23-year-old co-founder and CEO of AfterQuery, which went from YC W25 to a reported $3.2B valuation in about 18 months. He is the public face of the company and has positioned it as a research-led data vendor that proves data quality to labs with its own training runs and published benchmarks.
high confidenceSan FranciscoAt AfterQuery since 2025Updated 2026-09-19
Background
Met co-founder Carlos Georgescu in high school at a Google-run computer-science summer program; both later interned at Meta. Per his YC bio he also interned in tech private equity at Silver Lake, tech investment banking at Morgan Stanley and software engineering at Meta and Google.
He finished at Penn (BS Finance & Statistics at Wharton, MS Computer Science) while in YC. The founders applied to YC W25 in 48 hours with no idea; a first attempt at AI agents for finance failed because models did not know how professionals actually work, which led them into expert workflow data. In a January 2026 X post he said he, Georgescu and Danny Tang walked away from a combined $850K in post-grad jobs to move to SF.
What they run now
Runs the company and lab relationships; drives the research-as-marketing strategy (training models internally on AfterQuery data, publishing benchmarks such as UI-Bench) and the push into long-horizon tasks, professional/knowledge-work data and high-fidelity RL environments.
Career
2025 – presentCo-founder & CEO, AfterQuery
—Intern, technology private equity, Silver Lake
—Intern, technology investment banking, Morgan Stanley
—Software engineering intern, Meta / Google
—University of Pennsylvania (Wharton), BS Finance and Statistics
—University of Pennsylvania, MS Computer Science — Finished while in YC W25 (per Forbes)
On the record
Proving data quality · 2026-04
Argues AfterQuery objectively shows labs its data is high quality before they even look at it, via internal training and published results. source
Goldilocks data · 2026-04
Frames good training data as tasks in a range that is challenging but still learnable for current models (company positioning reported by Forbes). source
Catching the giants · 2026
Said AfterQuery is closing the gap on the incumbents and has grown multiples past the $100M run rate; pitched long-horizon tasks, knowledge-work data and RL environments. source
Design evaluation · 2025-08
Co-authored UI-Bench, arguing text-to-app tools promise quality that no public benchmark had rigorously verified; used expert pairwise judgments. source
Recent posts
38 posts archived · most engaged first, then the latest
To the researchers impacted by the recent Amazon AGI layoffs: AfterQuery is hiring.
We're looking for post-training researchers who care deeply about improving frontier model capabilities.
At AfterQuery, you'll help tackle some of the hardest problems in the space and run experiments that support teams across all frontier AI labs.
DMs open, or apply at the link in the comments.
AfterQuery is quickly closing the gap on the
“giants.”
We’ve already grown multiples past the $100M revenue run rate cited here.
If you’re a post-training researcher or lab that needs long-horizon tasks, professional / knowledge work data, high-fidelity RL environments, code generation data, or internal evals done right, my DMs are open or reach out to us at research [at] afterquery [dot] com.
Every safety taxonomy today is a list of scenarios that already went wrong. Coverage lags the incident that created the category.
Getting ahead of safety incidents takes a stated view on how a model should behave, not a long list of failures. Many labs today have a list and not a view.
Cyber is the current example. Code and cyber safety matter now, two years after RL made agents good at offensive coding.
People still underestimate how large the data market will become.
Better models do not reduce demand for data. They increase the number of useful things models can learn.
The frontier keeps moving, so the data has to keep getting harder.
Sam Jung — UI-Bench co-author / AfterQuery researcher
Gustaf Alströmer — YC partner who commented on the unicorn milestone
Who they amplify
Accounts whose posts Spencer has reposted recently: Nancy Fairbank, AfterQuery.
Why it matters here
The most direct competitor in Julian's lane: AfterQuery already claimed 'design evaluation' cheaply with UI-Bench and an expert-designer judging panel, and now has lab budgets at $3.2B scale. Mateega decides whether design/taste becomes a real AfterQuery product line or stays a marketing benchmark. He is also a potential buyer/subcontracting partner for a small specialist supplying vetted design judges.
How to reach
Speculative: he is very active on X and posts hiring/recruiting calls, so a public, concrete reply about UI-Bench methodology (e.g. inter-rater agreement of design judges) is a credible opener. Best ask: whether they source design experts themselves or would buy a vetted panel. Warm path possibly via Sam Jung (UPenn researcher on UI-Bench).
What we could not establish
Lead investor and round size of the $3.2B round not disclosed
No podcast/long-form interview found
Whether UI-Bench became a paid design-data product line is unknown