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ARK Innovation Predictions and Big Ideas 2023

Fortune via Yahoo Finance | | Feb 1, 2023

Ark invest big ideas 2023 - ARK Innovation Predictions and Big Ideas 2023

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Hypersonic flight, 3D printed humanoid robots, groceries delivered by drones, molecular biomarkers for early detection of malignant tumors

  • Report: That's the conclusion from her annual Big Ideas report published on Tuesday by her money management firm. This 153-page deep dive attempts to handicap the potential commercial opportunities awaiting those startups and incumbents quick to embrace promising new technologies set to supplant older, obsolete ones.
  • Wood believes companies that succeed in disrupting existing industries will experience “super-exponential growth”, lifting their cumulative value by an average annual rate of 40% in the process to reach a staggering $200 trillion by 2030.
    • To put that gargantuan figure into perspective, the International Monetary Fund estimated last April that the size of the entire world's economy would cross the $100 trillion mark in nominal GDP terms by the end of the year.

See:  a16z: Big Fintech Ideas to Tackle in 2023

  • Forecasting winners: Instead of emphasizing spreadsheets and valuation models that often focus on short-term fundamentals like a company’s forward-year cash flow or earnings per share, her ARK Invest research team prefers a top-down analysis of what macroeconomic problems inhibit social progress before examining which innovators are doing the most to solve them.
    • Much of the research conclusions in Big Ideas is based on predicting when technologies may reach mass market maturity by employing Wright’s Law, a general theory from 1936 that attempts to model cost degradation curves over time.
  • Wood’s team identifies 14 distinct technologies they believe will feed off each other, broadly converging into five overarching investment themes ("innovation platforms") grouped around artificial intelligence, robotics, energy storage, public blockchains and the multi-omic sequencing of digitalized biological data.

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