AI alignment is the research and engineering discipline focused on making AI systems pursue intended human goals reliably. It covers both the technical problem of specifying goals correctly and the engineering problem of building systems that follow those goals under pressure, drift, and distributional shift.
Alignment is distinct from AI safety in the narrow sense, though they overlap. Safety covers the broader question of whether AI systems are trustworthy. Alignment focuses specifically on the goal-pursuit question.
For most engineering organizations, alignment is not directly actionable — the work happens at the model layer, in labs. What organizations can do is build the governance layer around AI so that alignment failures are caught and contained, not amplified.
Why AI alignment Matters for Distributed Teams
Alignment is the hardest open problem in AI research, but most organizational risk does not come from misaligned models. It comes from aligned models being deployed without governance around their authority. Solve the second first; the first will continue to be solved by labs.
Frequently Asked Questions
What is AI alignment?
AI alignment is the research and engineering discipline focused on making AI systems pursue intended human goals reliably. It includes both specifying goals correctly and building systems that follow them under pressure and distributional shift.
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StandIn is built around these concepts. Engineers publish declared state before going offline. The next shift starts with full context.