The Cost of Specificity
There are fewer than one hundred registered cases of sialidosis in the world.
It’s a lysosomal storage disorder — a rare metabolic condition that attacks the nervous system. If you’ve never heard of it, you’re not alone. Most doctors haven’t either. When Sarah brings her daughter Lily to a new specialist, she spells the name, explains the mechanism, and watches the doctor look it up. Then she fills them in. The seizure patterns. The vision changes. The motor regression. The medication timing. What the geneticist said last month. What the clinical trial coordinator needs tracked.
Sarah is the expert in every room. She carries the entire picture alone — because until recently, there was no other way to carry it.
That’s the kind of problem that doesn’t get solved. The market is too small. The condition too rare. The use case too specific. By any rational product calculus, you don’t build for fewer than one hundred families.
Except now you do.
This is what actually changed when AI arrived — not what most people think changed.
The dominant story is about scale. AI makes things faster. AI makes things cheaper. AI lets one person do what used to take ten. That’s all true, and it’s all beside the point.
The deeper shift is this: AI collapsed the cost of specificity.
Before, building something specific meant paying in one of three ways. You paid in time — manual effort, custom work, one-off solutions that couldn’t be reused. You paid in money — hiring domain expertise, building narrow products for thin margins. Or you paid in quality — generalizing the product until it fit more people and served none of them particularly well.
So most products generalized. They had to. The economics demanded it.
This wasn’t a failure of imagination. No-code tools, templates, and SaaS platforms all tried to close the gap — and they helped. But they hit the same ceiling. Templates scale structure. They can’t scale judgment. The moment a problem required real domain-specific decision-making — what to flag, what to deprioritize, how to interpret an ambiguous signal — the generic tool ran out of road. You either hired an expert or you went without.
AI changes that specific thing. Not tasks. Judgment. Domain-specific decision-making could always be encoded — expert systems, clinical pathways, rules engines all tried. What’s different now isn’t just cost. It’s capability. Models that handle ambiguity, not just rules. Data that doesn’t have to be pre-structured to be usable. Build cycles that compress from years to weeks. The economics shifted because the underlying technology crossed a threshold. For the first time, encoding that judgment for fewer than one hundred families is financially rational.
That’s the shift.
A care coordination tool for Sarah isn’t just a shared timeline. It’s a set of structures shaped around the specific situation: what to capture after a neurology appointment, how to compress a week of fragmented observations into something a geneticist can use in ten minutes, what a new specialist actually needs to know before Sarah opens her mouth.
Togetherly doesn’t solve this by being flexible. It solves it by starting specific. When Sarah opens the app, she isn’t configuring a blank tool — she’s entering a structure already shaped around her situation. The observation prompts aren’t generic — they’re drawn from the real vocabulary of that condition, iterated into a starting set that covers what families navigating it actually track. And as her family uses the app, their own language gets absorbed: tags they add consistently become part of the circle’s vocabulary automatically. The log she builds becomes something she can hand a new specialist. The update she posts becomes something her family can actually read. The system doesn’t make the calls. But it means Sarah stops making them alone, from scratch, every time.
The Honest Part
This is early. The encoded judgment is partial, the system is still being built. That’s not a caveat — it’s the point. The cost has fallen enough to start.
The dominant pattern in AI products has been to bet on generalization — build one flexible tool that handles everything. The universal assistant. The blank canvas. Maximum optionality.
This is exactly backwards.
The winning pattern is the opposite: constrain harder, and deliver sharper outcomes. The more specifically a product understands your situation — not your category, your actual context — the more it can do that a general tool cannot. Flexibility pushes decision-making back onto the user. Constraint absorbs it.
Which means the real opportunity isn’t a better general tool. It’s systematic niche creation.
Once the system exists, the next niche isn’t a new product. It’s a configuration. Togetherly already does this across 24 conditions — ALS, Parkinson’s, cancer, dementia, organ transplant, long COVID, autism, sialidosis, and more. Each condition gets a dedicated landing page, an observation tag template drawn from that condition’s clinical vocabulary, and a seeded demo circle with a week of realistic family observations — accessible without signing up. The core product doesn’t change. What changes is the starting context: instead of a blank interface, a family navigating post-transplant care opens something that already speaks their language. The constraint shifts from build cost to problem clarity. The question stops being is the market big enough and becomes is the problem sharp enough.
That inversion matters. It means the limiting factor is no longer capital or scale. It’s understanding.
When specificity gets cheap, expectations shift.
People who’ve experienced a tool that genuinely fits their situation — that doesn’t require them to translate their problem into terms the software can handle — find it difficult to go back. Generic tools start to feel like friction. The question stops being does this work and starts being does this understand me.
That’s the fragmentation coming. Not the death of general tools — those will survive for general problems. But wherever a problem has real shape, a new standard is being set. And the builders setting it aren’t the ones building bigger platforms. They’re the ones willing to go narrow enough to actually think.
Sarah was always there. The problem was always real. The care coordination burden she carries — the 2am texts, the 45-minute phone calls, the exhaustion of being the only person who holds the full picture — existed long before anyone built anything for it.
The problem didn’t become worth solving.
The cost fell until it couldn’t be ignored.
Togetherly is a care coordination platform for families navigating complex medical situations. togetherly.care