To continue from last week, I asked Claude AI to source a summary of Gianni Giacommeli’s article “What to learn in the age of AI” to suggest four “pillars of content” for me to write about for the next 1-2 years.
Here’s what it suggested regarding the fourth pillar:
Write about culture, leadership, collaboration tools, and organization design. How teams, organizations, and networks think together—and how AI changes that dynamic.
So today, I’ll write about my journey finding and utilizing collaboration tools.
I asked Claude to unpack what it means by writing about how teams, organizations, and networks think together—and ways AI changes that dynamic.
Here’s part of what it said:
1 — Find the Nodes
Identify who participates in your collective cognition around tooling
- Internal scouts: Designate “tool champions” in each team or department — people who naturally experiment with new software and share findings
- External nodes: Tap communities like Product Hunt, Reddit (r/productivity, r/remotework), Hacker News, and niche Slack/Discord groups dedicated to tooling
- Machine nodes: Set up AI-powered monitoring agents (e.g., via Perplexity, Feedly, or custom GPT workflows) that continuously scan for new collaboration tools, reviews, and comparisons
- Vendor nodes: Include SaaS vendors themselves as nodes — their product roadmaps and release notes are intelligence sources
Thankfully, I currently find and designate nodes to and for the research and creative work I do. Champions include Alicia Lyttle, Justin Burns, and Jonathan Mast. Communities include Indie Hackers, Executive Office Hours, and Founders Forge. I license AI agents to small business owners in health/fitness/wellness industries through my company, Synergy Vitality Works, and I’ve begun creating agents for my own needs as well.
2 — Align Incentives
Give nodes a reason to surface and evaluate tools honestly
- Recognition systems: Create a lightweight internal “tool discovery” leaderboard or shoutout channel so contributors feel valued
- Clear business case framing: Ask nodes to submit tool candidates with a brief structured pitch (problem it solves, estimated time saved, cost) — this filters noise and rewards rigor
- Reduce friction: Make it effortless to nominate a tool (a simple form, a Slack command, a shared doc) so the act of contributing costs almost nothing
- Feedback loops: Show contributors what happened to their suggestions — adopted, rejected with reason, or in trial — to sustain engagement
I don’t get to engage these kinds of activities often. This alignment seems more suited to larger teams and/or companies. Might be an opportunity for me to explore in the future.
3 — Harvest the Right Information
Prevent insularity by pulling in fresh, diverse signals
- Structured external harvesting: Run periodic “tool landscape reviews” using sources outside your usual bubble — analyst reports (G2, Gartner, Capterra), academic papers on CSCW (Computer-Supported Cooperative Work), and startup funding news (tools getting Series A funding are worth watching)
- Contrast mapping: Deliberately harvest what competitors or adjacent industries use — not to copy, but to surface tools you’d never organically encounter
- Weak signal tracking: Monitor fringe communities (indie hackers, open-source contributors, research labs) where genuinely novel tools emerge before mainstream adoption
- AI-assisted synthesis: Use LLMs to periodically summarize and cluster tool nominations from your nodes, surfacing patterns and unexpected associations (e.g., “three separate people mentioned async video tools — is that a gap?”)
I intuitively engage activities like contrast mapping, weak signal tracking, and AI-assisted synthesis. Claude AI, however, seems to suggest to do so deliberately and routinely. I foresee me creating a personal, personalized dashboard soon for this purpose.
4 — Help the Supermind Collaborate on Tool Decisions
Structure the collective decision-making process itself
- Tiered evaluation framework: Give the supermind a shared rubric — e.g., score tools on integration fit, learning curve, cost, privacy, and scalability — so comparison is apples-to-apples
- Time-boxed trials with shared debrief: Run structured 2-week pilots with a defined debrief format (what worked, what didn’t, would you pay for it) fed back into the collective
- Debate scaffolding: For high-stakes tool decisions, run a formal red team / blue team exercise — one group argues for adoption, one against, with a neutral moderator synthesizing
- Decision log: Maintain a living document of tools evaluated, decisions made, and the reasoning — this becomes institutional memory that new nodes can onboard from, preventing the collective from re-litigating settled questions
- Tooling-for-tooling: Use a collaboration tool (e.g., a shared Notion/Confluence space, a structured Miro board, or a Loom async review system) specifically dedicated to the tool evaluation process itself — meta, but powerful
I responsibly, if not vulnerably, confess I am neither a detailed-oriented analyst nor project manager. I gladly delegate this kind of work to Sterling, my AI Wealth Dynamics Accumulator profile equivalent (until I can hire it’s human counterpart).
Claude AI proffers this final note for thought:
The Flywheel Effect
As your nodes get better at discovering tools, those tools in turn improve the augmented collective intelligence process itself — better async communication, smarter voting, richer knowledge bases — creating a self-reinforcing loop between the process and its output.