Earlier this week, I hosted the smallest meetup I've ever run: eight members from our DDVC Slack group, two hours, and one table in our Munich office.

No slides. No agenda. Just a roundtable of investors, researchers, founders, and operators who all share one obsession: how do we actually use AI to change the way we work in private markets?

Here are the 10 takeaways that stuck with me (+ the hottest tools used by all participants at the end of this article) 👇

This was the most consistent finding across the table, regardless of fund size or seniority. The awareness is universal. The execution gap is enormous. Those that just get started make the difference.

Deal flow, IC prep, portfolio firefighting: the urgency of daily VC work eats every block of time you set aside to redesign how you work. You cannot sprint and rebuild the track at the same time.

There are firms that have decided they are technology companies deploying capital, and firms that are still waiting for a plug-and-play solution to appear. The gap between these two postures is not about awareness or tool access. It is about a single explicit decision made at the partnership level.

Public data layers like Harmonic, Crunchbase, and PitchBook are commoditized. You won’t generate alpha but instead - as Harmonic founder and CEO Max put it many years ago already - you’ll face negative alpha by not having it. What cannot be bought is your internal data: Years of portfolio reportings, investment memos, IC voting, meeting transcripts. Most firms haven’t even started to record it, and those that do rarely leverage it.

Once you have connected your private data to an AI layer, the differentiator becomes codified process. How your firm evaluates a founding team, structures a competitive landscape, or weights signals in diligence: that is unique to you, and it can be extracted into reusable skills. Two firms can have access to the same data. They cannot buy each other's accumulated process knowledge.

Below roughly 200 to 300 million fund sizes, the management fee math does not support a real engineering team once you factor in salaries, infrastructure, and maintenance. For most funds, the right model is one AI-native operator embedded in the investment team, automating workflows one by one, combined with best-in-class off-the-shelf tooling.

The cost of cleaning up a junior team's technical debt is five times higher than starting with experienced engineers. Many of us learned this the hard way. And if you think of freelancers to build one-off projects: Don’t! Rather consider Fractional CTO or Automation Officers.

For regulated firms, this is real. One person at the table had built an entire workflow around a transcription tool that got flagged by IT overnight, and the whole operation stopped. The firms navigating this best have built a clean separation between what is in production (whitelisted, auditable) and what is in experimentation (sandboxed, fast-moving, non-sensitive data only).

The VC stack is deeply fragmented: sourcing, CRM, transcription, diligence, portfolio monitoring, all disconnected, all requiring manual glue. Many are trying to build a unified layer on top. None have cracked it. The core reason is that VC workflows are too idiosyncratic to productize cleanly. Any horizontal solution either stays too shallow to replace existing tools, or goes so deep it becomes a custom implementation per customer. Add a buyer-user mismatch where GPs decide and analysts use, and customers who believe they can build it themselves, and you have a category that presents a super strong pull but is really hard to serve.

Granola for transcription, Notion as a hub, n8n for orchestration, Exa AI for semantic search, Claude for everything that needs reasoning: most people at the table had landed on similar conclusions. What separates leaders from followers is not which tools they have access to. It is whether they have committed the time to actually wire them together.

The bottom-line from our meetup: The tools exist. The data exists. The bottleneck is always the same: someone in the room who decides to stop talking about it and start.

Most professionals are stuck in the hamster wheel - chasing the next deal, the next reporting deadline, or the next LP. Unfortunately, few take the time to zoom out, document their workflows, note the frequency and time spent, and explore simple tools to automate them - knowing that this would be the single most important thing to do.

But hey, next week you’ll surely find the time to test OpenClaw.

Sounds familiar? Then take action now: Join our Virtual DDVC Summit 2026 to learn how Accel, Bessemer, BlackRock and other firms leverage modern tools and AI to generate alpha

Stay driven,
Andre