The holiday period is a time for family, switching off, reflecting on the year that's been, and preparing for the year ahead.
The global AI ecosystem will not slow down over this period. Big Tech will continue shipping releases because that’s what it takes to maintain a lead and dominate in this space at the current pace. And the Northern Hemisphere holiday shutdown is a matter of days, not weeks like it is down under. But that doesn't mean you should completely lose touch. It's a great chance to take a step back, explore something new, and build additional mental muscle to hit the ground running in 2026.
This list isn't prescriptive. It’s not a set of courses or “follow these steps to build” (most won't want to touch a laptop over the break - if you feel differently and want resource recommendations to focus on getting hands to keyboard over the break, ping me a note).
Here are a few reads and listens to consider adding to your list this summer:
Theme 1: The AI race: power, pace, and who wins
Jensen Huang on BG2 podcast If you want one “zoom out” take on why capability keeps compounding, this is it. This cemented for me that the AI shift we're seeing is underhyped and that we're not yet close to an AI bubble.
Brian Balfour's write up on the Next Distribution Shift We're seeing this play out with ChatGPT making a run at App ecosystems, Ads and other monetisation models. The playbook is clear, and key for operators and founders to figure out is whether their lunch is about to be eaten, or whether the entire supply chain is at risk of being swallowed.
The Coming Wave by Mustafa Suleyman Great insight into how AI and synthetic biology are about to proliferate power fast, with massive upside and serious risks. Mustafa's belief is “containment” becomes the real challenge of this decade.
Vibe Coding is the future (Lightcone Podcast) Don't mistake this discussion for a misguided LinkedIn hype conversation about vibe coding as a fad or about creating budget software. This one sits in the bucket of macro calibration, because it reinforces how quickly the ground rules are shifting for rethinking software as an Enterprise toolset to an individual leverage advantage if used correctly.
The Dual Customer If Agentic Commerce means AI Agents are becoming your new biggest customer, how best to prepare? Paul Pritchard's write up encapsulates brilliantly how building for Persuasion and Logic are the two frameworks to think through.
The AI Dilemma (documentary) This is my go-to counterbalance on the risks of AI. Produced by the Tristan Harris and Aza Raskin, co-founders of the Center for Humane Technology (produced The Social Dilemma documentary on the societal impacts of Social Media), the risks of AI they predict are alarming and a reminder that Responsible AI development is paramount to human survival and societal stability.
AI Superpowers and AI 2041 (Kai-Fu Lee): Both grounded reads on the US vs China AI race, why China’s playbook is different, and what AI does to jobs and power as it scales. Brilliant write up from someone who's worked on both sides of the aisle. These are reads from a few years ago, but the perspective is incredibly valid and useful to understand the emphasis placed on AI by the CCP in particular.
Theme 2: How work changes: becoming AI-native without losing your edge
Co-Intelligence by Ethan Mollick Ethan is one of the great AI thinkers of our time. His book is practical (not preachy) because it changes how you actually work with AI. One of the best foundational books for anyone getting started in figuring out where to begin. All of Ethan Mollick's articles on One Useful Thing are worth your time.
Aaron Levie's X and LinkedIn channels hold core principles about how work will get done that all leaders and knowledge workers should be inhaling. The lightbulb moment that continuously hits and helps me pull others into this sphere of thinking is about how work becomes all about context, workflows, and governance.
AI Instinct: the most important professional muscle I wrote about how tools continuously improve and churn. But an instinct in the AI arena, knowing what works and what doesn't through experience, is an instinct that compounds. Your edge is built through from reps and judgement, not feature awareness.
Becoming an AI-native employee (Elena Verna) This is the behaviour benchmark. Elena accurately describes how AI-native employees default to AI and ship end-to-end with way less coordination drag, turning velocity into the moat and making “manager-as-middle-layer without real craft” a liability. Ultimately, being “AI-native” is becoming a baseline expectation in some industries, and a mega competitive advantage in legacy spaces.
The real threat of AI: over-attachment to how you do your craft This is the career durability lens. The trap is clinging to the workflow that made you “good” in the old world. I'm a big believer that we all need to reinvent the 'how' we do our roles every quarter / half year in today's working world. The 'why' doesn't change, but the way you do your work can instantly render you with an advantage to competitors, or heading towards being obsolete.
Tim (@timgedenk) on “The death of the average” (shout out to Noah Horner for surfacing) This is a gut-punch to most marketers who want AI to deliver the silver bullet of a result. Big believer that content / outputs / results driven by humans drive a premium in the new age of digital distribution (much like a hand carved table or chef dish sit head and shoulders above their commoditised alternatives). It's why you'll see us leaning into video / physical and other high-friction marketing activities in 2026 that are a) harder to replace and compete with, and b) win and build trust faster/
Theme 3: How organisations adopt: from tools → workflows → leverage
Climbing AI Everest: the 5-step AI adoption playbook for business leaders This is how I’d sequence adoption in most companies, because tool play without process change becomes theatre fast.
Paul Roetzer's: AI Answers series Frontline operator and leadership gold. Discusses the day to day challenges leaders face in driving AI transformation and adoption. Pick any episode (available on Spotify also, not just YouTube), you'll certainly learn something.
Faster horses vs teleporters (Tobias Zwingmann) Great mental model of avoiding confusion on whether you're running an AI tool rollout or an AI transformation.
My current AI stack update (Nov 2025) This is my “don’t be platform-monogamous” take, because stacks and workflows survive churn better than loyalties do. Read through for insights on how to think about using the best tools for the job rather than being locked into an ecosystem.
Theme 4: Building with AI: agents, evals, and “what good looks like”
All About AI Evals - Brendan Foody (Merkor) on Lenny’s Podcast This makes “evals” feel real, because it frames measurement as the bottleneck to progress, not ideas or capital.
Getting AI to Work in Complex Codebases article (shout out to Zach Martin for the share) This is the antidote to agents going feral. By understanding how context really works when building AI systems and interfacing with LLMs. Don't let the title scare you, this applies to no-coders worker in standard AI tooling (ChatGPT, Claude, Copilot, Gemini). It's all about understanding the constraints of context windows.
The FDE Playbook for AI Startups (Bob McGrew on the Lightcone podcast) This is builder content I trust because it’s about loops, speed, and doing the painful parts that actually ship value.
The best listen about building AI Moats as a start-up (Lightcone podcast) Useful framing for builders. Speed wins first and moats come after you’ve earned something worth defending.
Theme 5: Foundational pre-GPT 3.5, writing before GenAI hype fogged the conversation.
These two books in particular shaped my early-on thinking about AI. Having recently re-read Life 3.0, although some of the predictions might now be outdated, the discussion points still hold up as foundational for understanding the broader impacts of AI.
Superintelligence (Nick Bostrom): A map of how we might create superintelligent AI, why control is the whole game, and what “good outcomes” could take in practice.
Life 3.0 (Max Tegmark): A clear tour of the futures AI could drive, the choices we actually get to make, and how to stack the odds toward outcomes we’d want to live in. The concepts in the third section are still indescribable after all of these years.
The three focus areas for me over the holiday
The single AI book I'll be reading is Reshuffle : https://www.amazon.com/Reshuffle-wins-restacks-knowledge-economy-ebook/dp/B0DTKW6NQV
A variety of Claude Code YouTube videos I've been harvesting to absorb, model and learn from.
Three reads on start-up growth and distribution focused on differentitation to the competition, not being better.
Do you have anything that tops your AI list to read this summer break? Let me know. Have a great break!
Written by Mike ✌
Passionate about all things AI, emerging tech and start-ups, Mike is the Founder of The AI Corner.
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