Investor
First Round CapitalCheck size: First Round's published firm-wide initial checks are typically $1M–$7M, averaging about $3.5M; it has participated in rounds from $100K to $20M. No personal range or signing authority is published for Maggie.
Maggie's documented lens is research depth: she respects founders who do the work required to understand a problem at first principles and prefers to dig alongside them. Her technical background in signal processing, deep learning, automated pricing, and fraud detection makes AI/ML systems and technically demanding software natural areas of fluency; First Round has not published a personal portfolio or narrower standalone thesis for her.
Research-minded founders who have reached the bottom of a hard problem, can explain the signal and system rather than only the trend, and welcome an investor working beside them in the details. First Round's broader criteria add contrarian insight, an outlier founder skill, a large market, and evidence of passionate early demand when a product exists.
Her published comments and the firm's criteria imply weak fit for shallow AI wrappers, claims unsupported by technical or customer evidence, founders unwilling to examine assumptions deeply, small markets, later-stage rounds, and teams that cannot explain why their specific insight creates a durable advantage.
Lead with the research question and the non-obvious result: what signal did you find, how did you validate it, and why does it unlock a product now? Be prepared to go into data, model or system design, failure modes, customer willingness to pay, and the large-market path. Distinguish a durable technical insight from a thin wrapper. First Round accepts cold outreach and does not require revenue at this stage.
Ask whether the founder has investigated the problem deeply enough to explain the underlying signal, hard constraints, failed approaches, and the specific insight that makes a new solution possible.
For an AI or ML company, trace the path from data and model behavior to a reliable workflow, measurable customer value, deployment constraints, and a defensible learning loop.
For decision systems, examine labels, false-positive cost, feedback loops, distribution shift, unit economics, and whether automated outputs improve a real operational decision.
No writing has been linked yet. Kit can search for posts, interviews, and fund essays worth reading before outreach.
No podcast appearances are linked yet. Ask Kit to check for interviews that reveal how this investor thinks.
“There is nothing I love more than being in the trenches with someone.”
— https://www.firstround.com/team/investing/maggie-basta
“It’s a privilege to be able to show up for founders.”
— https://www.firstround.com/team/investing/maggie-basta
Maggie studied Electrical Engineering, Computer Science, and Comparative Religion at Harvard, where her research covered signal processing and deep learning; she also played competitive soccer internationally and for Harvard. She began as a machine-learning engineer at QuantCo, building automated-pricing and fraud-detection systems, and entered venture at Scale Venture Partners in 2022 before joining First Round.