The article predicts that the Singularity, a point where AI surpasses human intelligence, will occur on February 10, 2026. In San Francisco, discussions around the Singularity are rampant, yet they often lack a concrete timeframe. The author challenges this uncertainty by analyzing five key metrics of AI progress: MMLU scores (a benchmark for language models), tokens per dollar (indicating the cost of AI processing), intervals between major AI breakthroughs, the number of emergent AI research papers, and the fraction of code generated by AI through tools like Copilot. After extensive analysis, only the count of emergent papers shows a clear hyperbolic trajectory, suggesting that human attention to AI developments is accelerating faster than machine capabilities. The author argues that most predictions about AI growth rely on exponential models, which are flawed. Instead, a hyperbolic model captures the essence of accelerating AI advancements better. The analysis reveals that while metrics like MMLU and costs per token show linear growth, the excitement within the field—measured through emergent papers—indicates a looming inflection point. This suggests humans are increasingly overwhelmed by rapid developments in AI, raising concerns about societal impacts rather than the machines themselves. Economic implications are significant. In 2025, layoffs reached 1.1 million, with over 55,000 directly tied to AI. Companies are preemptively downsizing based on perceived threats from AI, rather than current performance metrics. Legislative efforts, such as the EU AI Act, are lagging behind technology, creating a trust crisis in AI governance. In the stock market, AI-related companies are dominating, with the top ten S&P 500 stocks hitting a peak index weight not seen since the dot-com bubble. The concentration of capital and the acceleration of AI-related advancements are reshaping the job market and society's ability to adapt.