Nandu Anilal

Nandu Anilal grew up in the Chicago suburbs and studied Industrial Engineering at the University of Illinois at Urbana-Champaign. He began his career as a management consultant at ZS Associates, helping clients build and scale their go-to-market efforts — an experience he later described as "a good training ground." He then joined Thumbtack on the product strategy & operations team, where he led strategic projects that informed and executed the product roadmap. He has said that time gave him "a much better look into how software-driven companies operate," and shaped his emphasis on user research and customer interviews when he now evaluates early companies.

He joined Canaan as an Analyst on the firm's enterprise team and focuses on "investments in data, AI/ML, and enterprise software more broadly." Over time his title has progressed — later profiles describe him as an enterprise investor (and, on some listings, Vice President) at the firm. Canaan is an early-stage venture capital firm founded in 1987 through a management buyout of General Electric's venture capital unit, with offices including San Francisco, Menlo Park, New York, and Stamford, CT.

Anilal is unusually public about his thinking. He publishes long-form research through Canaan's blog and his personal Substack, "notes & upside," writing on data engineering, ML observability, open source, and AI infrastructure. His writing gives founders direct transparency into his technical theses. He has also voiced contrarian views — for example, predicting that within ~20 years the college experience will be "verticalized and unbundled" by startups, driven by rising student-loan burdens and rapidly changing work environments.

• Core focus: Data infrastructure and tooling, ML/AI, and enterprise software — invested at seed and Series A.
• Guiding "data flywheel" thesis: declining storage costs → increased data volume → a larger data-talent pool → broader adoption of data-driven business practices across every industry.
• 2021 stated sub-themes he was actively looking at: (1) data transport/movement between lakes, warehouses, and business apps; (2) data engineering automation and orchestration; (3) "fixing data swamps" via cataloging, observability, and governance; (4) ML explainability, governance, and fairness.
• Bullish on open source: "Open source removes the friction of trying new software, shifting more decision-making power into the hands of users rather than managers."
• What makes him lean in: early, product-led companies. In his words, "That context is helpful when we look at companies early on and they largely are product-driven with only early investment into GTM." He values founders who have done real user research and customer discovery.
• Reference landscape he benchmarks against in the data stack: Snowflake, Databricks, Fivetran, dbt, Census, Segment, Airflow/Prefect, Monte Carlo, Collibra, Alation, BigID, Databand, and Delta Lake.