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.
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Ultra-late-stage companies like Ramp and Stripe represent a new category: "private as public." They could be public but choose not to be. Investors should expect returns similar to mid-cap public stocks (e.g., 30-40% YoY), not the 2-3x multiples of traditional venture rounds. The asset class is different, so the return profile must be too.
Traditional valuation models assume growth decays over time. However, when a company at scale, like Databricks, begins to reaccelerate, it defies these models. This rare phenomenon signals an expanding market or competitive advantage, justifying massive valuation premiums that seem disconnected from public comps.
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.
Mega-funds can justify paying "stupid prices" at the seed stage because they aren't underwriting a seed-stage return. Instead, they are buying an option on the next, much larger round where they'll deploy real capital. This allows them to outbid smaller funds who need to generate returns from the initial investment itself.
Some companies execute a 3-5 year plan and then revert to average returns. Others 'win by winning'—their success creates new opportunities and network effects, turning them into decade-long compounders that investors often sell too early.
Success in late-stage venture resembles trading more than traditional investing—it's about buying and selling on momentum. However, this "new public market" has a critical flaw: while liquidity exists on the way up, it vanishes on the downside, making it impossible to execute a true trading strategy when a correction occurs.
The most lucrative exit for a startup is often not an IPO, but an M&A deal within an oligopolistic industry. When 3-4 major players exist, they can be forced into an irrational bidding war driven by the fear of a competitor acquiring the asset, leading to outcomes that are even better than going public.
Unlike SaaS startups focused on finding product-market fit (market risk), deep tech ventures tackle immense technical challenges. If they succeed, they enter massive, pre-existing trillion-dollar markets like energy or shipping where demand is virtually guaranteed, eliminating market risk entirely.
This provides a simple but powerful framework for venture investing. For companies in markets with demonstrably huge TAMs (e.g., AI coding), valuation is secondary to backing the winner. For markets with a more uncertain or constrained TAM (e.g., vertical SaaS), traditional valuation discipline and entry price matter significantly.
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.
