Experts Loop
250511
Artificial intelligence is not missing raw horsepower. It is missing the right steering. Too often, founders put AI through the wrong kind of validation, asking non-experts to test-drive systems meant for domains that run on decades of training and judgment. It feels democratic, but it is actually negligent. As Plato wrote, “a good decision is based on knowledge and not on numbers.” When startups substitute generic user counts for domain wisdom, they confuse popularity with truth.
The real power of AI is unlocked through what some have called the Human Multiplier effect. AI on its own can surface patterns. Domain experts on their own can interpret complexity. Put them together and the output is not incremental but exponential. A radiologist with AI support detects cancers earlier than either could alone. A financial auditor armed with AI can flag fraud at scales impossible for a human team. This is not human-in-the-loop (HiTL) in the generic sense. This is expert-in-the-loop (EiTL), and it makes the difference between tools that impress in demo day slide decks and tools that truly transform billions of lives.
Consider how many AI dashboards today collapse into noise. They flood clinicians, traders, or analysts with alerts without context, hoping that visualization alone creates value. Chris Lovejoy has pointed out that dashboards without a domain-specific frame do not aid decision-making. They create cognitive overload, forcing experts to sift signal from noise. Domain framing is what transforms an alert into insight. Without it, your “intelligent system” becomes another inbox of false positives.
Domain experts are not beta testers or just stakeholders, as Daniel Ruiz-Riquelme plainly put. They are the primary users. Treating them as afterthoughts or edge cases ensures misalignment from day one. This mirrors the design paradox of AI. If you flatten interfaces into consumer-grade simplicity, you strip away the nuance experts rely on. A cardiologist does not want a pastel graph that says “high risk.” They want decision support aligned with the exact diagnostic protocols they use daily. Yet if you lean only into complexity, adoption stalls. Balancing this paradox is only possible if experts are there from inception, guiding what simplicity and complexity should mean in context.
The problem is startups often mistake any “human feedback” as sufficient. The Expert-in-the-Loop framing is sharper. A soccer mom can tell you if last week is a few day ago, but she cannot tell you if your AI diagnostic missed an early-stage melanoma. A baseball dad can say whether the chart looks confusing and the numberes don’t add up, but he cannot validate whether your compliance AI reflects the realities of regulatory audits. Human-in-the-loop without expertise risks creating false confidence. You end up with AI that feels polished but breaks under real-world pressure.
History proves this point. The Renaissance did not rise because average citizens gave feedback on Da Vinci’s sketches. It was because painters worked with anatomists, mathematicians studied alongside philosophers, astronomers debated theologians in a townsquare. Cross-pollination of mastery created acceleration. The same is true now. Industry expertise is the multiplier that gives AI its edge.
Builders who know how important this matter is but do not know where to begin, the principle is simple: the domain expert is the baseline and requirement. Map their workflows before you design anything. Mirror their heuristics before you visualize and embed their language before you fine-tune your model. Your goal is not to design AI that anyone could use but is to design AI that the people who bear the consequences of failure cannot live without.
The future of AI adoption hinges on trust, and trust is won by respecting expertise. Soccer moms and baseball dads bring value to many conversations, but not enough here. If you are building AI for medicine, law, finance, or government, look at cardiologists, prosecutors, auditors, and mayors as your co-designers. The next wave of AI will not be defined by how clever the models are or how sleek the interfaces look. It will be defined by whether we have the humility to center expertise. To build AI that matters, stop designing for hypothetical averages and start designing with the people who hold the burden of consequence, and you build the future.
Topics
Evals / Observability / Domain Experts / In the Loop