Odd Lots

James van Geelen on His Viral AI Doom Scenario

February 28, 2026

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  • The unexpected virality of the Citrini Research piece, "The 2028 Global Intelligence Crisis," highlights significant underlying nervousness and uncertainty within the market regarding the speed and implications of AI advancement. 
  • The core argument of the scenario analysis is that the exponential capability curve of AI, if sustained, presents a transition period that the financial system has never been stress-tested for, even if the scenario itself is assigned a low probability (10-15%). 
  • The rapid improvement in AI capabilities, particularly the reduction in the cost of inference and the introduction of agentic features into already-adopted technologies, suggests that traditional S-curve adoption models for consumer technology may not accurately capture the speed of enterprise disruption and disintermediation. 

Segments

Viral Piece Context and Reaction
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(00:02:10)
  • Key Takeaway: The viral spread of the Citrini Research piece demonstrated the market’s extreme nervousness and lack of consensus regarding AI’s future impact.
  • Summary: The hosts noted that the article’s extraordinary traction, drawing responses from major firms like Citadel Securities, indicated a high level of market anxiety. The piece was intended as a low-probability scenario analysis (10-15%) focusing on AI-driven white-collar job losses leading to a financial crisis. The immediate and widespread reaction, including the creation of a prediction market on the scenario, underscored how keenly investors are seeking any framework to understand potential AI outcomes.
Citrini Research Overview
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(00:05:59)
  • Key Takeaway: Citrini Research is a thematic equity and macro research firm that focuses on connecting disparate market moves through cohesive narratives, often centered on technology trends like AI infrastructure.
  • Summary: James van Geelen described Citrini Research as a pure investment research firm specializing in thematic equity and macro analysis. The firm previously published successful calls on GLP-1s and the AI infrastructure complex, tracking bottlenecks like optics and memory. The viral piece was specifically motivated by connecting the rally in bonds with the sell-off in software and fintech seen year-to-date.
AI Capability Curve Acceleration
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(00:08:01)
  • Key Takeaway: The AI capability curve is progressing exponentially, moving from agents capable of two minutes of autonomy on complex tasks to eight to sixteen hours in just two years, far exceeding prior expectations.
  • Summary: The co-author projected the AI capability curve as a diagonal line on a logarithmic chart, noting that attempts to level this curve off have failed. The speed of improvement means tasks that were uneconomical last year might become viable this year due to significant cost reductions in inference per cognitive task. This rapid acceleration, rather than the historical 50-year transition seen in previous technological revolutions, forms the core of the scenario being analyzed.
Bear Case and Financial System Stress
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(00:08:54)
  • Key Takeaway: The purpose of the scenario analysis was to articulate a bear case for investors who are otherwise uncomfortable when they cannot envision downside risks in a rapidly advancing market.
  • Summary: The piece aimed to provide investors with a framework encompassing the bull, bear, and base cases, as being unable to see the bear case is the most uncomfortable position. While the scenario is not the most likely outcome, it explores the implications if the fast capability curve continues, asking if the financial system is prepared for such a transition, even if it takes five to seven years. The authors noted that the scenario begins with a bullish S&P 500 (to 8,000) driven by AI infrastructure before detailing the potential downturn.
Disruption of Moats via Agentic Commerce
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(00:28:06)
  • Key Takeaway: Agentic AI, by eliminating human tedium in tasks like price matching, threatens to evaporate moats built on network effects and intermediation by prioritizing the cheapest option.
  • Summary: The scenario suggests that AI agents, unlike humans, do not experience tedium and can execute complex instructions like finding the absolute cheapest price across multiple platforms. This capability could dismantle rent extraction layers in the economy, such as those in delivery platforms, where an agent can aggregate supply and demand optimally. This differs from past comparison shopping websites because the agentic assistant performs the effort automatically on the user’s behalf.
Enterprise Software and Pricing Power
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(00:38:05)
  • Key Takeaway: Incumbent enterprise software companies, particularly systems of record, may benefit initially from AI-driven cost reduction in coding, but their pricing power faces downside risk from customers leveraging AI tools as negotiation leverage.
  • Summary: Systems of record are positioned to improve margins by using AI to reduce internal coding and maintenance costs, which is a short-term positive. However, sales teams face challenges when customers can credibly bluff or actually use AI tools (like those from OpenAI or Anthropic) to demand lower renewal prices. Anthropic’s release of simple, pre-packaged AI tools serves as a reminder to users of what is possible, effectively hammering suppliers over pricing power.
Market Reaction and Policy Vacuum
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(00:44:48)
  • Key Takeaway: The market’s immediate sell-off following the article, and the subsequent rally after rebuttals, confirms that no one has a clear understanding of AI’s trajectory, highlighting deep anxiety and a lack of substantive policy discussion in Washington.
  • Summary: The episode concluded that the market’s volatile reaction to a single scenario piece proves that participants are deeply anxious and operating in uncharted territory regarding AI’s speed. The fact that the market moved based on a single think piece, and then moved again based on a rebuttal from a market maker, shows a lack of conviction. Furthermore, there is a strange chasm where the technology’s impact is intensely discussed privately, yet virtually ignored in substantive policy discussions in Washington D.C.