1/ "We can't simulate a thousand years of research in-silico yet but automation gets you there today through throughput." The unlock isn't a perfect virtual cell. It's running and iterating on enough real experiments, fast.

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2/ Contrarian take I loved: R&D and automation should be best friends. Most teams say "we'll automate once things are stable." He argues that's backwards automation is how you iterate faster in R&D itself.

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3/ Manufacturing-cost reduction as the highest-leverage place for automation, specifically because cell therapies are so expensive that cost is what makes them inaccessible.

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4/ Underrated point: a great assay beats the best hardware running an older, worse assay. Before optimizing the robot, ask the first-principles questions like what's the context, how do we design this, what are we optimizing for, and is this even the right assay for the question.

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5/ The space is heating up. Toronto's Acceleration Consortium landed a ~$200M grant for autonomous labs across disciplines. Berkeley's A-Lab synthesizes AI-predicted materials from inorganic powders. ARPA-E is funding autonomous labs for next-gen catalysts.

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