AI is changing a few specific parts of how funds operate—and leaving most of the job untouched.
Blake Aber · Predicate Ventures · 2026
The question of how venture capitalists and private equity firms use AI usually gets answered by people selling something. Vendors describe a fund where deals source themselves and diligence runs on autopilot. That fund does not exist.
What does exist is narrower and more useful. AI is measurably changing a handful of tasks inside funds, mostly on the operations side, and having almost no effect on others. The difference between the two is worth being precise about.
Where AI is doing real work
The clearest results come from portfolio operations at large PE firms, not from investment decisions.
Apollo built a proprietary AI system that analyzed 15,000 software purchase agreements across its portfolio in minutes, which let one portfolio company cut procurement costs by more than 65% (MIT Sloan Management Review). The mechanism is simple. When you own many companies, they buy many of the same things at different prices, and nobody has ever compared the invoices.
Apollo's system compares purchasing contracts and invoices across more than 40 portfolio companies to find the best price paid for a given product (MIT Sloan Management Review). PitchBook reported a concrete example: the firm found that doormats were being bought across its hospitality holdings at very different prices, then negotiated portfolio-wide contracts (PitchBook).
This is the part vendors get wrong. The value here is not intelligence. It is scale of comparison. A person could find the doormat discrepancy given enough time and access to every invoice. No person has that time or access. AI reads all of it at once, and the return comes from the reading, not from any judgment.
AI inside the portfolio companies
The second category of real results is AI deployed operationally within portfolio companies, where the fund pushes capability down rather than running it centrally.
At Apollo portfolio company Cengage, AI process automation cut content production costs by 40% and lead generation costs by 20% (MIT Sloan). At Yahoo, AI-generated code improved engineering productivity by more than 20% (MIT Sloan).
These are operational gains at the company level. The fund's role is to identify the opportunity, fund it, and repeat the pattern across holdings. That repetition is the actual product of a large firm's AI effort—a playbook applied many times, not a single clever model.
Notice what these examples share. Content production, lead generation, and code generation are all high-volume tasks with clear inputs and outputs. AI performs well where the task is repetitive and the correct answer is verifiable. It performs poorly everywhere else.
Where AI is not changing much
The investment decision itself is largely untouched, and the claims that it is deserve skepticism.
Sourcing is the most common pitch. AI can rank companies by growth signals, hiring data, and web traffic, which is genuinely useful for building a top-of-funnel list. But sourcing was never the binding constraint at most funds. The constraint is winning the allocation and being right about the outcome. AI does neither.
Diligence is the second pitch. Language models summarize data rooms, extract terms from contracts, and flag inconsistencies. This saves associate hours, and saved hours are real. It does not tell you whether a founder will execute, whether a market will hold, or whether a management team will fracture under pressure. Those judgments carry the return, and they remain human.
The pattern is consistent. AI compresses the cost of reading and processing. It does not produce conviction. A fund that confuses the two will automate its analysts and keep making the same picking errors, faster.
The asymmetry between VC and PE
The evidence so far skews toward private equity, and that is not an accident.
PE firms own controlling stakes in many mature companies with real operating expenses. That gives them two things AI needs: authority to change how a business runs, and enough volume for comparison to matter. Apollo's procurement work depends on both.
Venture funds have neither in the same measure. They hold minority stakes in young companies, and they cannot dictate a portfolio company's software contracts. Their AI use tends to sit in their own operations—deal screening, portfolio monitoring, reporting—where the gains are quieter and harder to measure.
This means the honest answer to the keyword question splits by asset class. PE is finding measurable cost savings through portfolio-wide comparison and operational deployment. VC is mostly speeding up back-office work and building better lists.
What an operator should actually do
If you run a fund, the useful moves follow from the evidence rather than the marketing.
Start with tasks that are high-volume, repetitive, and verifiable. Contract analysis across a portfolio qualifies. Reading every invoice qualifies. Summarizing standardized documents qualifies. These are where the Apollo-style returns come from, and they require ownership scale to pay off.
Be honest about what you own. If you hold minority positions, portfolio-wide procurement is not available to you. Point your AI spend at your own operations and at helping companies help themselves, which is closer to the Cengage and Yahoo model than the doormat model.
Do not automate judgment. Use AI to give your team more time and cleaner inputs, then spend that time on the decisions that determine returns. The moment a tool promises to replace conviction, treat the promise as the tell.
The short version
Venture capitalists and private equity firms are using AI in two places that work and one place that mostly does not.
The two that work: reading and comparing high-volume documents at portfolio scale, and deploying operational automation inside owned companies. Both depend on scale and verifiable tasks, which is why PE shows the strongest numbers.
The one that does not: the investment judgment itself. AI makes the surrounding work cheaper. It does not make the pick better. Funds that keep those two ideas separate will get value from AI. Funds that blur them will get speed and call it insight.
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