Dismissing AI's current capabilities is a mistake due to its exponential improvement rate, evidenced by rapid advances in video generation. Markets selling off established companies based on nascent AI competitors are rationally pricing in this non-linear progress, rather than overreacting.
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Stock market investors are pricing in rapid, significant productivity gains from AI to justify high valuations. This sets up a binary outcome: either investors are correct, leading to massive productivity growth that could disrupt the job market, or they are wrong, resulting in a painful stock market correction when those gains fail to materialize.
AI is expected to create a new generation of "model busters": companies that grow so rapidly and for so long that they consistently shatter conventional financial forecasts. Like Apple post-iPhone, whose performance was underestimated by 3x, these AI firms will deliver value far exceeding any spreadsheet's predictions.
The surprisingly smooth, exponential trend in AI capabilities is viewed as more than just a technical machine learning phenomenon. It reflects broader economic dynamics, such as competition between firms, resource allocation, and investment cycles. This economic underpinning suggests the trend may be more robust and systematic than if it were based on isolated technical breakthroughs alone.
Cresta's CEO argues that while the internet's evolution from 1995-2001 was somewhat foreseeable, the advancements in AI since 2019 would have been unimaginable even to the experts who wrote the foundational papers. This highlights the unprecedented nature of the current technological shift.
Unlike previous tech bubbles characterized by speculative oversupply, the current AI market is demand-driven. Every time a major player like OpenAI 3x-es its compute capacity, the new supply is immediately consumed. This sustained, unmet demand indicates real utility, not just speculative froth.
Criticizing AI developers for being a few months off on predictions is a distraction. The underlying trend is one of exponential growth. Like criticizing Elon Musk's Mars timeline while ignoring his historic rocket launches, it's a failure to grasp the scale and direction of the technological shift that is already happening.
Karpathy pushes back against the idea of an AI-driven economic singularity. He argues that transformative technologies like computers and the internet were absorbed into the existing GDP exponential curve without creating a visible discontinuity. AI will act similarly, fueling the existing trend of recursive self-improvement rather than breaking it.
While the long-term trend for AI capability shows a seven-month doubling time, data since 2024 suggests an acceleration to a four-month doubling time. This faster pace has been a much better predictor of recent model performance, indicating a potential shift to a super-exponential trajectory.
For decades, AI only offered incremental improvements (e.g., 20% better fraud detection), which benefited large incumbents. Generative AI is a step-change, enabling entirely new user behaviors like creativity and emotional connection, creating the "1000x better" disruption needed to build new, iconic companies.
Contrary to the 'winner-takes-all' narrative, the rapid pace of innovation in AI is leading to a different outcome. As rival labs quickly match or exceed each other's model capabilities, the underlying Large Language Models (LLMs) risk becoming commodities, making it difficult for any single player to justify stratospheric valuations long-term.
