AI Exchanges: Will falling costs drive new opportunities?
Feb 4, 2025
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Kim Posnett, Global co-head of Investment Banking at Goldman Sachs, shares insights on the impact of low-cost AI models on tech spending and deal-making. The conversation delves into how these advancements may encourage greater AI adoption, even amidst market volatility. They explore the evolving role of AI agents and the shift towards new data economies. Posnett highlights the balance between reduced costs and the need for robust infrastructure, painting an optimistic picture of AI's future in various sectors.
The emergence of low-cost AI tools like DeepSeek could significantly reduce the capital expenditures necessary for AI development, leading to broader adoption across industries.
As operational costs of AI decline, businesses can automate complex processes, enhancing productivity and paving the way for innovative applications in various sectors.
Deep dives
Emergence of Low-Cost AI Tools
The rollout of a low-cost AI tool by DeepSeek has raised significant questions about the future of AI infrastructure and capital expenditures by major tech companies. This tool has the potential to mitigate prior concerns about the high costs of developing AI technology, particularly in terms of pre-training infrastructure. As costs associated with AI training evolve, projections indicate a trend toward lower capital needs, which might lead to increased adoption and new applications. This development suggests a shift in the market dynamics, potentially justifying investments made by companies while pivoting the focus toward more efficient AI utilization.
Exploring New Use Cases for AI
The decline in AI operational costs heralds a new era of cost-effective applications across various industries, including legal, financial, and healthcare sectors. Improved efficiency from low-cost AI tools means that businesses can automate more complex processes, significantly enhancing productivity. As the cost of AI applications diminishes, it encourages broader adoption, making technologies that were once deemed too expensive now feasible. This expansion could lead to innovative use cases that revolutionize industries, driven by advancements in AI capabilities.
The Role of Power and Data in AI Advancement
The ongoing conversation around AI infrastructure includes significant considerations about the demand for power and data as driving factors for innovation. With AI models requiring substantially more power than traditional servers, the need for improved energy sources and management practices becomes critical. Concurrently, discussions about data scarcity are evolving towards synthetic data solutions, highlighting a burgeoning economy around data generation and sharing. Together, these factors illuminate the path forward for AI development while emphasizing the interplay between technology, power usage, and data accessibility.
The introduction of low-cost AI models is raising questions about AI infrastructure and the high spending on AI by the world’s largest technology companies. In this inaugural episode of a special podcast series, AI Exchanges, Co-Hosts Allison Nathan and George Lee discuss the issues surrounding the costs of AI development and implementation, and the impact on deal-making, with Kim Posnett, global co-head of Investment Banking in Goldman Sachs’ Global Banking & Markets business.