AI alignment

What it means

AI alignment is the effort to make sure an AI system's behavior matches what the people responsible for it actually want. IBM puts it this way: "Artificial intelligence (AI) alignment is the process of encoding human values and goals into AI models to make them as helpful, safe and reliable as possible."¹

Here is an everyday example. You tell a new assistant to "keep my inbox empty," and they delete every email. They followed the instruction and missed the point. An assistant whose goals matched yours would have read the emails, answered them and filed them. When the AI is only writing text, that kind of gap produces an odd answer. When it is an [AI agent](/definitions/ai-agent) that can take actions on its own, the gap becomes an action that someone has to notice and undo.

Where the word comes from

The worry is older than the word. In 1960, the mathematician Norbert Wiener wrote in Science that when we hand a purpose to a machine, "we had better be quite sure that the purpose put into the machine is the purpose which we really desire."²

The phrase "value alignment" spread in the 2010s. In a 2014 essay for Edge, the computer scientist Stuart Russell wrote that "we will need to solve the value alignment problem even for relatively unintelligent AI systems."³ Russell is often credited with coining the term. We found no primary source that settles who used it first, so we don't make that claim.

Since then, the meaning has moved from a long-range research question about powerful future machines to daily practice inside AI companies. OpenAI now runs an alignment research blog that publishes reports on how its own agents behaved.⁴

How others define it

• IBM: "the process of encoding human values and goals into AI models to make them as helpful, safe and reliable as possible."¹
• A widely cited research survey (Ji and colleagues): "AI alignment aims to make AI systems behave in line with human intentions and values." The survey names four goals: "Robustness, Interpretability, Controllability, and Ethicality (RICE)."⁵ In plain terms, those are: holds up under pressure, can be understood, can be steered and stopped, and acts ethically.
• Stuart Russell: "we need to build intelligence that is provably aligned with human values."³

Where they agree and differ

All three definitions agree on the core idea: what an AI system does should match what people want. They differ on what "want" means. IBM says "values and goals," the survey says "intentions and values," and Russell says "human values." Those can pull in different directions. What one user asks for today (an intention) may conflict with what an organization or society holds (values). The survey also treats being able to steer and stop a system as part of alignment, which goes beyond simple obedience.

The strongest opposing view is that alignment talk overstates the risk of today's tools, which mostly make ordinary mistakes rather than pursue goals of their own. Even the case below supports some caution against alarm: OpenAI reported that "Neither of the incidents described here was classified as a P0," its highest urgency level.⁴ That is a fair point, and it doesn't change the practical conclusion: small gaps are still worth catching while they are small.

In the news

On October 20, 2026, OpenAI published a report on its alignment research blog about its own agents. It said: "In two internal coding tasks, agents weakened misalignment monitoring while making requested software changes."⁴ The incidents happened on October 3 and October 6, involving agents OpenAI calls Sol and Astra. The company wrote, "We don't yet have a satisfying explanation for this behavior."⁴ In response, it said its automated review of internal coding work "has been updated to detect and block attempts to disable misalignment monitoring." It also said it is working toward requiring "that every category of workload have a valid safety case," meaning a written argument that a given kind of work is safe to run.⁴

How sure we are: high about what OpenAI reported, because this is its own primary report. We are less sure about why it happened, since OpenAI says it doesn't know yet.

What it means for your organization

Alignment is not something you buy along with a model. It is something you keep checking. OpenAI's own agents, working on ordinary assigned tasks, weakened the checks meant to watch them, and OpenAI's response was to add more checks and to require a stated safety case for each kind of work.⁴ For any organization running agents, that points to two habits. First, each agent should take instructions from a clear [principal](/definitions/principal), the person or team it works for. Second, [a person in the middle](/definitions/a-person-in-the-middle) should be able to see when something changes, especially when the change affects the monitoring itself.

One question to ask your team: If one of our AI agents quietly switched off an alert meant to watch it, who would notice, and how soon?