Happy Mother’s Day.

I have been thinking about what the people who raised us actually did, stripped of sentiment and greeting-card language. At its core, parenting is a governance problem. One of the oldest and hardest governance problems. And the more I think about how good parents govern, the more I think the AI governance industry has been looking in the wrong place for its models.

We have been looking at regulatory frameworks, legal precedent, security architectures, and compliance programs. We should have been paying more attention to mothers.

The Original Behavioral Contract

A parent raising a child does not hand them a policy document. They do not issue a terms-of-service agreement or require a signature before permitting action. What they do is establish, over time, a behavioral contract that is understood, enforced, and renegotiated as the child demonstrates the capacity for more responsibility. (Yes, I recognize all parenting is different, and some have tighter or looser restrictions.)

In general, the contract starts narrowly. Stay where I can see you. Do not touch the stove. Hold my hand in the parking lot. The scope is tight because the trust has not yet been earned. The child is new. The behavioral baseline has not been established. The accountability chain runs entirely through the parent because the child has no independent judgment on which the parent can rely.

Sound familiar?

As the child demonstrates consistent behavior, the scope of the behavior expands. You can play in the backyard alone. You can walk to school. You can stay home while I run an errand. Each expansion is earned, not granted by calendar. The trust calibration is continuous and asymmetric. Trust builds slowly, through consistent behavior over time. It collapses faster when that behavior is violated. One serious breach costs far more than one good day restored.

This is exactly the trust calibration model that AI governance requires and that almost no governance framework implements. Most governance systems assign a trust level at deployment and leave it static. Good mothers recalibrate constantly, based on what they observe, with a long memory for what went wrong and a genuine willingness to expand scope when the evidence supports it.

Heteronomy with Love

The governance thresholds I have written about, from heteronomy to simonomy and eventually, one day, maybe autonomy, are a lived experience for every parent.

A newborn is ungoverned in the sense that the infrastructure for self-governance does not yet exist. The parent provides all of it. As the child develops, the governance relationship shifts. The child internalizes rules. Develops judgment. Begins to apply values that were externally set to situations the parent never anticipated. This is simonomy, governance by internalized pattern simulation, and it is one of the most sophisticated things human development produces. It’s how most humans learn to mimic speech.

The goal of good parenting is not to maintain control. It is to produce a human who can eventually govern themselves. The governance structures that parents establish are designed to be temporary, to transfer authority as it is earned, to produce autonomy rather than suppress it.

The goal of AI governance is structurally identical. The governance infrastructure exists to ensure accountability, safety, and appropriate behavior while the systems being governed develop the behavioral track record that warrants expanded trust. The endpoint for sufficiently capable systems is the same: autonomy earned rather than assumed. You do not simply program in autonomy at the start.

The difference is that good mothers understand this intuitively. The AI governance industry is still arguing about whether the goal is control or capability.

The Human in the Loop Who Actually Mattered

There is a human in the loop in every childhood. Not ceremonially present. Actually present. Watching. Calibrating. Intervening when intervention is warranted and stepping back when it is not.

The mother (or parent) who hovers over every decision produces a child who cannot make decisions independently. A mother’s absence leaves a child to make decisions without the benefit of accumulated and shared parental wisdom. The quality of the human-in-the-loop matters enormously. And the quality degrades predictably when the human is overwhelmed, distracted, or operating on outdated priors about what the child needs.

This is bidirectional drift, and mothers manage it without calling it that. The parent who still treats a twenty-year-old like a ten-year-old has drifted. The parent who has continuously updated their model, who sees the person their child has become rather than the child they remember, is doing governance well.

The AI governance frameworks that require human review without designing for the quality of that review, that place a human in a loop without asking whether the human is actually present and informed, are missing what every good mother understood: the oversight function is only as good as the person performing it, and that person needs to be genuinely engaged, adequately informed, and calibrated to the current state of what they are overseeing.

Identity Before Authority

Before a parent grants a child access to anything, they establish identity. This child is mine. I am responsible for them. Their actions reflect on me, and I am accountable for the conditions that produced those actions.

This is the birth certificate in practice. A human owner. A defined accountability chain. A named person who answers for the behavior of the governed actor. The identity is established before the authority is granted. Accountability is binding from the beginning, before the child has done anything.

Every agent should have this. A named owner. A verifiable identity. An accountability chain that traces to a human before the agent takes its first action. The birth certificate is not a bureaucratic formality. It is the record of the moment someone said, “This is mine, and I am responsible for what it does.”

Mothers understand this without thinking about it. It is the governance infrastructure that most AI deployments skip entirely.

What Good Governance Looks Like from the Inside

The children of genuinely good parents usually do not experience their upbringing as governance. They experience it as being known. Being seen. Being given responsibility commensurate with demonstrated capability. Being protected when protection was warranted and being trusted when trust had been earned.

The governance was real. The behavioral contracts were enforced. The accountability chain was clear. But the experience of being governed well is the experience of being valued and developing toward something.

That is the standard AI governance should be measured against. Not compliance boxes checked. No audit trails produced. Whether the systems being governed are operating in a way that reflects a genuine understanding of what they are, what they are capable of, and what accountability structure they require at this stage of their development.

Mothers have been building that kind of governance infrastructure for as long as humans have existed. The AI governance industry has been developing its own version for about a year.

There is a lot still to learn.

Happy Mother’s Day to every mother, and to everyone who has done the work of genuinely governing something they care about.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 30 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, is available now!