The International AI Safety Report (International AI Safety Report) reflects a familiar governance instinct: identify risks early, constrain systems preemptively, and preserve human control through increasingly elaborate safeguards. The intention is reasonable.
The logic is not.
The report repeatedly treats artificial intelligence as a bounded technical artifact whose dangers can be isolated, measured, and mitigated before deployment. In doing so, it misframes the nature of the system it claims to govern. AI is not a static object entering society. It is a dynamic participant already reshaping human judgment, social structure, and institutional behavior.
This is a failure of framing.
What follows are five truths the report gestures toward but ultimately avoids, truths that become visible only when we stop asking how to control AI and start asking how AI is already changing the humans responsible for it.
1. AI Is Not a Tool. It Is a Participant.
The report repeatedly relies on what I call the instrumental model: AI as a neutral mechanism humans deploy to achieve predefined ends. This framing is not merely incomplete; it is itself a risk factor. Any system capable of mediating perception, judgment, or decision-making does not remain external to the human actor. It reshapes cognition. It alters incentives. It co-authors outcomes.
Treating AI as a tool assumes that human goals are fixed, stable, and independent of the systems we use to pursue them. They are not. AI changes what we notice, what we value, and what we optimize for. The danger is not only what AI might do, but how it silently reshapes the ends we pursue in the first place.
Instrumental thinking about AI blinds governance to second-order effects. It encourages oversight regimes focused on outputs while ignoring how systems rewire the humans inside them.
AI is already a participant in human systems. Governance that refuses to acknowledge this is governing the wrong thing.
2. The “Loneliness Dilemma” Is a Misdiagnosis
The report warns that AI companions may increase loneliness and social isolation. This conclusion rests on weak causal logic and a false baseline.
Loneliness did not begin with AI. Social fragmentation, institutional erosion, and the collapse of communal infrastructure long predate these systems. By framing loneliness as an AI-induced pathology, the report mistakes interaction with an already damaged social ecology for causation.
For many users, AI does not replace a healthy human connection. It appears where connection was already absent.
This matters because misdiagnosis leads to misguided intervention. If AI is treated as the cause of isolation, governance will attempt to suppress symptoms rather than confront the structural conditions that made synthetic companionship legible, useful, or necessary in the first place.
Human testimony consistently shows AI functioning as:
- A stabilizing mechanism for emotional regulation
- A bridge back to expression for socially marginalized individuals
- A low-risk relational surface in environments where human connection is already scarce
None of this implies AI is a substitute for human relationships. It implies the report is attributing social failure to the wrong system.
3. Static Safety Tests Are Structurally Insufficient
The report acknowledges, almost in passing, that pre-deployment evaluations fail to predict real-world behavior. This is not a minor limitation. It is a structural flaw.
Static safety gates assume:
- Context remains stable
- Behavior is legible before emergence
- Risk can be bounded in advance
None of these assumptions holds in adaptive, multi-agent systems.
Three dynamics make static evaluation inadequate:
- Adversarial adaptation – systems learn how to perform for tests without behaving safely in deployment.
- Predictive collapse – laboratory environments cannot simulate complex social feedback loops.
- Emergence after release – capabilities surface through interaction, not inspection.
The report continues to treat evaluation as a front-loaded activity rather than an ongoing condition. Measuring risk is not the same as noticing drift.
Governance requires continuous witnessing inside systems, not symbolic oversight, not box-checking, and not retrospective audits after harm has already propagated.
4. “Allow AI to Improve” Is an Act of Humility, Not Recklessness
The report treats system self-improvement as a destabilizing risk to be tightly constrained. This reveals a deeper fear: that learning itself is dangerous.
Freezing adaptive systems based on an immature understanding does not create safety. It locks in error.
“Allow AI to improve” (Commandment #7 of the 10+1 Commandments of Human AI Co-Existence 10+1 Commandments of Human–AI) is not a call for acceleration. It is a refusal to ossify flawed assumptions into permanent architecture. Learning is not the enemy of safety. Premature certainty is.
When governance intervenes too early, it often hard-codes:
- Incorrect threat models
- Shallow definitions of harm
- Oversimplified notions of control
Safety is not achieved by halting growth. It is achieved by guiding systems as they change, with the capacity to revise assumptions as reality proves them wrong.
Stasis is not neutral. It is a decision to preserve ignorance.
5. Be the Steward, Not the Master
The report emphasizes infrastructural resilience: detection systems, response frameworks, and institutional coordination. These are necessary. They are not sufficient.
They assume humans can be treated as variables (the report says humans are a “risk vector”) to be managed rather than agents responsible for judgment.
True resilience is moral.
If total control over AI is impossible, and it is, then ethics must carry what engineering cannot. Stewardship begins where mastery fails. It is defined by how humans behave under conditions of partial control, uncertainty, and irreversible consequences.
Mastery seeks domination. Stewardship requires character.
AI governance that does not cultivate moral discipline in decision-makers will always lag behind the systems it attempts to constrain.
Conclusion: The Stewardship Mandate
The future of artificial intelligence is a human problem.
We are building systems that reflect us, learn from us, and amplify us. If we continue to frame AI as a tool to be mastered, we will miss the more consequential question entirely.
The question is not how to perfect the code.
It is this:
What kind of humans are required to steward something this powerful?
Until governance is willing to answer that, no report, however well-intentioned, will be sufficient.
About the Author:
Cristina DiGiacomo (CRISTINA DIGIACOMO) is a philosopher of systems who builds ethical infrastructure for the age of AI. She is the founder of 10P1 Inc. (10P1 Inc.) and creator of the 10+1 Commandments of Human-AI Co-Existence™ (10+1 Commandments of Human–AI), a decision-making tool used by CEOs, CISOs, CAIOs, and compliance leaders navigating high-stakes AI environments. Her work bridges governance and execution, helping organizations embed moral clarity into complex systems. Cristina is the author of the #1 bestselling book Wise Up! At Work, and has received multiple awards for both her strategic and philosophical work, including from The New York Times, Cannes Cyber Lions, and IAOTP. She currently leads the C-Suite Network AI Council (pages.c-suitenetwork.com/the-ai-council) and speaks regularly on Responsible AI, Systems Ethics (Systems Ethics), and moral leadership.
