👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI. ICYMI, check out some of our most read episodes:
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What. A. Week🤯
We did it!
After months of preparation, we successfully hosted the Virtual Data Driven VC Summit 2026 this week with 40+ speakers and 550+ participants from the world’s leading private market investment firms.
Across 18 live demos, keynotes, fireside chats, and panels, we covered all things AI for investors:
How to automate VC with OpenClaw
Vibe-coding a VC operating system
Building products that investors actually use
How AI-powered network intelligence drives alpha
Automating legals with AI
How AI transforms the CFO function
Building an AI-native VC tech stack from scratch
The future of tech-enabled platform teams
Who should own the tech stack: investors vs engineers
….
You can access all recordings until 1st April here via the event platform.
Thereafter, they’ll be added to our library of 100+ recordings in The Lab, where members have unlimited access to all our resources.
If you only have 2 minutes now, here are my top 10 takeaways from the summit:
#1 Agentic VC Firms are Fully Possible with Today’s Technology
Current tools allow for "agentic" workflows where AI handles multi-step processes like sourcing, screening, deal-flow management, research, legal drafting, or portfolio monitoring. Firms are already using these autonomous systems to manage daily investment routines and automate back-office functions. The future is now.
#2 Small Steps Compound into Significant Long-Term Technical Returns
Success comes from building clean data infrastructure rather than chasing magical AI "silver bullets". Alpha is achieved by investing in small, foundational data primitives that compound over time to create a unique firm edge. But most importantly, adoption requires change management and the right culture.
#3 Every Investor Must Become a Builder to Prioritize Human Interaction
Investors should leverage automation for repetitive tasks like data entry and screening to free up capacity for high-value relationship building. By becoming "builders" of their own workflows, team members can eliminate manual "crappy work".
#4 Prioritize Personal "Toothbrushes" Over Complex Enterprise Silver Bullets
Instead of all-in-one platforms, teams should build "toothbrushes" = small, personal tools that do one specific job well. Hereby, professionals across the firm can transition from reactive roles to proactive strategic partners to founders and LPs.
#5 Empower Non-Technical Teams to Build Through "Vibe Coding"
The bar for software creation has lowered, allowing anyone from accounting to legal to build tools using natural language and AI editors. Encouraging team prototypes provides engineers with clear "living" specifications for building secure, scalable firm versions later.
#6 Tool Stacks are Consolidating Around Proven Category Winners
Avoid reinventing the wheel by purchasing established winners for standard needs like CRM (Affinity), entity matching (Foresight), or portfolio intelligence (Vestberry). Winners are evolving across categories; firms should buy what is available and only build what provides a proprietary edge.
#7 Access, Security, and Liability Remain Critical Open Topics
While AI lowers the barrier to building, exposing sensitive information to external models creates significant risk. Security and compliance must be managed by experts, especially when moving from personal "toothbrushes" to shared firm-wide tools.
#8 Collaborative Ownership: Bridging the Gap Between Investors and Engineers
Technology in VC is evolving from centralized engineering projects toward a collaborative model where investors use AI tools to build bespoke workflows. While technical experts still manage core data infrastructure and security, firms find that empowering investment teams to prototype their own solutions significantly increases adoption and better reflects individual investment "taste."
#9 No Coding Needed
To increase efficiency, firms should move beyond fragmented, "duct-taped" legacy tools and instead build a centralized AI command center that utilizes natural language (or "vibe coding") to define investment logic. By connecting core data sources like Slack, CRM, and email directly to an LLM, firms can automate high-level analyst tasks such as autonomous startup scouting, deep company research, and initial founder outreach. This approach allows the investment team to iteratively refine complex system behaviors through plain English feedback and persistent memory files rather than traditional code maintenance.
#10 The Future of VC Interfaces is Conversational and Console-Based
The industry is shifting from static, dashboard-heavy software to AI-native consoles where investors interact with data via natural language. This shift allows custom workflows to remain portable and independent of any single software provider.
That’s it for today.
You can watch the full recordings here.
Thanks again to all speakers, participants, our partners Affinity, Foresight, Goodwin, and Originalis, and our amazing team around Maryama and Georgiy - you rock! DDVC is just getting started.. <3
Stay driven,
Andre
