Probably.

That is a strange thing to say coming from someone who has spent the last several years building AI governance infrastructure, publishing research on the subject, and filing patents on the mechanisms that make it work. But intellectual honesty requires saying it anyway.

AI governance is partially solving a real problem. It is partially solving an imaginary one. And the ratio of real to imaginary shifts significantly depending on what you think AI actually is, what agents actually do, and whether the scenarios you are governing against exist in production today or only in the films you watched growing up.

Let me unpack it.

We Bolted “AI” onto Something That Wasn’t Broken

Most organizations already had governance. They had software management policies. They had data classification frameworks. They had access control structures. They had procurement processes, acceptable use policies, and human accountability chains that applied to every system in their environment.

Then AI arrived. And something strange happened. The word AI appeared on a product label, and three decades of institutional knowledge about how to govern software evaporated. The same organization that has a six-step procurement process for a new SaaS tool allows an AI system to access it through a browser tab and a personal credit card. The same compliance team that enforces email policies with genuine rigor treated an employee feeding proprietary data into a third-party language model as a fundamentally different category of problem requiring a fundamentally different category of solution.

It is not a fundamentally different category. It is software. The governance principles that apply to software apply to AI. What changed is not the governance requirement. What changed is the willingness to apply existing governance to something that feels new and different and consequential, overriding the institutional memory of how you actually run this.

AI governance is partly a gold rush solution to a problem that was already solved. The solution just needed to be applied rather than reinvented.

The Gold Rush of AI Governance

The regulatory activity is legitimate. The EU AI Act exists. NIST published its AI Risk Management Framework. Various sector-specific regulators have issued guidance. These are real requirements with real enforcement mechanisms arriving on real timelines.

What happened next is predictable. Anywhere there is a new compliance requirement, there is a market for products claiming to satisfy it. The market for AI governance products grew faster than the genuine understanding of what those products needed to do. Forty or fifty projects emerged claiming to solve runtime AI governance. Most of them are doing the same thing with different branding. A handful are doing genuinely novel work. The rest are governance masquerades that respond to a market signal rather than to a problem analysis.

The gold rush generated a lot of activity and noise. It produced fewer genuinely new solutions than the activity level implies. And it has made it significantly harder for organizations to assess what they actually need, because the vocabulary has been inflated to the point where the same word can describe architecturally incompatible approaches.

Most Agents Don’t Need AI Governance

I’ve been doing this work for a long time, and honestly, right now, what we are calling “agents” do not need AI governance. It’s more of a sign that the code was poorly written than a requirement. for a deterministic wrapper around a probabilistic system.

But I’m sure this argument will generate the most pushback. Most agents in production today do not need AI governance in the sophisticated behavioral sense that the market is selling.

I have written about this before. Most of what the industry calls agents are advanced macros. Automation scripts with an LLM somewhere in the middle. A human defined the goal, the tools, the scope, and the authorization. The agent executes. The accountability chain runs through the human who built and deployed it. The governance question is: Did this authorized human deploy this authorized system with appropriate access controls and in accordance with applicable policies?

That is software management. It is important. It is not the novel governance challenge being marketed.

The governance challenge being marketed is roughly “AI systems are making consequential decisions without human involvement, operating in emergent ways that no rule anticipates, exhibiting behavioral drift that compounds across millions of interactions.” This is more of a marketing statement than one rooted in reality. It is just not fully here yet. The systems that require the most sophisticated governance infrastructure are the systems being demonstrated in research labs and extrapolated into marketing copy. They are not, for the most part, what is running in enterprise production environments today.

I usually get an example, such as a self-driving vehicle that needs to operate in this capacity. Yet again, the simple answer is, yes, we govern those. But we never called it “AI governance,” and those regulatory systems have been in place for 10+ years now.

The mismatch between the governance being sold and the actual systems being deployed is a meaningful part of the overhype. Organizations are buying governance solutions for agents they haven’t built yet, governed against risks that don’t fully exist, driven by a belief in AI capabilities that run 7 years ahead of the technical reality.

The Autonomy Belief Fuels Everything

The belief that AI systems are autonomous is the engine under all of the governance hype. If AI agents are autonomous, the governance problem is urgent, novel, and existential. If they are not autonomous, which they are not, the governance problem is important but manageable within existing frameworks with targeted additions.

The autonomy belief did not come from technical literature. It came from a cultural narrative and a ton of marketing dollars. Decades of science fiction have built a vivid, emotionally resonant model of what AI is and what it does. WarGames gave us the AI that plays global thermonuclear war without being told to. Terminator gave us the AI that decides humanity is the problem. Wall-E gave us the AI that runs everything because humans stopped caring. These are not technical documents. They are myths. And they have done more to shape the governance conversation than any academic paper.

When someone argues for urgent, comprehensive, existential AI governance, they are often governing the AI from the films rather than the AI in their production environment. The governance is appropriate for the imagined system. It is overbuilt for the actual one.

This is not a reason to stop building governance infrastructure. The imagined system is closer to production with every passing month. But it is a reason to be precise about which problem you are solving and whether the problem is here yet or anticipated.

Bad Things Have Happened. Good Things Are Being Exaggerated.

The hype has a legitimate foundation. Bad things have happened.

AI-generated misinformation has influenced elections. Facial recognition systems have misidentified innocent people. Automated decision systems have denied loans, parole, and benefits on the basis of biased training data. Chatbots have pushed vulnerable users in dangerous directions. A man ordered 18,000 water cups at a Taco Bell drive-thru.

These are issues. They are documented. They justify governance attention.

They also justify a specific kind of governance attention. The governance that would have prevented most of these harms is not the sophisticated behavioral evaluation and runtime-interrupt authority that the AI governance market primarily sells. It is bias testing, model auditing, output review, and human oversight of high-stakes decisions, along with the kind of data governance that most organizations already know how to do but have not applied rigorously to AI systems.

The harms that have occurred are largely pre-deployment and post-deployment problems. Not runtime behavioral problems. The governance required to address them is largely pre-governance and post-governance. Not the runtime control plane that dominates the market conversation.

This is not a reason to ignore runtime governance. It is a reason to be honest about which governance layer addresses which harm, rather than treating the entire governance problem as a single undifferentiated urgency.

Everyone Has an Opinion and a Product

The volume of coverage and commentary on AI governance has created an impression of a field with robust consensus. The opposite is true. The field is highly active and has almost no consensus on fundamentals.

Ask what AI governance is. Ask what an AI agent is. Ask whether governance belongs inside the model, at the execution boundary, in the organizational policy layer, or at the societal and regulatory level. You will receive confident, incompatible answers from equally credentialed people.

The disagreement is not a sign that the field is failing. It is a sign that the field is young and that the subject is genuinely hard. But the disagreement, combined with the gold rush dynamic, produces a market where every product claims to solve a problem the market has not yet agreed to define. The press covers each announcement as though it represents a resolved question. The coverage volume creates an impression of urgency and consensus that neither fully exists.

So, Is It Overhyped?

Yes and no, which is an unsatisfying answer but an honest one.

The governance attention applied to real systems doing real things with real consequences is not overhyped. Those systems need governance. The governance infrastructure being built for them is valuable. The regulatory frameworks arriving to mandate it are appropriate.

The governance attention applied to imagined systems with capabilities that do not yet exist in production, driven by autonomy beliefs that are not technically grounded, responding to film plots rather than incident reports, producing forty-plus variants of the same architecture under forty different product names, that is overhyped.

The problem is that both things are happening simultaneously, and the conversation rarely distinguishes between them. The result is a market where genuine governance needs and manufactured governance urgency are packaged together and sold as one thing, at a price point that reflects the imagined threat rather than the actual one.

The actual threat warrants serious attention. It is earning attention now so that the infrastructure is in place when the imagined threat becomes real.

The manufactured urgency is worth examining. Not to dismiss the field, but to build the right things for the right reasons, rather than building governance theater for a show that hasn’t opened yet.

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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, will be published in 2026.