The Capital Cycle Podcast AI: Scaling Flaw
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Nov 29, 2024 Kai Chen, an Emerging Markets Analyst at Marathon Asset Management, sheds light on the high-stakes gamble of AI investments. He discusses the slow revenue returns despite massive funding and the job displacement risks posed by generative AI. The conversation also addresses the limitations of large language models and the rising costs of AI development. Chen emphasizes the intense competition in the AGI race and the potential for monopolies, cautioning investors about the uncertain future and sustainability of capital inflows in the AI landscape.
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Scaling Law and ROI
- AI models, despite billions invested, haven't yielded substantial revenue.
- The "scaling law" suggests more computational power leads to better AI, driving investment but overlooking potential limitations.
LLM Limitations
- Large language models (LLMs) excel at pattern matching, mimicking human-like responses.
- However, LLMs struggle with novel problems, lacking the data for effective pattern recognition.
ARC Prize Challenge
- The ARC Prize Challenge highlights LLMs' limitations with novel problem-solving.
- Humans outperform machines in this challenge, demonstrating superior reasoning abilities.