Harvey, an AI startup for the legal industry, exemplifies the hyper-growth funding environment for top-tier AI companies. The company raised capital three times in less than a year, with its valuation climbing from $3 billion (Sequoia) to $5 billion (Kleiner Perkins) and finally to $8 billion (a16z).
Related Insights
The venture capital benchmark for elite growth has shifted for AI companies. The old "T2D3" (Triple, Triple, Double, Double, Double) heuristic for SaaS is no longer the gold standard. Investors now consider achieving $100M ARR in under three years as the strongest signal of exceptional product-market fit in AI.
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.
The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.
The AI fundraising environment is fueled by investors' personal use of the products. Unlike B2B SaaS where VCs rely on customer interviews, they directly experience the value of tools like Perplexity. This firsthand intuition creates strong conviction, contributing to a highly competitive investment landscape.
Harvey's initial product was a tool for individual lawyers. The company found greater value by shifting focus to the productivity of entire legal teams and firms, tackling enterprise-level challenges like workflow orchestration, governance, and secure collaboration, which go far beyond simple model intelligence.
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.
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.
Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.
The traditional SaaS growth metric for top companies—reaching $1M, $3M, then $10M in annual recurring revenue—is outdated. For today's top-decile AI-native startups, the new expectation is an accelerated path of $1M, $10M, then $50M, reflecting the dramatically faster adoption cycles and larger market opportunities.
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.
