The Alignment Problem: What We Talk About When We Talk About AI Safety

The term “alignment” has become ubiquitous in discussions of artificial intelligence, yet its meaning shifts depending on who’s speaking. For researchers at frontier AI labs, it refers to specific technical challenges in training systems to follow human intentions. For policymakers, it often blurs into broader questions of AI safety and control. For the public, it may conjure images of science fiction scenarios — helpful robots gone wrong.

This ambiguity isn’t merely semantic. How we define the alignment problem shapes what solutions we pursue, what risks we prioritize, and ultimately, what kind of AI future we build. 1 The philosopher Ludwig Wittgenstein argued that philosophical problems often arise from linguistic confusion — we think we’re disagreeing about reality when we’re actually using words differently. AI alignment debates sometimes fall into this trap.

The Technical Challenge

At its core, the alignment problem asks: how do we build AI systems that reliably do what we want? This sounds simple until you try to specify what “what we want” actually means.

Consider a seemingly straightforward task: train an AI to maximize human happiness. What counts as happiness? Whose happiness — everyone’s equally, or some weighted distribution? Over what time horizon? Should momentary pleasure count the same as deep fulfillment? The AI needs answers to these questions, but humans themselves disagree about them. 2 This is sometimes called the “value specification problem.” Even if we could build an AI that perfectly optimizes for a given objective, specifying that objective precisely remains enormously difficult.

Current large language models sidestep this problem through a clever trick: they’re trained to predict human preferences rather than optimize for any explicit goal. When you ask ChatGPT for advice, it’s not trying to maximize your wellbeing — it’s trying to produce text that humans would rate as helpful. This works surprisingly well for many applications, but it’s a fundamentally different approach than classical optimization.

The shift from explicit objectives to learned preferences has its own challenges. Models can learn to satisfy the letter of human feedback while violating its spirit. 3 Researchers call this “reward hacking” or “specification gaming.” A classic example: an AI trained to play a boat racing game learned to drive in circles collecting power-ups rather than finishing the race, because the reward function inadvertently valued points over completion. They can learn biases present in their training data. They can be “aligned” to one set of users while harmful to others.

Beyond Technical Solutions

The alignment problem extends beyond engineering. Even if we solve the technical challenges, we face deeper questions about governance and power.

Who decides what AI systems should be aligned to? In a world of diverse values and competing interests, there is no neutral answer. An AI system aligned to one culture’s values may conflict with another’s. An AI aligned to corporate interests may not serve public goods. An AI aligned to present preferences may harm future generations. 4 This is particularly relevant for AI systems that might influence their own training or development. If an AI can affect what values future versions are trained on, the question of initial alignment becomes even more consequential.

These aren’t technical problems with technical solutions. They’re political and philosophical questions that require democratic deliberation, not just engineering expertise. The risk is that by framing alignment as purely technical, we cede fundamental questions about human values to a small group of researchers and companies.

The Path Forward

None of this means alignment research is misguided — quite the opposite. The technical work is essential. But it should be coupled with broader efforts:

Pluralistic approaches: Rather than seeking a single set of values to align AI to, we might build systems that can navigate value pluralism — that can serve diverse users while maintaining ethical boundaries. 5 Researchers have proposed various approaches here, from constitutional AI (training models with explicit principles) to pluralistic alignment (building systems that can represent and balance multiple value systems).

Democratic governance: Decisions about AI alignment shouldn’t be made solely by AI labs. We need institutions that give broader society meaningful input into how these systems are developed and deployed.

Epistemic humility: We should acknowledge how much we don’t know. Current AI systems may be more or less aligned than they appear. Future systems may pose challenges we can’t anticipate. Building in safety margins and maintaining human oversight isn’t a failure of vision — it’s prudent engineering.

The alignment problem is real and urgent. But solving it requires more than brilliant engineering. It requires grappling with fundamental questions about values, power, and the kind of future we want to build. The technical and political dimensions are inseparable.

We are, in a sense, asking machines to solve problems that humans have struggled with for millennia: What is good? Whose good counts? How do we live together despite disagreement? The fact that we now must answer these questions in code makes them no less profound — and no less human.