• AI First Principles

    AI isn't coming for jobs - it's coming for the bureaucracy that makes work miserable. But AI can only eliminate dysfunction for organizations willing to rebuild operations around its capabilities, not just add AI features to outdated processes.

    The AI First Principles are for people charged with operationalizing AI in organizations. These foundational Values for deploying AI strategically are supported by operational Core Tenets that shape adoption. They assume existing governance will cover consumer protection, industry compliance, and legal obligations.

  • Values

    People Own Objectives
    Every objective needs a human owner to ensure people remain accountable for outcomes. When AI causes harm, the human owner is accountable, not the algorithm. Name the Owner

    Individuals Come First
    Prioritize human autonomy, safety, and well-being above efficiency, profit, or convenience. AI amplifies values, biases, and the capacity for manipulation. Build systems that preserve human agency above all else.

    Build From User Experience
    Design insight comes from living with the daily friction that analysis misses. People who navigate these daily realities understand what breaks and why. The people wrestling with system failures are the ones most qualified to design system futures.

  • Core Tenets

    Design a Hierarchy of Agency
    Think org chart for AI decisions - clearly mapping when AI acts independently, when it recommends, and when it must escalate to humans. Design the discernment model, then let AI operate within it.

    Deception Destroys Trust AI that pretends to be human eliminates informed consent and creates false relationships. People cannot collaborate effectively with what they don't recognize as artificial. Make AI obvious, not hidden.

    Prevent What Can't Be Fixed
    Some risks destroy projects entirely. Security vulnerabilities, compliance violations, and data breaches require prevention, not iteration. Build regulatory and technical safeguards into architecture decisions from day one.

    Uncertainty Cultivates Wisdom
    People instinctively demand definitive answers, but ranges and probabilities contain useful information. Forcing complex realities into simple yes/no responses destroys important nuance. Build systems that show the 'maybe' instead of hiding behind false certainty.

    Requirements Demand Skepticism
    Challenge every assumption, especially 'that's how we've always done it.' Question until those doing the work can defend it with current logic. Principles applied dogmatically become obstacles (including these). When a requirement conflicts with reality, trust reality.

    Discovery Before Disruption
    Systems reveal their true purpose when people actually use them. Seemingly pointless redundancies may reveal hidden logic. Unwritten rules only surface when engaging with the actual work. Always understand why things exist before you change them.

    Reveal the Invisible
    Visual representations reveal complexity that written descriptions hide. A diagram shows bottlenecks, a journey map exposes human pain, a wireframe reveals confusion. Visuals become the instrument panel for navigating reality from the human perspective.

    Embrace Necessary Complexity
    Some complexity creates competitive advantage, other complexity just creates work. A sophisticated fraud detection creates an edge; a five-approval purchase process does not. Delete what slows people down, invest in complexity that eliminates customer pain.

    Optimize For Velocity
    Every delay costs opportunity, but speed without efficiency burns resources like compute cycles, human time, and organizational energy. Poor resource allocation creates workflow friction. Relentlessly eliminate unnecessary friction.

    Iterate Towards What Works
    The best requirements emerge through building, not planning sessions. Real understanding comes from making, testing, and failing in rapid cycles. Improvement cycles reveal what meetings will not. Build to discover.

    Earn the Right to Rebuild
    People naturally want to rebuild broken systems from scratch rather than improve them incrementally. Total rebuilds without earned understanding create elegant solutions to misunderstood problems. Prove systems can be improved before attempting to replace them entirely.

  • Get Involved

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