Beyond The Prompt - How to use AI in your company

After 15 years of building Zapier, Wade Foster is inviting his team to rethink almost everything. What started as a “code red” response to AI has become an opportunity to rediscover what the company could be. Wade describes the feeling as “10 out of 10 anxiety, but 10 out of 10 fun.” The challenge is getting an entire organization to see the possibilities, especially when change can feel threatening.

Wade shares what this looks like inside Zapier, including an experiment to let AI agents handle customer support emails. It turns out that handing work over to AI is much harder than simply adding an assistant to an existing process. The team has spent months figuring out how to make it work, with humans still playing an important role.

Henrik, Jeremy, and Wade also explore what it takes to thrive when the old rules no longer apply. They discuss curiosity, resourcefulness, and the instinct to act instead of waiting for instructions. And while Wade is giving his team permission to question everything, the conversation raises a harder question: how do you get people to challenge the way things have always been done when that's exactly what they've been taught to follow?

Key Takeaways

• The work is changing, even when the jobs aren't disappearing.
Henrik and Jeremy reflect on Wade's point that AI doesn't necessarily make a role obsolete, but it can fundamentally change what people do in that role.
• Resourcefulness is becoming a superpower.
Henrik and Jeremy discuss why curiosity, adaptability, and the willingness to act without all the answers are increasingly valuable. Sometimes the most important step is realizing you can just build something.
• Question everything, but bring a better idea.
Henrik points out that challenging the way things have always been done is only useful if you're willing to suggest or try something different.
• You can't refound a company by announcement alone.
Jeremy wonders how leaders can make questioning old assumptions part of everyday work. He and Henrik explore why established habits, defaults, and organizational structures can be harder to change than the rules themselves.

Wade's LinkedIn: LinkedIn.com/wadefoster
Wade's X: https://x.com/wadefoster
Zapier: https://zapier.com
Wade's articles: Zapier.com/wadefoster

00:00 Intro: The Work Is Changing
00:48 Meet Wade Foster
04:49 Is Your Strategy Too Small for AI?
06:14 From Code Red to Refounding Zapier
07:31 10/10 Anxiety, 10/10 Fun
08:50 How Much Has AI Actually Changed?
10:41 Getting People Excited About AI
13:56 Why Jobs Are Changing
15:34 Rethinking How Work Gets Done
16:39 Inside Zapier's AI Support Experiment
20:14 When Existing Workflows Break
21:45 Identity, Curiosity, and Adaptability
25:36 The Instinct to Act Without All the Answers
29:39 Stop Waiting for an Assignment
33:28 You Can Just Build Something
34:43 What Does Refounding Actually Mean?
36:40 Giving People Direction Without All the Rules
38:36 The Future of Automation at Zapier
39:18 Wade's Advice for Refounding a Company
41:45 What Would a New CEO Do?
43:23 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Nicholas Skytland spent 25 years at NASA, where he helped bring people from around the world together through hackathons. He joins Henrik and Jeremy to share what makes hackathons work, why different perspectives matter, and how to help people through the moment they want to give up.

Nick is now bringing that approach to kids. When his wife wanted to run an AI hackathon at her school, restrictions on AI tools led Nick and his team to build Launch.Kids. At one event, 36 teams created 500 applications in a single day.

Try Launch.Kids: If you're a student, parent, or teacher interested in learning how to build with AI, we'd love for you to try out Launch.Kids, mentioned on the show. The first 100 listeners can sign up at https://launch.kids/welcome/beyondtheprompt for a free month of credit.

The conversation then turns to a bigger question: how well do we really know our own values? Nick, Henrik and Jeremy explore how to communicate what matters to us when working with AI, and why we shouldn't hand over our judgment just because technology makes it easy.

Key Takeaways

• Hackathons are an underused tool
Henrik and Jeremy reflect on how hackathons bring different people together, encourage experimentation, and create value even when they don't lead to a finished product.
• Expect the moment of despair
Getting stuck is part of building something new. Nick explains why good hackathons need mentors, encouragement, and a bit of fun to help people push through it.
• The better you know yourself, the better you can use AI
Henrik and Jeremy discuss why understanding your own values, preferences, and ways of thinking helps you give AI better direction and recognize when its answers don't fit.
• Don't let AI do all the thinking for you
AI can make decisions easier, but that doesn't mean we should stop questioning its answers. Henrik and Jeremy reflect on the importance of bringing our own judgment to the conversation, especially when we're dealing with something we don't know much about.

Launch.Kids: https://launch.kids
Nicholas' LinkedIn: LinkedIn/NicholasSkytland

00:00 Intro: Don't Outsource Your Judgment to AI
00:20 Meet Nicholas Skytland
00:46 How NASA Opened Up Innovation
03:45 Measuring the ROI of Hackathons
05:04 Getting Past the Barriers to Open Innovation
07:51 Bringing Hackathons to Kids
09:19 Building Launch.Kids
14:44 Why “Just Play With AI” Isn't Enough
18:19 The Moment of Despair
21:36 How to Run a Hackathon
23:39 The Process, Not the Product
25:56 Inside Anthropic's Gathering of Faith Leaders
31:33 Be an Active Shaper of AI
34:36 Can AI learn good judgement?
39:00 Who Gets to Decide What's Good?
42:40 Communicating Your Values to AI
44:25 Don't Outsource Your Judgment
46:42 Henrik's Personal AI Playbook
51:10 The Debrief

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Alexander Kuilman works with CEOs and executive teams to bring more humanity into how they lead. He joins Henrik and Jeremy to explore why, if leaders know AI matters, getting people to actually use it can still be so difficult.

Using Bob Kegan and Lisa Lahey’s Immunity to Change framework, Alex argues that “I don’t have time” can mask deeper fears about competence, relevance, and what happens when AI can do work you previously thought made you valuable. They also explore why psychological safety matters if people are going to admit what they don’t know and actually experiment.

The conversation ends with the “AI vampire” and Alex’s idea of “corporate numbing”: AI makes us more productive, but instead of taking the time back, we often fill it with more work. Which leaves a bigger question: when AI gives us time back, what do we actually want to do with it?

Key Takeaways:

• AI resistance may be about fear, not time
“I don’t have time” can mask deeper fears about competence, relevance, and what happens when AI can do work you thought made you valuable.
• People need to feel safe enough to experiment
Psychological safety gives people room to admit what they don’t know, try something new, and risk looking incompetent.
• How leaders make people feel matters
Henrik and Jeremy realize that some of the AI leaders they admire don’t just have good tactics. They make people around them feel safe enough to try.
• Don’t let AI give you more work
The “AI vampire” turns productivity gains into more work. Alex’s idea of “corporate numbing” asks whether staying busy is sometimes easier than deciding what we actually want to do with the time AI gives back. Alex Kuilman (transcript - fina…

The Practice: https://www.bethepractice.com
Alex's LinkedIn: LinkedIn/AlexKuilman
Immunity to Change by Robert Kegan & Lisa Laskow
Amy Edmondson on Psychological Safety: Psychological Safety

00:00 Intro: The Problem of Corporate Numbing
00:23 Meet Alexander Kuilman
04:45 Why Aren’t People Using AI More?
06:49 The Hidden Assumptions Blocking Change
08:08 Jeremy’s Travel Problem
14:12 What Are People Really Afraid of With AI?
16:02 Stop Making Time to Experiment
22:25 Psychological Safety and AI Adoption
32:56 How to Hold Yourself
38:57 The Leaders Who Make People Feel Safe
40:33 What If AI Helps Us Think Less?
44:13 The AI Vampire
46:10 Corporate Numbing
54:08 Your Humanity Is the Advantage
58:05 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Jim VandeHei joins Henrik and Jeremy around the launch of his new book, Simplify: Do 50% More With 50% Less, co-authored with Mike Allen and Roy Schwartz. In the conversation, Jim shares how he’s leading Axios through AI, from turning himself into an “AI lab rat” to rethinking how the company works.

At Axios, Jim gave everyone access to ChatGPT, brought in training, and invited employees who took naturally to the technology to help others across the company. He also shares how AI fellows working directly with him prototype new ideas, why companies should delete unnecessary work before automating it, and how Axios is preparing for a world where more information moves from the open web into social platforms and personalized LLMs.

The conversation ends with a bigger question: what story are we telling people about an AI-enabled future? Jim worries that the negative case for AI is vivid and easy to understand, while the positive case often amounts to curing cancer or eliminating work. He makes the case for something more tangible: using AI to make people’s work and lives better while preserving human connection and the feeling that what people do still matters.

Jim's new book: Simplify: Do 50% more with 50% less
“Confessions of an AI Lab Rat”: Read here!
Axios: https://www.axios.com
Jim's LinkedIn: LinkedIn.com/JimVandeHei

Key Takeaways

• Leaders need to become AI lab rats
Leaders need firsthand experience with AI to understand what it can do and give others permission to experiment.
• Give the CEO an AI “SEAL team”
Jim’s AI fellows help him prototype ideas quickly, stay current, and experiment outside the normal company processes.
• Delete before you automate
Before using AI to make existing work faster, ask which meetings, processes, and activities shouldn’t exist at all.
• AI needs a better story about what goes right
The negative future of AI is easy to picture. Jim argues we need a more tangible vision of how AI can improve people’s lives without losing purpose and human connection.

00:00 Intro: Predictions for the future
00:46 Meet Jim VandeHei
01:06 Becoming an AI Lab Rat
04:21 How Axios Got Everyone Using AI
05:46 Who Gets the Most Out of AI?
08:27 The AI Understanding Gap
11:04 How Jim Keeps Up With AI
14:20 Building Axios for an AI Future
20:47 The CEO’s AI “SEAL Team”
23:52 Writing for Humans and LLMs
26:49 Delete Before You Automate
30:46 The Monthly Mentality
35:58 AI-Assisted Writing and Authenticity
41:47 The Coming Political Backlash
47:42 A Better Story for AI
55:25 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

After helping lead AI adoption at Moderna, Brice Chalamel has a new goal at OpenAI: help create 1,000 AI adoption success stories across large organizations.

Brice takes Henrik and Jeremy inside how work happens at OpenAI, where Slack has largely replaced internal email, agents help process information and draft responses, and some workflows are already moving agent to agent. But as AI takes on more responsibility, Brice argues that trust can’t be mandated. It has to be earned.

The conversation goes beyond tools into what it actually takes to lead people through change. Brice explains why, in a ten-step journey, nine steps may only be halfway, why our existing mental models shape how we respond to AI, and why leaders need to listen before trying to change someone’s mind.

They also explore Brice’s idea that AI could move us from “knowledge workers” to “intelligence workers,” a personal story about his mother using ChatGPT while caring for his father with Alzheimer’s, and why the benefits of AI need to extend beyond the people already living in abundance.

Key Takeaways

• Trust in AI has to be earned
As agents take on more responsibility, people need experience with them before they’re willing to hand over judgment and communication.
• AI adoption is a mind game and a heart game
Successful change depends not just on what people know about AI, but on what they believe, fear, and care about.
• Nine steps can be only halfway there
The final stage of transformation is often where the hardest work begins and assumptions need to be questioned.
• We’re moving from knowledge workers to intelligence workers
As AI handles more information processing, human value shifts toward judgment, perspective, influence, and decision-making.
• Listen before you think
Changing minds starts with understanding the experiences and mental models behind someone’s point of view, not simply making a better argument.

Brice's LinkedIn: https://www.linkedin.com/in/bricechallamel/
Website: https://powerofwhy.ai

00:00 Intro: From Knowledge Worker to Intelligence Worker
00:30 Meet Brice Chalamel
01:18 From Moderna to OpenAI
04:50 The Mission: 1,000 AI Success Stories
07:45 How Work Happens at OpenAI
08:59 When Agents Talk to Agents
10:56 Trust Has to Be Earned
15:22 Brice’s Principles for Change
16:45 Nine Steps Is Halfway There
25:21 The Mind Game
30:24 Why Leaders Resist AI
35:28 The Human Side of AI
37:37 When ChatGPT Became a Lifeline
41:56 From Knowledge Worker to Intelligence Worker
43:37 Agency in the AI Era
46:31 Who Gets to Benefit From AI?
51:39 What Our Fear of AI Reveals
56:18 Listen Before You Think
01:03:07 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

A year ago, Logitech had people experimenting with AI. Today, Eric says he can’t think of a single part of the company that isn’t building, exploring, or creating something with it.

Eric shares what helped make that happen. There’s a Build Advisor that helps employees figure out what to build and connects them with people who may have already worked on something similar. AI in Action moments are now part of company and leadership meetings. And before leadership presents to the board, there’s an expectation that their work goes through an AI Board Advisor first.

Henrik, Jeremy, and Eric also get into what comes next: how to measure whether all this AI activity actually creates value, why Eric built AI systems to manage his own information overload and sleep, and why creating something new should come with another question: what old report, process, or way of working can now disappear?

Key Takeaways

• Embed AI into how work gets done
AI becomes more valuable when it’s built into existing workflows and expectations, rather than simply made available for people to use.
• Make AI adoption visible and repeatable
Logitech keeps AI present through AI in Action moments, leadership routines, office hours, shared Gems, and a 175-person volunteer Champions Network.
• Build resources that help people help themselves
Tools like the Build Advisor give employees a place to start, surface work that already exists, and connect them with colleagues who have tackled similar problems.
• Ask what you can stop doing
Eric argues that every new AI-enabled artifact should come with another question: what old report, process, or way of working can now disappear?

Eric's website: https://porres.com/
Eric's LinkedIn: linkedin.com/eporres/
Logitech: https://www.logitech.com/

00:00 Embedding AI Into the Workflow
00:52 Meet Eric Porres
01:15 The Cambrian Explosion of AI
06:20 Measuring the Value of AI
11:16 The Build Advisor
16:12 Keeping Up With AI
19:43 Making AI Part of the Culture
21:37 The AI Board Advisor
25:51 Building an AI Champions Network
29:15 Eric’s Personal AI Stack
32:17 The AI Vampire Problem
40:59 Building a Deep Memory
46:49 What Can AI Help You Delete?
55:19 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Most companies know how to innovate. Far fewer know how to scale innovation.

Charles introduces the explore versus exploit framework, explaining why the same systems that help organizations succeed today can make them resistant to change tomorrow. As companies mature, they become better at serving existing customers, improving existing products, and optimizing existing processes. The harder question is how to create space for experimentation without undermining the business that already exists.

Henrik, Jeremy, and Charles explore what this means in the age of AI. They discuss whether AI should be viewed as a substitute or a complement, why Microsoft's transformation under Satya Nadella succeeded, how Amazon has built exploration into its operating system, and why leaders don't create adaptable organizations through vision alone. They do it by shaping culture through incentives, systems, and the behaviors they reward.

Key Takeaways:

• The biggest challenge isn't generating ideas. It's scaling them.
Many organizations are good at innovation. The difficult part is giving promising ideas the support they need to grow.
• Great companies become trapped by what made them successful.
The metrics, incentives, and culture that optimize today's business can make it harder to adapt to tomorrow's.
• Culture is built through systems.
Leadership principles only matter when they're reflected in hiring, incentives, performance reviews, and everyday behavior.
• The goal isn't to predict the future. It's to discover it.
The most adaptable organizations build processes that help them experiment, learn, and uncover new opportunities as the world changes.

Charles' Stanford profile: stanford.edu/faculty/charles-oreilly

00:00 Intro: Why Companies Die Fast
00:36 Meet Charles O'Reilly
02:01 Explore Versus Exploit
03:50 AI Substitute Or Complement
06:51 Adaptability As Culture
09:19 Resistance To Change
12:32 Microsoft Culture Turnaround
15:25 Ambidexterity And Lifespans
18:20 Ideate Incubate Scale
20:10 Scaling Needs Separation
24:44 Amazon PRFAQ Machine
34:33 Rituals And Failure Signals
38:05 The Debrief

📜 Read the transcript for this episode: here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

The conversation begins with an experiment that caught Jeremy's attention. Dan asked ChatGPT to tell him what his friends wouldn't. From questions about his blind spots to what people might say behind his back, some responses felt completely wrong, while others landed with surprising force. Rather than accepting every answer, Dan explains why the real value comes from wrestling with AI's perspective, not simply believing it.

From there, Henrik, Jeremy, and Dan explore what it takes to use AI well. They discuss intellectual humility, prompting models to challenge rather than flatter us, and why AI works best as a sparring partner that exposes weaknesses in our thinking. The conversation also touches on taste, agency, and why, in a world where execution is becoming easier, discernment and original thinking become even more valuable.

Key Takeaways:

• Use AI to make your thinking visible
The best AI conversations don't just generate answers. They help you understand your own assumptions, reactions, and ideas more clearly.
• Treat AI as a sparring partner
Challenge AI's responses, ask it to critique your work, and use it to strengthen your thinking rather than replace it.
• Taste is developed by creating
As AI makes execution easier, judgment and discernment become more valuable. The best way to develop both is by creating, not just consuming.
• Agency depends on context
People don't become more agentic through willpower alone. The right environment, with autonomy and room to take risks, makes initiative possible.

Daniel's website: https://www.danpink.com
LinkedIn: linkedin.com/danielpink
AI Self-Reflection Prompts: https://www.danpink.com/ai-guide/

00:00 AI as a Brutally Honest Advisor
00:32 Meet Daniel Pink
00:47 AI for Self-Knowledge
01:40 A Brutally Honest AI
06:10 What Do People Say Behind Your Back?
07:45 Should You Trust AI's Advice?
13:28 The Fear of Irrelevance
17:44 Intellectual Humility
20:04 AI as a Sparring Partner
24:35 Teaching AI to Think Like You
27:38 Why Taste Matters
29:59 Agency Starts with Context
37:08 A Future That's a Little Better
41:49 Nostalgia vs. Reality
45:18 The Debrief

📜 Read the transcript for this episode: Here!

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Geoff Woods returns to Beyond the Prompt to discuss the updated edition of The AI-Driven Leader and what has changed over the past 18 months. Rather than focusing on the latest AI models, Geoff argues that leaders need to use AI themselves before asking others to, using it to think more clearly, shape strategy, and make better decisions.

The conversation explores why many organizations confuse access with adoption, why strategy should come before use cases, and how AI can change the way leaders approach everything from business strategy to organizational design. Along the way, Henrik and Jeremy challenge Geoff's ideas on authorship, judgment, and whether understanding AI changes what leaders believe is possible.

Key Takeaways:

• Leaders need to use AI themselves
Using AI personally is what qualifies leaders to shape strategy and lead others from practice rather than theory.
• Use AI to improve your thinking
The biggest opportunity isn't automating work. It's using AI to think better, solve better problems, and imagine new possibilities.
• Start with problems, not use cases
Begin with the biggest challenges facing the business, then use AI to rethink how to solve them.
• AI still needs human judgment
AI can generate ideas, but people are still responsible for reviewing the output and standing behind it.
• Focus AI on your highest-value work
Use AI to amplify the small set of activities where your human strengths create the greatest impact.

The AI-Driven Leader: https://www.aileadership.com
Geoff's LinkedIn: linkedin/geoff-woods

00:00 Are You Qualified to Lead on AI?
00:35 Meet Geoff Woods
00:54 The AI Slop Dilemma
05:48 Putting Your Stamp of Approval
09:19 What Changed in 18 Months
12:13 Access Isn't Adoption
15:50 Why Leaders Can't Delegate AI
20:09 Strategy Before Use Cases
22:02 BarkBox's AI Strategy
26:23 Reinventing Strategy with AI
33:11 Compressing Months into Hours
37:22 Human Skills as Superpowers
42:46 The Debrief

📜 Read the transcript for this episode: are-you-qualified-to-challenge-your-team-on-ai-with-geoff-woods-author-of-the-ai-driven-leader/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Christian believes the AI era will be defined less by generating outputs and more by evaluating them. As intelligence becomes cheaper and more accessible, the people who create the most value may be those who can distinguish good work from exceptional work and help guide increasingly capable systems.

The conversation explores verification, judgment, and why expertise still matters in a world where AI can perform many tasks at a high level. Christian explains why today's experts are both highly valuable and simultaneously training the systems that may eventually replace parts of their work.

Jeremy and Henrik also explore what this means at a personal level. They discuss building AI agents that reflect your own preferences, creating personal verification systems, and why AI may make it easier to learn new skills, switch careers, and pursue more ambitious ideas.

Key Takeaways:

• Verification becomes more valuable as intelligence gets cheaper
As AI makes generating outputs easier, the ability to recognize what is actually good becomes increasingly important.
• Experts are training their own replacements
The people best positioned to verify AI outputs are also helping codify the expertise that trains future systems.
• Human value shifts from doing to directing
As AI handles more execution, people create value through judgment, direction, and orchestration.
• Build your own verification system
The best AI users are developing agents, workflows, and tools that reflect their own preferences and standards.

Christian's LinkedIn: linkedin.com/ccatalini/
Christian's X: https://x.com/ccatalini
Christian's website: http://www.catalini.com
Economics of AGI: full paper
Jeremy's Persona File Template: YouTube/The8Files

00:00 Non-Measurable Frontiers
00:32 Meet Christian Catalini
01:08 The Economics of AGI
03:09 Why Verification Matters
06:37 Can Everything Be Measured?
10:32 The Rise of the Verifier
14:35 When Intelligence Gets Cheap
21:46 Building Your Verification Harness
24:08 Human + AI Augmentation
30:18 Persona Files and Privacy
33:12 Reasons for Optimism
36:00 Career Switching in the AI Era
39:31 The Debrief

📜 Read the transcript for this episode: the-unexpected-economics-of-agi-with-christian-catalini-tech-founder-and-co-creator-of-libra/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Chantel Prat studies how different brains make sense of the world. Her work starts from a simple idea: every experience leaves a mark. The inputs we consume shape how we think, what we notice, and ultimately who we become.

The conversation explores why people often choose familiar rewards over uncertain opportunities to learn. Chantel explains the tension between exploration and exploitation, why curiosity is essential for growth, and how fear can prevent us from engaging with new technologies like AI.

They also discuss theory of mind, cognitive offloading, and what happens when we increasingly rely on AI for thinking. The goal is not simply to do better work, but to use AI in ways that help us become better versions of ourselves.

Key Takeaways:

• Curiosity requires safety
When people feel threatened, they become defensive rather than exploratory. Fear gets in the way of learning.
• Better inputs create better outputs
Every experience leaves a footprint on the brain. The ideas, conversations, and information we consume shape how we think and who we become.
• We naturally favor certainty over exploration
Our brains are biased toward familiar rewards, even when something new may offer greater long-term value.
• Curiosity starts with admitting you might be wrong
Learning requires recognizing that you do not already have the answer. Without that openness, exploration never begins.
• Use AI to become better, not just produce more
The most important question is not what AI can do for you, but what you still want to get better at yourself.

Chantel Prat: https://linktr.ee/chantelprat
The Neuroscience of You: The-Neuroscience-of-you/book

00:00 Curiosity Versus Threat
00:31 Meet Chantel Prat
01:02 Why Input Shapes Brains
04:08 The Output Pressure Trap
05:52 Exploration Versus Exploitation
10:05 Average Brains And Teams
15:35 Theory Of Mind Defined
22:12 Practicing With AI Feedback
24:31 Offloading Thinking To AI
29:50 Humans In The Loop
35:16 Age And Tech Reactions
42:15 Why Curiosity Requires Safety
48:15 Personal Codex And AI
50:54 Becoming More Yourself
54:34 The Debrief

📜 Read the transcript for this episode: why-fear-kills-curiosity-and-what-that-means-for-ai-with-chantel-prat-cognitive-neuroscientist/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Eric Ries, author of The Lean Startup and the newly released Incorruptible, joins Beyond the Prompt to explore why most companies drift from their original mission over time. The conversation dives into governance, shareholder primacy, Anthropic’s unusual structure, and why AI makes these questions more important than ever.

Eric Ries argues that most companies are built on a contradiction. Founders say they care about customers and impact, but legally, the company is structured to serve shareholders first. Over time, that mismatch tends to win.

The conversation explores what that looks like in practice, why it is so hard to fix, and how a small number of companies have tried to design around it from the beginning. Eric reflects on advising Anthropic in its earliest days and what it actually takes to protect a mission as a company scales.

A big part of the discussion is how governance gets treated as a legal formality when it is really a design problem. In the age of AI, Eric argues that the principles baked into a company’s structure early on may determine whether it stays true to its mission or slowly drifts away from it.

Key Takeaways:

• Mission drift is often built in from day one
Founders may say they care about customers and impact, but legally the company is structured to serve shareholders first. Over time, that mismatch tends to win.
• Governance is one of the highest leverage founder decisions
If the structure is misaligned early on, founders can lose control of the company and its mission no matter how strong the original vision was.
• The system is stacked against mission-driven founders
Even well-intentioned founders operate inside structures designed to prioritize short-term shareholder returns. Most do not realize it until it is too late.
• “Why not try?” is more powerful than it sounds
Eric’s argument is not that fixing governance is easy. It is that most founders never even ask the question.
• AI makes this more urgent than ever
As AI systems act more autonomously, the principles built into a company early on will shape whether it stays true to its mission or drifts away from it.

Eric's new book:
Amazon: Incorruptible
Website: https://www.incorruptible.co
Socials:
X: https://x.com/ericries
LinkedIn: linkedin.com/ericries
The Lean Startup: https://theleanstartup.com

00:00 Mission vs Shareholder Value
00:32 Meet Eric Ries
01:37 Why Anthropic Needed Governance
06:47 The Long-Term Benefit Trust
10:00 Why Great Companies Drift
13:47 From Lean Startup to Incorruptible
18:14 Is It Too Late To Fix?
23:06 Governance As A Superpower
25:48 The Lies Founders Tell Themselves
28:49 The Rise Of Shareholder Primacy
33:09 The Unaccountability Machine
35:51 Profit vs Human Flourishing
37:24 The ROI Trap
38:26 The H-E-B Loyalty Story
41:14 Principles Beyond Metrics
42:54 AI, Thick Data, And Human Judgment
46:43 The Debrief

📜 Read the transcript for this episode: why-your-favorite-brand-stopped-caring-about-you-eric-ries-author-of-the-lean-startup/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Ryan Holiday argues that while AI can generate outputs, it cannot generate wisdom. Drawing on a story from Seneca about a Roman who used educated slaves to sound intelligent, he compares outsourcing thinking to outsourcing exercise: the value comes from becoming the kind of person who can do the work, not simply producing the answer.

The conversation explores the difference between useful cognitive offloading and surrendering judgment entirely. Ryan explains that while tools like GPS may replace navigation skills without much consequence, writing, decision-making, and critical thinking shape the person on the other side of the process. AI, he argues, tends to amplify existing tendencies. People satisfied with mediocre work will settle faster, while people pushing for exceptional work can use AI to refine and challenge their thinking.

Throughout the episode, Stoicism serves as a counterweight to both panic and hype. Change and uncertainty are constants throughout history, not exceptions. Ryan reflects on leadership, family, adaptability, and skepticism, arguing that in a world where AI can confidently produce both insight and nonsense, the ability to question, verify, and think independently becomes increasingly valuable.

Key Takeaways:

• You cannot outsource wisdom
AI can generate answers, but judgment and understanding still come from doing the work yourself.
• AI amplifies who you already are
People who settle for mediocre work will do so faster with AI. People who push for better work can use it to deepen and refine their thinking.
• Bullshit detection is becoming a core skill
As AI produces increasingly convincing answers, skepticism and verification become essential.
• Change is not new
The Stoics viewed uncertainty and disruption as constants of human life. AI may feel unprecedented, but humans have always had to adapt to major change.
• Agency matters more than ever
You cannot control technological change, but you can control how you respond to it and how you choose to use it.

Ryan's website: https://ryanholiday.net
Daily Stoic: https://dailystoic.com/podcast/

00:00 Intro: You Can’t Outsource Wisdom
00:29 Meet Ryan Holiday
02:03 The Dream Was To Work Less
03:07 Who Actually Gets The Time?
06:32 Leadership, Culture, And Family First
08:38 How Will You Measure Your Life?
10:11 The Stoic View Of Change
14:44 AI Hallucinations And Shameless Confidence
17:21 You Cannot Outsource Wisdom
19:08 Cognitive Offloading Vs Real Understanding
20:22 Ego, Flattery, And AI
22:52 AI As Editor And Thought Partner
24:59 Mediocre Vs Exceptional Work
31:15 Why Bullshit Detection Matters
38:06 Stoicism, Agency, And Adapting To Change
43:31 The Debrief

📜 Read the transcript for this episode: you-cant-outsource-wisdom-bestselling-author-ryan-holiday-on-what-the-stoics-have-to-say-about-ai/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Laura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works.

She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign.

The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what’s generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer.

Key takeaways:

• Mediocre is easier than ever, which raises the bar for originality
When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher.
• Brand becomes trust when products converge
As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that.
• Only do what only you can do
Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view.
• Need-finding still requires real people
Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did.
• Human plus bot plus human
Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process.

Instacart: https://www.instacart.com
Super Bowl ad: Super Bowl (Instacart ad)
Laura LinkedIn: linkedin/laurajones
ro's post: https://ro.co/perspectives/super-bowl-economics/

00:00 Intro: Originality vs AI Complacency
00:27 Meet Laura Jones
01:23 Brand as trust when products converge
03:50 Personalization and reducing mental load
06:24 What still matters in marketing
10:33 Why need-finding cannot be shortcut
14:09 Using AI without losing judgment
16:33 New channels and where customers actually are
21:35 Why “dopey ideas” matter
25:42 Human plus bot plus human
28:44 Inside the Super Bowl ad
31:47 From banana insight to product
34:49 Taking creative risks at scale
37:34 Fear, pressure, and team chemistry
46:24 AI and faster prototyping
53:26 The debrief

📜 Read the transcript for this episode proof-of-craft-what-it-takes-to-stand-out-when-everything-looks-good-with-laura-jones-cmo-of-instacart/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Dan Klein, professor at UC Berkeley and CTO at Scaled Cognition, explains that AI systems generate answers based on patterns in language rather than verified knowledge. This makes them highly capable across many tasks, but also means they can produce confident answers even when they are not fully accurate.

He introduces the “jagged frontier,” where AI performs very well in some areas and less reliably in others. Because responses are fluent and convincing, it is often hard to see where those limits are, which makes it important to stay engaged when using these systems.

The conversation also explores hallucinations as a natural part of generative systems. In some cases, this is what makes them valuable, especially for creative or open-ended tasks, while in other cases reliability becomes more important.

Finally, Dan highlights that working effectively with AI is a skill. As more people start using these systems in their daily work, knowing how to guide them, evaluate outputs, and apply them in the right contexts becomes increasingly important. He also shares how his team at Scaled Cognition is tackling this challenge by building AI systems with fundamentally different architectures, focused on determinism and reliability — aiming to ensure systems follow rules, reflect underlying data accurately, and behave predictably in high-stakes, policy-driven use cases.

Key Takeaways:

• AI is designed to sound right, not to know it’s right
Models generate fluent answers without knowing whether they are correct, which means users need to actively evaluate outputs
• You have to learn where AI works and where it doesn’t
Capabilities are uneven, and understanding those limits is key to using AI effectively
• Working with AI shifts your role from creator to editor
Instead of starting from scratch, you are reviewing, refining, and validating what the model produces
• Most people are using AI without knowing how to manage it
Skills like delegation, verification, and judgment are becoming essential, but are not widely taught

Dan's LinkedIn: linkedin/dan-klein/
Scaled Cognition Website: https://www.scaledcognition.com
Scaled Cognition LinkedIn: linkedin/company/scaledcognition/
Scaled Cognition X: https://x.com/ScaledCognition

00:00 Intro: Fluency vs Truth
00:34 Meet Dan Klein
02:53 Why Fluency Misleads
05:11 How LLMs Guess
07:30 What Is Hallucination
08:54 Deception and Alignment
11:22 Why Agents Break
12:48 Chaining and Determinism
16:01 When Hallucination Helps
22:33 Beyond Scale for Reliability
30:40 Synthetic Data Training
31:10 Enterprise Agent Use Cases
33:44 Healthcare Risks
39:13 Enterprise Literacy Gap
41:27 Delegation and AI Management
54:37 The Debrief

📜 Read the transcript for this episode: nobody-is-getting-new-manager-training-for-their-ai-team-with-dan-klein-uc-berkeley/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Melissa Cheals leads Smartly, a payroll and people management platform serving 24,000 small and medium businesses in New Zealand. In this conversation, she shares how AI is reshaping product development, leadership, and how organizations operate.

A key moment comes when her team estimates new features will take 12 months and $1M to build. Instead of accepting it, Melissa pushes back, using AI to better understand her team’s perspective and communicate the need for change more effectively. This becomes a broader shift in how she approaches leadership, using AI to think more clearly and navigate conversations with less friction.

The discussion expands into strategy. Companies now face a fundamental choice: become AI-native or continue building on existing systems. As AI adoption increases, it also exposes silos and bottlenecks. Melissa shares why cross-functional collaboration—and leaders actively engaging with AI themselves—is critical to navigating this shift.

Key Takeaways:

• Becoming AI-native is a defining decision
It’s not just a technology shift. Leaders need to decide whether to rebuild around AI or continue layering it onto existing systems, and that choice shapes how the company operates.
• AI shifts us from scarcity to abundance
Many organizations still think in terms of limited time and resources, but AI changes what’s possible and forces leaders to rethink how big they can think and what they can achieve.
• AI is a leadership amplifier
Beyond productivity, AI helps leaders think more clearly, reframe conversations, and communicate change in a way that is both effective and respectful.
• Leaders can’t delegate AI
Without hands-on experience, it becomes difficult to challenge assumptions, guide teams, or make informed decisions about what’s possible.

Smartly: https://smartly.co.nz
LinkedIn Melissa: linkedin.com/melissa-cheals
LinkedIn Smartly: linkedin.com/company/smartlynz/

00:00 Intro: Challenging AI Assumptions
00:28 Meet Melissa Cheals
01:17 The Spark For Change
02:36 Vision And Early Signals
03:48 Hiring For Transformation
06:12 Unlocking Data With AI
08:27 Breaking Silos Across Teams
10:39 Why Leaders Must Learn AI
13:42 Leading With AI And Clarity
17:05 The AI-Native Decision
21:45 Thinking Bigger With AI
25:23 Less Meetings More Writing
26:33 The Self-Disruption Imperative
29:11 Breaking Silos With Value Streams
31:28 Managing Fear And Change
32:50 Learning And Shipping Faster
34:58 Debrief

📜 Read the transcript for this episode: ai-native-or-not-the-defining-choice-for-companies-right-now-with-melissa-cheals-ceo-of-smartly/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Greg Shove describes a growing gap between individual and organizational AI adoption. A small group of employees are already using AI effectively, while most companies are still early. AI is generating real productivity gains, but those gains are not being captured at the company level. Instead, they are absorbed by individuals who use AI to work faster, often without changing team outputs or structures — raising a central question: if AI creates time, where does that time go?

The conversation explores why enterprise AI adoption remains uneven. Many organizations lack a clear point of view on AI, and workflows take time to adapt, making it difficult to turn individual gains into coordinated results. At the same time, AI is breaking capability boundaries, allowing people to take on work across roles while companies remain structured around existing ways of operating.

From a leadership perspective, Greg emphasizes that the challenge is not just efficiency. AI creates capacity, but without clear direction on how to use it, that capacity disappears. Leaders must decide how to reinvest the time AI creates if they want to capture real business value.

Key Takeaways:

• AI’s ROI is leaking, not missing
Companies are generating value from AI, but it’s being captured by employees rather than the organization.
• A small group drives most of the impact
Roughly 10–15% of employees adopt AI early and use it effectively, creating an uneven distribution of gains.
• AI is breaking capability boundaries
Individuals can now take on work across roles, but organizations are still structured around fixed responsibilities.
• Most companies lack a clear point of view on AI
Without direction from leadership, adoption becomes fragmented and employees are left to figure it out themselves.
• Leaders must decide what to do with the time AI creates
Efficiency gains alone don’t create value. Organizations need to define new, higher-value work or the gains disappear.

Greg's LinkedIn: linkedin/gregshove
Section LinkedIn: linkedin/company/sectionai
Section AI: https://www.sectionai.com
Prof AI: https://prof.ai

00:00 Intro: Entering the Era of AI Chaos
00:31 Meet Greg Shove
01:32 Enterprise AI Is a C Minus
01:51 AI’s ROI Is “Leaking” to Employees
03:04 When Individuals Outrun the Organization
05:44 When AI Breaks Workflows
06:47 Disposable Software and New Ways of Building
09:10 Cut vs Create
12:01 Using the Calendar as a Lever
16:24 Why Enterprises Don’t Move
17:32 When Customers Force Change
21:31 AI Breaks Capability Boundaries
25:44 The Productivity Firehose
27:49 Who Actually Captures the Value
28:45 Why Everyone Needs Good AI
32:00 Adoption Beats Buying More Tools
40:17 Teaching the 90 Percent
43:48 Where Humans Still Matter
48:09 The Debrief

📜 Read the transcript for this episode: greg-shove-on-why-most-companies-are-not-seeing-roi-on-ai-yet/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Leidy Klotz has spent years studying a simple but overlooked phenomenon: when we try to improve something, our first instinct is to add rather than remove. He shares the Lego bridge experiment that sparked his research and explains how this additive bias scales from small design decisions to entire organizations. Over time, companies accumulate reporting lines, meetings, software, and policies without questioning what no longer serves them.

Henrik and Jeremy explore how AI tools intensify this pattern. When generating ideas, launching projects, writing code, or producing content becomes effortless, the temptation to add grows stronger. The cost of producing information drops, but the cost of consuming it rises. Without guardrails, organizations risk what Leidy calls “organizational indigestion.”

The discussion moves from insight to implementation. Leidy outlines practical ways to counteract additive bias, including stop-doing lists, default kill dates on projects, and designing environments that make subtraction visible and acceptable. In a world of accelerating AI output, leaders must intentionally decide what to remove, what to protect, and what truly matters.

Key Takeaways:

• We default to adding, not subtracting
When faced with a problem, our instinct is to introduce something new. Subtraction rarely occurs to us, even when removing something would improve clarity and performance.
• Generative AI amplifies additive bias
AI makes producing content, code, and ideas easier than ever. Without constraints, this frictionless creation can accelerate complexity instead of progress.
• More organizations die from indigestion than starvation
Over time, companies accumulate tools, processes, and policies that quietly slow them down. The real risk is often not too few ideas, but too many unexamined additions.
• Architecture beats willpower
Rather than relying on discipline alone, leaders can design systems that encourage subtraction. Stop-doing lists and default expiration dates make removal expected instead of exceptional.
• Protect what matters before adding more
Before introducing new tools, workflows, or AI systems, leaders must define what is already working and worth protecting. Subtraction requires clarity about what should stay, not just what should go.

Subtract: amazon/Subtract-Untapped-Science-Leidy-Klotz
In a Good Place: amazon/Good-Place-Spaces-Where-Thrive/
Leidy's Speaking: https://leidyklotz.com/
Clip from Bear: Subtract - this is how you do better

00:00 Intro: Our Instinct to Add
00:28 Meet Leidy Klotz
01:15 The Subtract Idea
02:56 Organizations Get Bloated
03:49 Scandinavian Design Mindset
04:32 New Book: In a Good Place
05:59 AI Abundance and Indigestion
08:12 Curate Context, Not More
11:38 Cues and Stop-Doing Lists
15:00 Default Debt and Kill Dates
17:10 Odysseus Contracts and Biases
21:28 Reengage the Physical World
29:17 Bike Shedding and Priorities
36:10 Making Is Thinking
49:16 The Debrief

📜 Read the transcript for this episode: how-to-subtract-the-most-underrated-skill-of-the-ai-era-with-leidy-klotz/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Fathom was built on the assumption that transcription would become commoditized and generative models would steadily improve. Rather than training proprietary models, Richard focused on building the infrastructure around them and waiting for model capabilities to reach the right threshold.

In this conversation, he explains why AI has made effort and impact harder to predict, and why that shifts product development from roadmap execution toward experimentation. He describes separating an exploratory AI team from core engineering, structuring that team to prototype and write specs, and expecting a meaningful portion of experiments not to work.
Richard introduces his Jenga model for AI development, testing different models and use cases to find where resistance is lowest. He also discusses the operational realities of rapid model updates, hallucination rates, and what he calls the LLM treadmill.

The discussion explores qualitative QA, organizational design, buy versus build decisions, and why leadership taste plays an increasingly important role as AI lowers the barrier to generating outputs.

Key takeaways:

• Estimating effort and impact is becoming harder
As model capabilities improve quickly, features that require months today may take far less time in the near future. This makes traditional planning assumptions less stable.
• Product development increasingly resembles R&D
With shifting capabilities and uncertain outcomes, teams must experiment, prototype, and iterate rather than rely solely on long term roadmaps.
• Organizational structure must reflect experimentation
Separating exploratory AI work from core engineering can allow faster iteration while maintaining stability elsewhere.
• Rapid model updates create operational pressure
Frequent improvements and changing performance levels can require teams to revisit and adjust features more often than in traditional software cycles.
• Qualitative judgment plays a larger role
As AI lowers the cost of generating outputs, evaluating quality and deciding what to ship becomes increasingly important.

Fathom: https://www.fathom.ai/
Fathom LinkedIn: linkedin/company/fathom-video/
Richard's LinkedIn: linkedin/in/rrwhite/

00:00 Intro: Why AI Breaks Roadmaps
00:19 Meet Richard White (Fathom AI)
02:16 From Roadmaps to R&D
04:49 Designing AI Teams for Speed
07:11 The Jenga Model
09:56 Failing 50% & AI Team Psychology
13:40 LLMs as Interns & Anti-Planning
21:01 QA, Data Pain & Developing Taste
24:59 Executive Taste & Culture Rules
27:20 Reacting to AI Waves
28:50 Fathom’s 4-Step Product Plan
30:47 What New Models Unlock
32:13 From Scribe to Second Brain
40:32 Build vs Buy in AI
45:32 The Debrief

📜 Read the transcript for this episode: from-roadmaps-to-rd-how-ai-is-changing-product-development-with-richard-white-founder-of-fathom-ai/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

In this episode, Bryan McCann joins Henrik and Jeremy to explore how search is evolving from simple queries into more conversational and agent-driven systems, and why prompting is likely a temporary skill. Bryan shares how his definition of productivity changed as an AI researcher, moving away from doing the work himself and toward designing plans and experiments that machines could run continuously.

The conversation expands to leadership and organizational design. Bryan explains why helping others learn how to work with AI became his highest-leverage activity, and offers a simple rule of thumb: try to get AI to do the task first, and treat anything it can’t do as an interesting research problem. Henrik and Jeremy connect this to Bryan’s view that organizations may increasingly resemble neural networks, with information flowing more freely and decisions less tied to rigid hierarchies.

Key Takeaways:

• Productivity can be measured by machine output, not human effort
Bryan explains how “keeping the GPUs full” became his primary measure of productivity.
• Prompting is useful, but likely temporary
The episode discusses why future systems may rely less on explicit prompts and more on inferred context.
• Try AI first, then learn from what it can’t do
Tasks AI struggles with can reveal meaningful research opportunities.
• Leadership is about scaling others
Bryan shares how his focus shifted from scaling himself to helping his team increase impact.
• Organizations may benefit from neural-network-like design
Better information flow and fewer bottlenecks can improve decision-making.

YOU: You.com
Bryan's website: https://www.bryanmccann.org
LinkedIn: linkedin/company/youdotcom/

00:00 Intro: Keeping the GPUs Full
00:22 Meet Bryan McCann: CTO & co-founder of You.com
00:43 Why Search Is Breaking - and Why It Becomes a Skill
01:41 From Search to Agents
03:18 The Case for Proactive, Context-Aware AI
04:30 We Don’t Need New Hardware - We Need Trust
05:43 The Trust Problem of Always-On Listening
07:57 Trust as the Real Bottleneck (Not AI Capability)
09:52 Delivering Immediate Value to Earn Trust
12:13 Business Models and Escaping the Attention Economy
17:27 What “Agents” Really Mean - and Why the Term Will Fade
20:37 Productivity, Parkinson’s Law, and Keeping the Machines Running
23:52 Scaling Yourself vs. Scaling Your Team
29:57 Building Culture: Automate, Throw Away, Rebuild
35:46 Designing Organizations Like Neural Networks
45:02 Recruiting for Initiative in an AI-Native Organization
49:18 The debrief

📜 Read the transcript for this episode: podcast.beyondtheprompt.ai/heres-how-to-know-if-youre-getting-the-most-out-of-ai-with-bryan-mccann-cto-of-youcom/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.