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: 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: 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.