Core Insights - The price of AI models has dramatically decreased, with costs dropping by up to 280 times for certain models over the past year, indicating a significant technological deflation in the AI sector [6][15][41] - As AI becomes cheaper, the cost of human labor in sectors that cannot be automated is rising, creating a paradox where technology deflation leads to human inflation [15][30][41] Price Dynamics - The cost of using models like GPT-3.5 has fallen from approximately $20 per million tokens to just $0.07, showcasing a free-fall in AI model pricing [6][15] - Different levels of models exhibit varying rates of price decline, with LLM inference costs decreasing at a rate comparable to Moore's Law, approximately tenfold annually [9][30] Economic Implications - The decline in AI model prices is leading to increased consumption and reliance on AI technologies, as businesses and individuals utilize these tools more frequently [21][24] - The rising costs of services that cannot be automated, such as home care and repairs, reflect a shift in labor value, where jobs requiring human presence are becoming more expensive [25][30] Jevons Paradox - The phenomenon where increased efficiency in AI leads to greater consumption aligns with Jevons Paradox, suggesting that cheaper AI will result in higher usage rather than savings [16][21] - As AI becomes a ubiquitous resource, it is transforming from a luxury service to a public utility, increasing dependency on AI technologies [21][32] Labor Market Changes - The labor market is experiencing a revaluation, where jobs that can be automated see their value decrease, while those that require human interaction become more valuable [31][41] - The rising costs of skilled labor in sectors like home repair and healthcare highlight the economic theory that productivity gains in one area can lead to increased costs in less efficient sectors [30][31] Power Dynamics - The decreasing costs of AI models are not leading to a democratization of technology but rather to a concentration of power among a few major companies that control the AI ecosystem [32][39] - As AI becomes cheaper, the dependency on major players like OpenAI and Google increases, leading to a more centralized control over AI technologies and data [33][39] Future Outlook - The ongoing price decline of AI models is not just a technological shift but a fundamental reorganization of value, where human creativity and emotional intelligence become the new high-value assets [41][42] - The future landscape may not be characterized by AI replacing humans but rather by AI redefining the value of human contributions in various sectors [41][42]