Core Insights - Large language models (LLMs) are reaching a performance bottleneck despite significant investments and data usage, leading to concerns about the sustainability of returns on investment [1][2][5] - Global spending on artificial intelligence (AI) is projected to reach nearly $1.5 trillion by 2025, a 50% increase from 2024, and could rise to $2 trillion by 2026, marking a further 37% increase [1][4] - Major tech companies are heavily investing in LLMs, but there is growing skepticism regarding the economic returns from these investments [1][4] Investment Trends - The competition among major tech firms like Google, Amazon, Meta, Microsoft, and OpenAI in LLM development is intensifying, with costs potentially reaching hundreds of billions [4][5] - In 2023, leading companies generated approximately $1 billion in public sales from LLM products, expected to grow to $4 billion in 2024 and potentially reach between $235 billion and $244 billion by 2025, although most of this revenue will be reinvested into infrastructure [4][5] - The UNCTAD forecasts that the AI market could reach $4.8 trillion by 2033, while CMR estimates global AI revenue could hit $3 trillion by then [4] Economic Viability - There is a significant gap between infrastructure investment and end-user software licensing revenue, raising questions about the sustainability of current investment levels [5][6] - The expectation that all major LLM companies will emerge as winners is based on the assumption that their core products are nearing the end of their useful lifecycle, which may not hold true for all [5][6] - The high training costs of new LLMs are increasing exponentially, with current costs reaching hundreds of millions, while performance improvements are becoming marginal [6] Market Sustainability - Deutsche Bank has raised concerns that the current AI investment boom may not be sustainable due to the difficulty in maintaining exponential growth in tech spending [7] - Bain & Company reports that AI may not generate sufficient revenue to support the required computational power, predicting a $800 billion funding gap by 2030 [7] - BCA Research warns of a potential shift from a shortage to an oversupply of computing resources, which could lead to a decline in capital expenditures [7] Long-term Outlook - Goldman Sachs remains optimistic, projecting that AI will significantly boost GDP growth, contributing approximately 0.4 percentage points annually in the coming years, with a cumulative potential of 1.5% growth in the long term [7] - UBS emphasizes that AI investment will be a key growth driver for investment portfolios in the medium to long term, with ongoing progress in monetizing AI solutions [7][8]