Artificial Insights: Conversations About AI

by Daniel Manary

Season 5

AI Training Data Meets Copyright: How Publishing Can License Content at Scale w/ Julie Trelstad, Head of US Publishing @ Amlet.ai

AI has been trained on the world’s writing. Now, we have to figure out how creators prove ownership, set permissions, and get paid when their work is used. Julie Trelstad has spent 30 years inside publishing’s biggest technology shifts, from desktop publishing to eBooks to print-on-demand to self-publishing. In this episode, she explains why AI is forcing publishing into a new kind of rights era, one where piracy and fast imitation can flood the market within days of a book launch. Julie is Head of US Publishing at Amlet.ai and runs Paperbacks & Pixels. She walks through what “AI rights” means in practice, why book rights sales already power much of the industry, and how systems like ISCC fingerprinting can make ownership and usage terms machine-readable without exposing the full text. Daniel and Julie also dig into what happens when high-value content moves behind paywalls, why smaller domain models will need licensed, high-quality sources, and what fair compensation could look like when content is used for training, research, or generation. 🔑 What You’ll Learn in This Episode ✅ The six publishing waves Julie has lived through, and why eBooks changed the ecosystem ✅ Why AI imitation and piracy hit bestsellers first, and how that changes incentives ✅ What “AI rights” includes beyond training, including research and generative use ✅ How ISCC-based fingerprinting can prove provenance without sharing the full book ✅ Why paywalls and licensing are reshaping what high-quality data is available to models ✅ A practical way to think about “original vs. derivative” in an AI-assisted world 🔗 Resources & Links 🤝 Connect with Julie on LinkedIn: https://www.linkedin.com/in/julietrelstad 🌐 Julie’s work at Paperbacks & Pixels: https://paperbacksandpixels.com/ 🧩 Amlet.ai (AI content registry): https://amlet.ai/ 📚 StreetLib (distribution + registration path mentioned in the episode): https://www.streetlib.com/ 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 💬 Know a product leader or publisher trying to source high-quality data responsibly? Send them this episode.

S5 · E5

Feb 6, 2026

39:29

Are You Holding Onto a Job AI Should Have? w/ Dave Boyce, Executive Chair @ Winning By Design

When AI threatens your job, the real risk is clinging to the parts of work that are already predictable. In this bonus short, Daniel Manary and Dave Boyce (Winning by Design, author of Freemium) talk about the fear many people feel right now: AI is coming for my job. Dave argues that AI doesn't replace you as a human unless you keep trying to do work AI should handle. The path forward is to automate what is routine, and double down on the human work: alignment, confidence, strategy, and helping people navigate uncertainty. 🎧 Want the bigger picture? In the full episode, Dave breaks down what actually separates successful AI experiments from the 90% that stall out. He explains why measurement matters, why most companies need a clear “theory of the case,” and how AI-native go-to-market motions are rewriting the speed limits for growth. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2481331/ 🔗 Resources & Links 🤝 Connect with Dave on LinkedIn: https://www.linkedin.com/in/boycedave 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://artificialinsights.manary.haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 💬 If your team is wrestling with job impact, workflow redesign, or where humans still add leverage, share this clip with a leader who needs language for the conversation.

S5 · E4

Jan 30, 2026

2:37

From Experiments to ROI: Measuring AI Inside Real Go-To-Market Systems w/ Dave Boyce, Executive Chair @ Winning By Design

"Run experiments" is easy advice. Measuring them inside a real customer journey is the hard part. Dave Boyce has lived through multiple SaaS eras, from $1.2M ACV enterprise deals to $1,200 self-serve motions. Now, as Product lead and Executive Chair at Winning by Design, he works with growth-stage companies to rebuild their revenue systems for an AI-shaped market. In this conversation, Daniel and Dave get concrete about what “AI-forward GTM” actually requires: a clear theory of the case, a real data model for the whole customer lifecycle (the bow tie), and an operating model that can survive handoffs between humans and agents. Dave also shares what he has learned from launching AI agents in the wild, including why measurement is the difference between “experiments” and vibes. 🔑 What You’ll Learn in This Episode ✅ Why most AI projects fail to show ROI when they launch as point solutions inside un-architected systems ✅ How the “bow tie” model reframes GTM around renewal, expansion, advocacy, and growth loops ✅ What it takes to encode customer context for clean handoffs (SPICED: Situation, Pain, Impact, Critical Event, Decision) ✅ How AI-native companies use self-serve + real-time data to grow faster than rep-led machines ✅ A practical way to think about job anxiety: automate the predictable, protect the human work that stays exceptional 🔗 Resources & Links 🤝 Dave Boyce on LinkedIn: https://www.linkedin.com/in/boycedave 🏢 Winning by Design: https://winningbydesign.com/ 📓 The Growth Journal (Winning by Design): https://winningbydesign.com/growth-journal/ 📘 Freemium (Stanford University Press): https://www.sup.org/books/freemium 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 💬 Know a founder or revenue leader trying to retrofit AI into an existing GTM machine? Send them this episode. It will give them better questions to ask before they ship another “agent.”

S5 · E3

Jan 23, 2026

35:12

Wikipedia's First Real Competitor Is an AI: What is Grokipedia? w/ Bill Beutler, Founder @ Beutler Ink & TheNotability.co

An AI-written encyclopedia can sound coherent and still be wrong and that gap matters more than it first appears. In this bonus short, Daniel Manary and William Beutler of Beutler Ink and The Notability Company look at the first real challenger to Wikipedia's long-standing dominance. They discuss Grokipedia, Elon Musk's AI-generated encyclopedia: why its entries can feel clearer and more complete than Wikipedia, and how that polish can mask weak or missing sources. 🎧 Want the bigger picture? In the full episode, Bill explains why Wikipedia remains the shared reference point for major AI systems, how citations shape model behavior, and why neutrality matters more in an AI-mediated web. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2451988 🔗 Resources & Links 🤝 Connect with Bill on LinkedIn: https://www.linkedin.com/in/billbeutler/ 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 💬 Thinking about trust, sourcing, or shared knowledge in AI systems? Share this clip with someone building in that space.

S5 · E2

Jan 16, 2026

8:28

Who Decides What AI Knows? Wikipedia, Sources, and Trust w/ Bill Beutler, Founder @ Beutler Ink & TheNotability.co

Wikipedia has become one of the quiet foundations of modern AI. In this episode, Daniel explores why large language models rely so heavily on Wikipedia, both as training data and as a real-time citation source. Its concise structure, dense linking, and topic-first writing make it ideal for machines, even as fewer humans read it directly. Daniel is joined by Bill Beutler, founder of Beutler Ink, who works inside Wikipedia’s rules to help organizations correct inaccuracies and navigate notability standards. Bill explains how Wikipedia’s volunteer model, conflict-of-interest policies, and reliance on independent journalism shape what appears there, and why most brands misunderstand how the system actually works. The conversation surfaces a deeper risk: if AI reduces traffic to publishers, the sources Wikipedia depends on may shrink, making verification harder over time. As AI leans more heavily on Wikipedia, the health of that knowledge pipeline becomes a shared concern. This episode looks past hype to examine how AI is reshaping truth, reputation, and the infrastructure we increasingly depend on but rarely see. 🔑 What You’ll Learn in This Episode ✅ Why Wikipedia is a top citation source in AI answers, and why that changed recently ✅ How Wikipedia’s conflict-of-interest rules actually work in practice ✅ Why “undisclosed paid editing” is so hard for the volunteer community to police ✅ What Wikipedia’s “notability” standard is really screening for ✅ How brands can improve accuracy without turning Wikipedia into marketing ✅ What might happen if AI hollows out the sources Wikipedia relies on 🔗 Resources & Links 🏢 Beutler Ink: https://www.beutlerink.com 🔎 The Notability Company: https://thenotability.co 📚 Beutler Ink’s Wiki Resource Library: https://www.beutlerink.com/wiki-resources 🤝 Connect with Bill on LinkedIn: https://www.linkedin.com/in/williambeutler 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 💬 If your brand is being “summarized” by AI, pull up the citations and audit the Wikipedia trail. Share this episode with the person who owns reputation, comms, or search in your org.

S5 · E1

Jan 9, 2026

51:02

Artificial Insights Season 5: How AI is Changing Economics and Work, and How We Are Changing With It

Trailer

This season, we’re not talking about models, benchmarks, or demos. We’re talking about how AI is reshaping how humans work, coordinate, and judge as tools get better at remembering, acting, and contextualizing. With context increasingly automated, wisdom and judgment matter more, not less. We’ll hear from builders, operators, researchers, and leaders wrestling with how experience, trust, value, and incentives shift as AI becomes part of the fabric of work. 👀 What to expect this season: Conversations about augmentation and friction, not replacement. How work changes shape when systems handle memory and routine execution. Real stories from people adapting, repricing value, and renegotiating trust between humans and machines. 🎙️ Recommended Episodes to Start With: If you’re new to Artificial Insights or want entry points that capture the show’s core questions and lessons, here are strong starting points from past bonus episodes. ✨ Foundational Conversations How Is AI Anxiety Changing the World? (w/ Jennifer Moss) https://rss.com/podcasts/manaryhaus/1931059/ Why the AI Revolution Feels Different (w/ Mike Kirkup) https://rss.com/podcasts/manaryhaus/2075898/ What Happens to Jobs When AI Scales Output 35x? (w/ Nicolas Tobis) https://rss.com/podcasts/manaryhaus/2143792/ 🧐 Product & Founder Perspectives After 0→1: I Built a Product, Now What? (w/ Alexander Millar) https://rss.com/podcasts/manaryhaus/1992180/ Is AI a New Medium? (w/ Ahmad Iqbal) https://rss.com/podcasts/manaryhaus/2315508/ When Is the Right Time to Start a Company in AI? (w/ Adeel Zaman) https://rss.com/podcasts/manaryhaus/2238954/ 🔗 Resources & Links 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus

Jan 2, 2026

1:55

Welcome to Artificial Insights: How AI is Changing Economics and Work

Trailer

Artificial Insights is a podcast about how AI is changing economics and work, and what happens once the technology moves beyond demos and into real organizations. The podcast features conversations with founders, operators, and thinkers who are close to the work. People building AI products. People deploying them inside teams. And people who have already learned where AI helps and where it quietly creates problems. Daniel started Artificial Insights to stand at the gate of businesses using AI, separating hype from lasting impact. If you are new here, the most recent season trailer is a good place to start. 🎧 Recommended Starting Points (Bonus Episodes) If you want a fast way into the kinds of conversations this show is known for, these short bonus episodes are good entry points: How Is AI Anxiety Changing the World? (Jennifer Moss): A clear look at how fear and uncertainty around AI show up inside leadership teams and shape real decisions. https://rss.com/podcasts/manaryhaus/1931059/ Why the AI Revolution Feels Different (Mike Kirkup): A grounded explanation of why this AI wave cuts across more roles and systems than previous tech shifts. https://rss.com/podcasts/manaryhaus/2075898/ What Happens to Jobs When AI Scales Output 35x? (Nicolas Tobis): A concrete, numbers-driven look at productivity jumps, role changes, and second-order effects. https://rss.com/podcasts/manaryhaus/2143792/ After 0→1: I Built a Product, Now What? (Alexander Millar): A practical founder conversation about distribution, timing, and why “the product works” is not the end. https://rss.com/podcasts/manaryhaus/1992180/ Is AI a New Medium? (Ahmad Iqbal): A big-picture starting point on AI as a creative medium and how new formats actually emerge. https://rss.com/podcasts/manaryhaus/2315508/ When Is the Right Time to Start a Company in AI? (Adeel Zaman): A sharp filter for founders thinking about “why now” instead of chasing surface-level trends. https://rss.com/podcasts/manaryhaus/2238954/ 🔗 Resources & Links 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus

Dec 26, 2025

1:07

Season 4

The Great Flip: How AI Changed the Shape of Work in 2025

S4 · E14

Dec 19, 2025

18:25

Season 4 Wrap Up: How AI Is Making Impossible Work Practical

Across healthcare, nonprofits, construction, and creative tools, the same shift kept appearing. AI is moving work from impossible to practical. This special wrap up episode closes out Season 4 by revisiting a handful of moments that captured that shift most clearly. Rather than focusing on replacement or marginal efficiency gains, AI is removing constraints around memory, time, coordination, and execution. When those constraints fall, entirely new kinds of work become feasible. Daniel reflects on examples from across the season, including systems that surface organizational context no one person could retain, workflows that compress months of effort into minutes, software that moves from analysis to action, and tools that lower the barrier to creation itself. This episode is both a look back and a transition point. Season 4 showed what becomes possible when humans stop being the bottleneck. Season 5 will build on that foundation and explore where these shifts are leading next. We can't wait to share it with you! 🔑 What This Season Taught Us ✅ Why context is often the real constraint inside organizations ✅ How AI compresses long, fragile workflows into short, reliable ones ✅ What changes when software can act, not just inform ✅ Why feedback loops matter as AI enters the physical world ✅ How lowering barriers reshapes who gets to participate 🔗 Resources & Links 🤝 Connect with Daniel on LinkedIn: https://www.linkedin.com/in/dmanary/ 🎧 Explore past episodes of Artificial Insights: https://rss.com/podcasts/manaryhaus/ 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 🙏 Thank you to all of our Season 4 guests for their generosity, time, and hard-earned insight. 🎧 Season 5 launches in January. Stay tuned for the next chapter!

S4 · E13

Dec 12, 2025

11:36

Short: Who Should Be Writing the Rules for AI? w/ David Proulx, CAIO & Co-Founder @ HoloMD

Regulation and innovation rarely move at the same speed. In this bonus clip, Daniel Manary and David Proulx talk about who should shape the future of AI. David argues that the people building modern systems understand both the risks and the capabilities far better than policymakers who lack technical grounding. He contrasts Europe’s tendency to regulate early with the United States’ focus on innovation, liability, and builder responsibility. He points to the “Winning the AI Race” framework as an example of policy written by people who clearly understand the field, from guardrails to energy use to how a system should shut itself down if needed. For David, the real danger for countries like Canada and those in Europe is asphyxiating innovation before fully understanding the technology. 🎧 Want the bigger picture? In the main episode, David explains how HoloMD built a clinical agent that has handled more than 100,000 mental-health conversations without hallucinating. He walks through long-term memory, safety testing with synthetic patients, and why incentives matter in healthcare AI. Listen to the full episode: https://rss.com/podcasts/manaryhaus/2365531/ 🔗 Resources & Links 🤝 Connect with David on LinkedIn: https://www.linkedin.com/in/davidproulxoui/ 🌐 Learn more about HoloMD: https://holomd.ai/ 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus 👉 Have a guest in mind? Reach out to Daniel at daniel@manary.haus 🚀 Know someone thinking about AI regulation or policy? Share this clip with them.

S4 · E12

Dec 5, 2025

6:31

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