The AI Boom’s Multi-Billion Dollar Blind Spot

AI Reasoning Capabilities & Limitations - The industry initially believed AI reasoning was the next leap towards superintelligence, enabling models to "think" and show their work by breaking problems into steps [3][4] - However, research papers, including one from Apple titled "The Illusion of Thinking," question the promise of reasoning models, suggesting they may only be pattern-matching rather than truly reasoning [7][10] - Apple's research indicates that reasoning models' performance collapses on complex tasks like the Towers of Hanoi puzzle with more than seven discs, achieving zero accuracy [9] - Salesforce terms the current state of AI as "jagged intelligence," highlighting a gap between LLM capabilities and real-world enterprise demands [13] - Research suggests current AI training methods struggle to elicit genuinely novel reasoning abilities, limiting generalization to new, untested scenarios [13][14][15] Investment & Market Implications - The industry has invested heavily in AI, with approximately $2 billion spent, and anticipates significant growth in use cases [2] - The potential failure of reasoning models to scale raises concerns about the return on investment in AI and whether enterprises are overspending [20][25] - If reasoning models prove effective, they would require significantly more compute, potentially extending the infrastructure boom for companies like Nvidia [19] - Discrepancies in AI progress could impact partnerships, such as the one between OpenAI and Microsoft, particularly concerning the definition and control of AGI [29] - The industry's pursuit of superintelligence may be further away than initially anticipated, potentially impacting the timeline and expectations for AGI [16][17][28]