Venture capital is an industry that is built off relationships, instincts, and apprenticeship.
You joined a firm, built pattern recognition by reading 1,000 pitch decks, and slowly earned the right to make bets. But things look different now.
AI has entered the industry in full force. Not just as an investment thesis, but as infrastructure. It’s changing how deals are sourced, decisions are made, and even who gets to learn the craft.
As the money flows toward AI startups, the tools those startups create are quietly replacing the way VC firms operate.
And the change isn’t subtle. It’s fast, structural, and is revolutionizing the whole VC industry.
The data speaks loud and clear
In Q1 2025, OpenAI raised $40 billion. That one deal turned a down quarter into a record-setting one.
Without it, venture funding would have fallen by 36% from the previous quarter, according to EY. Instead, the quarter ended up 28% higher.
Source: EY
That momentum carried into Q2. According to Crunchbase, more than $205 billion was raised in H1 2025. Almost half of that went to AI startups.
Source: Crunchbase
The top two rounds of the year were both AI deals: OpenAI ($40B) and Scale AI ($14.3B). Safe Superintelligence and Thinking Machines Lab raised $2 billion each.
Eleven companies alone absorbed $70 billion in funding, compressing the rest of the market.
IT and AI dominated every metric. In Q1, the information technology sector made up 74% of VC investment.
Even without OpenAI’s deal, the category still would have claimed more than 50%. The Bay Area, New York, and Austin led in AI deal count and volume.
The data clearly shows just how much AI is dominating VC funding.
What happens when AI takes over the pipeline?
AI is not just affecting venture capital funding. It’s also affecting how the industry operates from the inside.
Junior analysts in VC funds used to spend years developing pattern recognition.
They triaged decks, scraped LinkedIn profiles, read market maps, and sat through endless founder calls.
It wasn’t glamorous, but it was the apprenticeship that turned interns into partners.
Now, most of that work can be done faster and better by AI. Platforms like Harmonic, Affinity, and Termina are reportedly able to scan millions of companies and flag the outliers.
QuantumLight, a $250 million fund started by Revolut’s founder, uses a proprietary model called Aleph to identify founders worth backing. No junior team. Just an engine.
That may be good for productivity, but it raises some questions regarding the long-term outlook of the industry.
If no one learns by doing, who becomes the next generation of venture leaders? Marc Andreessen says venture is still fundamentally human about taste, trust, and instinct. But if those instincts are never trained, how long can that edge last?
A new VC operating model is emerging
AI doesn’t just replace sourcing. It’s transforming every part of the venture stack.
LLMs now write memos, scan pitch decks, and extract market data in seconds. Internal copilots help GPs prepare for founder calls, answer LP questions, and even model financials.
Firms like SignalFire, Tribe Capital, and others are pushing this even further by building custom tools that automate diligence and flag red flags early.
McKinsey estimates AI could reduce operating costs by 25-40% across asset management. Venture firms, especially lean ones, are already seeing similar effects.
But automation doesn’t just save time. It flattens the advantage. If every firm has the same tools, the edge comes from how they are used.
Some firms are responding by rewiring everything. That means consolidating data platforms, redesigning workflows, and retraining teams. Engineering talent is being replaced by prompt engineers and AI operations leads.
The fastest firms are organizing around skills instead of functions. They’re turning AI into infrastructure.
But fast exits come with a tradeoff
There’s another force reshaping the industry, one that’s less visible but just as disruptive.
Big Tech is buying AI talent before startups reach maturity.
Meta recently hired the founders of coding startup Windsurf and paid $2.4 billion to acquire the team.
Seed investors got 50x. Series B investors got 4x. On paper, these are strong returns. But they come with a hidden cost.
When top founders leave early, the upside is capped. The $10 billion company never becomes $100 billion.
This is happening more often. As AI startups raise at higher and higher valuations, the math shifts.
If a company raises at $10 billion and sells at $10 billion, founders can walk away with billions.
Investors, depending on the terms, might get nothing beyond their initial capital.
In a market where retention bonuses and early acquisitions are common, founder incentives are diverging from investor expectations.
It’s not just startups. The Wall Street Journal reported that when Meta hired Daniel Gross and Nat Friedman, the founders of NFDG, the firm shut down.
Limited partners were paid out early, but the long-term promise of backing the next fund disappeared.
What this all means
AI is making venture capital faster, leaner, and more automated. But it’s also hollowing out the structures that gave the industry its staying power.
Fewer junior hires means fewer apprentices. Founders are exiting earlier. Deals are being picked by models, not relationships.
That’s efficient, but it’s also brittle. The venture business was built on long-term trust, shared experiences, and human judgment.
Those things don’t show up in training data.
The industry now faces a choice. Some firms will become AI-native, treating venture like a throughput machine.
Others will double down on the human edge, like deep relationships, conviction calls, and hands-on support.
Most will try to do both. But the ones who succeed will be those who choose their model deliberately. Not just as a thesis, but as a way of working.
The post How AI is turning the Venture Capital industry upside down appeared first on Invezz
