There is a quiet truth we hesitate to admit about artificial intelligence:
We are trying to regulate something that doesn’t stand still long enough to be measured.
AI is no longer a traditional technology that evolves in predictable cycles; it is a moving landscape, a living system of breakthroughs, feedback loops, and accelerating capabilities: every six months, the terrain shifts. Every year, new “frontier” models redraw the boundaries of possibility.
Someone recently asked me a question that gets to the heart of the dilemma:
“How do we technically define and measure systemic risk when the goalposts are always moving?”
This question captures the Achilles’ heel of traditional governance.
Regulation assumes stability.
AI offers the opposite.
But here is the deeper point: the problem is not that the world is changing too fast; the problem is that our frameworks are still built for a world that moves slowly.
If we want to navigate what I call The Cognitive Revolution successfully, we need a different approach, one that shifts us away from trying to freeze AI in time and toward building systems capable of adapting to whatever comes next.
This is the essence of anticipatory governance, a discipline that blends systems thinking, strategic foresight, emotional intelligence, and agile regulatory design.
This essay explores what that looks like in practice.
1. Why Static Safety Does Not Work Anymore
Most regulatory systems are built around a simple idea:
Define the harm → quantify the risk → write the rule.
But AI breaks this model, because:
The harms evolve as models evolve
The risks multiply through interdependencies
The rules become obsolete before they are even implemented
Trying to regulate AI with static rules is like mapping a coastline during a rising tide.
The photograph is outdated the moment it is taken.
In an era where the terrain is constantly shifting, we need a new goal:
Stop measuring static safety. Start measuring systemic resilience.
Resilience is not the absence of disruption.
Resilience is the capacity to absorb disruption, adapt, and continue moving forward without collapsing the system we depend on.
Static safety asks: ‘Will this specific chatbot lie?’ Systemic resilience asks: ‘If millions of chatbots lie, does our information ecosystem have the immune system to debunk them quickly?
And resilience can be measured if we look in the right places.
2. Mapping the Terrain: When You Cannot Predict the Wave, Strengthen the Shoreline
Systems thinking helps us shift from measuring individual risks to measuring interconnectivity, the primary source of systemic vulnerability.
We stop asking:
“What is the probability that this harm occurs?”
And start asking:
“What happens if this system fails?”
“Where do the shockwaves travel?”
“Which nodes are fragile? Which are resilient?”
“What cascades into critical infrastructure? Into public trust?”
Instead of assigning numerical values to hypothetical scenarios, we map the dependencies and failure propagation pathways.
This tells us where to reinforce, even if we don’t know the precise threat ahead.
This is how we deal with uncertainty:
We cannot predict the wave, but we can strengthen the shoreline.
3. Scanning the Horizon: Governance That Plans for Many Futures, Not Just One
One reason governance fails is that policymakers feel pressure to predict the future, but predicting the future is useless in a domain defined by exponential change.
The solution is not better prediction; it is better preparation.
Strategic foresight gives us the tools:
• Horizon Scanning
Scan widely for “weak signals” and early indicators of emerging trends. Most technologies that “suddenly disrupt the world” were visible years earlier; we were not just paying attention.
• Scenario Stress-Testing
Instead of planning for one future, we prepare for multiple plausible futures.
For example:
What if compute becomes extremely cheap?
What if compute becomes a restricted resource?
What if AI becomes regulated like pharmaceuticals?
What if AI agents act autonomously?
The goal is not to be right.
The goal is not to be wrong across all plausible futures.
We measure a policy not by how perfect it is today, but by how robust it remains across a shifting horizon.
This is what adaptive governance looks like:
You do not aim for the ball; you cover the zone.
4. The Human Circuit Breaker: Why Emotional Intelligence Is Now a Governance Capability
One of the most dangerous assumptions in AI discourse is that algorithms will eventually handle edge cases better than humans.
This is partially true for tasks defined by logic. But AI breaks down in the face of novelty, ambiguity, and emotionally charged decisions.
Machines react; humans reflect.
This is why humans must remain in the loop as the moral and emotional backstop.
“Human in the loop” does not mean a human rubber-stamping outputs. It means:
Humans are close enough to intervene
With authority to override
Using values, not just data
The human circuit breaker is not for routine efficiency; it is for moral ambiguity. When the data says ‘maximize profit’ but the context says ‘this harms the vulnerable,’ only a human can feel the friction between those two commands.
We can measure this through Cognitive Distance, the distance between human judgment and algorithmic action.
If the system moves faster than human empathy can intervene, the system is unsafe, even if it is “statistically reliable.”
The point of AI governance is not to mimic machine efficiency; it is to preserve human conscience in the loop.
Because empathy is not a nice-to-have, it is safety.
5. Measuring Adaptation Velocity: The Most Important Metric of the Cognitive Age
In a world of exponential technology, the most dangerous risk is not the AI itself; it is the gap between the speed of change and the speed of institutional adaptation.
This is the core insight of anticipatory governance.
We measure adaptation velocity:
Time from weak signal → to informed recognition → to policy response.
If it takes governments 10 years to regulate something that evolves every 6 months, even the smartest regulations are doomed.
The challenge is not just building good rules. It is building institutions capable of keeping pace.
Risk emerges in the delta between threat velocity and governance velocity:
Risk = Threat Velocity - Governance Velocity.
If governance cannot close the gap, the system becomes fragile, no matter how well-intentioned the policy is.
We see this pacing paradox vividly in the European Union’s recent reforms to its AI Act. The EU began with a rigid, rules-based framework meant to lock risks in place. But as frontier models accelerated, legislators found themselves in an impossible bind: the law was solidifying even as the ground shifted beneath it. The result was the “Digital Simplification Package,” a pivot toward more agile, anticipatory oversight—delaying certain high-risk obligations, empowering the AI Office to update thresholds dynamically, and relying more heavily on soft-law codes of practice. In other words, the EU discovered in real time that when technology moves along exponential curves, only governance that does not lag and can keep pace can remain credible.
6. The New Mandate: Governing Through Motion, Not Against It
Traditional governance tries to control the system. Modern governance must move with the system.
This requires a shift:
From rigidity → to adaptability
From certainty → to scenario thinking
From prediction → to preparedness
From rule-based control → to values-based navigation
And most importantly:
From technical intelligence → to emotional and ethical intelligence
This is not the governance of machines. This is the governance of humanity in motion.
7. The Deeper Truth: The Real Fragility Is Not in the Code
The more I study AI, the more convinced I become of something simple:
The real fragility is not in the technology.
The real fragility is in us.
Our institutions are slow.
Our attention is fragmented.
Our public trust is brittle.
Our pace of moral deliberation is incompatible with the velocity of change.
Anticipatory governance is not just a regulatory framework.
It is a way to strengthen the human system, our capacity to learn, adapt, and remain anchored in our values as the world accelerates.
8. What This Means for the Cognitive Age
In my book, The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence, I argue that humanity is not at war with AI; we are in negotiation with it.
It is not about what the technology becomes, but about what we become.
Governance is not simply a legal issue; it is a cultural, emotional, and philosophical one:
How fast should we allow innovation to move?
How much uncertainty can society absorb?
How do we preserve dignity, agency, and trust?
How do we protect the vulnerable while empowering the capable?
These are not technical questions. These are human questions.
Which is why the heart of anticipatory governance is simple:
We do not regulate AI. We regulate the space where AI meets society.
And that space is human.
Final Reflection
We cannot predict every harm that may come from AI.
We cannot measure every risk.
We cannot freeze the future into a set of fixed rules.
But we can build systems and a society that is resilient enough to navigate what comes next.
And perhaps most importantly:
The pace of our technology does not define the future, but the pace of our wisdom does.
That is the essence of anticipatory governance.
And that is the essence of the Cognitive Revolution.
Dive deeper into this transformative landscape in “The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence“. Now Available on Amazon.
