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After months of work, the Data Driven VC Landscape 2026 went live last week. It picked up coverage across various media outlets, pulled in hundreds of thousands of impressions on social media across 50+ posts and 500+ comments, and brought over 1000 new subscribers to this newsletter. The report itself crossed 500 downloads on day one alone, a new DDVC record 🙏
50+ posts by DDVC community across LinkedIn
Feedback like this tells me we're building the right thing: sharing our work openly, learning from the community around us, and pushing private market investing toward being more efficient and effective, one step at a time.
The full Landscape report runs long with 52 pages, so today's episode boils it down to a TL;DR. Here are my key takeaways.
Top 10 Takeaways
The core question flipped. Investment firms used to ask whether to build internal tech stacks. Now they ask where to focus it to drive real alpha.
61% of firms build for effectiveness, 39% build for efficiency. Most DDVCs are building to generate alpha and find opportunities others can't see.
Two archetypes have emerged, and both work. Workflow Builders run lean with no dedicated engineering teams, stitching together off-the-shelf tools and custom automations for productivity. Fullstack Builders hire in-house engineering teams and build complex infrastructure to unlock the value of their proprietary data and create a unique edge. Both are working, and there's no correct answer yet on which path wins.
One engineer for every five investors. Efficiency gains are most visible in smaller firms. Compared to 2025, lower AUM firms run with 25% smaller investment teams, whereas bigger AUM firms run with 20% larger teams. On average, DDVC firms maintain roughly one engineer for every five investors across all AUM tiers, a ratio that holds regardless of fund size.
Engineer hiring is about to overtake junior investor hiring for the first time. 49% of DDVCs plan to hire an engineer in the next year. Only 2% plan to add junior investors, and 45% plan to cut those roles.
Tokenmaxxing is real as spend ratio between engineering HR vs data, tools, and tokens moved from 2:1 to 1:1 in a single year. Firms are now putting as much into data & tokens as into engineering salaries. AUM defines total DDVC budgets as follows: <$100m = $85k, $100-500m = $185k, $500-1B = $470k, $1B+ = $588k
Technical ownership drives AI adoption across firms. The more technical the functional owner of the stack, the higher the AI adoption across the firm. 1/2 of Fullstack VCs have a CTO, CPO, Engineering, or Data Lead owning the tech stack, the highest share across all archetypes. In Workflow VC, investors and partners/GPs own the stack in 80% of firms.
Claude leads the DDVC community at 90.5% adoption, ahead of ChatGPT at 73.3% and Gemini at 56.2%. Most firms run three providers in parallel.
Fullstack firms source more than double the share of deals through their own tools compared to Workflow firms. Adoption depth is starting to translate directly into deal flow.
Time and bandwidth are the biggest constraint DDVCs face, cited by 49% of respondents, ahead of data quality at 41%. The tools exist. Finding the hours to wire them into daily workflows is the harder part.
If this isn’t enough, check out the full report here for more insights like most frequently used tools, top use cases and automations across departments, and more.
Stay driven,
Andre
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