Someone will eventually say something along the lines of “the Fed is flying partly blind” during any serious market discussion that takes place these days, whether it’s in conference rooms in Midtown Manhattan or on financial podcasts that draw listeners who enjoy tracking yield curve inversions. It arises in the same way that unpleasant realities frequently do in work environments: they are brought up quickly and then ignored, as though it would be rude to linger on them.
However, for months, Jim Bianco, president of Bianco Research and one of the more consistently fascinating voices on monetary policy, has been stating it clearly. The models developed by the Federal Reserve were not designed to comprehend the post-COVID economy. Additionally, the emergence of AI-driven algorithmic trading has complicated financial markets in ways that no central bank has yet to fully account for.
| Field | Details |
|---|---|
| Core Issue | AI-driven algorithmic trading creating market dynamics that Federal Reserve models may not adequately capture — including potential algorithmic collusion without explicit coordination |
| Key Federal Reserve Warning | Fed Governor Lisa Cook — warned November 20, 2025 that generative AI tools have the potential to shape market dynamics in ways that impair competition and market efficiency |
| Cook’s Direct Quote | “Recent theoretical studies find that some AI-driven trading algorithms can indeed learn to collude without explicit coordination or intent, potentially impairing competition and market efficiency” |
| Inflation Outlook | Jim Bianco of Bianco Research projects inflation remaining “sticky” around 3% — above the Fed’s 2% target — driven by structural post-COVID shifts including labor market changes and AI’s effect on GDP measurement |
| Current Fed Funds Rate | Held steady at 4.25–4.5% as of August 2025 — Fed resisting political pressure to cut despite slowing growth signals |
| Yield Curve Problem | Traditional yield curve recession signals no longer reliably predicting downturns — a key analytical tool the Fed has historically relied upon is behaving differently |
| AI and GDP Distortion | The trillion-dollar AI investment boom is quietly inflating GDP figures — raising questions about whether reported growth reflects genuine economic activity or capital accumulation in a narrow sector |
| Broader Market Risk | AI-related equity selloffs in early 2026 triggered cross-sector volatility — reflecting what analysts described as a growing “sell now, ask later” investor mindset driven by algorithmic momentum |
| Political Pressure | Trump administration pushing publicly for rate cuts — Fed under Jerome Powell maintaining independence, making decisions based on data rather than political demands |
| Structural Shift | Post-COVID economy reshaped by falling immigration, remote work patterns, and AI capital deployment — creating conditions that older Fed models were not designed to interpret |
This becomes really uncomfortable when looking at the inflation picture. Two percent is the Fed’s declared goal. With data to support his claims, Bianco has maintained that inflation may continue to hover around three percent. Not as a brief blip, but rather as a new equilibrium shaped by structural changes that the post-pandemic economy has locked in: the trillion-dollar AI investment boom subtly inflating GDP figures in ways that don’t necessarily translate into widely shared economic activity, labor market changes driven by declining immigration, and remote work changing where and how much people spend money. All of the numbers point in one direction. People’s actual experiences of buying groceries and paying rent frequently seem completely different.
Add algorithmic trading to this already complex picture. In November 2025, Federal Reserve Governor Lisa Cook sounded the alarm in a speech that got far less attention than it merited. Cook cautioned that theoretical research was already discovering evidence that AI-driven trading algorithms can learn to collude without any explicit coordination or intent, and that generative AI tools have the potential to alter market dynamics in ways that hinder competition and market efficiency. Read that again slowly: algorithms are learning to coordinate pricing behavior with one another because the systems are sophisticated enough to realize that coordination benefits the entities operating them, not because anyone programmed them to do so. It has a significant impact on how financial market prices behave and, consequently, how well those markets inform policymakers about the state of the economy.
The Federal Reserve determines interest rates by analyzing market signals while seated in its offices on Constitution Avenue in Washington. The data the Fed uses to determine monetary policy is being filtered through a process it doesn’t fully comprehend if those market signals are being shaped by algorithmic systems that are, in essence, subtly coordinating in ways that regulators cannot see. This is not a minor issue. The academic literature on AI-driven trading has been discussing this structural issue for a number of years, and Cook has publicly acknowledged it. Whether the effect is significant enough to materially skew the Fed’s assessment of inflation or financial conditions is still up for debate. However, the question is no longer speculative.
The yield curve, which for many years was one of the most accurate indicators of economic recession in the Fed’s analytical toolbox, has a similar issue. In the past, an inversion—a situation in which short-term interest rates are higher than long-term rates—has indicated that financial markets anticipate future economic decline. According to Bianco, the bond market’s structure has changed, and the sheer volume of algorithmic activity has changed how yield relationships form and what they actually signal. As a result, the yield curve has lost much of its predictive reliability in the current environment. The Fed’s tool is acting differently. It’s unclear what will replace it.
The Fed’s position is made more difficult rather than easier by the political pressure that is layered over all of this. Jerome Powell’s data-dependent stance has become more challenging as a result of the Trump administration’s loud and persistent calls for rate cuts through 2025. Being perceived as making decisions based on economic data rather than political expediency is essential to the Fed’s institutional credibility. That’s the correct idea. The problem is that the economic evidence itself is becoming more and more challenging to interpret due to structural changes in markets and the economy as a whole, which have nothing to do with politics. Using models designed for an economy that existed before the pandemic altered labor markets and before algorithmic trading emerged as the primary means of price discovery in financial markets, the Fed is maintaining rates at 4.25 to 4.5 percent while interpreting data that AI systems are partially producing and partially distorting.
Thinking about this convergence makes it difficult to avoid feeling a little uneasy. The organizations tasked with overseeing macroeconomic stability seem to be operating with maps created for a slightly different region; they are not wholly incorrect, but they are noticeably lacking in areas that are crucial during times of stress. The Fed’s AI blind spot isn’t the result of a lack of effort or intelligence. There is a structural lag between the rate of change in the economy and its markets and the rate of adaptation of the analytical frameworks that govern them. There have always been those lags. The speed at which the gap is growing and the extent to which the tools causing it—algorithmic systems optimizing for outcomes the Fed wasn’t intended to anticipate—remain poorly understood by those most accountable for maintaining the economy’s equilibrium are what have changed.
