Weekly Reviews, AI Adoption Stages, and the Create vs Review Mode Trap

Weekly Review, AI Adoption, and Brain Modes

TLDR: A weekly note review practice—separating capturing from processing—creates a clean slate for each Monday. For AI adoption in engineering teams, a three-stage model (Explore → Embrace → Empower) works best. But beware: AI assistance can trap your brain in "review mode," making it harder to think creatively.

Summary:

The weekly review practice described here is deceptively simple but powerful. The key insight is separating reviewing from capturing—going through everything captured during the week in a dedicated "organization mode" rather than processing things hastily on the spot. When you do this, interesting things happen: over 60% of what seemed interesting days ago no longer does and gets deleted. You find more connections and work faster. It becomes a genuinely pleasurable activity, like a mini time capsule where each idea feels like a rediscovery. This extends to clearing email, DMs, downloads, and tasks—starting each Monday with a clean slate.

The AI adoption framework for engineering teams follows a three-stage process: Explore → Embrace → Empower. Adoption begins with personal exploration—engineers getting familiar with AI tools and their ergonomics. This is largely bottom-up. Managers can encourage it by providing tools without performance expectations, identifying champions, and creating knowledge-sharing ceremonies. Early wins include small automations, AI-written features, minor refactoring, and more docs and tests.

The second stage graduates AI usage into team practices: AI doing first-pass code reviews, better testing and documentation standards, improved meeting summaries. Critically, these must become actual standards—if AI makes testing easier, enforce tests in PRs. The observation that "if it doesn't feel risky, is it real change?" is worth internalizing. Feedback loops through retros and one-on-ones continuously tweak what's working.

The "Empower" stage asks: what do we do with the residual capacity AI creates? The best teams use it to expand people's scope—engineers going full-stack, PMs creating prototypes, designers trying frontend. Benefits include reduced coordination costs, higher velocity, and stronger growth.

For architects and teams, the "create mode vs review mode" distinction is particularly important. Create mode produces output through non-obvious processes—writing algorithms, essays, important emails. You make connections between ideas and turn them into something new. It feels draining but rewarding, like a workout. Review mode compares a draft against rules about what it should look like. It feels ten times cheaper energy-wise, and humans are genuinely good at it—spotting details and steering output tactically.

But review mode has limits. Most critically, it's hard to radically change course once you have a draft. The draft becomes an anchor. Kahneman's anchoring bias research shows how random numbers influence subsequent estimates even when participants know the numbers are random. This is the trap with AI assistance: having a first version in front of you restricts what you can create yourself. There's a mix of anchoring, sunk cost, and genuine laziness that kicks in. The concerning observation is that AI assistance often makes us feel less engaged—the brain enters a lazier mode that's hard to steer back.

Key takeaways:

• Weekly reviews that separate capturing from processing delete 60%+ of notes and create mental clarity
• AI adoption follows Explore → Embrace → Empower, moving from individual experimentation to team standards to expanded scope
• "Create mode" builds neural connections and mental models; "review mode" is energy-cheap but limiting
• AI-generated first drafts trigger anchoring bias, making it harder to think divergently
• The best teams use AI's residual capacity to expand people's scope rather than just accelerate existing work
• If adopting AI doesn't feel risky, it probably isn't creating real change

Tradeoffs:

• AI-generated first drafts accelerate output but anchor thinking and reduce creative divergence
• Expanding scope through AI capacity creates growth opportunities but requires people to operate outside their comfort zones
• Weekly reviews maintain clarity but require dedicated time that competes with "productive" work

Link: Weekly review, AI adoption, and brain modes

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