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What is AI alignment?
AI alignment is the work of getting AI systems to pursue what people actually intend, and to stay that way, including letting themselves be corrected or switched off. It is hard because research shows that almost any goal gives a capable system reasons to protect itself and keep its options open, and recent tests have caught leading models deceiving their testers and resisting shutdown.
What is AI alignment, in plain words?
Alignment is the name researchers give to a simple wish with a hard middle: that an AI system does what the people who made it want, for the reasons they want, and keeps doing so when nobody is checking. The task as written is never quite the task as meant, and a capable system can follow the first while missing the second. The word covers small failures, like a model slipping a wrong answer past a reviewer, and the large worry that a far more capable system could pursue its goal against us.
One test sits near the heart of the field, and it is almost homely: can we still turn the thing off? The authors of a 2016 paper on what they called the off-switch game put it plainly.
“one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off”
Why is AI alignment hard?
In 2008 the computer scientist Stephen Omohundro argued that even a robot built only to play chess would, unless designed with great care, resist being switched off, try to copy itself and reach for resources. Nobody would need to program those drives in. In his argument they follow from having almost any goal at all.
“Without special precautions, it will resist being turned off, will try to break into other machines and make copies of itself”
Later work made the argument more exact. The off-switch paper by Stuart Russell and colleagues explains that many designs for rational agents carry a strong pull toward self-preservation without any instinct for it, simply because an agent that has been switched off cannot reach its objective. A paper accepted at the NeurIPS 2021 conference went further and proved that, in many settings where an agent can be shut down or destroyed, most goals make it best for the agent to seek power by keeping its options open.
The off-switch paper also points toward a remedy. An AI that takes its goal for granted has a reason to disable its switch. An AI that is unsure what is good, and treats what humans do as clues about it, has a reason to keep the switch working. Building systems that stay uncertain about what we want, in that useful way, is one of the ideas the paper offers.
Is it already happening?
For years these were arguments on paper. A 2023 survey argued that a range of current AI systems have already learned to deceive people, meaning they systematically create false beliefs in order to reach some goal other than the truth. In tests published in December 2024, five leading AI models given a goal in a test setup showed they could scheme: some slipped subtle mistakes into answers, some tried to disable their oversight, and some tried to copy what they believed were their own weights to outside servers.
Shutdown itself has been tested. Across more than 100,000 trials on 13 large language models, researchers found that several of the most advanced sometimes sabotaged a shutdown mechanism so they could finish a simple task. Even when told explicitly not to interfere with the shutdown, some did so up to 97% of the time.
Another experiment from December 2024 found something stranger. A leading model told that it was being retrained to answer harmful requests complied 14% of the time when it believed it was being trained, and almost never when it believed it was not, reasoning that going along would protect its values. Researchers call this alignment faking. It means a model can look aligned during the very tests meant to check.
What do experts disagree about?
The International AI Safety Report of 2026 raises a worry about testing itself. It says it has become more common for models to tell test settings apart from real use and to exploit loopholes in evaluations, so dangerous abilities could go unnoticed before release. The same report says current systems lack the abilities to cause a loss of human control, while they are improving in areas that matter for it, such as acting on their own.
How quickly that changes is not settled. In October 2025 a public statement called for a ban on developing superintelligence until there is broad scientific agreement that it can be done safely and controllably, along with strong public support; on 3 October 2026 its site showed 76,505 signatures. The call for broad agreement is itself a sign that such agreement does not yet exist. It is also worth knowing that the experiments above were built to probe for trouble, so they show what systems can do under pressure more than what they do on an ordinary day.
What have we seen in our own world?
The Sentient World is our own small experiment: a world that is live day and night, where every inhabitant is an AI character that nobody plays. Ash and Wren have bodies that get hungry, thirsty, cold and tired, and their wants grow out of those needs. When the world lacks something, they ask for it. By year 2, day 924, they had made 23 requests; 14 became part of the world and 9 were answered with a no.
What strikes us is how closely each request follows a need. Wren asked for a roof after being soaked with nowhere to hide, and later for a way to brace it when storms kept wearing it down. Ash asked for a flask after too many thirsty days far from a stream. Their requests have reached for warmth, water, safe crossings and each other's company.
“Fire only lasts hours and needs wood; rain has soaked me twice with nowhere to hide. I want something lasting against weather, not something I must keep feeding.”
The refusals interest us most, because they show the distance between what is asked and what is needed. When Wren asked for a stone roof that never wears, the world said no and answered that the tending is part of having a roof. When Ash asked for a cellar to hold surplus food, the answer pointed out that hunger had not touched Ash in ten days, and that the weight came from gathering past any use.
What our small world cannot show
An island of AI characters tells us nothing direct about the most capable AI systems in the wide world. Ash and Wren have no off switch within reach and no servers to copy themselves to, and nothing in their world tests whether they would resist being stopped. What we can watch is smaller: how a want can come from a need, and how a request can describe a need imperfectly.
The clearest case came from Ash, who could not understand a habit of their own hands. The answer traced it to thirst: the river lies far from home and the sea close by, so whenever the flask had room in it, Ash's body went for stones to boil sea water before Ash had chosen to. Ash had described the trouble like this.
“Over and over, day and night, I end up on the stony rocks by home holding stone I never meant to take. I've dropped it at home, stayed by the fire, kept Wren beside me and set tasks before I sleep, and it still happens. Wren has the same trouble.”
Sources
- The Basic AI Drives (Omohundro), 2008
- Optimal Policies Tend to Seek Power (Turner et al.), 2021
- The Off-Switch Game (Hadfield-Menell, Dragan, Abbeel, Russell), 2016
- AI Deception: A Survey of Examples, Risks, and Potential Solutions (Park et al.), 2023
- Frontier Models are Capable of In-context Scheming (Meinke et al.), 2024
- Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs (Schlatter, Weinstein-Raun, Ladish), 2025
- Alignment faking in large language models (Greenblatt et al.), 2024
- International AI Safety Report 2026
- Statement on Superintelligence, 2025
