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There are three fundamental bottlenecks to AI progress: models, chips, and data. We have model builders and chip producers, but data is the bottleneck to AI progress. Protege is the trusted data infrastructure layer that’s here to solve that. Curated, licensed, privacy-protected data, ready for AI. 2025 was a...

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Protege

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29,873 Aufrufe • vor 7 Monaten •via X (Twitter)

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8 Kommentare

Profilbild von Protege
Protegevor 7 Monaten

More from @BobbySamuels, @daisydwolf, @evajsteinman on @a16z's Raising Health Podcast, now live!

Profilbild von Protege
Protegevor 7 Monaten

And @daisydwolf + @evajsteinman's investment thesis in us from last month!

Profilbild von Engy Ziedan
Engy Ziedanvor 7 Monaten

@daisydwolf And come join our data lab!

Profilbild von Auren Hoffman
Auren Hoffmanvor 7 Monaten

@daisydwolf go go go!

Profilbild von Tom Hollands
Tom Hollandsvor 7 Monaten

@daisydwolf amazing company + mission. so glad to work with you 🦾

Profilbild von Yiwei
Yiweivor 7 Monaten

@a16z @daisydwolf energy is the silent fourth. compute is a derivative of power capacity and we're already hitting the grid limits in some regions

Profilbild von Spencer Ingram
Spencer Ingramvor 6 Monaten

@daisydwolf Wait, so you can actually let AI know who has the world's best cup of coffee?

Profilbild von Ai Pulse
Ai Pulsevor 7 Monaten

@daisydwolf This. The paradox: we have infinite synthetic data but zero signal. Curated, licensed real-world data is worth 10x raw internet scraping. The winners in 2026 will be the ones with distribution + quality data moats, not just raw capacity.

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Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and Kingston Kuan spent years building large-scale infrastructure at Jane Street.

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