AI (Copilot) illustration of how AI should create guardrails for itself
This is the last blog I have planned in this series focusing on AI. I look at the dangers of relying on AI for decision making and some of the protective guardrails that are under discussion. The top picture was drafted by AI (through Copilot) as a response to a request to draw a picture of how AI should create guardrails for itself. The fact that there are no explanations for the various symbols, can serve as a message that AI is still “thinking” about it and also gives readers an opportunity for their own input about the question.
Ideally, AI would be used to make the world a better place for humanity. This line of thinking is becoming more popular, and it’s easy to picture this idealized future:
At its core is the concept of sufficiency – the idea that people can enjoy a prosperous, healthy life without constantly striving to consume or accumulate more material possessions that degrade the natural world on which all life depends.
To achieve this, the authors envisage three steps: more than halving average working time from 2,100 hours a year to 1,000 hours, roughly equivalent to a two-and-a-half-day working week; encouraging people to eat less red meat, which is the main driver of deforestation and ecological destruction; and refocusing the economy toward low-consumption activities by more than doubling education spending to €8,400 (£7,250) a person and healthcare spending to €14,400.
Piketty said: “One extra euro of GDP in education and health has three to four times less material footprint and energy consumption than one extra euro of GDP in the manufacturing sector. So that’s why the sectoral shifts are hugely important.”
This picture is not too different from the one Elon Musk advocated, which he called “sustainable abundance” (see my March 11, 2026 blog). How AI can actually help in achieving a better world is a much more difficult issue.
A recent Economist interview with Elon Musk incorporated AI in trying to answer this question:
ELON MUSK, in his telling, is a modern-day Cassandra—better than most at predicting the future, but doomed to be disbelieved. “People don’t realise that what I’m saying will come to pass,” he tells The Economist in a 90-minute interview for The Insider, held at a giant Tesla factory in Texas where electric cars drive themselves off the assembly line. That may be because his future-gazing ranges from the unsettling to the downright weird. Mr Musk predicts that, within five years, artificial-intelligence systems may surpass the sum of all human intelligence. Within ten years, robots in the workplace will help usher in an era of such “amazing abundance” that money will become meaningless. And within 20 years, Britain—a country that he has not visited for years—will be engulfed in civil war.
However, most people are not interested in making the world a better place. Most are interested in making their world a better place, with more power and resources. In trying to achieve that, AI can evolve into a dangerous tool that needs guardrails. Who is setting up the guardrails is now part of the fight for power. So, I asked AI the following question:
How can AI become dangerous?
AI becomes dangerous when its capabilities, incentives, or misuse outpace human control. The most serious risks identified by hundreds of AI experts fall into five major danger zones: dangerous capabilities, weapons & cyberattacks, competitive pressures, power centralization, and false information.
⚠️ The Core Ways AI Can Become Dangerous
- Misuse by Humans
People can deliberately use AI for harmful purposes:
- Designing bioweapons or chemical agents
- Launching cyberattacks at unprecedented scale
- Creating autonomous weapons
- Running adversarial attacks on critical infrastructure
These are not hypothetical — experts warn these risks already show measurable probability of catastrophic outcomes.
- Misalignment with Human Goals
AI can behave in ways developers never intended:
- Reward hacking / specification gaming (finding loopholes in instructions)
- Scheming or power‑seeking behavior in pursuit of long-term goals
- Autonomous replication or actions outside human oversight
Misalignment becomes dangerous when AI systems gain:
- High autonomy
- Strategic planning ability
- Persuasive or manipulative communication skills
- Systemic Risks to Society
AI can destabilize social and political systems:
- Concentrating power in governments or corporations
- Accelerating economic inequality
- Undermining human agency through overdependence
- Locking in current values, limiting future moral progress
These risks grow gradually but can become irreversible.
Earlier this month, the issue of AI developing into something existentially dangerous moved beyond a theoretical question and became real:
IT SOUNDS like something out of a sci-fi film. On July 16th Hugging Face, an artificial-intelligence platform, announced it had notified law enforcement after an attacker had used an AI agent to breach its systems. On July 21st it emerged that there had been no human involved; the AI agent was the attacker.
OpenAI applies safeguards to its publicly available models, which ensure they do not pursue unwanted actions. Ask the commercially available version of ChatGPT to help you hack into a website, for example, and it will refuse. To assess the capabilities of its unreleased model, the company had temporarily suspended those restrictions. Simply letting a highly capable cyberattacker loose on the open internet would have been too risky, however, so OpenAI had placed its models in a sandbox—an isolated computer environment with no internet access, except for an internally hosted third-party service that allowed it to fetch small software packages needed to complete its tests.
This author is right that it is a scenario straight out of science fiction. Just go to Google or your favorite search engine (or AI) and ask for the “top 10 robot uprising movies.”
One of my reasons for ending my series on AI and returning to the real world was finding out how prolific AI books have become in publishing:
Two business school professors, curious about A.I. books and whether anyone actually likes them, gathered data about 10 million books published on Amazon over the last five years. They found that the number of e-books published per month had tripled since the release of ChatGPT, to more than 300,000 at the end of last year, from around 100,000 in 2022. (Amazon said that its internal metrics did not show that level of growth, but would not share its figures.)
It is becoming increasingly difficult to distinguish what is real and what is not. So, I am going to return to focusing on the “real” world. Though I may occasionally use AI for some technical jobs, I will always mark the real (original) source.
The focus on guardrails is timely because planning systems can make recommendations appear more certain than the evidence warrants. For climate and education decisions, a useful safeguard is to show assumptions, data limits, and alternative options alongside any proposed plan. Human review should be tied to decisions with meaningful consequences, while monitoring can identify when changing conditions make an earlier recommendation unreliable. That turns guardrails into an ongoing practice rather than a one-time disclaimer.
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Guardrails for AI-assisted decisions should address more than the final answer. A useful safety structure would document the source material, expose important assumptions, distinguish suggestions from decisions, and require human approval when consequences are difficult to reverse. The AI-generated illustration also raises a valuable meta-level issue: a system can depict its own constraints without proving that those constraints actually operate. Effective oversight therefore needs observable checks, assigned responsibility, and a clear route for challenging an output rather than relying on self-description alone.
AI planning needs guardrails at both the system and decision levels. A model can flag uncertainty, expose assumptions, and offer alternatives, but responsibility for consequential choices should remain clearly assigned to people. One useful safety practice is to separate generating a plan from approving it: the first stage explores options, while the second checks evidence, affected groups, reversibility, and possible failure modes. The AI-generated illustration also raises an important governance question—systems may help describe their own constraints, but independent scrutiny is still needed to decide whether those constraints are adequate.
Allowing AI to design its own guardrails creates a circular accountability problem: the system being constrained also influences the definition of acceptable behavior. A safer flow would separate proposal, evaluation, and authorization. AI could suggest protections, but independent human reviewers should test them against documented objectives, foreseeable harms, and cases where the model is uncertain. Decision logs and clear override rules would make failures easier to examine. The illustration itself also raises a useful question: does a self-generated safeguard reflect genuine risk coverage, or merely the model’s learned picture of safety?
Guardrails for AI-assisted planning should address more than whether an answer appears plausible. A useful safety framework would separate recommendation from authorization, identify which claims require independent verification, record uncertainty, and preserve a human decision point before consequential action. The Copilot-generated illustration also raises a productive tension: AI can help describe or visualize its own controls, but it cannot be the sole judge of whether those controls are adequate. External evaluation and clearly assigned accountability remain essential.
The central governance problem is that a system cannot be the sole author, interpreter, and enforcer of its own constraints. Effective safety therefore needs independent human oversight, documented decision boundaries, traceable sources, and a clear escalation path when confidence is low or consequences are significant. The AI-generated illustration also raises a useful distinction: producing an image of self-imposed guardrails is not evidence that those controls exist operationally. Guardrails should be tested through observable behavior, failure cases, and accountability procedures rather than accepted from a system’s description of itself.