Microplastic Exposure Score
Discovery Lens
C Combination Innovation
Two separate worlds finally connect — and the intersection is a product
One-Liner
An app that estimates your personal daily microplastic exposure based on your habits and product choices, and recommends reductions.
Kill Reason
There is no viable consumer monetization path for a microplastic exposure estimator — it is an anxiety-inducing awareness app with no actionable output that users will pay to access repeatedly. The underlying exposure coefficient data is not proprietary, making the app trivially replicable by any health NGO or media publication, and no B2B buyer has sufficient incentive to pay for what amounts to a liability-generating awareness tool.
What do you think?
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killed: Consumer spectroscopy hardware at the $50–100 price point cannot reliably detect food contaminants — this is a documented technical limitation that already killed SCiO ($23M raised), Tellspec, and Consumer Physics before them. At the accuracy levels achievable with phone-attachment optical sensors, false-positive rates would be high enough to make the product dangerous as a safety device and useless as a consumer gadget. FDA regulation of food safety testing devices would require clinical validation the physics cannot support.
killed: The consumer water filtration and testing market is dominated by Brita, ZeroWater, and Culligan, all of which are adding PFAS-reduction claims to their products. The personalized AI layer doesn't create a defensible moat — any filter company can add an app, and the actual testing hardware for lab-grade PFAS detection remains expensive and regulated.
killed: Distributed AI inference across local networks is an active open-source development area — Petals, ExLlamaV2, and llama.cpp all address this problem, and NVIDIA and AMD are solving the same constraint with hardware. The business model is unclear: who pays for software to pool RAM when cloud inference is cheaper and simpler for most use cases?