Pierrick Bouffaron, Operating Partner for Entropia Capital, a global investor in technology with offices in Hong Kong, Luxembourg and NYC.
A quiet yet profound transformation is reshaping private equity and late-stage venture capital: the convergence of AI, operational value creation and long-hold ownership models. At the core of this evolution are AI-fueled roll-up strategies and the rise of permanent capital vehicles, both of which appear to be increasingly favored by firms ready to move beyond the rigid timelines of traditional fund cycles. We are entering a new era where algorithmic leverage, not just capital, is driving the next wave of scalable, operational value creation.
Over the past decade, I’ve worked at the intersection of deep tech investing, corporate innovation and startup acceleration, advising and co-building technology ventures across the U.S., Europe and Southeast Asia. I’ve seen firsthand how data infrastructure and operational control are becoming essential tools for private market outperformance.
Why Roll-Ups Are Ripe For AI
Roll-ups, where investors acquire and consolidate smaller businesses in fragmented sectors, have long been a private equity favorite. They can deliver efficiencies through scale, help negotiate better contracts, centralize functions and ultimately allow the group to be sold at a premium.
But executing a roll-up is hard. It requires deep market knowledge, relentless due diligence and seamless post-deal integration. That’s where AI is rewriting the playbook. Modern AI systems can crawl thousands of databases, parse regulatory filings and analyze web content to surface ideal acquisition targets using natural language processing. Machine learning models can flag customer churn risk, uncover margin levers and benchmark operational key performance before a term sheet is signed. After closing, AI can help facilitate faster onboarding, workflow automation, supply chain optimization and digital transformation across units.
This is no longer theory. Thrive Capital, an investor behind OpenAI and Stripe, has been fundraising for Thrive Holdings, a $1 billion permanent capital vehicle to acquire and operate “everyday” businesses, including homeowner associations and accounting firms, with AI as the operational backbone. The idea is to use algorithms and automation to drive improvements in margins and service across legacy sectors. And Thrive isn’t an outlier. This playbook builds on a proven model seen in industries like dental chains and plumbing services: Standardize systems, share overhead and scale intelligently. What’s new is that the engine now runs on code.
The Acceleration Of AI In Private Equity
AI in private markets may seem recent, but the data revolution has been gaining steam for decades. In the early 2000s, quantitative hedge funds like Renaissance Technologies led the way (paywall). Private equity generally followed with caution until firms like Two Six Capital began using data science to evaluate portfolio companies. Two Six participated in more than $27 billion worth of deals using these analytics. The pace accelerated in the 2020s, with some studies indicating that firms investing in data science capabilities outperformed their peers (paywall), highlighting a link between analytics and business success.
One example is Paris-based Jolt Capital, which developed Jolt.Ninja, an AI platform that’s been in use since 2016, according to the platform’s website. It scans the web to spot under-the-radar investment opportunities in tech firms. It tracks patent filings, executive shifts, market sentiment and financial signals, which can offer an edge in sourcing and diligence, particularly in Europe’s fragmented deep tech landscape. Another example is EQT, also in Europe, which uses its internal AI engine, Motherbrain, to help source investments.
Shaping Long-Term Plays
The other major trend I’m seeing reshape private markets is the rise of permanent capital vehicles (PCVs), investment structures without fixed exit deadlines. In my view, their popularity is likely increasing thanks to their compatibility with operationally intensive strategies like AI-led roll-ups. Traditional funds must return capital in seven to 10 years, in my experience. PCVs allow firms to take the long view, reinvest gains and build durable, cash-generating businesses over decades. It’s a model tailor-made for transformations that take time, like deploying AI across dozens of acquired companies.
Sequoia Capital helped pioneer this approach (paywall) in 2021 by launching The Sequoia Fund, a structure designed to hold public stocks indefinitely. Instead of being forced to exit positions in winners after an initial public offering, Sequoia now retains long-term upside and strategic optionality. Andreessen Horowitz took a similar approach in 2023 with its a16z Perennial Venture Capital Fund.
No public tally exists for how many firms run PCVs, but I’m seeing the trend accelerating. From my observations, top-tier firms with operational muscle and AI ambitions are increasingly choosing flexible timelines over forced exits.
Capital Meets Code: A Strategic Convergence
Together, AI-powered roll-ups and permanent capital vehicles signal a structural shift in how investment firms deploy capital and build value. I believe the boundary between late-stage venture and traditional private equity is fading, as operational control becomes the new priority. Firms are hiring engineers as core team members, building proprietary tooling and behaving less like investors and more like operators. A new class of fund is emerging: They aggregate assets, standardize them with AI and create long-term cash flow engines.
To me, this means the competitive edge is increasingly found in the data stack. This isn’t a tactical update; it’s a redefinition of what happens after the deal closes.
For other firms looking to adapt to these shifts, start by embedding technical talent—such as data scientists, machine learning engineers and AI product leads—into your core deal and operations teams. Second, consider piloting internal tools that can track portfolio performance, not only financially but also operationally, and layer in data streams that surface risks and opportunities in real time. I believe those who adapt could not only see better internal rates of return but also build a compounding, self-improving edge that defines the next generation of value creation.
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