The line usually attributed to Einstein goes like this: “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.” It is a warning against racing to the answer. Gale Robins, writing for UX Collective, makes the same case for product teams—then moves it one step earlier than Einstein did.

Her subject is the decision that rarely gets scheduled: whether a customer signal deserves discovery at all. She calls it Signal Evaluation, and she describes it bluntly:

Signal Evaluation is not a box to tick on the way to the real work. It is a filter, and a good filter is defined by what it keeps out. If most of what reaches your team makes it into discovery, the filter is not working; it is just a turnstile.

This is Einstein’s 55 minutes, relocated:

It is tempting to think discovery begins when you start talking to customers. It begins one step earlier, with the call to research this rather than something else. Every signal that reaches a team […] arrives carrying an implicit claim: this is worth your attention. Evaluating a signal is the act of testing that claim before you spend anything on it.

The difference she leans on is between a feature signal and a job signal:

The distinction that matters is whether the signal is about the customer’s job or about your product […]. “The export button is confusing” is a feature signal: it concerns your solution and usually warrants a quick fix rather than a discovery effort. “I cannot get my insights to my stakeholders” is a job signal: it is about what the customer is trying to accomplish, and it may hide an underserved need worth real investigation.

That is the trap Einstein was guarding against. The seductive request arrives pre-packaged as its own solution—add the widget, fix the button—and it is tempting precisely because it lets you skip the 55 minutes. Robins’s point is that a signal can be strong, genuine, and still aimed at the wrong job.

Here is where she goes past Einstein. His hour is already committed: he has a problem and is deciding how to spend time inside it. Robins is working a layer earlier. Her question is whether the signal has earned the hour in the first place. In her words, the skipped judgment is “whether to begin it at all.” Signal Evaluation is the gate before Einstein’s clock even starts.

AI is what makes that gate matter more now, not less.

AI can scan your entire feedback corpus, cluster signals by frequency, surface correlations with churn, and tell you in minutes that the widget request appeared in twenty-three of forty calls. That is genuinely useful and genuinely predictive: pattern-finding at a scale no human can match.

But notice what AI cannot do in that example. It can tell you the signal is strong. It cannot tell you the signal is pointing at the wrong job. That judgment, that “more widgets” is really “I cannot see what matters,” and that building the literal request might make things worse, is meaning-making, not pattern-matching.