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:

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.

In this episode, Humza Teherany breaks down how he bridges deep technical fluency with strategic leadership at MLSE, home to the Raptors, Maple Leafs, and more. He shares how a vacation turned into an AI reawakening and how that hands-on immersion led to a fundamental shift in how his organization builds and experiments.

Humza walks through MLSE’s build in a day practice, their internal AI platform, and why speed to prototype now unlocks more than just efficiency. It changes who gets to shape the future. He, Jeremy, and Henrik explore the limits of traditional enterprise AI rollouts and how to build spaces for superusers that enable company-wide transformation. The conversation covers how technical literacy impacts credibility, why idea execution is the new differentiator, and how Humza’s five-year-old inspired a bedtime story app powered by AI.

Whether you're a CTO, a founder, or just figuring out where to start, Humza makes a compelling case. The best leaders don’t delegate this moment. They build.

Key Takeaways

• Leaders should not delegate the AI moment
  Humza, Henrik, and Jeremy agree that this is a moment for leaders to be hands-on. The ones who build and explore the tools themselves are the ones unlocking real impact.
• Technical fluency builds credibility and better decisions
  Humza’s return to his technical roots has changed how he leads. Understanding how AI works helps leaders earn trust and make smarter, faster choices.
• Speed enables inclusion
  MLSE’s build in a day model allows more people to contribute ideas and see them turned into real prototypes. Moving fast isn’t just efficient - it changes who gets to participate.
• Empower your superusers first
  Rather than starting with enterprise-wide training, Humza focuses on enabling the small group already eager to build. That early energy helps drive broader culture change.

MLSE: mlse.com
LinkedIn: Humza Teherany - LinkedIn

00:00 Intro: Humza Teherany and MLSE
00:27 The Role of C-Suite Leaders in AI
01:08 Reconnecting with Technical Skills
02:08 Diving Deep into AI Tools
03:03 The Importance of Hands-On Learning
04:25 Progression from Consumer to Technical AI Tools
07:28 Building a Business Case for AI
10:03 Creating a Culture of Innovation
14:00 Implementing AI in Business Operations
21:05 Challenges and Strategies in AI Adoption
26:17 Organizational Structure for AI Success
32:02 The Importance of Reviewing and Planning Code
33:01 The Future of Solo Developers and New Technologists
34:58 Reimagining Company Structures with AI
38:55 Key Skills for Future Technology Leaders
41:19 Personal AI Experiments and Innovations
46:52 Encouraging Creativity in Children with AI
49:11 The Debrief

📜 Read the transcript for this episode: building-an-enterprise-ai-innovation-lab-a-master-class-with-humza-teherany-chief-strategy-officer-of-maple-leaf-sports-and-entertainment/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.