Valuations don't jump dramatically; they 'sneak up on you.' An investor might balk at a $45M cap when they expected $40M. But the fear of missing a potential unicorn is stronger than the desire for a slightly better price, causing a gradual, batch-over-batch inflation of valuation norms.
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A massive valuation for a "seed" round can be misleading. Often, insiders have participated in several unannounced, cheaper tranches. The headline number is just the final, most expensive tier, used to create FOMO and set a high watermark for new investors.
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.
Raising too much money at a high valuation puts a "bogey on your back." It forces a "shoot the moon" strategy, which can decrease capital efficiency, make future fundraising harder, and limit potential exit opportunities by making the company too expensive for acquirers.
Seed-focused funds have a powerful, non-obvious advantage over multi-stage giants: incentive alignment. A seed fund's goal is to maximize the next round's valuation for the founder. A multi-stage firm, hoping to lead the next round themselves, is implicitly motivated to keep that valuation lower, creating a conflict of interest.
Startup valuation calculators are systematically biased towards optimism. Their datasets are built on companies that successfully secured funding, excluding the vast majority that did not. This means the resulting valuations reflect only the "winners," creating an inflated perception of worth.
For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.
The standard VC heuristic—that each investment must potentially return the entire fund—is strained by hyper-valuations. For a company raising at ~$200M, a typical fund needs a 60x return, meaning a $12 billion exit is the minimum for the investment to be a success, not a grand slam.
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 paradigm has inverted. Historically, private companies traded at an "illiquidity discount" to their public counterparts. Now, for elite companies, there is an "access premium" where investors pay more for private shares due to scarcity and hype. This makes staying private longer more attractive.
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.