I recently searched “AI governance” on Substack.

What came back wasn’t a coherent field so much as a crowded bazaar of meanings: essays, threads, frameworks, manifestos, warnings, and product pitches, all using the same phrase to point at very different things.

That alone is worth noting. Before we argue about how to govern AI, it’s clear we don’t agree on what we mean when we say the words. This post is not a theory of AI governance. It’s a snapshot of how the term is being used—on the surface—right now.

What’s Being Pointed To (at a Glance)

Across posts and snippets, “AI governance” is doing several jobs at once:

  • A way to talk about regulation finally arriving (EU AI Act, U.S. state laws, “2026 as an enforcement year”)
  • A way to talk about corporate risk and valuation protection
  • A way to talk about ethics, responsibility, and trust
  • A way to talk about enterprise controls, dashboards, and “rogue agents”
  • A way to talk about democracy, legitimacy, and public oversight
  • And sometimes, a way to signal seriousness without saying much at all

Most pieces gesture toward urgency. Many insist governance is no longer optional. Quite a few complain—explicitly or implicitly—that we’ve been talking about this for years without agreeing on what actually counts.

What’s Worth Reading Further (Based on Titles and Snippets Alone)

Even at the surface level, a few strands stand out as more than rhetorical noise:

  • Analyses of the EU AI Act that frame it as a shift from voluntary “trustworthy AI” language to binding, risk-based regulation
  • Essays arguing that governance is being miscast as behavior or ethics rather than systems, controls, and architecture
  • Pieces framing governance as infrastructure, not policy paperwork
  • Political critiques pushing back on the idea that AI governance can be “apolitical” or purely technocratic
  • Enterprise-focused writing on AI agents, internal controls, and why governance tooling is becoming a product feature

These don’t all agree—but they’re at least arguing about structure rather than vibes.

Persistent Misperceptions That Keep Showing Up

A few confusions repeat across posts, regardless of author or ideology:

  1. Governance = ethics statements
    Many discussions still treat codes of conduct, principles, or “responsible AI” pledges as governance. Even critics often frame failure as moral weakness rather than structural absence.
  2. Governance = compliance checklists
    Others reduce governance to documentation, audits, or certifications—something you prepare for regulators rather than something that shapes real behavior.
  3. Governance is boring until disaster
    There’s a recurring trope that governance only matters after something goes wrong, which quietly implies it’s a reactive add-on rather than a continuous constraint.
  4. Governance can avoid politics
    Some writing still suggests that if we just let experts handle AI governance, we can bypass messy public disagreement—an assumption others explicitly challenge.

Themes, Rejections, and Likely Failure Modes (Collapsed)

When you put the surface discourse together, a pattern emerges.

What many writers seem to want:

  • Clear rules
  • Fewer bad outcomes
  • Less reputational risk
  • Some sense that “someone is in charge”

What gets rejected (often implicitly):

  • Governance as mere bureaucracy
  • Governance as empty virtue signaling
  • Governance as endless debate with no enforcement

What is likely to fail, based on how it’s framed:

  • Governance programs built mostly on policies, committees, or training
  • “Governance dashboards” that visualize risk without real authority behind them
  • Frameworks that promise safety but don’t say who can stop what, when, or how
  • Public-interest visions of AI governance that never resolve who decides tradeoffs or bears responsibility

There’s a quiet acknowledgment running through much of the writing: once enforcement begins, a lot of existing “governance” will turn out not to govern much at all.

Where This Leaves the Conversation

At the surface level, AI governance discourse feels like a field transitioning from aspiration to pressure.

The language is shifting—from trust, ethics, and principles toward enforcement, controls, and consequences—but the structures often lag behind the rhetoric.

Whether this leads to better outcomes or just more elaborate theater remains unresolved.

But one thing is already clear from the surface alone:
when people say “AI governance,” they are rarely talking about the same thing—and that confusion is now becoming a liability.