Too early to tell if AI is in a bubble, says Goldman Sachs' Kim Posnett

Core Insights - AI is perceived as transformative and value-creating across various industries, with potential for broader applicability compared to previous tech innovations [2][4] - Historical analysis indicates that 75% of major tech innovations over the past 175 years led to equity price bubbles, but the current environment does not yet reflect an AI bubble [3][4] - The path of AI development is expected to be upward over time, albeit with fluctuations and varying success among companies [4][5] Capital Allocation and Debt Issuance - Significant capital raising is primarily driven by hyperscalers, with debt investors eager to gain exposure to these large, diversified businesses [7][8] - Despite increased debt issuance, hyperscalers maintain healthy credit metrics and still possess substantial debt capacity [8][9] - The heavy supply of debt has led to a slight widening of credit spreads, reflecting a recalibration by debt investors [9] Strategic Partnerships and Market Dynamics - The interconnectedness of deals among model companies, infrastructure providers, and hyperscalers raises concerns about elevated risks, especially during market corrections [10][12] - The current macroeconomic environment is favorable, with a pro-growth US administration and robust public market valuations contributing to increased CEO confidence [14][15] - This confidence is driving a rise in M&A and IPO activity, fueled by the growth potential of AI across sectors [15]