AI is a Better Investor Than You ?!
The bold bet that's reshaping venture capital and private equity
That's the audacious claim from Nik Storonsky, Revolut's founder, with his new $250 million QuantumLight fund. His thesis? Artificial intelligence can spot golden opportunities better than any human being. The fund leverages its proprietary AI model "Aleph" to analyze 50 million companies and has already made 17 investments since 2023 — all algorithm-recommended. It's a radical approach that could redefine the entire private equity industry.
AI: Private Equity's Not-So-Secret Weapon
This automation and big data logic isn't exactly groundbreaking news. Jolt Capital, a top-quartile private equity fund, hired Philippe Laval back in 2017 and invested millions to create "Ninja" — an AI designed to replace all analyst work. The results? Spectacular.
The system now continuously analyzes over 600,000 companies, with Jolt partners reviewing 70 automatically generated opportunities weekly. That's massive efficiency gains, with opportunity retention rates jumping from 5% at launch to 20% today.
AI-Powered Deal Detection: The New Gold Rush
The first AI revolution in asset management focuses on dealflow — the ability to identify investment gems. Funds like EQT (with Motherbrain launched in 2016), Jolt, and Sequoia have developed sophisticated predictive algorithms that are mind-boggling in scale: EQT Motherbrain analyzes 50 million companies across 50+ data sources.
These algorithms scrutinize hundreds of parameters: early employees' CVs, Google search volumes for problems being addressed, team GitHub activity, filed patents, social media growth signals. This automation frees up partners to focus on negotiation and human relationships — the stuff that still matters.
Due Diligence Gets the AI Treatment
But it's in due diligence where AI delivers its most spectacular revolution right now. What's better than a multimodal algorithm for deep-diving into a target's internal data, market positioning, and digital footprint? The efficiency gains are staggering: 60% reduction in due diligence time according to fund feedback, instant cross-analysis of millions of financial and reputational data points, and detection of anomalies invisible to human eyes.
This transformation particularly impacts large Private Equity and LBO firms where data volumes are massive — far more than in Venture Capital, where early-stage startup data remains sparse.
Venture Capital: AI's Perfect Playground
VC presents a unique case that justifies this AI arms race. It's the only asset class with a globally negative consolidated DPI (Distributions to Paid-In). The stats are brutal: only 15% of deals actually make money for funds, the top decile shows a net TVPI (Total Value to Paid-In) of 3.06x while the top quartile caps at 2.39x, and everyone else is just trying to hit a 1x DPI — breaking even.
This VC market singularity, where everything hinges on a few spectacular outliers, justifies massive AI deployment to maximize chances of identifying future leaders.
The Dark Side: Market Homogenization and Polarization
Yet this massive dealflow algorithmization creates a disturbing side effect: homogenization of funded entrepreneur profiles. AI models, trained on past successes, favor known patterns — founders from the same prestigious schools, experiences at the same established companies, similar problem-solving approaches.
Jason Calacanis, one of Sequoia's first scouts, illustrates this perfectly: "My first investment memo for Uber contained two words: 'Cabs suck.' AI would never have validated that." This anecdote highlights the risk of missing true disruptions.
To counter this bias, Sequoia developed an interesting hybrid model: human scouts identify talent in local ecosystems for pre-seed, then algorithms take over at Series A where metrics become analyzable. As Alexandra Lutz, EQT's AI head, puts it: "AI acts as an amplifier of human expertise, not a replacement."
The New Barrier to Entry
The colossal cost of these AI systems — several million euros in initial investment, teams of 30+ data scientists, continuous model maintenance — creates a dizzying new barrier to entry. The market is polarizing between ultra-specialized funds that must leverage unique human and sectoral expertise, and giants like EQT, KKR, or Blackstone with the resources for these tools, scooping up the best deals alongside specialists.
Middle-market funds ($100 million to $5 billion AUM) are caught in the squeeze. According to 2024 PitchBook data, their market share is eroding, and their numbers are gradually declining.
An Efficient but Less Diverse Future?
AI implementation in private markets promises radical but ambivalent sector transformation. As Ilya Kondrashov, QuantumLight's CEO, puts it: "Our goal is to make visible and reproducible the invisible systems behind iconic companies."
AI doesn't replace investor instinct — it augments it. It doesn't eliminate risk — it quantifies it better.
The future sketches a more efficient but less diverse industry, more data-driven and potentially less disruptive. However, I believe the next revolution will continue coming not from numbers, but from an entrepreneur that all algorithms will have overlooked.
Because ultimately, venture capital remains the art of identifying the exception, not the rule. And that's something no algorithm truly knows how to do.
Yet from Series A onward, the question doesn't arise — algorithms will sweep the table!
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for sure!
The notion.vc stack is called RISTA - seven years in production, source & select focused and is analysing 1m plus European Start-ups 🤖 - super interesting write up on the space 💙