Acknowledging venture capital's power-law returns makes winner-picking nearly impossible. Vested's quantitative model doesn't try. Instead, it identifies the top quintile of all startups to create a high-potential "pond." The strategy is then to achieve broad diversification within this pre-qualified group, ensuring they capture the eventual outliers.
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Contrary to the 'get in early' mantra, the certainty of a 3-5x return on a category-defining company like Databricks can be a more attractive investment than a high-risk seed deal. The time and risk-adjusted returns for late-stage winners are often superior.
An analysis of 547 Series B deals reveals two-thirds return less than 2x. This data demonstrates that a "spray and pray" strategy fails at this stage. The cost of misses is too high, and being even slightly worse than average in your picks will result in a failed fund. Discipline and picking are paramount.
The current fundraising environment is the most binary in recent memory. Startups with the "right" narrative—AI-native, elite incubator pedigree, explosive growth—get funded easily. Companies with solid but non-hype metrics, like classic SaaS growers, are finding it nearly impossible to raise capital. The middle market has vanished.
Vested's investment model gains an edge from proprietary data on employee sentiment and behavior. Signals like unsolicited negative comments, willingness to counter on price, or selling more shares than necessary provide unique insights into a company's health that traditional financial analysis lacks, forming a data moat.
Many LPs focus solely on backing the 'best people.' However, a manager's chosen strategy and market (the 'neighborhood') is a more critical determinant of success. A brilliant manager playing a difficult game may underperform a good manager in a structurally advantaged area.
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 venture capital return model has shifted so dramatically that even some multi-billion-dollar exits are insufficient. This forces VCs to screen for 'immortal' founders capable of building $10B+ companies from inception, making traditionally solid businesses run by 'mortal founders' increasingly uninvestable by top funds.
AI-powered VC introduction platforms are not just connectors; they are stringent gatekeepers reflecting the high bar of the current market. By assigning a "grade" and only facilitating introductions for high-scoring decks, these systems programmatically enforce VC standards at scale.
Conventional venture capital wisdom of 'winner-take-all' may not apply to AI applications. The market is expanding so rapidly that it can sustain multiple, fast-growing, highly valuable companies, each capturing a significant niche. For VCs, this means huge returns don't necessarily require backing a monopoly.
The majority of venture capital funds fail to return capital, with a 60% loss-making base rate. This highlights that VC is a power-law-driven asset class. The key to success is not picking consistently good funds, but ensuring access to the tiny fraction of funds that generate extraordinary, outlier returns.