VC Funds Face Slow Returns as AI and Deep-Tech Risks Grow
New research highlights mounting pressures across venture capital (VC) funds, AI investment and deep-tech due diligence. Carta data show that the median US VC fund launched in 2017 had returned only 0.37 times investors’ capital after about nine years; the top quartile returned 0.70 times. Most funds had distributed some cash, but few had returned investors’ full contributions, potentially affecting their appetite for follow-on funding or early exits.
Odin research found that selected emerging managers invested in outlier seed companies at nearly twice the rate of five large firms: 24.7% versus 11.5%. The large firms’ portfolios were heavily concentrated in AI, while emerging managers’ differing mandates produced more varied exposure. The research does not show that outlier investments are more successful.
AI spending varies sharply across more than 70,000 companies tracked by Ramp: median monthly spending was $11.38 per employee, compared with $611 for the top 10% and $7,449 for the top 1%. Separately, data cited by Theory Ventures suggest mid-tier AI models account for a substantial share of usage and spending, while competition, open-weight models and fine-tuning are putting pressure on prices.
The article also warns that deep-tech investment may lack independent technical scrutiny. Private quantum investment reached $4.9 billion in 2025, while estimated industry revenue was about $1.4 billion. For founders and investors, fund age, portfolio concentration, AI costs and technical validation are increasingly important factors in financing and risk decisions.
Neutral
The article does not report a direct catalyst for cryptocurrency prices, such as a regulatory decision, token-specific event, or change in network activity. Its central findings concern VC fund returns, AI spending and deep-tech diligence, so the likely immediate effect on crypto trading and overall market stability is limited.
There are some indirect links. The article says emerging managers’ portfolios are more varied and notes that blockchain represented 46.7% of their early-stage investments before falling to 20.9%. It also names Multicoin as an example of a specialist fund with relatively low AI exposure. These details may inform sentiment about crypto venture funding, but they do not establish a change in capital flows into digital assets or predict token performance.
In the short term, traders are therefore more likely to respond to established market drivers such as Bitcoin and Ether price action, liquidity, ETF flows, interest-rate expectations and regulation. The VC findings could modestly shape sentiment toward crypto startups if investors interpret aging funds and weak cash distributions as a reason to reduce follow-on commitments. Historically, tighter venture financing can weigh on startup activity and risk appetite, but its effect on liquid token prices is usually indirect and may be overshadowed by macroeconomic or market-specific news.
Over the longer term, fund liquidity pressure could encourage earlier exits, secondary share sales or more selective financing. That may affect private crypto companies and the pace of new projects, while the article’s warning about inadequate deep-tech scrutiny could prompt more cautious diligence across emerging sectors. Neither outcome points clearly toward a broad rise or fall in cryptocurrency prices. With no specific token catalyst and no clear directional evidence, a neutral market view is appropriate.