BlackRock’s Tony Kim on the ‘dawn of a new intelligence era’

Episode Summary

Executive Summary: Tony Kim argues tech is entering a new AI-driven era after two prior phases: the 2009-19 “golden age” of low rates and the 2021-22 correction. He sees public/private valuations still misaligned, IPO supply too large, and only elite companies winning. AI and quantum computing will reshape the stack, with compute, data, and proprietary information becoming decisive.

Main Topics: Tony Kim’s investment background and tech ecosystem map (Priority: 5/5): Kim explains how engineering, Silicon Valley exposure, and a historical/map-based framework led him to build a comprehensive model of the tech landscape across layers and geographies. Tech market cycles: golden age, correction, and AI era (Priority: 5/5): He divides the last decade-plus into a low-rate golden age (2009-19), a painful inflation/rate reset (2021-22), and the current AI age that is redefining the sector. Public vs. private market valuation disconnect (Priority: 5/5): Kim says late-stage private valuations remain too high versus public comps, keeping the IPO window constrained and creating a backlog of unmarketable unicorns. AI as a new technology stack (Priority: 5/5): He frames AI as more than a tool: a new intelligence layer built on semiconductors, compute, data, foundation models, and applications, with huge implications for winners and losers. Quantum computing as the next platform shift (Priority: 4/5): Kim sees quantum as a complementary breakthrough that could unlock biology, chemistry, encryption, and synthetic data generation within roughly a decade. What makes a durable tech company and team (Priority: 4/5): He emphasizes differentiated products, moats, founder-led businesses, and teams that combine brilliance, curiosity, and maniacal focus; he also argues talent archetypes are changing because AI automates much of implementation work. Macro, geopolitics, and valuation sensitivity (Priority: 4/5): Higher rates, recession risk, and reshoring/supply-chain fragmentation raise discount rates and inflationary pressure, especially for growth and hardware-heavy tech businesses.

Key Arguments: Tech should be analyzed as an interconnected ecosystem, not isolated subsectors, because silicon, hardware, software, data, and geography all interact. Public and late-stage private markets are increasingly linked; private valuations should be judged against public comps, not venture-era narratives. The 2013-21 environment was powered by low rates and easy capital; the post-2021 environment demands discipline and a reset in risk assumptions. The current private market is split between a small group of AI winners and a much larger group of overvalued or underpowered companies. The IPO market remains constrained because the valuation gap between private and public markets has not fully closed. AI is different from prior tech waves because it challenges human intelligence and can potentially become the new software layer. The AI stack is likely to be concentrated in large firms because compute-heavy infrastructure and foundation models require billions in capital and years of R&D. Smaller companies can still win in vertical applications where proprietary data, domain expertise, and regulated workflows matter. Quantum computing is a credible next frontier because classical computing is nearing physical limits and cannot efficiently model nature, chemistry, or biology. In the age of AI, human advantage shifts toward asking the right questions, exercising judgment, and creating ideas rather than merely assembling models or summaries.

Data Points: BlackRock technology AUM overseen by Tony Kim: about $20 billion - Kim’s current responsibility as head of Tech Sector Fundamental Equities at BlackRock Tenure running BlackRock Global Technology Funds: since 2013 - Kim’s leadership of the tech investment platform Golden age of tech period: 2009-2019 - Post-financial-crisis decade of low rates and rapid tech growth Tech sector weight in S&P 500 (20 years ago): 15% - Kim compares historical versus current concentration Tech sector weight in S&P 500 (today): almost 40% - Shows the rise in sector dominance over two decades Public tech unicorns globally: roughly 1,000 - Kim’s estimate of public tech unicorn market cap universe Public tech unicorn market cap: 28 trillion - Approximate total market capitalization of public tech unicorns Private tech unicorns globally: 1,300 - Kim’s estimate of private tech unicorns at current marks Valuation peak differential in private markets: maybe 200% - Kim’s estimate of how much higher private valuations were versus public comps at the peak Example SaaS public valuation at peak: 15x revenue - Used as a comparison point for frothy public market software valuations Example private SaaS valuation at peak: 40-50x ARR - Illustrates the premium paid in private markets versus public comps SaaS public valuation after repricing: about 6x revenue - Kim cites the compression in public software multiples Private SaaS valuation after repricing: about 20x ARR - Still elevated relative to public comps, in Kim’s view IPO/SPAC count in 2021 peak era: about 400 globally - Kim combines IPOs and SPACs to illustrate the prior exit window Typical annual global IPO count: 30-50 globally - Kim’s rough range for normal market conditions Private market dispersion in 2022: median tech valuations down 60%-65% - Referenced to show the severity of the correction in smaller/private names Tech sector downside in 2022: 20%-80% decline depending on subsector - Kim describes wide dispersion across the tech stack AI stock performance in early phase: 100%-200% up - He cites semiconductor and infrastructure beneficiaries during the early AI surge Discount-rate sensitivity: 1% rate change ≈ 10% valuation change - Kim’s simplified DCF rule of thumb for growth companies Potential impact of a 4% rate increase: ≈40% off valuation - Illustrative effect on present value of future cash flows Estimated fab cost today: $25 billion - Kim’s example of the capital intensity of advanced semiconductor manufacturing Future fab cost estimate: $50 billion - Kim warns costs may double as nodes become more advanced Quantum computing timeline estimate: 3-7 years for breakthrough; possibly 4-10 years - Kim’s view on when quantum could reach practical, supercomputer-like capability Recorded episode date: Tuesday, October 3rd, 2023 - Podcast timestamp provided in the closing disclaimer

Pivotal Quotes: "The technology industry is as diverse as the S&P 500 within the sector itself." — Tony Kim: Explaining why he built a full map of the tech ecosystem "There are more private unicorns at those marks and not the real value." — Tony Kim: Arguing that late-stage private valuations remain disconnected from public-market reality "If AI consumes what we think of software, it could write the software. It will become the new software." — Tony Kim: Describing AI as a structural replacement rather than a mere feature layer

Implications: Investors should expect continued dispersion: AI infrastructure and select vertical applications may compound, while overvalued private companies face more down-rounds, consolidation, or failure. For workers, the edge shifts toward creativity, judgment, and domain expertise.