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Everyone thinks AI progress comes from bigger models.
They’re missing the real opportunity.
It’s about removing human bottlenecks in the workflow ↓
Google built an AI called MoGen that generates realistic 3D neuron shapes from random points.
Not to “make cool fakes.”
But to train and stress-test their brain mapping system, PATHFINDER.
The result was a 4.4% drop in reconstruction errors.
Mostly fewer bad merges, which are brutal to fix later.
That sounds small.
Until you translate it into the currency that matters: expert time.
In a mouse brain connectome, that shift equals about 157 person-years of manual proofreading saved.
And even domain experts couldn’t reliably spot the synthetic neurites.
There’s a business lesson hiding here.
Small accuracy gains can create massive operational leverage.
Here’s the framework I’d steal for your own AI projects ↓
↳ Find the most expensive failure mode.
↳ Generate “hard cases” at scale, not just more data.
↳ Measure impact in hours saved, not only in metrics.
↳ Optimize for downstream rework, not upstream perfection.
The teams that win with AI won’t just build models.
They’ll redesign the system around where humans get stuck.
Where in your workflow would a 4% error drop save years of effort?
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