🔥 Here's the Modern VC Tech Stack
1. Key Themes
The VC Tech Stack Has Commoditized — Access Is No Longer Alpha
The article's core argument is that tool access has equalized across the industry, shifting the competitive question from what you use to how you use it.
"Every serious firm now has access to the same data providers, the same CRM, the same models. While the unique blend of vendors granted alpha for many years, it has become sort of a negative alpha to not have this access today."
AI Adoption in VC Is Wildly Uneven Across Functions
Based on survey data from 345 funds, the article reveals a stark internal adoption gap — sourcing and engineering lead, while legal and compliance lag severely.
"Sourcing, screening, and DD score 3.4 out of 5 on our AI adoption scale. Engineering and infrastructure sit close behind at 3.3. Legal and compliance sits at 1.8, the lowest score on the value chain."
Bigger Funds Have a Bigger Gap — Not a Smaller One
Counterintuitively, the firms with the most resources to fix AI adoption in compliance are the ones falling furthest behind.
"At firms managing $1 to 5 billion, engineering and product adoption climbs to 3.9, the highest score in the entire dataset. Legal and compliance at that same cohort drops to 1.5, the lowest. A 2.4-point gap between the two functions."
The New Alpha Is Proprietary Workflow + Decision Data
With public data and tools commoditized, the article argues the next competitive moat is encoding firm-specific judgment into governed AI systems.
"What separates firms now is whether their own judgment, in investing and in legal work, has been turned into something a system can run under a governance model compliance actually trusts."
Compliance Is the Gatekeeper to the Data That Actually Matters
Unlocking legal/compliance AI adoption isn't just an operational efficiency play — it's what enables VC firms to train AI on their most valuable proprietary data.
"Fixing it turns compliance into the permission slip that lets a firm point AI at the data that actually matters: proprietary deal flow, portfolio signal, internal knowledge nobody can buy off a vendor."
2. Contrarian Perspectives
Larger firms are less AI-mature than smaller ones, not more. The conventional assumption is that bigger AUM means bigger budgets and faster adoption. The data shows the opposite in the most critical function.
"Interestingly, at the largest funds, that gap gets wider. Indication that the bigger the teams, the higher the inertia to get going."
At $1–5B AUM firms, the engineering/compliance gap reaches 2.4 points (3.9 vs. 1.5), the widest spread in the dataset — suggesting organizational complexity and bureaucracy slow adoption more than budget constraints.
More tools is worse, not better. The received wisdom in VC has been to assemble a comprehensive tech stack. The author deliberately moved in the opposite direction.
"I've tracked VC tools since 2017, when the list I kept ran about 100 deep. It passed 1,000+ tools in 2025. My own working stack went the other direction. I consolidated it from the peak of 80+ tools in 2024 down to about 30 tools in January this year."
Compliance resistance is the biggest untapped lever in VC AI — not sourcing. Most discourse focuses on sourcing and screening as the frontier of AI adoption, but the article argues the real unlock is the function nobody is talking about.
"Most rollouts stall on five concerns nobody writes down. MNPI leaking into a model. LP data that legally can't touch a third-party system. No audit trail. Vendor risk. Access nobody can account for. Name them and answer them in order. Compliance stops blocking the build and starts writing the guardrails alongside it."
3. Companies Identified
- Description: AI-first private capital CRM; newsletter sponsor
- Why mentioned: Held up as a mature CRM solution and cited as a proactive vendor pushing AI forward via its new agent platform, Affinity Ascend
- Quote: "Affinity Ascend handles the operational work that slows deal teams down, so investors can focus on relationships, decisions, and returns. Ascend's agents automatically prepare meeting briefs, propose CRM updates from meeting notes, and uncover ranked warm introduction paths across your firm's network."
- Description: Company and talent data platform for sourcing
- Why mentioned: Listed as a top-tier sourcing/intelligence tool in the author's personal 30-tool stack
- Quote: "Harmonic, Dealroom, and Evertrace for data"
- Description: Portfolio management and reporting platform
- Why mentioned: Identified as a proven CRM/fund ops solution in the author's personal stack
- Quote: "Affinity, Vestberry, and Carta for CRM"
Carta
- Description: Cap table and fund administration platform
- Why mentioned: Named as a core fund operations tool
- Quote: "Vestberry, Carta, Affinity, Attio, Visible, Pipedrive, HubSpot, Salesforce, Rundit: the backbone for managing deal flow, cap tables, and LP/portfolio reporting."
Kruncher
- Description: AI-powered deal research/automation tool for VCs
- Why mentioned: Listed as part of the "agents and automations" cluster — described as the most valuable layer of the stack
- Quote: "Agents and automations... Kruncher, Claude, Cursor, ChatGPT, n8n, Langdock, Zapier, Perplexity, Gemini: the most valuable layer of the stack, where VCs are implementing AI for research and workflows."
n8n
- Description: Open-source workflow automation platform
- Why mentioned: Part of the author's personal 30-tool stack for automation
- Quote: "n8n and Zapier running the automation layer."
Evertrace
- Description: Company tracking/signal data tool
- Why mentioned: Listed as a top sourcing intelligence tool in the author's personal stack
- Quote: "Harmonic, Dealroom, and Evertrace for data"
Dealroom
- Description: European startup data and intelligence platform
- Why mentioned: Part of the author's personal data stack
- Quote: "Harmonic, Dealroom, and Evertrace for data"
Granola
- Description: AI meeting notes tool
- Why mentioned: Listed under productivity tools VCs actually use
- Quote: "Productivity. Slack, Granola, Wispr, Gamma, Notion, Airtable: how teams communicate, run meetings, and capture notes day to day."
Pinecone
- Description: Vector database for AI applications
- Why mentioned: Listed in the infrastructure cluster powering VC search and data systems
- Quote: "Infrastructure. Foresight, Exa, Supabase, GitHub, Pinecone, PhantomBuster, Cloudflare, Google BigQuery, AWS: the systems powering search, data, and hosting."
4. People Identified
Andre Retterath
- Description: Author of Data Driven VC newsletter; VC investor and data/AI practitioner
- Why mentioned: Primary author; provides first-person operational data and the consolidated 30-tool stack as a case study
- Quote: "I consolidated it from the peak of 80+ tools in 2024 down to about 30 tools in January this year, and most of them map straight onto the five clusters above."
5. Operating Insights
Consolidate your tool stack rather than expanding it. The proliferation of 1,000+ VC tools creates noise, not advantage. The author's own experience shows that moving from 80+ tools to 30 focused ones maps more clearly to value-generating functions.
"Most of this list is now a commodity purchase. Any firm with a budget and the ability to cut through the noise can stand up a comparable stack in no time."
Sequence your compliance AI rollout to name and neutralize objections upfront. Compliance gatekeeping stalls most AI initiatives not because the technology fails, but because five specific concerns go unaddressed. The tactical fix is to make them explicit and sequence the rollout accordingly.
"Name them and answer them in order. Compliance stops blocking the build and starts writing the guardrails alongside it. Low-risk data first, then read-only systems, then the firm's own playbooks encoded as skills."
Build custom workflows on top of commodity tools — that's where differentiation lives now. With the base stack commoditized, the edge comes from proprietary automation and data fusion, not vendor selection.
"My clear answer is yes — via actual adoption (don't underestimate how difficult it is to change habits…), custom workflows, and most critically by fusing public data with proprietary workflow and decision data."
6. Overlooked Insights
The emergence of two new VC archetypes: "Fullstack VCs" and "Workflow VCs." The article briefly coins these terms to describe how coding agents are reshaping who can build internal tools — not just technical investors, but non-technical ones too. This has significant implications for team composition and hiring at VC firms.
"Coding agents allow existing engineers to accelerate (=Fullstack VCs) and non-technical investors (=Workflow VCs) to finally build what wasn't possible before."
Seat-based pricing in the CRM/fund ops tier may be insulating those tools from disruption — for now. The middle tier (CRM, fund ops, portfolio intelligence) scores only 2.1–2.6 on AI adoption. The article suggests the pricing model itself reduces urgency to change, but flags that leading vendors are proactively pushing the frontier before disruption forces their hand.
"Most of this tier also runs on seat-based pricing, not usage-based agentic spend. That keeps the cost predictable. Overall the question remains 'why change a running system?' — until it doesn't."