Every product has a shimmer problem. Not the audio kind, though the audio kind is instructive: Suno, one of the most-used AI music generators on the market, produces songs with a distinctive artifact in the vocals and drums. A shimmer. A giveaway. Anyone who's spent time in a recording studio hears it instantly and knows exactly what made that track. It's not a catastrophic flaw. The song still plays. But it marks the work as something less than it could be.
Your product has one of those. So does ours. So does every piece of software, every service offering, every onboarding flow that's been shipped under pressure and patched in the dark. The question isn't whether the shimmer exists. It's whether you hear it before your customers do.
Here's the thread we keep seeing across every domain right now: the systems that survive are the ones with anomaly detection built in. Not error detection. Anomaly detection. There's a difference. Error detection catches what breaks. Anomaly detection catches what drifts. Drifts are almost always worse, because they don't trigger alarms. They just quietly degrade the experience until someone hits a wall or leaves without explaining why.
The wearable health research makes this concrete. Smartwatches paired with AI catch illness not by knowing what sick looks like, but by knowing what you look like when you're healthy. The deviation from your own baseline is the signal. Same principle applies to your product, your team, your revenue curve. You're not looking for broken. You're looking for off-pattern. A deal cycle that runs three weeks longer than usual. A support ticket category that grew 20% without a corresponding feature change. A churn spike that arrived two months before you noticed it in the monthly dashboard.
Most founders we work with are drowning in data and starving for signal. They've got Stripe, they've got Google Analytics, maybe they've got a CRM with six months of dust on it. But none of those tools are watching the baseline. They're watching the metrics. Those are not the same thing.
The Marginal Revolution finding that firms that adopt AI heavily grow headcount 10% over two years after adoption is one of the most underread data points in the current AI conversation. Everyone hears "AI" and thinks "automation replaces people." The actual pattern is different. AI gives smart teams the capacity to notice more, act on more, build more. The headcount grows because the floor on what's worth doing gets lower. More signals get caught. More anomalies get addressed. More shimmer gets fixed before the customer hears it.
That's not a case for throwing AI at everything. It's a case for building systems that surface deviation before it becomes damage. The specific tool is almost irrelevant. The discipline is not.
There's a cultural reason this is hard, and it shows up even in the strangest places: we are wired to want narrative closure. We want the arc to resolve. We want to ship the feature, close the quarter, hit the milestone, and move on. Anomaly detection is the opposite of that. It's a permanent state of mild suspicion about what's drifting. It's an open loop. It never closes. And most founder brains are not built to hold open loops comfortably after years of sprinting to ship.
This is the operational gap we see in almost every engagement we take. The product works. The revenue is real. But there's no one watching the baseline. No system asking "is this normal for us?" Just dashboards that report what happened after it already happened.
The smartest operators we see right now are baking anomaly reviews into quarterly planning, not just OKR tracking. Not "did we hit the number" but "what moved that we didn't expect, and why." It's a small ritual that does something the dashboards can't: it forces the team to compare the current state against the baseline, not against the goal. Goals are aspirational. Baselines are honest.
The Desunofier exists because someone who knew what healthy audio sounded like refused to accept that the shimmer was just the price of using the tool. They built the fix. That's the move. Not tolerating the artifact because "it mostly works." Knowing your baseline well enough to hear when something's off, and caring enough to address it before your customers build it into their mental model of who you are.
What this looks like in practice
If you're running a product or service business in the $1M to $5M range, here's what we'd actually do. Pick three metrics that matter to your business and define what normal looks like for each one, not what good looks like. Normal. The average behavior in a healthy week. Then build a dead-simple alert, manual or automated, that fires when any of them deviate more than 15% without an obvious cause. That's it. That's your anomaly detection layer. You're not building a data warehouse. You're just deciding in advance what "off-pattern" means so you're not deciding in a panic when it happens.
The shimmer in Suno's tracks isn't loud. It's subtle. That's what makes it a tell. Your product's shimmer is probably subtle too. The checkout flow that confuses exactly the kind of buyer you most want to convert. The onboarding step where 30% of trials quietly stop logging in. The service deliverable that's technically complete but leaves clients feeling slightly, inexplicably flat. Nobody's complaining. Nobody's filing a bug report. But it's there, and the customers who know your space hear it instantly.
The businesses that win at scale are not the ones that shipped the most features or ran the most campaigns. They're the ones that heard the shimmer earliest and cared enough to go fix it. Build that into how you operate, and you stop reacting to the symptom two quarters after it starts and start catching the drift before it becomes a crisis.
The open loop is the feature, not the bug. Stay suspicious of your own baseline. That's where the work actually lives.