Core Insights - The report titled "State of AI: An Empirical 100 Trillion Token Study with OpenRouter" analyzes the usage of over 300 models on the OpenRouter platform from November 2024 to November 2025, focusing on real token consumption rather than benchmark scores [3][6][8]. Group 1: Open Source vs. Closed Source Models - Open source models (OSS) have evolved from being seen as alternatives to closed source models to finding their unique positioning, becoming the preferred choice in specific scenarios [9]. - The relationship between open source and closed source models is now more complementary, with developers often using both types simultaneously [10]. - The usage of open source models is expected to reach approximately one-third by the end of 2025, with Chinese models experiencing significant growth from 1.2% to 30% in weekly usage share [12][13]. Group 2: Market Dynamics and Model Diversity - The dominance of DeepSeek as the largest contributor to open source model usage is diminishing as more models enter the market, leading to a diversified landscape [16]. - By the end of 2025, no single model is expected to maintain over 25% of token usage, with the market likely to be shared among 5 to 7 models [17][18]. - The report indicates a shift towards medium-sized models, which are gaining market favor, while small models are losing traction [20][21]. Group 3: Evolution of Model Functionality - Language models are transitioning from dialogue systems to reasoning and execution systems, with reasoning token usage surpassing 50% [22]. - The use of model invocation tools is increasing, indicating a more competitive and diverse ecosystem [29][31]. - AI models are evolving into "intelligent agents" capable of independently completing tasks rather than just responding to queries [43]. Group 4: Usage Patterns and User Retention - The complexity of tasks assigned to AI has increased, with users now requiring models to analyze extensive documents or codebases [35]. - The average input to models has quadrupled, reflecting a growing reliance on contextual information [36]. - The "glass slipper effect" describes how certain users become highly attached to models that perfectly meet their needs upon release, leading to high retention rates [67][70]. Group 5: Regional Insights and Market Trends - The share of paid usage in Asia has doubled from 13% to 31%, indicating a shift in the global AI landscape [71]. - North America's AI market share has declined to below 50%, while English remains dominant at 82%, with Simplified Chinese holding nearly 5% [80]. - The impact of model pricing on usage is less significant than expected, with a 10% price drop resulting in only a 0.5%-0.7% increase in usage [80].

Core Insights - The article discusses the significant outperformance of AI leading companies in both the US and China stock markets since the launch of ChatGPT, highlighting concerns about potential asset price bubbles due to high valuations and low risk premiums [2][3]. Group 1: Market Dynamics - The relationship between interest rates and stock prices is explored, suggesting that a decline in interest rates could support high stock valuations, but the traditional cause-and-effect relationship may not hold in the current environment [3][4]. - AI-related capital expenditures have contributed to one-third of the US GDP growth this year, indicating that the stock market's wealth effect is driving consumer spending and influencing interest rates [3][4]. Group 2: Investment Trends - The article notes that foreign investors hold $21.2 trillion in US stocks, representing 31.3% of the total market capitalization, the highest since World War II, which reflects global confidence in US tech giants [4]. - The emergence of a "herd effect" among individual investors in the AI narrative is highlighted, which can amplify both upward and downward market movements [5]. Group 3: AI Economic Impact - The potential economic impact of AI is debated, with estimates suggesting that AI could contribute an additional 0.8-1.3 percentage points to GDP growth annually over the next decade [8][9]. - The article emphasizes the uncertainty surrounding the economic benefits of AI applications, particularly in measuring direct and indirect returns [7][9]. Group 4: Cost-Benefit Analysis - The need for capital market support for AI development is stressed, with a focus on the high costs associated with research and application, including computing power and energy consumption [6][9]. - The shift from capital-light software models to capital-intensive hardware production in AI investment is noted, with major tech companies taking on roles traditionally held by venture capitalists [6]. Group 5: Competitive Landscape - The article discusses the implications of the open-source model in AI, particularly how China's approach is reshaping global competition and reducing monopolistic advantages held by a few companies [14]. - The differences in energy sources between the US and China are highlighted, with potential future constraints on AI development due to the economic characteristics of fossil fuels versus renewable energy [14]. Group 6: Long-term Considerations - The article concludes that the high valuations of AI-related stocks may be driven by overly optimistic long-term profit growth expectations, which could lead to a market correction if these expectations are not met [15][16]. - The concept of creative destruction is introduced, suggesting that while short-term market disruptions may occur, they could ultimately lead to long-term technological advancements and innovation [16].

Core Viewpoint - The article discusses the relationship between AI-driven stock prices and macroeconomic factors, highlighting the potential for both optimism and risk in the current market environment [2][4]. Group 1: Stock Valuation and Economic Impact - Since the launch of ChatGPT at the end of 2022, leading AI companies in the US and China have significantly outperformed the overall market, with AI-related capital expenditures contributing one-third to US GDP growth this year [2][3]. - The relationship between interest rates and stock prices can be viewed in three ways: traditional causation, reverse causation, or both being influenced by external factors [3][4]. - The wealth effect from stock market gains, particularly among the wealthiest 10% of the population, is driving consumer spending and influencing interest rates [3]. Group 2: AI Investment Dynamics - The current AI technology development is characterized by low application maturity and high profit expectations, necessitating support from capital markets [5][6]. - The shift from capital-light software distribution to capital-intensive hardware production is led by major tech companies, which are now primary supporters of large AI startups [6]. - The uncertainty surrounding the economic benefits of AI applications poses challenges for investors, as the direct and indirect economic benefits are difficult to quantify [6][8]. Group 3: Economic Growth Projections - Different methodologies estimate AI's impact on economic growth, with projections suggesting an additional GDP growth of 0.8-1.3 percentage points annually over the next decade [7][8]. - The introduction of AI is expected to contribute approximately 9.8% to China's GDP by 2035, translating to an annual growth rate of about 0.8% [8]. Group 4: Scale Economics in AI - The breakthrough of DeepSeek demonstrates how algorithmic improvements can compensate for computational limitations, impacting the semiconductor industry [9][10]. - The concept of scale economics applies to chip production, where increased production leads to lower unit costs, contrasting with the scale inefficiencies seen in natural resource extraction [10][11]. - The relationship between demand increase and pricing dynamics in the chip industry suggests that while production may increase, prices are likely to trend downward due to scale economics [11][12]. Group 5: Market Dynamics and Future Outlook - The high valuation of AI-related stocks may stem from overly optimistic long-term profit growth expectations, which could lead to market corrections if these expectations are not met [14]. - The potential for a bubble in AI stocks is influenced by the competitive landscape, particularly with advancements in China's semiconductor industry and improvements in algorithmic efficiency [14]. - The article concludes that while short-term market corrections may occur, the long-term effects of technological advancements could lead to constructive disruption and innovation [14].

Core Viewpoint - The article discusses the relationship between AI-driven stock valuations and macroeconomic factors, emphasizing the potential for both overvaluation and sustainable growth in the context of AI advancements and capital investments [4][5][17]. Group 1: Stock Valuation and Economic Impact - Since the launch of ChatGPT at the end of 2022, AI leading companies in the US and China have significantly outperformed the overall market, with AI-related capital expenditures contributing one-third to US GDP growth this year [4][5]. - The relationship between interest rates and stock prices can be viewed in three ways: traditional cause-and-effect, reverse causation, or both being influenced by external factors [5][6]. - The wealth effect from stock market gains, particularly among the wealthiest 10% of the population, has driven consumer spending, which in turn supports higher natural interest rates [5][6]. Group 2: Cost and Benefit Analysis of AI - The current AI technology development is characterized by low application maturity and high profit expectations, necessitating substantial capital market support [8][9]. - The shift from capital-light software distribution to capital-intensive hardware production is led by major tech companies, which are now primary supporters of AI startups [8][9]. - The economic potential of AI applications remains uncertain, with challenges in quantifying direct and indirect economic benefits [9][10]. Group 3: Economic Growth Projections - Different methodologies estimate AI's impact on economic growth, with projections suggesting an additional annual GDP growth of 0.8-1.3 percentage points over the next decade [10][11]. - The introduction of AI is expected to contribute approximately 9.8% to China's GDP by 2035, translating to an annual growth rate of about 0.8% [11]. Group 4: Scale Economics and Market Dynamics - The breakthrough of DeepSeek illustrates how algorithmic improvements can compensate for computational limitations, impacting the semiconductor industry [12][13]. - The distinction between scale economies in chip production and scale diseconomies in natural resources like coal highlights different market dynamics [13][14]. - The pricing power of large tech firms, driven by scale economies, raises questions about the sustainability of their monopolistic profits in the face of potential regulatory changes [14][15]. Group 5: Open Source and Competitive Landscape - The open-source model of AI development in China is reshaping the global competitive landscape, enhancing China's influence in AI and prompting adjustments in strategies by Western firms [15][16]. - The energy consumption of AI technologies poses a significant concern, with the contrasting approaches to energy sources in China and the US potentially impacting future AI applications [16]. Group 6: Creative Destruction and Market Risks - The high valuations of AI-related stocks may stem from overly optimistic long-term profit growth expectations, which could lead to unsustainable stock prices [17]. - The potential for a market correction exists, driven by changes in the semiconductor industry and the realization that AI applications may not meet current optimistic projections [17].

Group 1 - Microsoft is facing a significant issue with a surplus of GPUs that are currently idle due to a lack of power and space in data centers [4][5][6] - The primary challenge is not the supply of chips but rather the infrastructure needed to support their operation, particularly in terms of power supply and data center readiness [7][9] - The demand for electricity has surged in the past five years, driven by the rapid expansion of AI and cloud computing, outpacing the capacity of utility companies to meet this demand [11][12] Group 2 - Data center developers are increasingly opting for "behind-the-meter" power solutions to bypass public grids and meet their energy needs directly [12][14] - Solar energy is seen as a flexible and quick-to-deploy solution, but its implementation still takes considerable time, which does not align with the fast-paced demands of AI [14] - Concerns exist that if AI demand slows down, the investments in power plants and energy storage to support AI may become underutilized, although some industry leaders believe AI energy needs will continue to grow [15][17] Group 3 - Microsoft has decided not to stockpile single-generation GPUs due to the risk of obsolescence and depreciation if they cannot be powered in time [15][19] - The shift in focus from chip performance to energy efficiency is becoming more relevant as power shortages become a critical issue [17][19] - Microsoft announced plans to invest $8 billion in data centers and AI projects in the Middle East over the next four years, indicating a strategic move to regions with abundant energy resources [20]

Core Insights - AI is not leading to job losses but rather increasing the importance of radiologists, with demand for their services rising significantly [2][3][22] - The phenomenon of "Jevons Paradox" suggests that increased efficiency from AI can lead to greater consumption and job creation rather than reduction [4][12] - The "Baumol's Cost Disease" indicates that as some industries become more profitable due to efficiency gains, wages in other sectors must rise to retain talent, leading to increased costs across the board [16][18] AI in Radiology - Over 700 radiology AI models have received FDA approval, representing more than 75% of medical AI devices [22] - By 2025, the average salary for radiologists in the U.S. is projected to reach $520,000, making it the second-highest paid medical specialty [2][22] - The number of positions in diagnostic radiology is at a record high, with a vacancy rate also reaching historical levels [22] Economic Implications - The rise in AI efficiency is expected to increase service costs in unrelated sectors, as wages rise to compete for labor [18][20] - AI's impact on productivity may lead to a paradox where jobs that cannot be automated become more valuable, as they represent the final human touch in processes [21][23] - The overall economic growth driven by AI may lead to a situation where even low-skill jobs, like dog walking, become more expensive due to rising living costs [20][21] Future Workforce Dynamics - As AI takes over 99% of tasks, the remaining human roles may become highly specialized and valuable, leading to a unique labor market [21][23] - The potential for new job categories may emerge, focusing on tasks that require human presence or decision-making, despite the automation of many processes [23][24] - The ongoing challenge will be to enhance productivity while managing the societal changes that come with technological advancements [24]

Core Viewpoint - Microsoft is facing a significant challenge with a surplus of GPUs that are currently idle due to insufficient power supply and inadequate data center infrastructure [1][4][36]. Group 1: Power Supply Issues - The primary issue is not a surplus of chips but rather the lack of power capacity and the speed at which data centers can be built near power sources [2][4]. - Microsoft has a large number of NVIDIA AI chips that are currently unused due to power shortages [3][5]. - The demand for electricity has surged in the past five years, driven by the rapid expansion of AI and cloud computing, outpacing utility companies' capacity to generate additional power [15][16]. Group 2: Infrastructure Challenges - There is a significant lag in the supply side of electricity generation, with traditional power plants taking years to come online, while AI industry expansion occurs on a quarterly basis [17][18]. - Data center developers are increasingly opting for "behind-the-meter" power solutions to bypass public utilities and meet their energy needs [17]. - The construction of data centers and their associated power and cooling systems is not keeping pace with the actual demand for AI computing power [18][20]. Group 3: Future Outlook and Strategies - There is a belief among some industry leaders that AI's electricity demand will continue to grow, leading to more applications and increased overall demand [24][26]. - Microsoft has decided not to stockpile a single generation of GPUs due to the risk of obsolescence and depreciation over time [30][31][32]. - The industry is shifting focus towards energy-efficient chips as power supply becomes a more pressing concern than chip availability [39]. Group 4: Investment and Expansion - Microsoft has received approval to export NVIDIA chips to the UAE for building data centers necessary for AI model training [41]. - The company plans to invest $8 billion over the next four years in the Gulf region for data centers, cloud computing, and AI projects, indicating a shift of AI infrastructure from Silicon Valley to energy-rich emerging markets [42][43].

Core Viewpoint - Microsoft is facing an unprecedented issue with a surplus of GPUs that are idly stored due to insufficient power supply and data center infrastructure [1][2][3][4]. Group 1: Current Challenges - The primary challenge is not a surplus of computing power but rather the lack of electrical capacity and the inability to quickly build data centers close to power sources [2][4]. - Microsoft has a significant number of NVIDIA AI chips that are currently unused due to these infrastructure limitations [3][4]. - The industry is experiencing a collective challenge where energy and infrastructure must match the growing demand for AI computing power [10][11]. Group 2: Energy Supply Issues - The overall electricity demand in the U.S. has surged over the past five years, driven by the rapid expansion of AI and cloud computing data centers [15][16]. - Traditional power plants take years to develop, while the AI industry expands at a quarterly pace, leading to a mismatch in supply and demand [17][18]. - Many data center developers are opting for "behind-the-meter" power solutions to bypass public utilities and meet their energy needs [17]. Group 3: Future Outlook - There are concerns that if AI demand slows down, the investments in power plants and energy storage could become underutilized [22]. - However, some industry leaders believe that AI's energy demand will only continue to grow, leading to more applications and higher overall demand [24][25]. - The call for increased power generation capacity is seen as a strategic asset for AI development, with suggestions for the U.S. government to add 100 gigawatts of power annually [28]. Group 4: Strategic Adjustments - Microsoft has decided not to stockpile single-generation GPUs due to the risk of obsolescence and depreciation if they cannot be powered [30][32][33]. - The industry is shifting focus from peak performance to energy efficiency, as the limitations have transitioned from computing power to energy supply [39][40]. - Microsoft has announced plans to invest $8 billion in data centers and AI projects in the Gulf region over the next four years, indicating a shift in AI infrastructure towards energy-rich emerging markets [43][44].

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]

Core Viewpoint - Microsoft is facing an unprecedented issue of having a surplus of GPUs that are idle due to power shortages and insufficient data center infrastructure to support their operation [2][5]. Group 1: Power Shortages and Infrastructure - The primary challenge is not the supply of chips but the availability of power and the speed at which data centers can be built near power sources [3][5]. - Microsoft has a significant number of Nvidia AI chips that are not being utilized because the existing infrastructure cannot support their operation due to power and cooling limitations [5][7]. - The overall power demand in the U.S. has surged in the past five years, driven by the rapid expansion of AI and cloud computing, which has outpaced the growth of power generation capacity [7][10]. Group 2: Industry Response and Future Outlook - Data center developers are increasingly opting for "behind-the-meter" power supply methods to bypass public grids and meet energy needs directly [7][8]. - There are concerns that if AI demand slows down, the investments in power plants and energy storage projects to support AI might become underutilized [8]. - However, some industry leaders believe that AI's power demand will continue to grow, leading to a call for increased power generation capacity as a strategic asset for AI [8][12]. Group 3: Strategic Shifts in Chip Production - Microsoft has decided not to stockpile single-generation GPUs due to the risk of obsolescence and depreciation if they remain unused for extended periods [8][10]. - The focus may shift from maximizing peak performance to developing energy-efficient chips, as the industry grapples with power limitations rather than chip shortages [10][11]. - Microsoft has announced plans to invest $8 billion in data centers and AI projects in the Middle East, indicating a shift of AI infrastructure to regions with abundant energy resources [12].