Key takeaways
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- AI is increasing uncertainty, not reducing it, according to Howard Marks.
- Investors are underestimating both the scale and disruption of AI.
- Marks warns that new technologies often attract capital before risks are understood.
- Equity may be a better way to play AI than credit, given high business-model uncertainty.
What Happened?
Oaktree co-founder Howard Marks said AI is making the investment landscape more unpredictable, not more predictable. Speaking at a conference, he emphasized that traditional investing — based on forecasting outcomes — may be less reliable in an AI-driven world where second-order effects are harder to model.
Marks also highlighted how investors tend to over-allocate to new technologies based on promise rather than proven outcomes, drawing parallels to past boom-and-bust cycles in innovation-driven sectors.
Why It Matters
The key insight is structural: AI introduces non-linear outcomes. That makes it harder to price risk, especially in areas like private markets where transparency is limited.
Marks’ distinction between equity vs credit exposure is critical. If AI companies face uncertain business models, fixed-income investors may not be adequately compensated for the risk. Equity holders, by contrast, benefit from upside if the technology succeeds — making it a more asymmetric bet.
His comments also reinforce a broader concern: capital is flowing rapidly into AI without a full understanding of downside risks. That dynamic has historically led to mispricing and eventual corrections.
What’s Next?
Investors should focus on how AI impacts business models, not just revenue growth narratives. The key questions are durability of competitive advantage, pricing power, and whether AI-driven gains translate into sustainable cash flows.
The broader takeaway is that AI is both an opportunity and a source of instability. Markets are still in the early phase of pricing that reality — which means both upside and risk are likely being underestimated.
