Cathie Wood: Did The Market Overreact To The Election? | In The Know

Summary

Cathie Wood argues that the market’s recent weakness reflects election uncertainty and inflation concerns, but does not signal a systemic financial crisis. She remains bullish on AI-led productivity and economic growth, while cautioning that data-center development, technology spending, and adoption need to be managed more efficiently.

Markets, elections, and policy

• Wood says markets have generally risen in the year after U.S. midterm elections, citing historical returns of roughly 13–14% in the examples discussed. She also points to the 1987 market decline as a reminder that corrections can occur even within longer-term uptrends.
• Although congressional results looked like a Democratic sweep, she says state and local ballot measures were more mixed. In her view, measures affecting household finances—including wealth taxes and tax limits—often leaned in a more conservative direction.
• Wood says the prolonged Iran war and associated oil and gasoline inflation contributed to public anger and market uncertainty. She says ARK was wrong to expect the war to end quickly, and that markets had been pricing political uncertainty since around May or June.
• She says defense stocks have been hit by expectations that higher healthcare spending could require cuts elsewhere. She also flags possible taxes on banks and energy companies, but presents these as possibilities, not settled policy.
• Wood expects political gridlock could help bring the federal deficit to 2.8% of GDP by the end of 2028, though she describes this as uncertain.

Data centers and AI investment

• Wood acknowledges that some companies moved too quickly in building data centers without adequately considering local concerns, including electricity costs, water use, pollution, visual impact, and displacement. She expects companies to respond with more community-sensitive development.
• She cites a Texas proposal associated with Elon Musk’s data-center project that would reportedly reduce property taxes by 20%, cut electricity bills by 40%, and employ 5,000 people in a town of 20,000. She also relays a Brainstorm participant’s account that Virginia data-center communities have seen lower property taxes and enough economic development to build roughly one high school per year.
• Wood says constraints in power, chips, and data-center capacity could encourage efficiency and reduce wasteful investment. She argues that this could make the AI-driven technology cycle healthier than the dot-com boom and bust.
• Ramp data shows a wide gap in corporate AI spending per employee per month:
  • Top 1% of companies: nearly $10,000
  • Top 10%: a little over $800
  • Everyone else: about $15
• She says AI inference costs—the cost of generating an answer or completing a task—are falling about 99% per year for a fixed level of performance. She also cites AI training and inference cost declines of roughly 75–99%, arguing that official measures may understate software deflation.
• Wood sees AI as a catalyst for robotics, energy storage, blockchain, and multiomics. She cites a projected $26 trillion humanoid-robot opportunity over 10–15 years, equivalent to about 20% of current global GDP, and a $10 trillion robotaxi opportunity.
• She expects AI adoption to reward companies that improve productivity and put pressure on those that do not. She argues that some transaction-focused apps could face challenges as AI personal assistants reduce the need to search and transact within individual apps.

Credit risk and company-specific discussion

• Wood says high-yield credit-default-swap (CDS) levels have risen somewhat but remain below their COVID-era highs; investment-grade CDS levels are also not showing broad distress.
• CDS on hyperscalers are elevated relative to investment-grade credit and, she says, comparable to investment-grade levels during the worst of COVID. She links investor concern to the companies’ shift from large free-cash-flow generation toward negative free cash flow as they invest in AI infrastructure.
• She says hyperscaler CDS eased somewhat after the election amid expectations that data-center construction could slow.
• CoreWeave CDS are described as above 1,000, compared with high-yield levels closer to 300. Wood says CoreWeave is more exposed to a data-center slowdown because its business is focused on cloud computing infrastructure, while its contracts are primarily with hyperscalers. She expects its CDS spreads could settle as markets calm.
• Wood says spending on frontier AI models is shifting toward leading labs, naming OpenAI, Anthropic, and Gemini. She also highlights SpaceX AI, suggesting orbital computing could eventually help address data-center and power constraints.
• She describes the market’s response to Atlassian and ServiceNow as evidence of concern about software disruption. ServiceNow reportedly fell 27% in one day, despite its CEO describing it as an AI “execution layer.” She also mentions Apple as seeking more cost-efficient AI use.
• Wood says ARK’s estimate of an approximately $26 trillion humanoid-robot opportunity comes from work by Tasha Keiny and Daniel.

Inflation, productivity, and the economy

• Wood says quarterly productivity growth was 3.6% annualized, while unit labor costs fell 0.7%. She interprets the combination as compensation per hour growing at just under 3% and expects faster productivity to benefit businesses and consumers.
• She says productivity gains could be distributed through higher wages, wider company margins, more R&D and investment, or lower prices.
• In AI-intensive sectors, real output per payroll job is reportedly about 11% above trend, a gain Wood says has emerged in the past few years. She says the data do not yet show similar improvement across the rest of the economy.
• Wood argues that CPI and PPI may understate software and technology deflation. She notes that equipment inflation has diverged from software inflation, and sees a need for more equipment and data-center capacity.
• She says U.S. natural-gas prices have fallen a little over 35% over the past year, while gasoline prices have risen roughly 30%. She sees lower natural-gas prices as a potential U.S. advantage for power generation and data centers, and notes that U.S. gas storage was near typical winter levels while European storage was below them.
• Wood says third-quarter GDP grew 3.4% and forecasts real GDP growth could reach 7% through 2030, or 9% through 2035 if the technology revolution proceeds as expected. These are her projections, not reported consensus forecasts.
• She says manufacturing has recovered from its “rolling recession,” while housing remains weak. Homebuilder inventories are high, and builders are buying down mortgage rates by about one percentage point for buyers—an incentive not reflected in the cited home-price data.
• Average hourly earnings rose 0.4% for the month, about a 5% annualized rate but roughly 3% year over year. Wood says the monthly reading may indicate companies are beginning to share productivity gains with workers.
• The unemployment rate fell, and the unemployment rate for people aged 16–24 declined from 9.9% to 9.3%. Wood says baby-boomer retirements and reduced immigration could contribute to labor shortages. Ramp data, she adds, suggest AI-intensive companies are hiring faster than less AI-intensive firms.
• She says the U.S. effective corporate tax rate is about 8%, profit margins are near highs, and accelerated depreciation is supporting investment. She also attributes recent import growth partly to data-center-related purchases.

Assets, rates, and performance views

• Gold: Wood says the price fell below $4,000 after reaching roughly $5,600. She expects gold could fall further if inflation risks ease and Fed Chair Warsh follows the policy path she anticipates.
• Oil and electric vehicles: She expects oil prices to decline, arguing that high oil prices have accelerated the shift to EVs. She says transportation accounts for 57% of oil use and expects substitution toward electricity generated from natural gas, hydro, nuclear, solar, and wind.
• Bitcoin: Wood says Bitcoin and gold have had little historical correlation—about 0.1 since 2019—and that the correlation has recently turned negative. ARK expects Bitcoin to rise relative to gold, while acknowledging uncertainty over whether Bitcoin could be treated as “digital gold” during a decline in gold.
• Equities relative to commodities: She says the S&P 500’s performance relative to oil and gold remains healthier than during past inflationary periods.
• Rates and bonds: Wood says the yield curve is flattening and that recent rate increases have been driven more by real yields. She expects productivity and technology-driven disinflation to constrain long-term rates relative to short-term rates, even as stronger nominal GDP growth could lift rates overall. She cautions that bond-heavy investors—particularly retirees with little financial flexibility—could be vulnerable to capital losses.
• Systemic risk: Bank CDS and high-yield credit indicators show some movement but, in her assessment, remain far from levels associated with the 2008–09 crisis, the European sovereign-debt crisis, or COVID.

Framework and cautions

Wood’s investment framework and main cautions are:

• Assess election and market headlines against historical market behavior and underlying economic data.
• Evaluate data-center projects not only by their investment potential, but also by their effects on local electricity, water, taxes, employment, and infrastructure.
• Track corporate AI spending alongside productivity gains; early adopters may pressure competitors to adopt AI or lose market share.
• Distinguish investment-grade, high-yield, hyperscaler, and specialized data-center credit risks rather than treating all AI-related borrowers alike.
• Watch productivity, labor costs, energy prices, and software-cost declines when assessing inflation and interest-rate risks.
• Wood urges workers and businesses to adapt to AI rather than remain in areas she believes will be disrupted. Her market, growth, and asset-price views are forecasts and carry substantial uncertainty.

Disclosures

No explicit “not financial advice” disclaimer appears in the subtitles.

Presenter and sources mentioned

• Presenter: Cathie Wood, ARK Invest CEO and CIO.
• Data and references: Ramp corporate-spending data; credit-default-swap data; U.S. economic and labor statistics; and Brainstorm participants Matt and Larry Goldberg. Wood also refers to robotics research by Tasha Keiny and Daniel.
• Other named people and organizations: John Nosta, whose book The Borrowed Mind is discussed in an opening tribute; the Federal Reserve and Chair Powell; Anthropic and its CEO Dario Amodei; OpenAI; Gemini; SpaceX AI; Apple; Atlassian; ServiceNow; CoreWeave; Meta; Amazon; Google; Tesla; Archer; and Uber.