An LP's diversification strategy across different venture funds is undermined when every fund converges on a single theme like AI. This creates a highly correlated portfolio, concentrating systemic risk rather than spreading it. The traditional diversification benefits of investing across multiple managers, stages, and geographies are nullified.
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Unlike past platform shifts that caught many off-guard, the AI wave is universally anticipated. This 'consensus innovation' intensifies all existing competitive pressures, as every investor—from mega-funds to accelerators—is aggressively pursuing the same perceived opportunities, pushing factors like Power Law belief to an extreme.
Active managers are struggling against the S&P 500 not just from bad picks, but because the market is dominated by a few AI stocks they can't fully concentrate in. Many also became too defensive during April's volatility, causing them to miss the subsequent sharp market rebound.
The AI boom is fueled by 'club deals' where large companies invest in startups with the expectation that the funds will be spent on the investor's own products. This creates a circular, self-reinforcing valuation bubble that is highly vulnerable to collapse, as the failure of one company can trigger a cascading failure across the entire interconnected system.
WCM realized their portfolio became too correlated because their research pipeline itself was the root cause, with analysts naturally chasing what was working. To fix this, they built custom company categorization tools to force diversification at the idea generation stage, ensuring a broader set of opportunities is always available.
Today's market is more fragile than during the dot-com bubble because value is even more concentrated in a few tech giants. Ten companies now represent 40% of the S&P 500. This hyper-concentration means the failure of a single company or trend (like AI) doesn't just impact a sector; it threatens the entire global economy, removing all robustness from the system.
Aggregate venture capital investment figures are misleading. The market is becoming bimodal: a handful of elite AI companies absorb a disproportionate share of capital, while the vast majority of other startups, including 900+ unicorns, face a tougher fundraising and exit environment.
The increased volatility and shorter defensibility windows in the AI era challenge traditional VC portfolio construction. The logical response to this heightened risk is greater diversification. This implies that early-stage funds may need to be larger to support more investments or write smaller checks into more companies.
Private credit funds have taken massive market share by heavily lending to SaaS companies. This concentration, often 30-40% of public BDC portfolios, now poses a significant, underappreciated risk as AI threatens to disintermediate the cash flows of these legacy software businesses.
AI startups' explosive growth ($1M to $100M ARR in 2 years) will make venture's power law even more extreme. LPs may need a new evaluation model, underwriting VCs across "bundles of three funds" where they expect two modest performers (e.g., 1.5x) and one massive outlier (10x) to drive overall returns.
Investing in startups directly adjacent to OpenAI is risky, as they will inevitably build those features. A smarter strategy is backing "second-order effect" companies applying AI to niche, unsexy industries that are outside the core focus of top AI researchers.
