Cathie Wood: “AI and Bitcoin Are Moving Faster Than We Expected” | EP #226

Wood discusses the 2008 financial crisis as a catastrophic deflation that introduced massive counterparty risk. She argues Bitcoin serves as a hedge not only against inflation but also against such deflationary panics, provided one self-custodies. She contrasts with stablecoins, which are backed by dollars and thus exposed to fiat risk. In the context of disruption from converging technologies, she expects more bankruptcies in the traditional order, making self-custody more salient.

Wood explains that the discrepancy between GDP and GNI is widening because free services that become paid (robot labor) will show up in income. She warns that relying on flawed GDP numbers leads the Fed to over-tighten policy. She ties this to her broader thesis that we are in a deflationary boom mislabeled as inflation.

Wood explains that traditional financial analysis misses this because it operates in siloed sectors. Her firm, ARK Invest, instead structures research around 15 technologies that cut across industries, enabling analysts to spot convergences. She invokes Wright's Law—cost declines follow cumulative unit doubling—rather than Moore's Law, and notes that AI is the biggest catalyst. She acknowledges that almost all living economists dismiss this, but emphasizes that even Elon Musk is now talking about explosive GDP growth, lending credibility. She also suggests the 7% figure is conservative because it doesn't fully account for vertical integration and data center cost collapses from SpaceX and other innovations.

Wood is a strong advocate for open source, citing Linux as the poster child of success. She believes the current Claudebot (Moltbot) wave shows individual agency outpacing big companies. She notes that Meta's acquisition of Manis, a Chinese open-source company, has drawn no US government objection—contrasting with hardware restrictions. She sees the competition as healthy, potentially spurring US innovation, but warns that China's investment-to-GDP ratio of 40% versus the US's 20% is fueling an AI race, especially in healthcare where China is running far more clinical trials. She points out that the US government's silence on the software side suggests it may be missing the strategic shift.

Wood ties this to the broader energy picture for AI: massive investments into power ($10 trillion by 2030) will demand all low-carbon sources. She notes that the regulatory shift in 1974-75, along with going off the gold standard and price controls, set the US on a bad course. She praises the current administration's deregulation and tax incentives (immediate expensing of manufacturing structures) that could re-industrialize the US and favor innovation. She believes the renewed enthusiasm for nuclear, combined with solar and even orbital data centers, can bring costs down again. She mentions that China is half as energy-efficient as other major countries, but is compensating by building reactors.

Wood explains that Bitcoin's correlation with gold is only 0.14 lately, but gold's run suggests Bitcoin is ready for another big move. The flash crash on October 10th caused an automatic deleveraging of $28 billion in over-leveraged positions, which has now cleared, setting up a cleaner market. She also addresses the deflation critique: Bitcoin is not just an inflation hedge; in a deflationary disruption like 2008, self-custodied Bitcoin eliminates counterparty risk, making it a hedge against systemic financial collapse. She notes that Iran is now using Bitcoin for daily transactions, a real-world case study. She also points out that Bitcoin's daily trading volume is already a quarter of gold's, despite a market cap 1/20th, indicating high velocity.

Wood emphasizes that Tesla's factory strategy ('the machine that makes the machine') is a critical barrier to entry—legacy automakers can't pivot because they assemble third-party components and are burdened by unions and pension obligations. Even if autonomous cars reduce the need for car ownership, the total vehicle fleet needed in the US drops from 400M to 24M, so the auto market as we know it will be destroyed. However, demand for robots in different shapes will be effectively infinite, so the automotive sector will survive but with new winners. She sees Tesla also benefiting from distributed energy ecosystems, with robo-taxis acting as mobile inference engines and energy storage devices. She expects autonomous delivery (Zipline, Wing, Coco) to grow massively, possibly facing noise issues with drones.

Wood explains that if productivity is undermeasured, real GDP growth is underestimated and inflation overestimated, leading policymakers to make mistakes. The traditional belief that growth is inflationary (Keynesian) is wrong; she says growth is disinflationary, and in this tech-driven world, deflationary. She ties this to the GDP paradox: when technology makes things radically cheaper (e.g., AI inference, rocket launches), GDP can appear to drop even as real wealth increases. However, she counters that many unpaid activities (childcare, cooking) will move into the measured economy via robot purchases, boosting GDP. She also notes that the US dollar's share of global currency reserves is falling, adding urgency to getting economic measurement right. She advocates using GNI and focusing on per capita productivity, but acknowledges even productivity is hard to measure.