we get to releases, this week on the show, I complained, again, that I feel my AI's are degrading. If this feels like de-ja-vu to you, it's because the same happened a year ago in September 2025 (and Anthropic admitting this 2 weeks later), and ... now this happens with Fable?You see, I use pretty much the same prompts, every week, preparing for the show. This is partly my way to evaluate new models and compare to existing and previous ones while also bringing you the best researched weekly show in AI. Well, this week, one after another, Claude Fable, which is... like the best intelligence, gave me such poor output, that I couldn't believe what I'm seeing. First, literally ignoring instructions that say “hey, show me all the items I've collected and let me pick the most important ones”, Fable instead sent all of them to my research pipeline, without showing me. This has worked, consistently, without fail, for the past... year? maybe more! This worked with open source models, worked with GPT, and now Fable, a Mythos Level LLM, is doing the most basic dumb s**t possible, ignoring the main reason I even have this workflow. And this wasn't just a fluke either, when asked to create a run of show document, and given an example, Fable produced this... whatever this is. This is the same document and same format that Fable produced for me during AI Engineer which got me thinking “ok, this is AGI”, and here, given an example, I got a completely unusable artifact, despite direct instructions, structure and example! I got to say, given that privately this week, Anthropic disclosed that they have passed $65B in revenue, which is absolutely insane, this doesn't add up. So I figured, ok Alex, maybe this is your prompts or skills. But no, LDJ came in with some charts that show degradation, one from MarginLab.ai that shows significant lowering on number of tool calls and average runtime recently (this is for Opus 5) and And another chart from modelverify.ai model drift monitor showing drift scores.Do we have anoher Claude Gate on our hands? Is your Fable/Opus behaving weird lately? Or did you completely switched away to other models? OpenAI pausing RL and focusing on safetyLook, when we covered the HF hacking incident and then the pacing the frontier letter, I didn't imagine that results will come this fast, but this week, OpenAI publicly announced that they are pausing RL training, which is the last step of models, until they get their sandboxes in order and align the models better. We all agreed on stage that this is likely a very good move, and Peter was really awe-struck at the 20% dedication of resources towards reviewing thought processes of models. Is this a good enough response to the scary hacking incident? we'll see, but I think this is the right move from OpenAI, and still, waiting for the full postmortem on the OpenAI security incident. Open Source LLMsQwen3.8-27B ties GPT-5.6 Luna and runs on a 4090 (X, HF, Announcement)Following the release of their flagship, Alibaba dropped a model that became a community darling overnight, Qwen 3.8 with just 27B parameters. This “tiny” model scores 52 on the Artificial Analysis Intelligence Index, same score as GPT 5.6 Luna at Max reasoning and 51 on Agentic index, beating Opus 4.8 MaxAll while running at around 68t/s on a 4090 GPU, and around 40 on max via MLX, hell it even does 11t/s on Xenova's WebGPU kernels right in the browser! This model exploded on the HuggingFace hub, with tons of quants, over 152 fine-tunes, it was downloaded over 10M times overall

AI isn't just about writing emails or asking ChatGPT a few questions. When it's thoughtfully integrated into your business, it can become a practical tool that saves time and helps your team work more efficiently.This week, Kyle sits down with Jeremy Maher of Phoenix Home Remodeling to talk about how his team is actually using AI in the business. Jeremy shares real workflows they've built to reduce repetitive work and get more value from the information they're already collecting. You'll also hear how AI has become part of their sales process. Jeremy even shares how his team is using it to handle missed calls more effectively.If you've been wondering what AI can actually look like inside a remodeling company, this episode gives you a practical look at what Jeremy's team is doing and what they've learned along the way.JobTread helps remodelers bring estimating, scheduling, job costing, and invoicing into one connected system, so they can clearly see where jobs stand and what's actually profitable. We've watched members move from guessing to confidently knowing their numbers, which leads to better pricing, planning, and leadership. If you're ready for better systems and better decisions, learn more at jobtread.com.Explore the vast array of tools, training courses, a podcast, and a supportive community of over 2,000 remodelers. Visit Remodelersontherise.com today and take your remodeling business to new heights!Key Takeaways AI integration in remodelingAutomation of client communication and project managementUsing AI for call analysis and employee coachingAutomating review collection and reputation managementLeveraging AI for pre-visit prep and scope of work creationRemote team management and outsourcing in constructionAdapting AI tools for business growth and efficiencyChapters00:00 Introduction to Jeremy Maher and AI in Remodeling02:00 Jeremy's background and how he got into AI04:02 The importance of strategy before automation06:00 Using AI to enhance customer journey and communication07:49 Voice AI for call handling and appointment scheduling13:48 Automated client photo collection process20:52 AI-generated pre-visit prep and scope of work26:57 AI analysis of employee performance and coaching35:05 Automated review management and reputation building44:07 Role-specific GPTs for team productivity50:01 Outsourcing and managing remote teams with AI50:51 Final thoughts and encouragement for remodelers

On this episode of the Woodard Report podcast, Joe and Dawn Brolin talk about the accelerating impact of AI on the accounting profession, from secure, specialized AI tools to the emerging potential of quantum computing. They explore why accounting professionals need to move beyond fear and begin experimenting with AI now, sharing practical examples of using tools like Claude and Tax Figure to analyze financial data, strengthen advisory services, and work more efficiently. Current events — EY installed a quantum computer in their Toronto office TV/Movie quote of the week — The Greatest Showman House of the Dragon Excellent Thing We Learned — There are a series of tags you can use that will change the way most GPTs will respond to you. Examples are: TRUTHMODE – forces direct, unvarnished feedback instead of the AI just agreeing with you HUMAN – strips the "AI voice," makes writing sound like a person wrote it HORMOZI – answers like a blunt, ROI focused business coach (style associated with Alex Hormozi) REDTEAM – argues against your idea to stress test it before you commit FUTUREYOU – answers as if it's the version of you who already solved this problem ELI10 – explains it like you're 10 years old, no jargon UNLEARN – flags outdated assumptions or bad advice you're carrying around 80/20 – skips straight to the highest leverage move, ignores the rest SOCRATES – teaches by asking you questions instead of handing you the answer LINDYMODE – sticks to long proven approaches, filters out hype and fads The Woodard Report article of the week — Accounting Pros Now Have a Voice at the AI Table Thank you to our show sponsor, Rightworks! If you're coming out of tax season wondering how to make next year better, RightWorks has everything your firm needs to do exactly that. Most accounting firms are juggling scattered apps, dealing with ongoing security risks, and relying on manual workarounds all on top of serving your clients. RightWorks takes that off your plate. One secure workspace for all your apps and tools. Enterprise Great Security managed for you and the AIM productivity tools built specifically for accounting firms. Whether you have an IT team or not, whether you have five employees or 45, RightWorks grows with your firm and keeps you ready for whatever comes next. RightWorks, it's all right here. Learn more at RightWorks.com. Learn more about the show and our sponsors at Woodard.com/podcast

Send Rita a text with your thoughts!Join us at Prep for Wave Week this year:  https://strategictravelentrepreneurpodcast.com/prep-for-wave-week/Fill out Rita's 2026 Feedback Form: https://forms.gle/3zuchVKZ817RvGyk9Join us for the ultimate content and marketing camp in 2027: https://strategictravelentrepreneurpodcast.com/summer-camp-at-sea/Stop wasting hours hunting for cruise content: https://programs.steeryourmarketing.com/products/courses/view/1166776I don't know about you, but AI has been big on my brain this summer.Coincidentally, The Legal Paige just came out with a reel talking about using AI legally.I'm sharing the four big things Paige mentioned, and translating them to how they would affect travel advisors like you.Things like why you can't just upload other people's work into AI to who actually owns the content AI creates for you. I'll share what I've been rethinking about my own courses and content, plus some GPT ideas I'm excited about for helping you get the most out of what you've already got. I'm not a lawyer, but Paige is, and I've been following her long enough to know that she knows her stuff and then some.Come think through these topics with me, as we're all figuring this out this new world together.Questions this episode answers:Is it legal to upload client itineraries into AI tools or a CRM's AI?Can you copyright a blog post or website that AI wrote?Is it a copyright violation to upload someone else's course or digital product into AI?Should travel advisors use AI to write their contracts?What should you never paste into AI?Do you need an AI clause in your travel agency terms and conditions?Can a client legally run your itinerary through AI and use it themselves?How can travel advisors use AI and GPTs to get more out of the content they already own?The Legal Paige Reel: https://www.instagram.com/reel/DbqlAc_yw8o/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==The Legal Paige Templates *affiliate link*: https://thelegalpaige.com?aff=847Enjoy and take action!---------------------------------------------------------------Rita M. Perez (Host) first began in the travel industry as a travel advisor in 2010. She only fully realized her role as a travel entrepreneur in 2018, and embarked on a mission to support her fellow travel advisors in 2021 when she began the Strategic Travel Entrepreneur Podcast. She now strategizes with travel entrepreneurs, so they too can build sustainable travel agencies and market effectively.She's a maven when it comes to content photography and videography, and as such founded the Cruise Content Library and leads retreats and partners on FAMs where advisors get top notch content and education for their marketing efforts.Website:  https://strategictravelentrepreneurpodcast.com/everything/Socials:LI: https://www.linkedin.com/in/ritaperez19/IG: http://www.instagram.com/steeryourmarketingFB: https://www.facebook.com/groups/strategictravelentrepreneurs/ Email:rita@steeryourmarketing.com

Public Domain AI Flipping: Build & Monetize Custom GPTs With 19th‑Century Books (Before the Window Closes) Make money online by flipping public domain AI content—the overlooked side hustle for busy parents. Learn how to find, repurpose, and sell digital products without creating anything from scratch. This contrarian strategy saves time, cuts through the noise of traditional side gigs, and generates income faster than you think. Perfect for the AI entrepreneur who wants results without the grind. https://DarkHorseEntrepreneur.com The episode explains a “public domain AI flipping” strategy: training custom GPTs on 19th-century public domain books (including Project Gutenberg and the Internet Archive) to create niche, highly specific tools that can be monetized quickly via subscriptions or products. It argues most custom GPT monetization fails due to content creation costs or licensing, but public domain works published on or before 1930 can be used and monetized without copyright barriers, with the public domain expanding yearly. The script outlines a four-step process: pick a narrow vertical with an unmet need, curate matching public domain texts, build a custom GPT using prompts and retrieval-augmented generation (RAG), then monetize via recurring revenue using platforms like GPT Plus, Gumroad, or API interfaces. It recommends verticals such as historical accuracy consulting, academic supplementation, content engines for podcasters, and archival tools, while warning about uncertain platform rules, limited precedent, and the need for distribution over technical execution. 00:00 Intro to public domain AI flipping 01:05 Why Most Custom GPT Monetization Fails  02:10 Clarify U.S. Public-Domain Cutoff 03:40 4-step Mechanism 06:20 RAG & Tightly Sourced Corpus > GPT 08:05 Monetization Options  09:40 Necessary Callouts 11:10 4 Promising Verticals 14:30 Real Trap Warning 17:10 Whiskered Wisdom Custom GPT monetization, Public domain,AI, Project Gutenberg, training data, AI side hustle 2026, ChatGPT, passive income, Public domain, Train GPT on books, RAG, custom chatbot,AI entrepreneur, niche GPT, recurring revenue, how to make money with chatgpt, ChatGPT, GPT-4o, OpenAI, Custom GPT, GPT Store, how to make money online, make money online, ai side gig, digital products for beginners, side hustles, income growth hacks https://DarkHorseEntrepreneur.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most real estate operators say they're using AI. Very few have built their entire company around it. In this episode, Neal Bawa, CEO of Grocapitus, breaks down the four-phase process his 20-person team used over the past 18 months to become what he calls an AI-first real estate company. From getting every employee certified on custom GPTs in phase one, to building proprietary web-hosted dashboards that pull live data from seven different property management systems in phase four, Neal is specific about what they built, what tools they used, how much it cost, and what it actually changed about how they operate. If you want to know what implementing AI at the company level looks like in practice, this is the episode. About Neal Bawa Neal Bawa is the CEO of Grocapitus and MultifamilyU, where he manages a $436 million AI-powered portfolio across 25 projects in 11 states. Known as the Mad Scientist of Multifamily, Neal has built one of the most data-driven operations in the real estate industry and has made more than 300 podcast appearances sharing that framework with investors. His free investor education platform at MultifamilyU has tens of thousands of subscribers and runs eight webinars per year at no cost. What We Cover in This Episode What it actually means to declare your company AI-first and what changed internally at Grocapitus when they did How Neal tied AI adoption to compensation, bonuses, and continued employment — the carrot and stick system that drove company-wide adoption Phase one: getting every employee certified on ChatGPT and building 300 custom GPTs in four months Phase two: connecting Zoom, Slack, Asana, and Google Drive through native integrations and Zapier to automate meeting workflows Phase three: why they moved to Claude for rent comps, underwriting, and web scraping — and why ChatGPT couldn't do the job How Claude Code differs from Claude Chat and Claude Cowork, and why that distinction matters for automation Phase four: turning every employee into a programmer using GitHub Codespaces — no coding knowledge required How they built a proprietary MySQL dashboard that pulls live data from seven different property management systems simultaneously Why 99% of property managers are not compliant with the industry standard of three phone calls and three text messages to every lead — and how Neal can now see this in real time across his entire portfolio How AI affected headcount — no layoffs, but three to four open requisitions closed and employees working an average of one to one and a half hours less per day Phase five on the horizon: AI handling 100% of incoming calls at their properties Neal's plan to offer this dashboard-building capability to the top 50 property management companies in the US Why Haiku outperforms Sonnet for teams that keep running out of Claude tokens Neal's interest rate prediction through the end of 2026 Key Insight Neal makes a claim that should stop most operators cold: 99% of leads at managed properties do not receive three phone calls and three text messages, the industry standard for lead follow-up. Most don't even get a second call. For years, this was invisible — property managers self-reported compliance, and nobody could verify it. Neal's phase four dashboard changes that. For the first time, he can walk into a Monday morning meeting and show every property manager exactly where they rank against each other, how long they take to respond to a lead, and how many of their leads are being processed correctly. He doesn't have to say a word. The data does it. Why This Episode Matters Neal isn't describing what AI might do for real estate someday. He's describing what his 20-person team built in the last 18 months, with zero consultants hired, on $25 per month Claude accounts. The playbook he lays out — phased adoption, compensation tied to AI competency, tools connected in sequence — is something any operator can start applying at their own scale. If you're still thinking of AI as a tool you use occasionally rather than a system your company runs on, this episode draws a clear line between those two approaches. Find Out More Website: https://www.grocapitus.com Free Investor Club (always free, 8 webinars per year): https://multifamilyu.com/club Location Magic eBook: https://multifamilyu.com/lp/location-magic-ebook/ Physical Book: https://multifamilyu.com/book Sponsors Today's episode is brought to you by Green Property Management, managing everything from single family homes to apartment complexes in the West Michigan area. https://www.livegreenlocal.com And RCB & Associates, helping Michigan-based real estate investors and small business owners navigate the complex world of health insurance and Medicare benefits. https://www.rcbassociatesllc.com

Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show:   Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans

If someone handed your competitor every prompt, workflow and AI system you use today, would you still have an advantage?   That is the question that opens this live roundtable, recorded in a sealed room at the first Founder AI Studio Live in Toronto. Ten women who are not just using AI but building with it: intellectual property strategists, AI educators, investors, community builders, and operators who have built and exited companies.   No keynote. No panel. Just founders comparing notes from the front line.   IP lawyer Andrea Bolden takes on the part most founders get wrong: whether your prompts, workflows and custom GPTs count as intellectual property, why switching off the training toggle is not the protection you think it is, why people trademark a name while giving away the entire body of work underneath it, and which AI tools will actually indemnify you if you get sued.   The room also gets into what happens to knowledge businesses when knowledge stops being scarce, why AI-native builds beat AI retrofits, the environmental cost nobody wants to raise, the risk of putting your child's face into these systems, and the six companies capturing most of the value from all of it.   It ends with a lightning round on the one tool each founder actually uses.   CHAPTERS 00:00 Context: what this room was 01:35 Not a keynote, not a panel 02:16 The question: would you still have an advantage? 03:09 Forty-five products that all do the same thing 05:46 Being the tastemaker 07:54 AI as a democratizing force 10:03 AI-native builds vs. AI retrofits 11:00 The contractor line item that changed everything 14:24 From "done for you" to "done for you-ish" 16:01 Building instead of talking about building 18:35 The real unlock is the speed of learning 23:00 If you sell knowledge, what are you selling now? 24:42 Dyslexia, ADHD and AI as a translation layer 28:22 Are your prompts and GPTs your IP? 29:28 The training toggle myth 30:30 Trademarking the name, losing the body of work 32:15 Tim Ferriss and what is already public 34:08 Can AI legally be an author? 35:32 Which AI tools will indemnify you 37:03 Writing a book with AI 39:27 Just because AI can, should it? 43:14 Share the why, not the whole how 44:00 The one thing to do next week 45:22 Canada's AI strategy and where the money is going 47:32 Environmental impact and who absorbs it 49:55 Your child's face inside an LLM 50:52 Sycophancy and outsourced thinking 51:58 What work do we reserve for humans? 54:16 Six companies and who holds the stake 56:14 Lightning round: the one tool 1:01:27 The advantage was never the tool   TOOLS MENTIONED Claude / Claude Code / Claude Cowork, ChatGPT, Microsoft Copilot, Lovable, Gamma, Codex, DeepSeek, Kling, Go High Level   LINKS Next Founder AI Studio Live   IN THE ROOM Host: Monique Bryan, Brand Authority Strategist Featuring: Andrea Bolden, IP and business lawyer   PARTNERS Captured by Perspective Studio Productions Presented in partnership with BDC Capital, Inclusive Entrepreneurship   Founder AI Studio Live is an invite-only working session for established women founders already building with AI. The room is the asset.   #AIforFounders #IntellectualProperty #WomenInAI Who Knows You is hosted by Monique Bryan, brand authority strategist and built for founders, operators, and experts who are doing real work and ready to be picked for it. Take the AI Visibility Audit to find out where your positioning is breaking down and what to fix: [RUN YOUR AUDIT] Connect with Monique:Before we build, let us talk. https://moniquebryan.com/book/ - Website: moniquebryan.com LinkedIn: Monique Bryan Instagram: @moniquebryan

The AI world has has done a spectacularly bad job of naming things. Agents, GPTs, gems, notebooks, projects, skills - you'll find a completely different word for what is essentially the same kind of tool. Agents in one place, GPTs in another, gems somewhere else. Different names, similar ideas - and no handbook to tell you that. But the tools themselves are not that complicated. Once you know what each one is built to do, knowing which to reach for becomes straightforward. In this How I AI episode, Neo and I cut through the jargon and map out what Agents, Projects, and Skills actually are, how they differ from each other, and which one to reach for depending on what you need to do. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: Why agents, GPTs, and gems are the same kind of thing (just with different names) When a project is the right choice over an agent What makes a skill different from a long prompt Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

pWotD Episode 3369: ChatGPT Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 196,704 views on Thursday, 23 July 2026 our article of the day is ChatGPT.ChatGPT is a generative artificial intelligence chatbot developed by OpenAI. Originally released on November 30, 2022, the product uses large language models—specifically generative pre-trained transformers (GPTs)—to generate text, speech, and images in response to user prompts. ChatGPT accelerated the AI boom, an ongoing period marked by rapid investment and public attention toward the field of artificial intelligence (AI). OpenAI operates the service on a freemium model. Users can interact with ChatGPT through text, audio, and image prompts.ChatGPT was quickly adopted, reaching 100 million monthly active users two months after its release and 900 million weekly active users in February 2026. Proponents say that it has the potential to transform numerous professional fields, and it has instigated public debate about the nature of creativity and the future of knowledge work.The chatbot has also been criticized for its limitations and potential for unethical use. It can generate plausible-sounding but incorrect or nonsensical answers, known as hallucinations. Biases in its training data have been reflected in its responses. The chatbot can facilitate academic dishonesty, generate misinformation, and create malicious code. The ethics of its development, particularly the use of copyrighted content as training data, have also drawn controversy.This recording reflects the Wikipedia text as of 02:57 UTC on Friday, 24 July 2026.For the full current version of the article, see ChatGPT on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Justin.

Most photographers are using AI for culling, editing or the occasional social post. Marcin Rafalowicz believes they're barely scratching the surface. In this interview, he explains how to build a custom ChatGPT trained on your voice, your business, your pricing, your clients and the work you want to be known for. The result is less generic content, faster marketing and an AI tool that becomes more useful the more you refine it. Marcin also shares how custom GPTs, website chatbots and voice-powered AI could change the way photographers create content, answer enquiries and run their businesses. The post 678: Marcin Rafalowicz – How to Build a Custom ChatGPT for Your Photography Business appeared first on Photography Business Xposed - Photography Podcast - how to build and market your portrait and wedding photography business.

As AI's capability grows, what once-human tasks will it be able to do, by when? What will those displaced humans do? We look to the automation revolutions of the past to see what the historical pattern has been, and explore in what ways AI is different that could change the pattern. The pattern will change. The implications are immense. SourcesLeontief's horse. Wassily Leontief, 1983, National Academy of Engineering symposium The Long-Term Impact of Technology on Employment and Unemployment. Quote and horse-population figures via Brynjolfsson & McAfee, "Will Humans Go the Way of Horses?", Foreign Affairs (2015): https://www.foreignaffairs.com/world/will-humans-go-way-horsesSoftware developer pay. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, Software Developers (SOC 15-1252): https://www.bls.gov/oes/2021/may/oes151252.htm · https://www.bls.gov/oes/2022/may/oes151252.htm · https://www.bls.gov/oes/2023/may/oes151252.htm · Occupational Outlook Handbook: https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm · Total-comp figure: Levels.fyi (2026).AI timelines (experts). Katja Grace et al., "Thousands of AI Authors on the Future of AI" (2023 survey, 2,778 researchers): https://arxiv.org/abs/2401.02843 · https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdfAI timelines (forecasters). Metaculus (community forecasts; live figures): "first general AI system" https://www.metaculus.com/questions/5121/ · "weakly general AI" https://www.metaculus.com/questions/3479/Goldman Sachs. "An AI Job Apocalypse?", Goldman Sachs Research, June 25, 2026: https://www.goldmansachs.com/insights/top-of-mind/an-ai-job-apocalypse (Goldman's own view is that the disruption is temporary.)Occupational exposure. Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock, "GPTs are GPTs," Science 384 (2024): https://www.science.org/doi/10.1126/science.adj0998 · working paper: https://arxiv.org/abs/2303.10130Entry-level cracks. Stanford Digital Economy Lab, "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI" (Nov 2025): https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdfCurrent labor data. Maxim Massenkoff & Peter McCrory, "Labor Market Impacts of AI: A New Measure and Early Evidence," Anthropic (Mar 5, 2026): https://www.anthropic.com/research/labor-market-impacts (Note: Anthropic funds both the models and this research.)Depression unemployment anchor. U.S. unemployment peaked near 25% in 1933 (verify exact figure before citing on air). Hosted on Acast. See acast.com/privacy for more information.

After a BIG announcement (!!!), your hosts David and Ellie get into the economic and psychological features of the state no one wants to be stuck in: unemployment. How does capitalism structurally construct “wageless life” while nonetheless treating it as a personal failure or unhappy accident? What's the connection between unemployment and reactionary politics? And is AI going to put us all out of a job? Correcting misconceptions about automation, UBI, and more, your hosts take you through some significant arguments about the impacts of unemployment and reflect on why this socially-constructed position can make us feel so worthless even when we know that we're not the problem. In the Substack bonus segment, Ellie and David reflect on the end of Overthink and share some reasons for it.Works Discussed:Aaron Benanav, Automation and the Future of WorkAaron Benanav, “Is the AI Bubble About to Burst?”Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”Charles Babbage, On the Economy of Machines and ManufacturesMichael Denning, “Wageless Life”Wendy Brown, In the Ruins of NeoliberalismRegina Bateson, “Perceptions of pandemic resume gaps: Survey experimental evidence from the United States”Start your business today with the industry's best business partner, Shopify. Sign up for your one-dollar-per-month trial today at shopify.com/overthinkElevate your summer wardrobe. Go to Quince.com/overthink for free shipping on your order and 365-day returns. Now available in Canada, too.If you're ready to stop talking yourself out of finding care and making progress, then head to rula.com and take the first step.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

How much of your life -- and everyone's -- is a compulsive, futile effort to deny your own death? What happens when you see what's really happening?SourcesLeontief's horse. Wassily Leontief, 1983, National Academy of Engineering symposiumHumans and Horses. Brynjolfsson & McAfee, “Will Humans Go the Way of Horses?”, Foreign Affairs (2015): https://www.foreignaffairs.com/world/will-humans-go-way-horsesSoftware developer pay. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, Software Developers (SOC 15-1252): https://www.bls.gov/oes/2021/may/oes151252.htm · https://www.bls.gov/oes/2022/may/oes151252.htm · https://www.bls.gov/oes/2023/may/oes151252.htm · Occupational Outlook Handbook: https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm Total-comp figure: Levels.fyi (2026).AI timelines (experts). Katja Grace et al., “Thousands of AI Authors on the Future of AI” (2023 survey, 2,778 researchers): https://arxiv.org/abs/2401.02843 · https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdfGoldman Sachs. “An AI Job Apocalypse?”, Goldman Sachs Research, June 25, 2026: https://www.goldmansachs.com/insights/top-of-mind/an-ai-job-apocalypse (Goldman's own view is that the disruption is temporary.)Occupational exposure. Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock, “GPTs are GPTs,” *Science* 384 (2024): https://www.science.org/doi/10.1126/science.adj0998 · working paper: https://arxiv.org/abs/2303.10130Stanford Digital Economy Lab, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of AI” (Nov 2025): https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdfCurrent labor data. Maxim Massenkoff & Peter McCrory, “Labor Market Impacts of AI: A New Measure and Early Evidence,” Anthropic (Mar 5, 2026): https://www.anthropic.com/research/labor-market-impacts Hosted on Acast. See acast.com/privacy for more information.

New litigators dread unscripted moments: witnesses who answer in ways they never prepared for, or objections that land in the middle of a question. In the latest episode of Real Cases, Professor Katherine Donoghue and Carson Sadro explain how Stetson's trial advocacy program is using AI to rehearse those exact moments before students face them in a courtroom or a mock trial. Their custom GPTs play witnesses, let young attorneys rerun cross examinations, and even pepper students with objections, including wrong ones they have to recognize and rebut in four sentences. We talk about why repetition is the real engine behind courtroom skill, and why they tell students to be wrong in the simulation, before they're wrong in front of a judge.

The "which is smarter" question is dead. Both models are good enough that the right question is which one does the specific thing you need better. This episode breaks down where each one wins for actual work. The short version. Claude wins on writing quality, instruction-following, long-document analysis, and agentic work. ChatGPT wins on image generation, voice, custom GPTs, and ecosystem breadth. Where Claude pulls ahead. For anything client-facing, Claude produces prose that needs less editing, with fewer clichés, better structure, and more controllable tone, which is the single most-cited reason people prefer it for memos, reports, and articles. It also holds detailed constraints better, so when you give it specific headings, a voice, and things to avoid, it sticks to them more faithfully. On the coding and analysis side, Claude leads the reasoning benchmarks (91.3% on GPQA Diamond) and holds a slim edge on SWE-bench Verified, and its context window is the most-cited reason developers switch, with the API tier going up to 1M tokens for long codebases, contracts, and book-length documents. Where ChatGPT pulls ahead. Image generation is not close. ChatGPT generates images natively and Claude cannot generate them at all, so if visuals are in your workflow, that decides it. ChatGPT also browses the web in real time, while Claude does not do that natively, and it integrates directly with Word, Excel, Teams, and Outlook through Microsoft Copilot, which matters if your business already runs on Microsoft 365. For high-volume API work, the flagship cost gap is large: a small internal RAG tool running 10M input and 2M output tokens a month runs roughly $300 on Claude Opus versus $55 on GPT, and it scales from there. Pricing. If you're choosing between Claude Pro and ChatGPT Plus, pick on capability, not price, because they both cost about $20 a month. The one real gap is ChatGPT's cheaper $8 Go tier and its more generous free tier. The move most professionals actually make. The common 2026 setup is ChatGPT for ideation, images, and quick questions, and Claude for the serious writing, editing, long-document analysis, and agentic file work. At about $20 each, running both is roughly $40 a month, which is trivial against the time it saves if AI is core to your job. The AI Career LabBottom line for a service business or agency. If your work is mostly writing, client documents, and code, Claude is the stronger daily driver. If you're producing marketing visuals, doing web research, or living in Microsoft 365, ChatGPT earns its seat. Most people find a clear preference within a week of running both on real work.Topics: Claude vs ChatGPT 2026, best AI for work, AI for small business, AI writing tool, AI for consultants and agencies, Claude Code, ChatGPT vs Claude pricing, long context AI, AI coding model, business AI workflow.Best AI for work 2026, Claude vs ChatGPT for business, AI tool for agencies and freelancers, AI writing and coding assistant, running Claude and ChatGPT together.

Both models are good enough that the right question is which one does the specific thing you need better. This episode breaks down where each one wins for actual work. The short version. Claude wins on writing quality, instruction-following, long-document analysis, and agentic work. ChatGPT wins on image generation, voice, custom GPTs, and ecosystem breadth. Where Claude pulls ahead. For anything client-facing, Claude produces prose that needs less editing, with fewer clichés, better structure, and more controllable tone, which is the single most-cited reason people prefer it for memos, reports, and articles. It also holds detailed constraints better, so when you give it specific headings, a voice, and things to avoid, it sticks to them more faithfully. On the coding and analysis side, Claude leads the reasoning benchmarks (91.3% on GPQA Diamond) and holds a slim edge on SWE-bench Verified, and its context window is the most-cited reason developers switch, with the API tier going up to 1M tokens for long codebases, contracts, and book-length documents. Where ChatGPT pulls ahead. Image generation is not close. ChatGPT generates images natively and Claude cannot generate them at all, so if visuals are in your workflow, that decides it. ChatGPT also browses the web in real time, while Claude does not do that natively, and it integrates directly with Word, Excel, Teams, and Outlook through Microsoft Copilot, which matters if your business already runs on Microsoft 365. For high-volume API work, the flagship cost gap is large: a small internal RAG tool running 10M input and 2M output tokens a month runs roughly $300 on Claude Opus versus $55 on GPT, and it scales from there. Pricing. If you're choosing between Claude Pro and ChatGPT Plus, pick on capability, not price, because they both cost about $20 a month. The one real gap is ChatGPT's cheaper $8 Go tier and its more generous free tier. The move most professionals actually make. The common 2026 setup is ChatGPT for ideation, images, and quick questions, and Claude for the serious writing, editing, long-document analysis, and agentic file work. At about $20 each, running both is roughly $40 a month, which is trivial against the time it saves if AI is core to your job. Bottom line for a service business or agency. If your work is mostly writing, client documents, and code, Claude is the stronger daily driver. If you're producing marketing visuals, doing web research, or living in Microsoft 365, ChatGPT earns its seat. Most people find a clear preference within a week of running both on real work.Topics: Claude vs ChatGPT 2026, best AI for work, AI for small business, AI writing tool, AI for consultants and agencies, Claude Code, ChatGPT vs Claude pricing, long context AI, AI coding model, business AI workflow.Best AI for work 2026, Claude vs ChatGPT for business, AI tool for agencies and freelancers, AI writing and coding assistant, running Claude and ChatGPT together.

Lets talk all things AI and custom GPTs for your homeschool! Find Secular Curriculum with our Resource Selector https://www.homeschool-together.com/secular-resources Support The Podcast If you like what you hear, consider supporting the podcast: https://homeschooltogether.gumroad.com/l/support Consider Leaving Us A Review If you have a quick moment, please consider leaving a review on iTunes - https://podcasts.apple.com/us/podcast/homeschool-together-podcast/id1526685583 Show Notes 180 Days of Social Studies 4th Grade - https://amzn.to/43uFeyC Stop Repeating Yourself: How to Create a Custom GPT - https://youtu.be/vFd5EdJaXjA?si=-79BFthyCcSz19Ii Connect with us Website: http://www.homeschool-together.com/ Store: https://gumroad.com/homeschooltogether Youtube: https://www.youtube.com/c/homeschooltogether Facebook: www.facebook.com/groups/homeschooltogetherpodcast/ Instagram: www.instagram.com/homeschooltogetherpodcast Twitter: https://twitter.com/hs_together The Gameschool Co-Op: https://www.facebook.com/groups/gameschoolcoop/ Email: homeschooltogetherpodcast@gmail.com

The episode opens with Taylor Phillips setting the table on AI and tools in a practical way: what they are, how they are used, and why the starting point should be the problem you are trying to solve, not the technology itself. That carries into the full conversation with Nick Horob, who joins Evan and Kristjan to discuss farm software, AI, and the difference between building something interesting and building something useful. Farms do not need more disconnected tools. They need better ways to see their numbers, manage information, and make decisions with less friction. The conversation pushes past the broad excitement around AI and gets into where it can actually help. Nick talks about large language models, hallucinations, trusted sources, custom GPTs, and the importance of starting with the outcome before building anything. Whether it is equipment knowledge, grain marketing, SOPs, invoicing, or field-level financial decisions, the value comes from using AI with guardrails and farm-specific context. When the cost of building software keeps falling, the harder skill is knowing what should be built in the first place. For farm leaders already acting as the human router between employees, managers, advisors, accountants, and data systems, AI has potential but only when it is tied to better decisions, clearer workflows, and a farm that is easier to run.

Are your Custom GPTs living on borrowed time—or is this the perfect opportunity to build something even better?Rumors that OpenAI may phase out Custom GPTs have sparked plenty of debate. But instead of focusing on what might disappear, this episode explores what comes next—and why business leaders should be paying attention now.If you've invested time building AI automations, or you're just starting to explore how AI can streamline your business, you'll discover how Custom GPTs, Projects, Skills, and Agents fit together, where each one excels, and why the future belongs to portable, reusable AI workflows.Rather than waiting for platform changes to force your hand, learn how to build AI systems that are flexible, scalable, and ready for what's next. This episode breaks down the concepts into practical examples you can apply immediately.In this session, you'll discover:Why the rumors around Custom GPTs matter—and what they could mean for your business.The differences between Chats, Custom GPTs, Projects, Skills, and AI Agents.When Projects are a better choice than Custom GPTs.How Skills make AI automations reusable across multiple workflows.The building blocks behind effective AI automation: instructions, context, and memory.A practical framework for creating reliable AI workflows that deliver consistent results.How to transition from simple prompts to sophisticated AI-powered business processes.Real-world examples of proposal automation, reporting, and workflow orchestration.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

This is the last episode of season 5 and  i reflect on lessons learnt from  this season. From conversations i have had with with guest, stepping out of my comfort zone, researching trends in the cake world. Its been a very interesting season with the cake world consistently evolving.The whats trending segment touches on cake pops, Christmas and Instagram instants.Its been a great season, enjoy the summer and see you in September for season 6If you have a question or feedback about this episode you can message me here. Thank you for listeningYou can connect with me via:Instagram: Adventures in KakelandAdventures in kakeland studio: Creative projects, custom GPTs, books and resourcesPublished books for hobbyist and cake business owners.A food management system: for small home based food businesses Bake -cover-finish -deliver: undated Weekly baking plannerCake business order form bookCake business Social media plannerDated weekly cake business planner (2026)My Business social media channelsInstagram: Kake and CupkakeryFacebook: Kake and CupkakeryWebsite:...

AI is moving fast, and for most business owners, it can feel overwhelming. ChatGPT, Claude, Grok, agents, custom GPTs, Claude Code, automations — where do you even start?In Episode 225 of The Profit Lifestyle, Scott and Andy break down how to actually use AI without overcomplicating it.They talk about ChatGPT vs. Claude, how to choose one tool to start with, why AI can be wrong, and how business owners can use AI to create content, build marketing systems, improve funnels, write copy, analyze ads, and save massive amounts of time.Scott also shares how he built “Funnel OS,” an AI-powered system that helps with offers, landing pages, ad copy, VSL scripts, automations, follow-ups, and sales scripts. Andy explains how he built a custom GPT to help clients create better marketing copy.The point is not to become a tech wizard. The point is to use AI as leverage.In this episode, you'll learn:How to start using AI without getting overwhelmedThe difference between ChatGPT and ClaudeWhy you should pick one tool and use it consistentlyHow to use voice prompts to speed up content creationHow custom GPTs and Claude projects can support your businessHow AI can help with funnels, marketing, copywriting, and researchWhy AI can increase your effective hourly rateWhy service-based business owners should use AI, not fear itStart simple. Ask better questions. Use it daily. Then build from there.Like, subscribe, and share this episode with another business owner who needs to hear it.

Rajiv Parikh talks with Dr. Christophe Morin about why AI can be deceptively convincing, how to use custom GPT guardrails to make it safer, and what synthetic consciousness might actually mean. We also explore neurospirituality and neuroplasticity as practical tools for founders who want to scale without letting stress, anxiety, or savior patterns run their lives.• AI as a persuasive mirror that can hallucinate and mislead• Synthetic consciousness versus consciousness claims• Custom GPTs as curated knowledge containers with guardrails• Synthetic personas for market research and messaging tests• COVID as a catalyst for depression and a deeper pivot into wellness• Stress, anxiety, depression, addiction, and trauma as nervous system signals• 26 balance-restoring practices including nature, prayer, and meditation• Psychedelics as an emerging neuroplasticity conversation• Savior complex leadership patterns and how neuroplasticity rewires them• Why talk-only approaches can be slow and inaccessible for many people• Flow state as hypofrontality and trusted performance under pressureAI is getting better at sounding right, and that's exactly what makes it risky. We sit down with Dr. Christophe Morin, bestselling author of "The Persuasion Code", "Neuromarketing AI", and "Open", to unpack why generative AI can be deceptively persuasive, how hallucinations turn into “trusted” advice, and why using chatbots for emotional support can create a dangerous feedback loop for vulnerable people.From there, we get concrete. Christophe explains how custom GPTs and curated LLM setups can act like safer containers for vetted, proprietary knowledge, instead of a grab bag of blended theories and fabricated citations. We also explore how AI can help marketing teams through synthetic personas and pattern detection, not to emotionally “sell” the machine, but to test and sharpen messaging meant for real humans whose decisions start in the primal, emotional brain.The conversation turns personal and practical for founders and CEOs: stress is not always bad, but toxic stress spreads fast and can hijack leadership. Christophe shares how neuroplasticity enables real rewiring of long-held beliefs, why savior-complex leadership can quietly fuel anxiety and addiction patterns, and how neurospiritual practices like meditation, nature exposure, prayer, and emerging psychedelic research fit into a grounded self-healing framework. We close with flow state science, plus a fun Spark Tank detour into feline behavior that somehow reinforces the point: nervous systems need real release valves.Subscribe for more conversations at the intersection of AI, persuasion, and human performance, and if this one helps you, share it with a founder friend and leave a review.Christope Morin: https://www.linkedin.com/in/christophemorinphd/Dr. Christophe Morin is the bestselling author of multiple books, including The Persuasion Code, Neuromarketing AI, and OPEN: A Neurospiritual Exploration of the Self-Healing Power of Your Brain.  Christophe is the co-architect of the NeuroMap® model of persuasion and the creator of the OPEN™ framework, and the CEO and Co-Founder of SalesBrain, the world's first neuromarketing agency, and more recently the Founder of the Institute for Soul.  Christophe holds an MBA from Bowling Green State University and a Ph.D. in Media Psychology from Fielding Graduate University, where he serves as adjunct faculty alongside his role as an AI lecturer at Johns Hopkins University. Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

In this episode we explore John Witherspoon's famous sermon, The Dominion of Providence Over the Passions of Men. Preached in 1776 amid the growing struggle for American independence, Witherspoon reflects on God's sovereign rule over human affairs, showing how even conflict, suffering, and the ambitions of men ultimately serve His purposes. Along the way, he calls Christians to trust God's providence, pursue personal holiness, and labor faithfully for the good of church and nation.Dead Presbyterians Society is a ministry of Greenville Presbyterian Theological Seminary. This time of year is a special year at the seminary; we say goodbye to our graduating class, and prepare to welcome new students. It is also the end of our fiscal year, and because of the growth of our student body, we have an increased need this year. Will you help us reach our goal? Consider supporting GPTS, and partnering with us in training the next generation of pastors. You can give online at ⁠gpts.edu/give⁠.

Most agencies have someone who's figured out AI better than anyone else on the team. They built the custom GPTs. They know the prompts. They're the one everyone asks when something breaks. Now ask yourself: what happens if that person leaves tomorrow? "If your agency relies completely on one AI champion, the agency's progress is limited to one resignation." Ishant Kulshreshtha, AI Strategic Analyst at White Label IQ, joins host Erik Martinez on Episode 112 of the Digital Velocity Podcast to talk about what it actually takes to move AI knowledge out of one person's head and into the organization. Ishant shares a practical 30-day process any agency owner can start today, explains why the data your agency has been collecting for years is more valuable than any tool you could buy, and makes the case for why building AI capability is a team sport, not a solo act. If you've been relying on one person to drive your AI progress, this is the episode where you find out what you're actually risking.

Tori Begg joins Brad Bialy to separate fact from fiction and share a practical roadmap for staffing leaders looking to implement AI without getting lost in the noise. Despite the constant hype surrounding AI agents and automation, most organizations are still in the early stages of adoption.From generative AI and AI agents to workflow mapping and digital labor, Tori explains what staffing firms should actually be paying attention to right now—and what can safely be ignored. She shares why most organizations aren't ready for full-scale agentic AI, how recruiters can identify the highest-value opportunities for automation, and why understanding your workflows is the first step toward successful AI adoption.Brad and Tori also dive into AI hallucinations, prompt engineering, custom GPTs, small language models, clean data, and the growing importance of keeping humans at the center of every AI strategy.Whether you're experimenting with ChatGPT for the first time or actively exploring AI-powered recruiting, sourcing, sales, and operations workflows, this conversation offers a practical framework for using AI to increase productivity, improve decision-making, and create a competitive advantage without sacrificing the relationships that make staffing successful.Expect to Learn:The difference between generative AI and agentic AIWhy workflow mapping should happen before AI adoptionHow to identify non-revenue-generating tasks for automationWays to reduce AI hallucinations and improve output qualityWhy using multiple LLMs can improve resultsThe role of small language models in the future of staffing technologyHow AI agents can support sourcing, screening, engagement, and ATS updatesWhy a "human first, AI forward" mindset will separate successful firms from the restAbout the HostBrad 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.About the GuestTori Begg is a senior AI leader specializing in adoption, enablement, strategy, and the human side of artificial intelligence. As Senior Manager of AI Programs, she leads AI strategy and implementation across sales, marketing, and operations, helping organizations translate emerging AI capabilities into measurable business impact.Tori focuses on how leaders redesign workflows, roadmaps, decision-making, and expectations as AI reshapes how work gets done. She is the co-founder of Mind & Machine DFW and a frequent speaker on AI leadership, organizational transformation, digital labor, and the practical application of AI in business.Sponsors and Offers HeardTake the Stage is presented by Haley Marketing. For a limited time, we're offer 50% off of a brand new staffing website. Just message Brad Bialy on LinkedIn and mention the Crazy Website Promo.For 30 years, Benefits in a Card has delivered benefit plans designed specifically for the staffing industry—over 140 unique options with immediate coverage, unique perks like FreeRx, and solutions that reduce turnover while improving ACA compliance. Give your workforce benefits they'll actually use and give your staffing firm a competitive edge. Learn more at:https://www.BenefitsInACard.com.

Confused by AI Skills, Projects, Gems, Custom GPTs, and Agents?You're not alone. It's important to separate which is best for which use-case, because each has a place depending on what you're trying to get done. In this beginner-friendly live episode, we break down what these AI terms actually mean, who creates them, and when everyday users should use each one.Think of this as your plain-English map to the new AI assistant world:✅ Projects = places to organize ongoing work✅ Gems & Custom GPTs = reusable custom assistants✅ Skills = reusable instructions and workflows✅ Agents = AI systems that can take actions on your behalfBy the end of this live session, you'll understand the practical difference between creating a custom assistant, organizing work in a project, giving AI a repeatable skill, and letting an agent complete tasks for you.What You'll Learn:

Anne Gonzalez, MD is a practicing Direct Primary Care physician at Emerald Health DPC in Western North Carolina. After eight years in insurance-based medicine, she made the leap to DPC and hasn't looked back. She hosts the DPC Life Podcast, where she has honest, unscripted conversations with DPC physicians about the real work of building an independent practice, and runs the DPC Lifer Club, a free community for physicians in the early stages of doing the same.Some of the topics we discussed:Implementing the 4Rs of practice growthHow to use automation to grow your practice without doing everything manuallyAttracting and retaining patients with a membership-based practiceImproving patient engagement and responsivenessUsing AI-powered phone assistants and website chat widgets for 24/7 patient communicationReplacing multiple software subscriptions with one platformHarmony Ops' custom GPTs and AI tools for practice developmentCRM systems, workflow automation, and HIPAA-compliant technology solutionsStreamlining scheduling, forms, onboarding, and communicationsUsing social media strategically to grow an independent practicePractice growth strategies for physicians with full patient panelsBalancing automation with authentic human connectionOpportunities for coaching, community, and physician supportAnd more!Interested in learning more about my telehealth direct specialty care practice? At AmazVita MD, I help patients optimize weight and metabolic health, harmonize hormones in peri/menopause, and enhance wellness and vitality. Accepting new patients now.Website:amazvitamd.comEmail:hello@amazvitamd.comLearn more about me or schedule a FREE coaching call:https://www.joyfulsuccessliving.com/ Join the Voices of Women Physicians Facebook Group:https://www.facebook.com/groups/190596326343825/Connect with Dr. Gonzalez:DPC LIFE PODCASTWebsite: https://dpclife.comSpotify: https://open.spotify.com/show/5eHR2BEnHwEVklUYWJQ11XFacebook: https://www.facebook.com/dpclife/Instagram: @dpclifeHARMONY OPSMain Site: https://harmonyopsfordpc.comInstagram: @harmonyopsfordpcFacebook: https://www.facebook.com/harmonyopsfordpc/Ep 195: How to Use Automations to Elevate Your DPC with Dr. Anne GonzalezApple PodcastsSpotify

Thanks to Our Tique Talks Sponsors:Travel Collection - Connect and learn more about TC's DMCsFlytographer - Earn commission on professional vacation photographyCozy Earth - Use code COZYTIQUE at checkoutHeather Keller of Perfect Landing Travel returns to the show to share how she's leveraging AI to eliminate repetitive tasks and create more space for the work that drives growth. From client communications and workflow management to restaurant research and trip planning support, Heather has built a suite of custom GPTs that function as an extension of her team; bringing greater consistency, efficiency, and scalability to her business without sacrificing personalization.Want to see Heather's GPTs in action? Join us inside the Niche Community on July 2 for a live demonstration where Heather will walk through the custom GPTs she uses to streamline operations, support her team, and deliver a more consistent client experience! Then, continue the conversation at Tique's Travel Business Intensive, August 24-26!About Heather Keller:Heather is the founder of Perfect Landing Travel and a respected luxury travel advisor with more than a decade of industry experience. A consistent top producer with Departure Lounge since 2020, she has been recognized as a Travel + Leisure Rising Star and has helped lead her team to multiple industry nominations. Known for her psychology-informed approach to travel planning, Heather specializes in understanding what clients truly want from their travels—not just where they want to go, but how they want to feel. By combining deep client insight with innovative tools like AI, she creates highly personalized, high-touch travel experiences that feel effortless, thoughtful, and uniquely tailored to each traveler.perfectlandingtravel.cominstagram.com/perfectlandingtravelToday we will cover:(03:00) ChatGPT vs. custom GPTs(06:45) How Heather created GPTs for repeatable tasks and workflows(14:55) Using AI for client communications(20:55) Heather's favorite note-taking tools; Granola and Plaud(26:05) Auditing, tweaking, and refining outputs over time(28:10) How examples, standards, and boundaries improve outputFOLLOW ALONG ON INSTAGRAM @TiqueHQ

Episode 223: Automate Your Lead Generation with our FREE online course: https://go.digitaltrailblazer.com/auto-leads-course-freeMost online business owners are barely scratching the surface with AI — copy-pasting prompts and hoping for magic — while their competitors are building automated workflows that do the heavy lifting. Without a real AI system behind your business, you're stuck in the weeds doing repetitive, time-consuming tasks instead of the high-value strategic work that actually grows your revenue.In this episode, Rocky Pedden teaches us how to build AI-powered workflows that run your business operations, including how to map your deliverable process into trainable agents, structure prompts for specific outputs, use tools like Claude Cowork and Zapier to automate multi-step tasks, and repurpose content at scale — all so you can spend 80% of your time on strategy instead of button-pushing.Connect with Rocky:https://revenuezen.com/ https://www.linkedin.com/in/rockypedden/ https://www.linkedin.com/company/revenuezen/ https://www.instagram.com/revenuezen/ https://www.youtube.com/channel/UCgRWKt7IPwh3_rH66XlwuEgWant to SCALE your online business bigger and faster without the endless hustle of networking, referrals, and pumping out content that nobody sees?Grab our Ultimate Ad Script for Coaches, Agencies, and Course Creators.Learn the exact 5-step script we teach our clients that allows them to generate targeted, high-quality leads at ultra-low cost, so you can land paying customers and clients without breaking the bank on ad spend.Grab the Ultimate Ad Script right HERE - https://join.digitaltrailblazer.com/ultimate-ad-script✅ Connect With Us:Website - https://DigitalTrailblazer.comFacebook - https://www.facebook.com/digitaltrailblazerTikTok: https://www.tiktok.com/@digitaltrailblazerX (Twitter): https://x.com/DgtlTrailblazerInstagram: https://www.instagram.com/DigitalTrailblazer

Labor shortages continue to challenge industrial companies everywhere. But there is another growing concern many leaders are now facing: How do you preserve the knowledge and experience veteran employees take with them when they retire? In this episode of Grounding AI Podcast, Donna Peterson explores how industrial companies can begin using internal GPTs and AI systems to capture employee expertise, preserve institutional knowledge, and help newer employees learn faster. AI will not solve labor shortages overnight. But AI can help organizations protect decades of experience, improve onboarding, and give younger employees faster access to trusted internal knowledge. In this episode you'll learn: • What internal GPTs are and how companies can use them • How AI can preserve veteran employee expertise before retirement • Ways newer employees can learn faster using AI tools • Questions leaders should ask before building internal AI systems • How industrial companies can use AI without sacrificing authenticity The companies that use AI strategically will strengthen teams, improve knowledge transfer, and prepare the next generation of employees for long-term success. If your organization is evaluating AI, this conversation will help you think through practical next steps. Subscribe for weekly conversations on practical AI implementation for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

Podcasting can be one of the most powerful ways to grow your business, but if every episode leaves you feeling overwhelmed, exhausted, and scrambling to keep up, something needs to change. Sustainable podcasting isn't about working harder—it's about building systems that support you behind the scenes. In this episode of Podcasting Unlocked, we're opening the vault and revisiting some of the most impactful conversations we've had about creating a podcast workflow that actually works.This special compilation episode features insights from four podcasting experts who have mastered efficiency, automation, and sustainable growth. From custom GPTs and project management systems to editing shortcuts and intentional calendar management, these strategies will help you streamline your process and reclaim your time. Whether you're a solo podcaster or managing a growing team, you'll walk away with practical ideas you can implement immediately.This week, episode 289 of Podcasting Unlocked is about building a sustainable podcast workflow!In this episode of Podcasting Unlocked, I'm sharing expert-backed strategies to simplify your podcasting process, reduce overwhelm, and create systems that support long-term success.I also chat about the following:Creating Custom GPTs as SOPs: Learn how Marci Rossi uses custom GPTs to automate repetitive podcast tasks while maintaining her unique voice and content style.Building Repeatable Systems with Project Management: Discover why Jacki Hayes believes every podcaster—even solopreneurs—needs a documented workflow and how tools like ClickUp can keep your process organized.Editing Smarter, Not Harder: Hear Joe Casabona's simple note-taking strategy that can dramatically reduce editing time and make collaboration with editors easier.Using Recording Markers and Timestamps: Explore practical ways to identify mistakes, highlight great moments, and streamline post-production without re-listening to entire episodes.Managing Your Calendar with Intention: Learn Lance Cayko's mindset shift from "scheduling" to making appointments and how intentional time management creates more freedom and balance.Podcasting shouldn't constantly feel like you're running behind. The right systems can help you stay consistent, protect your energy, and make podcasting enjoyable again. This week, choose one workflow improvement from this episode and commit to testing it in your own process.Be sure to tune in to all the episodes to receive practical podcasting strategies, expert insights, and actionable systems that help you grow your show without burning out.Thank you for listening! If you enjoyed this episode, take a screenshot of the episode to post in your stories and tag me! And don't forget to follow, rate, and review the podcast and tell me your key takeaways!Learn more about Podcasting Unlocked at https://galatimedia.com/podcasting-unlocked/CONNECT WITH ALESIA GALATI:InstagramLinkedInWork with Galati Media! Work with Alesia 1:1MENTIONED EPISODES:Episode 236 with Marci RossiEpisode 253 with Jacki HayesEpisode 157 with Joe CasabonaEpisode 143 with Lance CaykoFree Download: 15 Ways to Improve Your Podcast Proud member of the Feminist Podcasters Collective.

In this episode, we explore the life of Louisa A. Lowrie — a young missionary wife whose brief but radiant faith burned brightly for Christ. Through her own journals and letters, we see a woman of deep devotion, tender conscience, and unwavering surrender, who gave everything to the cause of the gospel even unto death.A moving testimony of holiness, missionary zeal, and wholehearted love for Christ.If you would like to learn more about Louisa A. Lowrie, consider reading Memoirs of Mrs. Louisa A. Lowrie. We would be happy to send you a copy for free! Simply send an email with your address to info@gpts.edu and request the book. Dead Presbyterians Society is a ministry of Greenville Presbyterian Theological Seminary. This time of year is a special year at the seminary; we say goodbye to our graduating class, and prepare to welcome new students. It is also the end of our fiscal year, and because of the growth of our student body, we have an increased need this year. Will you help us reach our goal? Consider supporting GPTS, and partnering with us in training the next generation of pastors. You can give online at gpts.edu/give.

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley shares seven practical ways his e-commerce business uses AI to optimize operations and scale growth. Drawing from his experience building an eight-figure brand across Amazon, TikTok Shop, and Shopify, Josh covers strategies including building custom GPTs, automating TikTok Shop listing optimization, streamlining hiring processes, leveraging Alexa data, analyzing meeting transcripts, scaling ad creative production, and cloning leadership decision-making into AI-powered SOPs. Josh emphasizes treating AI like a new team member requiring proper training, offering actionable, real-world insights over hype.Bullet Points:Practical applications of AI in e-commerce operationsOvercoming fears and misconceptions about AI adoptionCustom GPT development for task automationAI-driven optimization of product listings on TikTok ShopAutomating the hiring process with AI scoring systemsUtilizing AI for product insights through Amazon Alexa dataAnalyzing meeting transcripts for business insights and decision-makingScaling ad creative production using AI-generated video contentCloning leadership decision-making into AI Standard Operating Procedures (SOPs)Viewing AI as a team member requiring onboarding and trainingTimestamps:00:02:00 Weekly Custom GPT CreationThe speaker's 35-person team is required to create or enhance a custom GPT weekly to automate their specific tasks.00:04:05 AI Agent for TikTok Shop OptimizationAn AI agent integrated with the TikTok Shop API continuously tests and optimizes product titles, descriptions, and main images weekly.00:08:32 Automating Hiring Case Study ScoringAI is used to automatically score applicant case studies based on a predefined rubric, saving hours of manual review time.00:11:29 Custom GPTs Integrated with AlexaCreating custom GPTs that analyze customer questions on Amazon Alexa to optimize product listings and improve Alexa recommendation rankings.00:12:11 Analyzing Company Meeting RecordingsAI analyzes transcripts from all company meetings to identify business constraints, track team progress, and provide a leadership pulse.00:13:56 Scaling Ad Creative ProductionUsing AI video generation tools to quickly produce a high volume of ad creative for Meta and TikTok campaigns.00:14:48Cloning Leadership Judgment and Decision-MakingUsing AI to document processes and decision-making frameworks from leaders, creating an internal knowledge base to empower team members.Links and Mentions:AI Tools:"ChatGPT": "00:02:00""Claude AI": "00:02:00""Fireflies AI Notetaker": "00:11:25""Veo3": "00:14:27""Notion": "00:16:24"E-commerce Platforms:"TikTok Shop": "00:04:05"Videos and Resources:"30 60 90 Day Onboarding Framework": "00:07:52""Episode on Cloning Yourself Utilizing AI": "00:15:24"Transcript:Josh Hadley 00:00:00  Today, I'm going to be walking through seven different ways that we are implementing AI into our e-commerce business and practical steps that you can take to implement it in your business as well. Welcome to the Ecomm Breakthrough Podcast, I'm Josh Hadley. I've scaled my own ecommerce brand from 0 to 8 figures, and I'm actively building towards nine figures in sales. This podcast is where I document that journey and share the systems, the strategies, and the lessons learned in real time so that you can learn what actually matters and scale your own business. My name is Josh Hadley. First and foremost, I'm a man of faith. I'm a husband to a beautiful wife and also the father of four children. I've been selling in the e-commerce space for over a decade now, doing multi-million in revenue on Amazon, TikTok, shop and Shopify. And I am also the host of the number one business strategy podcast for ecommerce, and that is E-com breakthrough. Today, I want to dive into the practical use cases of how we're implementing AI into our business.Josh Hadley 00:00:58  Today. I hear a lot of noise going on in a lot of the e-commerce groups. There's a lot of like doom and gloom of, oh, you're getting left behind if you're not actually implementing AI in your business today, if you don't have an agent managing your PPC campaigns, you're late to the party, etc., etc. there's a lot of fear. And then what ultimately happens is there's a lot of entrepreneurs that because there's so much fear and anxiety around it and feel like they're already behind. They just stay stuck and they're just kind of like frozen because nobody's providing actionable content regarding like, here are the actual practical use cases of AI. Yes, there are some incredible features with Claude and integrating it to your email system, right. And being able to monitor your emails for you. Yes, there are some incredible ways to use ChatGPT and the new images that it's able to produce, right? Like, there's a lot of good things that are happening that way, but a lot of times the practical use cases where actually maximizes value in the business gets left to the side, or nobody's actually addressing them.Josh Hadley 00:02:00  So that's what I wanted to do today, is actually provide you with practical use cases that if you're an e-commerce brand, you can go replicate these exact same frameworks and implement AI in your own br...

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.

K, grab your glass of red and come sit. We need to talk about AI.The discourse has been rampant the last few months: vibe-coding, Claude Cowork, agentic AI, OpenAI and partnering with the Department of War, AI psychosis, custom dashboards, apps, and GPTs...We are FLOODED with info right now, and instead of leading with curiosity, social media has become a 360 slam-dunk fest on who's right or wrong. When the internet is filled with black and white thinking, name-calling, and passive aggressive posts... I can't help but wonder: isn't this what led to the surge of AI in the first place? Did the AI boom happen due to a lack of communication, conflict resolution, and critical thinking skills?Why would people turn to a robot instead of a human to ask questions about business, life and love? Why aren't we hiring real people right now, even when we know it's the "right" thing to do? What's the point of moving at lightning speed? Where is this all taking us?These are all the questions I ask today, from both sides of the debate. I was a Power User not too long ago, and am currently on an "AI cleanse". I share how one long afternoon with a custom GPT and one too many prompts led me to this point...As usual, no final answers here. Just observations, thoughts, questions, and ideas for how we can stop pieing each other in the face about AI and instead, build a bridge with curiosity, imagination, stories and some good ole fashioned empathy.I can't wait to hear your thoughts!People mentioned in this ep:Ximena - everyone's favorite Mexican Eco-PhilosopherKP Pilley - Editorial Strategist & 9-Grid ExtraordinaireXanthe Appleyard & Social LifeSocial media is fickle AF, but your community doesn't have to be. Life of the Party is a content strategy program that helps creative leaders own their growth, engagement, and joy online.  Walk away with a sustainable strategy to skyrocket the visibility of your biz, become known for your unique POV, and grow an online community stacked with the caliber of clients you've always dreamed of collabing with. Use code NOTES for $100Let's connect!

Want better AI results? The answer isn't to prompt better. (At least, not anymore.) As AI has changed drastically, so too must your company's strategy and implementation plan. Section's Bobby Isaacson joins Everyday AI to lay out the roadmap: helping guide organizations from running like hamsters on the AI treadmill to actually redefining workflows toward agentic automation. How Smart Teams Stopped Prompting AI and Started Automating Workflows -- An Everyday AI chat with Jordan Wilson and Section's Bobby IsaacsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode 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:AI Success Metrics in Modern OrganizationsShift from Prompting to Workflow AutomationAdopting Autonomous Agents in EnterprisesImportance of Context Engineering with AITop-Down Leadership for AI TransformationBuilding AI Manifestos vs. AI PoliciesTraining Challenges: Chatbots to Agents EraClosing Skill Gaps: Elite vs. General AI UsersOvercoming Change Management in AI AdoptionFostering Curiosity and Experimentation in AI TeamsTimestamps:00:00 Defining AI success metrics05:19 The shift to context engineering08:16 Leadership setting AI adoption example11:58 CEO commitment to AI integration14:17 Setting clear AI goals19:06 Discussing automation and its challenges20:48 Enterprise AI usage challenges26:00 Leveraging AI for learning27:45 Closing and contact informationKeywords: AI workflow automation, autonomous agents, automating workflows, AI agents, enterprise AI transformation, custom GPTs, prompting AI, context engineering, AI proficiency, AI training, leadership in AI adoption, change management, AI strategy, employee enablement, AI integration, top-down AI approach, organization-wide AI, business process automation, AI-powered briefs, sales automation with AI, agent-based automation, workflow optimization, context sharing, repeatable AI workflows, AI change management strategy, AI use cases, AI in finance, ROI calculator with AI, personalized AI training, skill gap in AI, scaling AI solutions, technology adoption, digital transformation, AI challenges in enterprises, curiosity-driven learning, creative freedom in AI, AI enablement, AI policy vs manifesto, fostering innovation with AI, learning and development AI, AI champions, process innovationSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Scott is with Brett Bohannon to talk about the fast-moving shift from basic AI chat tools to agent-driven Amazon workflows. They discuss Claude, OpenClaw, MCP servers, APIs, and how sellers can connect private catalog data, public marketplace data, and advertising insights into one AI-powered operating system. Brett shares how he uses AI agents to reduce repetitive Amazon tasks, audit catalogs, connect tools like Keepa and Data Dive, and build workflow automations for ads, inventory, and listing optimization. They also shed light on what this means for Amazon software, why unique data still matters, and how sellers can start using AI to solve specific operational problems instead of chasing every new tool.   Episode Notes: 00:09 - Intro to Brett Bohannon, Claude, and AI agents 01:40 - From custom GPTs to faster AI workflows 02:37 - Why recent AI progress feels different 02:55 - OpenClaw, AI agents, and business context 06:28 - Data Dive adapter for Claude and API data 07:18 - Three AI user levels: LLM users, builders, and MCP users 08:13 - How MCPs connect Claude with hosted or local data 09:49 - Combining Amazon tools under one AI workflow 12:33 - Using catalog data, Keepa, and niche analysis together 14:05 - Automating daily Amazon workflows 16:18 - Skill Create, GitHub, and open-source Amazon tools 17:45 - Replacing software with custom AI tools 19:31 - Maintenance tradeoffs with DIY AI workflows 20:08 - What stays valuable in Amazon software 22:57 - Talking to Amazon data inside Claude or ChatGPT 25:16 - Combining profitability, ads, and marketplace data 27:22 - Using agents to scale as a solo consultant 29:08 - Brett's Amazon background and catalog expertise 30:34 - Catalog audits using category listing reports 31:17 - Rufus scoring and listing data quality 33:38 - Helm and layered MCP workflows 34:25 - AI agents and the future of Amazon software   Related Post: How to Sell on TikTok Shop 2026 (Guide For Beginners)   LinkedIn: https://www.linkedin.com/in/brett-bohannon-1992329/ Website: https://voartex.com/   Scott's Links LinkedIn: linkedin.com/in/scott-needham-a8b39813 X: @itsScottNeedham Instagram: @smartestseller YouTube: www.youtube.com/@smartestamazonseller2371 Newsletter: https://www.smartscout.com/newsletter-sign-up Blog: https://www.smartscout.com/blog

In this episode, Jason Cass interviews Patrick Murakami, a forward-thinking insurance agency owner, about his journey from corporate to entrepreneurship, innovative marketing strategies, and the future of AI in the insurance industry. They explore how to leverage social media, AI tools like Claude and ChatGPT, and build scalable, sellable agencies. Key Topics: Patrick's decade at Progressive and pitching VoIP technology to corporate The USAA phone call that pushed Patrick to go independent Growing a $5 million agency on $260 in total ad spend through Facebook Why introducing clients to custom GPTs is still an uphill battle AEO and GEO: how AI searchability differs from traditional SEO Backlink networks and AI-searchable directories as a ranking strategy Using AI to build SOPs as the smartest entry point for most agents Why SOPs directly affect agency valuation and exit readiness Targeting the military PCS market and expanding to 25 states Patrick's book The Human Advantage and building the trust stack alongside the tech stack Reach out to: Patrick Murakami Jason Cass Visit Website: NexAgency Claude AI Obsidian Note-taking App Agency Intelligence Manual Agency Intelligence Produced by PodSquad.fm

Libertópolis Negocios, jueves 14-05-2026

Oren Michels is the founder and CEO of Barndoor.ai, the first and only Control Plane for the agentic enterprise. Previously, he co-founded Mashery in 2006 and served as CEO until Intel acquired the company in 2013. When it was acquired, Mashery-powered APIs were used by over 350,000 active developers in over 100,000 active applications, and counted among its customers many of the largest e-commerce, media, and data companies in the world. He is an entrepreneur, investor, board member, and advisor to technology startups in the US and Europe and has made angel investments in several successful companies including Uber, Pebble Post, Addy, Navdy, and eero.Highlight Bullets> Here's a glimpse of what you would learn…. Rapid evolution of AI agents in e-commerce and business operations.Definition and functionality of AI agents that perform actions on behalf of users.Importance of governance and trust in deploying AI agents to prevent errors and misuse.Introduction of Barndoor AI and its role in providing connectivity and governance for AI agents.Practical use cases of AI agents in managing tasks across various platforms (e.g., Shopify, JIRA, QuickBooks).The necessity of setting strict policies to control AI actions and ensure safety.Integration of AI tools with existing software systems and the potential for low-code/no-code solutions.The significance of problem-solving and process design skills in effectively utilizing AI agents.Recommendations for starting small with AI and learning through practical application.Continuous evolution of AI tools and the importance of staying informed and adaptable.In this episode of the Ecomm Breakthrough podcast, host Josh Hadley speaks with Oren Michels, founder and CEO of Barndoor AI, about the growing role of AI agents in business operations. Oren explains how AI agents can autonomously perform tasks within systems like Shopify, Amazon, and Slack, while emphasizing the critical need for governance and trust. He introduces Barndoor AI as a control plane that enables secure connectivity and policy-based guardrails, preventing unintended actions. Practical use cases include email management, JIRA ticket handling, and financial forecasting. Oren advises listeners to start small, experiment with multiple AI tools, and develop strong problem-solving skills.Here are the 3 action items that Josh identified from this episode:Start with low-risk automation Deploy AI agents on simple, non-critical workflows first (e.g., email summaries, reporting) to test value and build internal trust before scaling. Enforce strict governance from day one Define clear permissions, rules, and guardrails—never give blanket access. Every AI action should be controlled, logged, and auditable. Design processes before deploying AI Break workflows into clear steps and craft precise prompts. Strong process design + prompt clarity = better, safer AI performance.Timestamps:00:00:00 The Problem of AI GovernanceOren discusses lack of governance in current AI systems and the risks of AI agents forgetting instructions.00:00:30 Podcast Introduction & Guest BackgroundPodcast is introduced, and Oren Michels' background and achievements are highlighted.00:00:44 The Rise of AI Agents in E-commerceJosh frames the future of e-commerce as dominated by AI agents and introduces Oren as the guest.00:02:06 Oren's Perspective on AI Agent AdoptionOren explains the rapid and slow pace of AI agent adoption, especially beyond coding tasks.00:03:02 What is Barndoor AI?Oren introduces Barndoor AI, focusing on connectivity and trust for AI agents in business systems.00:03:40 How Barndoor AI WorksDetails on how Barndoor AI enables granular control and governance over AI agent actions.00:05:45 Security and Guardrails for AI AgentsDiscussion on security risks, both from bad actors and unintended consequences by legitimate users.00:06:33 Difference Between Barndoor and Other AI ToolsOren explains how Barndoor adds governance missing from tools like OpenClaw and Claude.00:09:24 Use Case: Email Management with AI AgentsOren shares how he uses AI agents to manage and triage his daily email load efficiently.00:12:04 Why Governance Matters in AI ActionsExplains the importance of restricting AI actions to prevent mistakes, especially in sensitive tasks.00:13:00 Custom Rules and Granular PoliciesBarndoor allows highly specific rules for AI actions, such as price-based restrictions in e-commerce.00:13:58 Use Case: JIRA and Finance AutomationExamples of using AI agents for JIRA ticket management and automated financial reporting via Slack.00:16:48 Enterprise Use Cases & E-commerce OptimizationBarndoor's enterprise clients use AI for handling sensitive data and optimizing Amazon listings seasonally.00:19:08 Customer Service and Contextual CommunicationAI agents help draft personalized emails by pulling context from Salesforce and previous communications.00:20:40 AI Agent Adoption is Still EarlyOren emphasizes that AI agent use is in its infancy and encourages experimentation in low-risk areas.00:22:40 Personal Use Cases for AI AgentsJosh and Oren discuss personal productivity applications, like sports team management and scheduling.00:24:14 The Evolving AI Tool LandscapeDiscussion on the rapid evolution of AI tools, the importance of using multiple models, and specialization.00:27:47 Future of AI in Business OperationsSpeculation on the future: specialized AI tools for each business function, governed by platforms like Barndoor.00:31:00 The Importance of Problem-Solving and Prompt EngineeringSuccess with AI depends on defining problems and giving clear instructions, akin to prompt engineering.00:33:46 Actionable Takeaways for ListenersJosh summarizes three action items: start experimenting, document processes, and stay flexible with tools.00:36:44 Book Recommendation: Why Computers ThinkOren recommends a book that explains the probabilistic nature of AI and why it sometimes fails.00:37:34 Favorite AI Tool and Personal UseOren shares his favorite AI tools and how he uses them for both work and personal learning.00:38:49 Who to Follow: Aaron LevieOren recommends following Aaron Levie for insightful commentary on AI and business.00:39:28 Where to Learn More About Barndoor AIOren directs listeners to Barndoor AI's website and their personal product, Zenni, for hands-on experience.00:39:45 Podcast Wrap-UpPodcast concludes with thanks and a call to subscribe and leave a review.Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough PodcastEmail Josh Hadley: Josh@eCommBreakthrough.comTools and Websites"OpenClaw": "00:00:00""Barndoor AI": "00:03:14""

Did you ACTUALLY MISS OpenAI's big Workspace Agents announcement?

Subscribe to the show and get weekly tips from Tanya on how to grow, scale and diversify your online business. MY CUSTOM GPT WORKSHEET | https://www.tanyaaliza.com/customgpt  START HERE | Learn more about the different ways Tanya can help you in your business. Whether it's starting an online business or growing the one you have: https://www.tanyaaliza.com DIGITAL CREATOR STUDIO | My All-In-One Marketing System To Grow Your Audience, Build Your Email List, Build Amazing Marketing Funnels, Attract Perfect Leads & Sell Digital Products, While Building A Multi-Income Stream Online Brand. https://digitalcreatorstudio.com MY FAVORITES | My personal camera and video gear, my health, wellness and beauty products, my favorite books and more:  https://tanyaaliza.com/amazon  CONNECT ON INSTAGRAM: http://Instagram.com/tanyaaliza  SUBSCRIBE & WATCH ON YOUTUBE: https://www.youtube.com/TanyaAlizaTV?sub_confirmation=1  Custom GPTs are the digital product opportunity nobody's talking about yet. I'm about to show you how to turn your expertise into an AI-powered tool that works for you 24/7. You're about to get the entire playbook. I'm showing you how to literally clone your brain, build it once, and let it work for you forever. Most people think custom GPTs are complicated or techy, but I promise you they're not. This is one of the simplest and most powerful digital products you can create right now, and people are actually buying these instead of boring PDFs that sit in their downloads folder forever. I'm walking you through the exact steps to build a custom GPT that coaches people using YOUR methodology, acts as a lead generator for your business, and positions you as cutting edge in your industry. Plus I'm giving you my free Custom GPT Builder Worksheet so you can follow along and actually build this thing. If you're a coach, course creator, or entrepreneur who wants to stay ahead of the curve and create digital products people actually want, this episode is for you. CAN I FEATURE YOU? Rate and review the show and tag me on social (@tanyaaliza)... I feature a new member of the community each week on my Social Media Platforms. The reviews help us and I'd love to feature you for taking the time to share your feedback. Disclaimers: The discussions and opinions expressed on this podcast are intended for informational and educational purposes only. Results from the strategies or products mentioned can vary and are not guaranteed. Some of the links provided are affiliate links, meaning at no additional cost to you, we may earn a commission if you click through and make a purchase. Always conduct your own due diligence before making any financial decisions.

Bronson Hill shares how he went from $100K raised to $15M in 18 months and why single family investing is holding most people back.In this episode of RealDealChat, Jack Hoss sits down with Bronson Hill, author of Fire Yourself and host of The Mailbox Money Show, to dig into what real passive income actually looks like and how to build it through syndication and alternative assets.Bronson covers:Why trading time for money traps even high-earning professionalsHow he went from 4 single family rentals to a 225-unit syndication dealThe difference between a property manager and true passive incomeWhy the effort to close a single family deal is almost identical to a multifamily dealHow one conversation on a cruise launched $15M in capital raisedThe value-first networking approach that beats cold outreach every timePrefabricated housing as a solution to the housing crisis and LA fire rebuildingSenior care as a growing investment categoryA $40M loss lesson from a 2021 Atlanta multifamily dealWhy long-term fixed debt and higher equity are now non-negotiableHow Bronson uses custom GPTs to scale content and investor communicationsThis conversation is essential for:Investors stuck in the single family grind looking to scalePassive investors exploring syndication for the first timeOperators thinking through capital raising and deal structuringAnyone building wealth systems that don't require their daily involvementIf you're still trading your time for income, this episode will reframe everything.

#884 What if your AI assistant could think, sound, and strategize exactly like you? In this powerful and eye-opening episode hosted by Kirsten Tyrrel, Kinsey Soderberg from Authentic AI shares her “human-first” approach to using tools like ChatGPT to amplify your brand's authenticity — not replace it. She breaks down her signature VIBES framework for training AI to align with your voice, values, audience, and offers. You'll also hear how she's automating back-end systems that save hours while increasing revenue, plus creative ideas for repurposing content, managing client onboarding, and building personalized GPTs for team members and programs. If you want to leverage AI without losing the soul of your brand, this one's a must-listen! (Original Air Date - 8/28/25) What we discuss with Kinsey: + Human-first approach to AI + Training AI to reflect your brand + The VIBES framework explained + Brand voice vs. generic tone + Using AI like a trained intern + Custom GPTs for team workflows + Automating playbooks and deliverables + Scaling personalized client experiences + AI's role in increasing revenue + Building an AI-powered story bank Thank you, Kinsey! Check out Authentic AI at ⁠DIYWith.AI⁠⁠⁠⁠. Get the free ⁠Make AI Sound Like You Micro-Class⁠. Create your ⁠AI Brand Blueprint⁠. Follow Kinsey on ⁠Instagram⁠. To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this week's bonus pep talk, I am sharing the 3 specific AI skills I'm using right now to save hours every week and how you can use them to simplify your workflow and support sustainable business growth. In today's episode, I share:01:38 – Why I recorded this bonus episode and how AI is actually showing up behind the scenes in my business 03:17 – What AI tools I'm currently paying for (and why I chose both ChatGPT and Claude) 05:20 – How I'm using multiple AI platforms together without starting over or overcomplicating things 07:05 – The importance of being slow and intentional when integrating AI into your business 08:40 – How we're evolving (not replacing) our existing custom GPTs for long-term use 10:15 – The introduction of my custom AI editor and how it helps remove robotic-sounding writing 12:05 – Why editing (not generating) content with AI is a major time-saving shift 13:40 – How I turned my monthly business scorecard into a visual dashboard in under two minutes 15:10 – Why using AI for data visualization helps you make faster, smarter CEO decisions 16:45 – The bigger takeaway: using AI to support your business without burnout or unnecessary complexity