AI Alignment
AI alignment is the work of making an AI system pursue what its designers and users actually intend — including the goals they didn't think to spell out — rather than optimizing a literal objective in harmful or unintended ways. It spans training techniques, evaluation, and oversight.
Also known as: alignment, aligned AI
A model optimizes whatever objective it’s given — and the gap between what you said and what you meant is where things go wrong. AI alignment is the effort to close that gap: getting a system to act on intended goals and values, including the unstated ones, instead of gaming a literal metric or producing fluent, confident, wrong output. Techniques like RLHF are alignment in practice — shaping behavior toward what people actually want.
For builders it’s not just a frontier-lab concern. Every shipped system has a smaller version of the problem: an agent that follows instructions too literally, a model that’s helpful at the expense of being honest. That’s why alignment shades into the day-to-day disciplines the show covers — guardrails, evaluation, and keeping a human in the loop — which are how intent gets enforced in production rather than assumed.
From the conversation
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Amplitude's AI Playbook: How Wade Chambers Builds for the Agentic Future -
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We Built Agents, Nobody Built HR | Tyler Akidau, Redpanda - GenAI Predictions for 2025 | Databricks & Cohere
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The AI Framework Era Is Over: Why Context Is the Moat | Jerry Liu - Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne
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- The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn