How Other Biotech & Life Sciences leaders Actually Make Decisions

• •achieving radical transparency in food origin
• •applying machine learning to make sense of vast genomic data
• •quantitatively tracking human performance improvements
• •integrating phenotypic information with genomic data for understanding
• •bridging machine learning and biomedical data disciplines

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• •reducing disease to single variables is 'asinine'
• •extreme burden of 24/7/365 operational requirements for living organsNew
• •tech and health tech companies losing massive valuation and access to capital simultaneously
• •very few success stories to train machine learning models on in pharma
• •sprinkling llm fairy dust without real implementationNew

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• •contributing to the top line and bottom line of the company
• •metrics alignment with cfo on profitability and roi, not just revenue growth
• •cost-effective for patients in the overall system
• •driving greater improvement in business processes
• •prediction of heart disease or alzheimer's in 10 years

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• •customer segmentation and targeting - identify most important/profitable segments, find them efficiently
• •the three m's (moment, metric, motion) - evaluating product and market fitNew
• •mindset-driven approach: targeting consumers based on their desire to optimize life and create progress
• •company strategy first: ensuring technology choices and decisions align with and advance the overall company strategy
• •cost reduction curve: aim for 'million fold reduction in costs' similar to genome sequencing

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• •only facilitating meetings as a project manager (not bringing expertise)
• •consuming processed foods with heavy preservatives and emulsifiers
• •claims of ai drug discovery without experimental validation at every step
• •ignoring complex biological mechanisms like ecdna
• •cmos not building strong relationships across ceo, cfo, cto, chr organizational anchors

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