LAST UPDATED: NOVEMBER 28, 2025
This manifesto is part of the stack and interprets our vision, mission, values, and collaborative work principles for the age of AI.
It is a living companion to our overarching policy and ways-of-working, not a separate value system.
OUR AI PRINCIPLES AS AN ECOSYSTEM
Every organization will soon run on AI leverage, but the real differentiator will be how many different forms of intelligence it can bring into conversation: technical, sociological, cultural, and embodied.
An ecosystem organization treats AI as a set of instruments in a larger ensemble, helping people think with more disciplines, across more perspectives, in service of a thicker sense of value than efficiency alone.
Our mission, in the age of AI, is to help people and organizations tend living pathways of knowledge—multidisciplinary trails where ideas from many disciplines cross-pollinate—so that value ripens slowly and durably for teams, communities, and the traditions of innovation we inherit and renew.
Scaling with AI, for us, means scaling discernment, dialogue, and care alongside speed.
01.
Practice many ways of knowing
Use AI as a collaborator among others: conversation with colleagues, attention to customers, reading, field observation, and reflection.
Pair large language models with curated knowledge graphs, so decisions grow from a diverse ecology of select inputs rather than a single oracle.
02.
Let machines work, let humans play
Regularly surface tasks that are repetitive, low‑leverage, or depleting, and invite AI to absorb or streamline them.
Reinvest the freed time into playful experiences: relationship-building, creative synthesis, learning across domains, mentoring, and long-horizon strategy that machines cannot own.
03.
Create imaginatively, not just efficiently
Treat AI as a speculative partner: use it to imagine new products, services, and experiences that emerge at the intersection of disciplines—education, art, civic life, science, spirituality.
Ask regularly, “What could exist here that does not exist yet because we have never combined these forms of knowledge in this way?”
04.
Experiment responsibly in living systems
Prototype AI workflows boldly, but remember that every workflow touches real people, cultures, and ecosystems.
Before deployment, explore edge cases, unintended incentives, and long‑term second‑order effects, and build in red‑team reviews that include non‑technical voices.
05.
Share patterns, stories, and failures
When you discover a useful AI pattern, share not only the prompt or playbook but the story: what you were trying to do, what mattered, what went wrong, and how you adjusted.
Treat failures as communal learning artifacts and invite cross‑functional retrospectives so that designers, engineers, operators, and facilitators can read them from different angles.
06.
Ask AI before adding, ask humans before automating
Before adding new headcount, software, or vendors, explore how AI might extend existing people and systems—but do so in conversation with the people whose work will change.
When considering automation that displaces tasks, co‑design transitions, training, or role evolutions with those affected, so AI becomes a tool for shared agency, not unilateral replacement.
07.
Build an AI-plural team
Hire and develop people who are not only AI‑proficient but also philosophically curious, socially attuned, and capable of learning across fields.
Seek polymaths and bridge‑builders: people who can hold tension between metrics and meaning, code and culture, speed and reflection.
08.
Measure multidimensional impact
In weekly and quarterly updates, track AI’s impact on more than output: learning, well‑being, customer trust, inclusion, and environmental footprint.
Let these multidimensional metrics influence strategy, so that “doing well” is always evaluated alongside “doing right” and “doing deep work.”
09.
Evolve platforms towards commons
Treat internal AI platforms and agent ecosystems as living commons that everyone can improve through use, critique, and contribution.
Design interfaces and knowledge bases so that diverse teams—including non‑technical roles—can encode their expertise, stories, and judgment into the system.
10.
Keep human judgment at the center
Use AI to surface options, simulate scenarios, and challenge your assumptions, but make decisions through accountable human judgment informed by dialogue.
Document why decisions are made, whose values are being served, and what tradeoffs are being accepted, so responsibility never disappears behind a model’s output.
11.
Stay compassionate, stay connected
As teams grow, shift, or shrink with AI, attend to transitions with care: reskilling, time for integration, honest communication, and when needed, generous exits.
Celebrate people who expand their contributions through AI and through service to others—teaching, mentoring, community-building—not only those who maximize individual output.