AI Startup Funding in Silicon Valley Hits $150B in 2025 — Big Rounds Dominate

The report from 36Kr claims that Silicon Valley’s largest unlisted (primarily AI) startups have amassed US$150 billion in financing through 2025—roughly RMB 1.05 trillion—recording a new high well beyond previous peaks such as the US$92 billion in 2021. [1],[2] The article roots this surge in the AI boom and investor behavior that prefers certainty: late-stage deals, infrastructure build-out, and resource hoarding in expectation of tighter capital conditions ahead. [2]

Three megadeals dominate the round-off: first, the US$41 billion round for OpenAI led by SoftBank; second, Anthropic’s US$13 billion round in September; third, Meta’s more than US$14 billion investment in Scale AI. [2] These few transactions contribute massively to the total, creating distortion: they pull up aggregate totals but may not be reflective of the funding landscape for typical startups. [3]

The strategic posture across founders and investors is defensive: with concern about overinvestment, valuation resets, or macroeconomic pullbacks, many firms are raising more frequently than usual—some after just months, not years—and emphasizing cash reserves or reducing burn. [2] The term “fortress‐like balance sheets” signifies a shift from growth‐at-all-costs modeling to resilience. [1],[2]

However, this boom is uneven. While top AI players have access to abundant capital, many early-stage firms are reporting difficulty raising funds. Also, the overall number of active venture firms is declining; data shows venture capital investment in Silicon Valley and San Francisco was US$69 billion in 2024, down from the breakout US$84.8 billion peak in 2021. [3] This polarization raises risks: fewer companies receiving large sums inflate valuations, but exit activity and revenue generation may lag.

From a risk and strategy standpoint, there is clear signal that capital allocation is reaching saturation at the top end. Investors should watch for valuation compressions, regulatory changes (especially around AI), macroeconomic tightening (interest rates, inflation), and exit inflection points (IPOs, M&A). For startups: securing differentiation, building defensible moats (technical, data, regulatory), and focus on unit economics will be crucial.

Open questions include: how many of the top‐funded firms will reach profitable or self-sustaining states; whether newer startups can break through amid intense competition; when (and how sharply) investor sentiment may shift; and the role of policy, regulation, and competitive dynamics to shape which firms succeed.