The Illusion of Insight
AI is everywhere in venture now. It’s parsing decks, auto-sourcing deals, benchmarking portfolios, and helping analysts look smarter than they are. On paper, it’s a dream: more deals, faster decisions, cleaner data. But there’s a risk that gets glossed over. AI makes it easy to confuse motion with progress.
Venture capital was never a speed game. It’s about seeing what others miss, and that still takes time, intuition, and uncomfortable conversations. A good GPT summary can’t replace sitting down with a founder and asking why the roadmap changed three times this quarter.
AI in Venture Capital: Key Numbers
| Metric | 2023–2024 Data | Source |
| AI VC investment (global) | US $131.5 bn (+52% YoY) | PitchBook (2024) |
| Share of VC funding into AI | 35.7% globally; 33% in the U.S. (first 9 months 2024) | PitchBook |
| AI deal share in Q3 2024 | 37% of all U.S. VC fundraising | EY VC Trends Q3 2024 |
| U.S. Q1 2025 VC funding | US $91.5 bn (71% to AI startups) | WSJ / PitchBook-NVCA |
Source: EY analysis; Figures are for equity financings into VC-backed companies headquartered in the US. Cash investments came from VC firms, corporate investors, other private equity firms, individuals and other sources.
“Despite accounting for 42% of deal volume, IT (largely AI) soaked up 74% of invested capital—underscoring how VC is doubling down on data-native bets.”
Biased Inputs, False Confidence
AI is only as good as the data it’s trained on—and that’s a problem. Most models rely on historical venture data: prior investments, LinkedIn patterns, headline traction. But history is biased. It favors polished, well-networked founders who fit a certain mould. The risk? You miss the unconventional bet that doesn’t score well but will outperform.
If everyone uses the same inputs, flags the same trends, and sources from the same pools, the real edge becomes even more scarce. You can’t out-model the market if you’re using the same tools in the same way.
Diligence Still Requires Curiosity
AI can now compress weeks of diligence into hours. Financial models, GTM diagnostics, org charts—all neat, exportable, and visualized. But this creates a false sense of clarity. Numbers are tidy; businesses aren’t.
Real diligence lives in nuance. Why is churn really rising? What does the team dynamic feel like? Is the CEO just tired—or actually done? These are things you pick up in conversations, not dashboards. AI can summarize; it can’t empathize.
Post-Investment: Support > Surveillance
Post-close, many firms deploy AI to track OKRs, benchmark performance, or flag red flags. That’s fine, but founders don’t need automated nudges. They need actual help.
The best support VCs give isn’t performance monitoring—it’s being in the trenches. Helping hire the right VP. Talking through a tough product bet. Reading the room during a board meeting. AI can’t replace that kind of judgment or presence.
Use the Tools, But Keep Your Edge
AI has a role. It should take the grunt work off your plate. But it won’t make you a better investor. That still depends on taste, judgment, and time spent listening.
The firms that win won’t just be “AI-enabled”—they’ll be AI-aware. They’ll use tools to clear noise, not shortcut conviction.
Final Thought: Stay Human
The job hasn’t changed. See what others miss. Build real relationships. Stay curious when others rely on output.
AI is here to help. But edge still comes from the questions you ask, not the answers you automate.
At Quickers Venture, we’re not just adopting AI—we’re integrating it into how we think, support, and build conviction. The future of venture is informed, not automated. And we’re here for the founders who still believe insight starts with a conversation, not a dashboard.
