What we believe about AI.
AI First Principles names the governance and ownership decisions that separate running AI from pilot theater. We are a founding contributor. Twelve principles for teams charged with operationalizing AI inside a real business.
The principles.
AI isn't coming for jobs, it's coming for the bureaucracy that makes work miserable. But bolting AI onto broken processes just scales the mess faster. Eliminating organizational dysfunction demands rethinking both how you design technology and how you rebuild the operations around it.
The twelve AI First Principles guide teams charged with operationalizing AI. They emerged from decades of building for users, understanding what breaks and why, and codifying what holds under pressure.
The twelve principles below are the canonical statement, presented as they appear on aifirstprinciples.org. Lightly reformatted for this page.
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01
AI Inherits Messiness.Define what's prohibited over what's required.
AI learns from people. Therefore AI systems, like people, are inconsistent and operate more effectively with structure. Trying to engineer them to operate like deterministic code will result in system failures. Variation is inevitable, not accidental.
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02
AI Fails Silently.Build feedback loops over post-mortems.
AI accumulates errors across thousands of interactions before patterns become visible. Traditional systems failed loudly with clear signals; AI fails quietly on repeat.
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03
People Own Objectives.Name the owner.
AI shouldn't be used in place of human discernment, judgment, or taste. When AI makes mistakes or causes harm, a person should be held accountable, not the algorithm.
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04
Deception Destroys Trust.Make AI obvious, not hidden.
When AI pretends to be human, people cannot calibrate their expectations, recognize its limitations, and protect themselves from its failures.
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05
Individuals First.Prioritize individual agency above efficiency, profit, or convenience.
AI industrializes manipulation by personalizing it at scale. Mass persuasion becomes individual manipulation. Build tools that people control, not tools that control people.
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06
Build from User Experience.Design systems from lived experience, not distant observation.
Without input from end users, AI solutions won't solve real problems. People wrestling with system failures are the ones qualified to design system futures.
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07
Discovery Before Disruption.Identify purpose before simplifying.
Changing systems that aren't understood creates unpredictable failures. Redundancies prevent edge cases and manual steps catch exceptions. Existing inefficiencies are containers of knowledge.
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08
Ambiguity Is Wisdom.Surface the probabilities.
Concealing ambiguity removes opportunities for critical judgment. Not all decisions are binary yes/no. Wisdom lives in gray areas. AI produces probabilities that demand judgment, not facts that replace it.
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09
Reveal the Invisible.Pursue what is hard to explain.
There's a wealth of ignorance hiding in document theater. Expose what you don't yet understand by learning how to articulate it.
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10
Iterate Towards What Works.Learn by doing, not planning.
Grand plans commit to solutions without validating problems. Iteration tests assumptions and measures impact, revealing what works gradually over time. Inherited practices carry outdated logic that meetings can't expose.
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11
Decompose Incrementally.Dismantle legacy complexity piece-by-piece.
Legacy systems carry too much technical debt to replace and are too brittle to automate. AI systems should allow isolated components to decompose naturally.
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12
Justify Resource Consumption.Optimize the ratio of value per resource spent.
AI makes it trivially easy to waste resources. What costs pennies to create can cost millions to run. The friction that once prompted resource consideration has vanished. The resources remain real: energy, water, compute, time.
In practice.
Every engagement runs against these. We do not start an AI project that violates a principle, and we do not stop refining one until the principle holds in production.
WISER is how we put them on the floor. AI First Principles is the belief system; WISER is the applied method that operationalizes them inside a real business.
The source.
The full property lives on aifirstprinciples.org. The principles above are the canonical statement; the treatise behind them is where the research, the rationale, and the community of contributors live. The principles are open source under CC BY 4.0.