AI Reshapes Venture Capital as Mega-Deals Concentrate Funding

AI is changing venture capital both as an investment target and as an operating tool, while funding increasingly flows into a small number of very large deals. An OECD analysis found that AI firms received 61% of global venture capital in 2025, or $258.7 billion of $427.1 billion in total investment. AI's share has doubled from 30% in 2022. The shift is not simply a larger technology sector. It is also a change in how risk is distributed across the venture market. Deals worth more than $100 million accounted for about 73% of AI investment value in 2025, while deals above $1 billion represented almost half of the category's total value. The OECD says the average AI deal grew from $11.2 million in 2014 to $35.8 million in 2025, but the median 2025 deal was only $5 million, showing how a small number of outsized financings distort the average. The same concentration is visible geographically: US companies attracted about 75% of global AI venture deal value, or $194 billion. Capital is also moving upstream into the infrastructure required to train and run advanced systems. AI companies focused on IT infrastructure and hosting received $109.3 billion in 2025, more than 42% of all AI venture investment. That creates opportunities for compute, data-center and hosting businesses, but it raises the cost of competing outside the best-capitalized networks. The PitchBook-NVCA Venture Monitor describes a similar US market: more than $400 billion was raised by startups in the first half of 2026, yet the recovery remained highly concentrated among AI companies, financings of $100 million or more, and a small group of established funds. For venture firms themselves, AI is becoming a workflow layer. A 2026 industry briefing reports that 82% of venture firms use AI for deal sourcing and research, with applications spanning pipeline expansion, diligence research, portfolio monitoring and limited-partner communications. The practical appeal is speed and coverage: repetitive research can be compressed from days to hours, allowing smaller teams to review more companies. But the same briefing argues that conviction under uncertainty, founder trust, network building and timing remain human responsibilities. Research is also beginning to test whether models can improve selection rather than merely automate paperwork. The VCBench study evaluates nine large language models on 9,000 anonymized founder profiles. Its market-index baseline had 1.9% precision, compared with 5.6% for tier-one venture firms; DeepSeek-V3 delivered more than six times the baseline precision, while GPT-4o recorded the highest F0.5 score in the study. Those results are promising but narrow. Historical data can reflect existing access patterns, public-profile bias and the outcomes of a market that has already selected its winners. Models can also introduce hallucinations, herd behavior and data-security risks, making independent verification essential. The emerging playbook is therefore neither fully automated investing nor a return to instinct alone. It is a machine-assisted funnel in which AI broadens discovery and accelerates analysis, while partners remain accountable for judgment, relationships and portfolio construction. The central investment question is shifting from whether AI will attract capital to whether concentrated AI bets can produce durable returns without leaving the rest of the venture ecosystem starved of funding.