Raising AI: Why It’s More Like Having a Baby Than Building a House

Why AI Is More Like Having a Baby Than Building a House

Missteps and hallucinations are natural growing pains, not a verdict on the technology.

Most of the public conversation about AI still sounds like a construction meeting.

We talk about “building a model,” “deploying a system,” “rolling out a solution.” We expect that, after some testing and debugging, the system will do what it’s supposed to do, just like a new building is expected to stand straight, keep the rain out, and pass inspection. If there are flaws, we assume something went wrong in the build: bad specs, sloppy work, poor oversight.

So when people see AI systems hallucinate, make biased decisions, or deliver wrong answers, the reflex is to treat this as proof that “AI doesn’t work” or “AI is broken.”

But that reaction reveals the wrong mental model.

Serious AI—especially agentic AI that acts, not just chats—is not like constructing a house. It is much closer to bringing a child into the world.

Not in the sentimental sense, but in a very practical one: you are not done when it appears; you are just getting started. The missteps and misbehaviors we see early on are not evidence that the whole idea is doomed; they are the exact signals that tell us where governance, guidance, and “parenting” must go to work.

It’s time we retire the construction metaphor and adopt a parenting one.

Houses follow blueprints. Children don’t.

When you build a house, the process is beautifully linear:

• You start with a complete blueprint.

• Materials and trades are well known.

• Quality is defined in advance: straight walls, working plumbing, no leaks.

If the contractor follows the plan and passes inspection, you get what you ordered. If the roof leaks, something failed—either the design, the execution, or the materials. The answer is to fix it and move on.

That’s how many people unconsciously think about AI:

• Design the model,

• Train it,

• Test it,

• Deploy it,

• Expect compliance.

But AI systems, especially those built on large language models, are inherently different:

• They operate in open-ended, messy environments.

• They produce probabilistic answers, not exact outputs.

• They face questions and situations no one anticipated when they were first designed.

So what do we see at first? Errors. Awkward phrasing. Hallucinations. Biases. Overconfidence.

From the “build a house” mindset, this looks like failure. From the “raise a child” mindset, it looks like the first week of kindergarten: this is learning in progress.

You don’t abandon a child because they repeat something foolish they heard, or because they don’t yet understand the rules. You notice, you intervene, you explain, you correct. That’s not a sign that “children don’t work”—it’s a sign that parenting is required.

Early AI misbehavior is exactly that sort of signal: not a verdict, a call to govern.

Traditional systems don’t learn. AI does—and that’s the point.

Classic software is deterministic. You give it inputs; it applies rules. If it’s wrong, the rules are wrong, and you change the code. Once fixed, it stops making that specific mistake.

AI isn’t rule-based in the same way. It:

• Generalizes from examples.

• Makes best guesses in uncertain situations.

• Mirrors patterns—good and bad—in its training data.

So when AI produces biased outputs or nonsense answers, it’s actually telling us:

“This is what I’ve inferred from what you gave me and how you framed my job.If you don’t like this, teach me differently.”

That is not evidence that AI “can’t be trusted.” It is evidence that AI doesn’t come pre-aligned with your values or your organization’s reality. Just like a child, it arrives with raw capability and rough edges. The hard work is in the shaping.

Real governance is not a one-time inspection; it’s ongoing parenting:

• Reviewing logs and transcripts, not occasionally but routinely.

• Identifying patterns of harmful or low-quality behavior.

• Updating prompts, guardrails, and tools.

• Adding better examples and explicit “we do / we do not” instructions.

• Introducing tests and red-team scenarios to catch failure modes early.

Complaining that AI makes mistakes without investing in this is like complaining that your five-year-old doesn’t act like a seasoned professional. Of course it doesn’t. That’s why there are adults in the room.

Babies live in real families. AI agents live in described environments.

There’s another crucial difference that makes governance non-optional.

Children grow up in the real environment of a family and society. They absorb new rules and norms naturally:

• They hear about changed expectations at the dinner table.

• They see how adults respond to new laws, events, or crises.

• They adapt—sometimes clumsily, but constantly—to a moving world.

AI agents do not directly experience your evolving organization. They live inside a described environment:

• Policy documents

• Procedures and playbooks

• Knowledge bases

• Tool and data access rules

• Prompts and role descriptions

If your actual organization changes—and it always does—but your described environment doesn’t, the agent will cheerfully continue to act as if the old world were still in place.

When you see an AI agent make decisions that don’t match current policy, that’s not just “AI being dumb.” It’s a precise warning:

“The world you told me I live in no longer matches the world I’m actually deployed into.”

Again, that’s not failure; that’s a governance alarm. It’s pointing at stale documentation, misaligned policies, or missing updates. The right response is to update the described environment, not to throw out the agent.

Building a house is a project. Raising a child is a commitment.

In traditional IT, the cost curve is front-loaded:

• 90–95% of the effort is design, build, and go-live.

• 5–10% per year goes into maintenance and enhancements.

That model only works when behavior is fully specified up front and doesn’t need constant adaptation.

Raising a child is the opposite:

• Birth is just the starting line.

• The real investment is years of attention, teaching, boundaries, and support.

  
  

Serious AI is on the second curve.

Initial work—model selection, integrations, first prompts, basic tests—is like pregnancy and delivery. The real responsibility begins at deployment:

• Continuous monitoring

• Ongoing tuning and retraining

• Updating its referential sources as the organization changes

• Adjusting its role as you learn where it adds value and where it must defer

Early errors, hallucinations, and biases are not reasons to declare defeat; they are exactly the data you need to shape the next version of the “child” and the rules of the household.

If you budget AI as if it were a house—big upfront costs, trivial ongoing stewardship—you will either be disappointed or blindsided. If you budget and staff it more like a child—ongoing care, education, and guardrails—you can catch those “natural” early mistakes before they scale into real harm.

You don’t “accept delivery” of a child

At the end of a construction project, you do a final walk-through, compile a punch list, sign off, and accept the keys. The builder leaves. The house is yours. You expect it not to surprise you.

You don’t “accept delivery” of a child.

You accept responsibility.

You recognize that this new being will:

• Make mistakes

• Misread situations

• Echo the worst parts of what it absorbs unless you intervene

• Require more attention at precisely the moments you feel most tempted to give up

AI is no different at a governance level. When you deploy an agent to talk to customers, approve transactions, or guide employees, you are not just installing software. You are introducing a new actor into your social and operational system.

So instead of asking, “Why did this AI get it wrong?” we should first ask:

• “What did its behavior reveal about the data, rules, and examples we gave it?”

• “What does this misstep tell us about gaps in our governance?”

• “Who is responsible for teaching and correcting this agent over time?”

Early complaints about AI’s mistakes are not an indictment of AI’s potential. They are a mirror held up to our lack of parenting.

The mindset shift we need

If we keep treating AI like a house that should match the blueprint and mostly stay out of our way, we’ll see every error as proof of failure.

If we start treating AI more like a child—powerful, unfinished, and shaped by what we feed and tolerate—then errors, hallucinations, and biases become what they actually are: natural signals that demand attention, correction, and better governance.

The question is not, “Can AI be perfect?” No technology is.

The question is, “Are we willing to parent what we create?”