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Why you should read this report:
Lab automation looks hot, but the usual indicators are quiet: patents are flat, vendors report uneven demand, and standard market metrics barely move. This report shows what those signals miss—where recent AI-drug-discovery capital actually landed, why “Lab Automation Engineer” roles increasingly require Python and APIs over vendor scripting, and which operational metrics (orchestration and closed-loop activity) reveal the real shift.
What you’ll find inside:
- Original patent analysis showing lab-automation filings peaked in 2018 and declined 6% while core AI patents rose 150%.
- Job-market transformation data documenting how one title fractured into five specializations, with Python requirements jumping from 30% to 90% of postings.
- Funding timeline quantifying the pivot toward AI-first discovery and orchestration software (vs. hardware), including recent mega-rounds and multi-billion-dollar pharma partnerships.
- New metrics framework for what actually drives throughput: API call volumes, closed-loop run rates, and versioned-protocol adoption.
- Comparative case studies across AI-led discovery programs showing why integrated orchestration cut end-to-end timelines materially (including one program with a 67% reduction) while component-level optimization delivered smaller gains.
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