50-60% vs. 80-90% Gross Margins: AI businesses operate with materially lower gross margins due to compute and human-in-the-loop costs, which makes early unit economics and value-based pricing non-negotiable.

3 AI Models and 3 Core Charge Metrics: Copilots lean toward seat or consumption pricing, Agents toward ROI or outcome pricing, and AI-enabled services toward per-output models, while the main charge metrics span consumption, workflow, and outcome with increasing value alignment and cost risk.

$0.99 per Resolution and Hybrid Pricing Formulas: Outcome pricing examples like per-ticket resolution and hybrid structures such as platform fees plus outcome credits show how companies balance predictable revenue with expansion upside.

86% Report Heightened Client Concern Over Global Uncertainty: Wealth managers say their clients are deeply worried about tariffs and geopolitical risk, with most advisers indicating high concern levels being far more likely to decrease U.S. equity allocations. Only 31% expect to increase U.S. equity exposure, compared to 61% planning to increase allocations to developed non-U.S. markets and 48% to emerging markets.

95% Plan to Increase AI Investment, Yet 44% Believe Wealth Lags Broader Industry: Nearly all firms plan to boost AI spending over the next three years, and 68% view AI as vital to competitiveness. However, wealth managers see their segment behind other financial services in adoption, with top AI priorities centering on portfolio management, proposal generation, risk management and due diligence rather than client-facing applications.

71% Expect to Increase Private and Alternative Allocations, 83% Say Private Assets Are Now Essential: Private credit, private equity, digital assets and other alternatives are expected to see the largest allocation growth. A 15% allocation to private assets may increase expected returns by 40 basis points annually while maintaining similar risk levels. ETFs are simultaneously surging (73% expect them to become more common) as advisers pair liquid instruments with illiquid long-term holdings.

VC allocation has grown significantly relative to the market cap of venture-backed IPOs, but whether the industry is outgrowing its opportunity set hinges entirely on four anomalous years: 1999/00 and 2020/21. Both periods generated massive liquidity but ended in disaster, and both are unlikely to repeat due to post-Enron regulation and the unique conditions of pandemic-era zero rates. Excluding those years, the trend suggests VC may already be oversized relative to its exit capacity.

The real scaling opportunity is at the base, not the top: expanding the network of independent managers to surface more novel companies, then getting them public around year 8 instead of year 15. Trading public market growth for extended private timelines is actively degrading company health. Private capital is expensive, incentives misalign with long-term value creation, and companies that IPO sooner show better growth, stronger profitability, lower risk at listing, and superior long-run returns.

Scaled VC funds face a math problem (larger multiples get harder), scaled VC firms face a systematic problem (consensus decision-making, market distortion, distance from founders), but the deeper question is whether VC as an asset class is properly sized relative to actual innovation output. The goal should not be inflating venture capital or IPO market caps but increasing the total proper capitalization of innovation, public and private combined.

Record 2-Year Return Spread Between NASDAQ and VC-Backed IPOs: The analysis highlights the widest gap in more than a decade between strong forward NASDAQ returns and some of the weakest median post-IPO results for venture-backed companies at the 6-month lock-up expiry window.

Public Market Performance Concentrated in a Few Mega-Caps: Recent index gains are shown to be driven by a small group of large incumbents that sit outside venture portfolios, which means the rally does not generate liquidity or distributions for VC LPs.

Fundraising Undershooting 30-Year Model Levels in 2024-2025: Current fundraising totals are described as the largest deviation from historically correlated public market returns and venture capital formation in three decades.

8.30% → 2.88% New Hire Share (Q4 ’23-Q3 ’25): Across 386,500 hires, the share going into support fell by roughly 65%, with nearly half of that decline occurring in the last three quarters, indicating an accelerating adoption curve.

20+ → 3 Humans, +47% YoY Revenue: One example shows a transition from more than 20 support staff to three humans supported by AI agents, while revenue moved from -19% to +47% YoY as AI handled most inbound volume.

$50K Generalists → $100K+ Technical Hybrid Roles: Tier 1 work is increasingly automated, leaving fewer entry-level ticket handlers and more specialized escalation and CS-style roles as AI deflects 40-60%+ of requests.

Early Graduations: About 10% of startups make the jump to Series A in under 12 months, and another 15.6% do so between 12-24 months. By the end of Year 3, roughly 80% of startups that will ever graduate have already done so.

Investor Signals: Graduation rate acts as a baseline for "markup rate," helping LPs evaluate fund managers long before cash returns are realized. It informs early judgments about fund performance in a decade-long venture cycle.

Bridges and Extensions: Relying on bridge rounds or extensions is discouraged, as it can misalign incentives between founders and investors. Startups should plan funding rounds strategically to avoid signaling delays in growth or maturity.