Algorithms vs. Fundamentals: Katie Wood on the Real Causes of Market Turbulence

Recently, ARK Invest founder and investment director Kathy Wood explained that the sharp fluctuations in the U.S. stock market are not due to changes in economic fundamentals but are driven by the mechanical logic of algorithmic trading. She believes that the current wave of panic selling is a chain reaction of software systems that handle nearly 90% of trading volume but cannot distinguish good companies from bad ones. This thesis warrants a detailed analysis, as it shifts the usual perspective on the nature of modern market volatility.

How Machine Trading Creates the Illusion of Volatility

According to Kathy Wood, algorithmic trading operates on a “sell first — understand later” principle. These algorithms do not analyze the competitive environment or a company’s potential; instead, they monitor technical signals: price drops, increased volatility, asset correlations, and risk limits within portfolios.

The mechanism is triggered as follows: when an asset’s price falls or volatility rises, the model automatically reduces risky positions to stay within set limits. This reduction itself increases volatility and asset correlation, prompting other algorithms to sell. A self-reinforcing loop emerges — a feedback cycle where even fundamentally healthy companies are sold off in haste.

At this point, Wood describes this phenomenon as astonishing: machine systems “pour out the baby with the bathwater,” failing to differentiate asset quality. This process is especially destructive in over-saturated sectors where many portfolios have similar structures. Companies with solid prospects are hit not because of fundamental issues but due to cold, mathematical risk management.

Structural Transformation of the AI Era: What the Market Doesn’t Understand

Wood believes that behind the current turbulence lies a brilliant transformation in the tech sector. She argues that the market is shifting from a universal SaaS (Software as a Service) model to highly specialized AI-agent platforms. This process inevitably puts pressure on traditional software, but the market reacts overly emotionally.

The problem is that algorithms cannot comprehend this transformation. When they detect a slowdown in SaaS growth, they automatically sell off the entire sector indiscriminately. Machine logic cannot distinguish which companies are successfully adapting to the AI paradigm and which are lagging. These mispricings, caused by a complete lack of deep analysis, create opportunities for active investors who conduct thorough fundamental research.

“That’s why we focus our portfolio on the most compelling ideas,” Wood emphasizes. “It is during these moments that the market offers us the best opportunities.” She describes the current situation as a “climb up the wall of worry” — a phenomenon that historically preceded the strongest bull markets, not a crash.

Productivity Instead of Inflation: A New Economic Paradigm

One of Wood’s most radical theses concerns the macroeconomic impact of AI: the AI revolution could fundamentally change the traditional view that growth inevitably leads to inflation.

She argues that a surge in productivity will reduce the deficit-to-GDP ratio so much that the U.S. could return to a budget surplus by the end of 2028. While optimistic, her reasoning is based on concrete data: Palantir has shown a 142% increase in U.S. commercial revenue, even as the number of salespeople slightly decreased. Such a productivity leap is not a hypothesis but a measurable reality.

Wood forecasts that by the end of the decade, global real GDP will grow at 7–8% annually. She considers this projection conservative. Contrary to Keynesian economists who expected labor costs to grow 5–7% due to labor shortages, the reality has been different: labor costs are rising only about 1.2% annually. The reason is not wage suppression but a spike in productivity.

Regarding inflation, Wood highlights data from the Truflation index, which tracks 10,000 goods and services in the real economy. The latest figure is around 0.7% annually, significantly lower than official CPI figures, indicating that current inflation calculations are based on distorted foundations. She also points to deflation in critical sectors: inflation on existing housing has fallen below 1%, and prices for new homes remain negative. Oil prices have decreased by double digits year-over-year.

American Exceptionalism Through the Dollar and Cutting-Edge Technologies

Wood dispels the misconception of America’s decline as a global power. She believes the U.S. is on the verge of an economic breakthrough, not decline. A key factor is the dollar’s dynamics.

While in recent years the hryvnia has depreciated due to geopolitical factors — as countries diversify reserves into gold and alternative currencies — Wood thinks this trend will reverse. Technical analysts are already seeing the dollar bounce off previous support levels. If the dollar rises, it will serve as a “powerful anti-inflationary factor,” as imported goods will become cheaper in dollar terms.

M2 money supply is still recovering from pandemic-related contraction but has stabilized around a 5% annual growth rate. Meanwhile, the velocity of money is flattening or declining, partially offsetting inflation risks associated with money supply expansion. All this paints a picture of an economy that is not in an inflationary spiral but shows signs of healing.

The Labor Market in Transition: From Fear to Entrepreneurship

Low consumer confidence remains one of the main mysteries of the U.S. economy. People feel insecure despite strong macroeconomic indicators. The reasons are obvious: a weak labor market and a severe housing affordability crisis.

Wood notes the figures: in the latest employment revision for 2025, numbers were adjusted downward by 861,000, meaning a loss of about 75–80 thousand jobs per month. It’s no surprise that consumers fear for their jobs even when official unemployment remains low.

However, she also sees positive signals. The youth unemployment rate (ages 16–24), previously above 12%, has fallen below 10%. This is not just a recovery in employment — Wood considers it a sign of an “entrepreneurial explosion.” According to her observations, people unable to find traditional jobs are starting their own businesses. AI has become so powerful and accessible that an individual or small team can launch an effective startup.

This phenomenon will have profound implications: people will use AI not just for efficiency at work but to create new companies. This will become another significant driver of global productivity, as Wood predicts.

Challenges in the Crypto Market: Builders Are More Active Than Ever

The cryptocurrency market is going through tough times. Compared to gold, which has recently performed better and even outpaced BTC, Bitcoin has become a risk-off asset during panic sell-offs.

Wood acknowledges this reality but offers a different interpretation: she believes that gold’s supply is increasing faster than Bitcoin’s. She points to the gold/M2 ratio, which has reached record levels — even higher than during the Great Depression and the high-inflation 1970s. In her view, this indicates that gold is “overextended.”

Bitcoin remains in a long-term upward trend — higher highs and higher lows remain intact. The market is currently testing a key level around 2024, with technical support near $20,000–$23,000. This is where Bitcoin has become a refuge for those fearing systemic counterparty risk.

Recently, Wood became an advisor to LayerZero — a DeFi project aimed at creating an ecosystem for the new AI era, requiring 2–4 million transactions per second. Ethereum can currently handle 13 transactions per second, Solana about 2000. During the most intense times, DeFi builders are working hardest, developing new architectures for the future.

1996, Not 1999: Choosing the Path During Turbulence

One of Wood’s most compelling comparisons is to the internet revolution. She believes we are not at the peak of the 1999 bubble but rather at 1996, when the internet was just beginning its march toward mass adoption.

The key difference: during the tech and telecom bubble peak, Jeff Bezos could come out and say, “We’re losing more and more money because we’re investing aggressively,” and the market celebrated — Amazon’s stock rose 10–15% on the news. Today, it’s the opposite. When Google, Meta, Microsoft, and Amazon announce aggressive AI investments, the market punishes them: stocks fall rather than rise.

According to Wood, this is a strong signal that we are not in an irrational speculative frenzy but rather in a market filled with fear and doubt from those holding “bubbles from 2000.” This mood — a “climb up the wall of worry” — is a reliable foundation for sustained upward trends, not a sign of collapse.

Wood firmly states that Google, Meta, Microsoft, and Amazon should invest aggressively in AI because it is “the greatest opportunity of our lifetime.” The question is not whether money is being spent wisely but whether AI and chatbots will take time away from traditional social media or if intelligent agents will do all the shopping and routine tasks for us. These are market transition moments, not the end of value.

Conclusion: From Panic to Investment

Market turbulence that frightens many investors appears to Wood as a natural phenomenon accompanying major transitions. Just like in April last year during tariff turbulence — those who panicked and sold then spent the following year regretting it.

In Wood’s view, current extreme fluctuations are mostly generated by algorithms that lack human analysis, leading to mispricings. For those willing to do the work, this is a time of opportunity. “We are climbing the wall of worry, and this has always been a sign of the strongest bull markets,” she concludes.

Ultimately, each investor faces a choice: let emotions dictate decisions under machine-driven panic, or stand on the right side of change. The golden age of the AI revolution is just beginning.