EP 257 What My 30-Episode Sprint Is Teaching Me About AI, Energy, and Experimenting in Public

Midway through a 30-episodes-in-30-days podcast-to-book sprint, host Susan Diaz gets honest about what’s working, what’s hard, and how she’s actually using AI as a thinking partner, draft machine, pattern spotter, and quiet project manager - plus what leaders can learn from this for their own AI experiments.

 

Episode summary

This solo episode is a behind-the-scenes check-in from Susan’s “completely unhinged” (her words) experiment to record 30 episodes in 30 days as the raw material for her next book.

Nine episodes into twelve days, she talks candidly about fatigue, capacity, and why she refused to skip this recording even though she could have. She pulls back the curtain on the very practical ways she’s using AI to structure ideas, draft assets, spot patterns across episodes, and manage the subtle project/energy load of a sprint like this.

Then she zooms out to translate those lessons for founders and teams: why consistency beats intensity, why experiments are allowed to be small and honest, and why capacity has to be part of your AI strategy instead of an afterthought.

Key takeaways

This sprint is a live experiment in sustainability, not heroics. The goal isn’t to “win” 30 episodes perfectly, it’s to see what pace, support, and structure actually make ambitious AI-powered work sustainable for a real human.

AI is a thinking partner first. Susan uses voice input in her LLM to dump messy thoughts, then asks it to shape them into outlines, angles, and talking points so she’s never facing a blank page. (Pro tip: the built-in mic usually cuts off around five minutes - annoying but survivable.)

Drafting support is where AI shines next. From show notes to extra research points to contextualising guest insights, custom GPTs help expand and refine ideas so she can focus on judgement and voice instead of first drafts.

Pattern spotting turns episodes into chapters. By feeding multiple conversations into AI and asking for common threads or how ideas map to her core pillars, she can see where book chapters naturally want to live - and build something far more cohesive than her first, fully manual book.

AI also helps with energy management. It quietly supports the admin around the sprint: drafting guest emails, summarizing notes, organizing ideas, and helping her see where there’s too much on the go so she can re-plan.

For organizations, three big lessons emerge:

• Consistency beats intensity - small, steady steps with AI are better than unsustainable bursts.

• Experiments can be small and honest - you don’t need a centre of excellence to start. A one-hour training or a tiny workflow tweak counts.

• Capacity is strategy - pretending people have unlimited time and energy guarantees failure. Designing AI work around real capacity gives it a chance to stick.

Good AI literacy lowers the cost of entry and raises the quality of thinking. Used well, AI doesn’t replace your brain, it gives your best ideas a better chance of making it out of your head and into the world.

Episode highlights

[00:02] Setting the scene: a 30-episode sprint at the end of 2025 to get the book out of her head.

[01:43] Nine episodes in twelve days, fatigue, and choosing to show up anyway.

[03:21] Why the sprint mirrors how leaders feel about AI: “We know it matters… but keeping the pace is hard.”

[05:02] Using AI as a structure-building thinking partner via voice dumps and outlines.

[05:30] The five-minute mic limit, word-vomit sessions, and how AI turns fuzz into flows.

[07:02] Drafting support: research, context around guests, and custom GPTs for show assets.

[07:44] Pattern spotting across episodes to find the book’s real chapters and through-lines.

[09:18] Why this AI-supported book will be “twice, thrice, ten times” better than the first one.

[10:24] Energy and project management: emails, reflections, and organising all the moving pieces.

[11:46] Lesson 1 – consistency over intensity for teams experimenting with AI.

[13:29] Lesson 2 – small, honest experiments beat grand, delayed programs.

[13:59] Lesson 3 – capacity as a core part of AI strategy, not a footnote.

[15:01] Gentle prompts for listeners: where you’re already experimenting, where AI can remove friction, and who your inside champions are.

Use this episode as a mirror, not a mandate.

Ask yourself and your team:

1. Where are we already experimenting with AI, even in tiny ways?

2. How could AI remove friction from that work instead of adding pressure?

3. Who are our quiet inside champions - and what support or validation could we offer them this week?

Answer even one of those honestly, and you’re already moving from vague AI interest to real AI literacy.

Connect with Susan Diaz on LinkedIn to get a conversation started.

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