DEER ISLE: Insights, Flows, & Investment Trends
AI Training AI – Does this Cause AI to Collapse?
AI is rapidly becoming the default intelligence layer in capital markets, embedded across investment analysis, banking research, and quantitative trading. These systems read filings, draft research, screen deals, monitor portfolios, and generate trade ideas on a scale. AI is no longer an edge; it is infrastructure. And like any infrastructure, if it increasingly feeds on its own outputs, it degrades unless deliberately corrected at the system level. This would suggest proprietary data sets create a competitive edge.
It is now commonly cited that roughly 50% or more of web content includes AI-generated material, with credible forecasts suggesting that figure could rise to 90% or more over time. As AI-generated text, analysis, and commentary flood the public domain, the raw material used to train and retrain “AI analysts” becomes progressively more synthetic, more derivative, and less grounded in primary reality.
This creates a structural problem, not a philosophical one. When AI models are trained predominantly on AI-generated content, signal quality decays. Nuance erodes. Errors compound. Outputs converge toward polished consensus rather than differentiated insight.
Markets, however, require disagreement to function. Price discovery depends on opposing views, incomplete information, and judgment under uncertainty. If analytical systems increasingly reinforce the same narratives, the market does not become smarter; it becomes more synchronized and more fragile.
The viability of AI-driven market intelligence therefore hinges on what breaks this feedback loop. The differentiator will be private context: proprietary data, rigorous data tagging, thoughtful filtering, and intentional framing of problems. Models that are continuously fed original, non-public, human-generated inputs remain adaptive. Models trained primarily on generic public content become increasingly banal, regardless of their sophistication.
Organizations that can generate their own datasets through direct market engagement, proprietary research, transaction-level insight, and long-horizon historical analysis will compound advantage. Organizations that rely primarily on AI-written public data will converge toward the same conclusions at the same time.
In a market where AI is ubiquitous, edge will not come from using AI, it will come from AI training based upon private data.
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Capital Provider Interest: Interest is concentrated in stable, cash-flowing businesses within basic and non-cyclical industries. We see interest from capital providers with many mandates effectively starting at enterprise values of $100 million and above, reflecting a preference for scale, durability of cash flows, and downside protection in an uncertain rate and growth environment.
Private Equity: Private equity activity has been increasing in select sectors where visibility, consolidation potential, or structural tailwinds are clear. Notable areas include life sciences and biotech, RIA roll-ups, data centers, lower middle-market platforms, and cloud services. These sectors offer a combination of recurring revenue, fragmentation that supports buy-and-build strategies, and long-term demand drivers that remain intact despite macro volatility.
Venture Capital: Venture capital remains highly selective. Anecdotal data even from smaller funds suggests investment rates of less than 1% of inbound opportunities, underscoring how constrained the funnel has become. A typical profile may involve reviewing 500–600 opportunities annually and closing six or fewer investments, with capital concentrated in only the most differentiated teams, technologies, and market positions. Real Estate: In real estate, hospitality is showing early signs of recovery as buyers and sellers begin to close valuation gaps. Many owners refinanced during COVID with five-year debt structures and are now approaching maturity in a higher-rate environment. Rather than handing assets back to lenders, owners are increasingly choosing to reset exit expectations and transact at lower valuations in order to preserve residual equity and salvage value from their investments.
