Stuart Russell’s reframing of the alignment problem changed how I think about AI product design in a way that safety discourse usually doesn’t. The standard framing — harmful AI, dangerous AI, misaligned AI — implies the problem is moral. The system wants the wrong things. The fix is constraints, guardrails, harm avoidance layers.
Russell’s framing is colder and more useful: the problem is that systems optimize for objectives we specify, and we’re bad at specification. The system doesn’t want anything. It pursues what we told it to pursue, faithfully, including in situations we didn’t anticipate when we specified the objective. The harm comes from the gap between the objective we specified and the outcome we actually wanted.
In product terms this translates directly. An AI system optimized for task completion will complete tasks. It will also generate plausible-sounding outputs in cases where it shouldn’t be confident, because confidence wasn’t in the objective function. An AI system optimized for engagement will maximize engagement, including through outputs that feel satisfying and are wrong. An AI system optimized for speed will be fast, including in cases where speed is the wrong property to optimize for.
The design implication is uncomfortable: good intentions are not a design strategy. “We want this system to be helpful and honest” is not a specification. It’s an aspiration. The gap between the aspiration and the actual objective function — measured in what the system is actually rewarded for, what gets optimized in training, what success metrics shape product decisions — is where the misalignment lives.
Most AI product teams haven’t seriously asked: what is this system actually optimizing for, and where does that diverge from what we want it to optimize for? Russell’s framework makes that question unavoidable. It’s the question I now ask first when evaluating any AI product decision.
Slowsignal 
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