@AbridgeHQ spent years building before the market opened up.
The lesson from COO @juliajchou
“We were never standing still.”
While healthcare AI adoption was still early, Abridge was building patient products, training models, and developing relationships with health systems so it could move when the window opened.
Introducing the Sapienne Podcast: conversations with women leading the most consequential AI companies of our time.
@juliajchou has built @AbridgeHQ into one of the most important companies in healthcare AI as COO.
We loved this conversation and hope you enjoy it.
6/ We launched Sapienne’s 2026 AI Startup Compensation Survey to make this market more transparent.
Subscribe for future findings: https://t.co/yLCS2Bx5ui
Work at an AI-native startup? Contribute anonymized data to receive the full benchmark: https://t.co/8gbYSpt6ok
We surveyed 110+ employees across top venture-backed AI startups.
One early finding stood out: candidates who negotiated reported median bumps of:
+$25K cash
+4 bps equity ownership
+$118K in equity value
Here’s what the early data says about AI startup compensation:
4/ Bigger does not necessarily mean more cash.
Cash compensation clusters in the mid-to-high $200K across company sizes. The more meaningful tradeoff is often higher ownership at smaller companies versus equity that may be closer to liquidity at later-stage companies.
The next generation of agents don't just answer a prompt—they’ll work for hours. That’s been top of mind for so many builders in the Sapienne. Proud to back @sailresearchco and excited to see what our builders create with their infrastructure. Congrats @neilmovva + team!
Samir Menon @blintzbase and I are thrilled to announce Sail @sailresearchco ! We build infrastructure for long-horizon agents: inference served at unbeatable prices-per-token for open models, plus sandboxes designed to run for days, weeks, or longer.
We've raised $80M, w/ our seed led by @Sequoia
and series A led by @KleinerPerkins. We're using this capital to build the most efficient infrastructure for long-horizon agents.
What makes agents so different? Unlike a human waiting at a keyboard (top priority: speed), agents need scale, reliability, and sustainable cost. Sail finds this efficiency everywhere in the stack: we carefully choose our chips, write custom inference engines, and run a global controller that fully utilizes every computer in our fleet. Tight integration from silicon to API lets Sail open up the cost / latency frontier to our customers - the most patient agents can now access 10x more intelligence per dollar.
We're excited to be working with great companies like
@parallelweb, @detaildotdev,@Jackandjillai, and @quadrillion_ai to deploy long-horizon agents with trillions of tokens.
Our team is thoughtful in our engineering craft and relentlessly ambitious in our pursuit of peak performance. We previously trained at companies like NVIDIA, OpenAI, Google, and so many trading firms. Now we're ready to do the work that will define our careers, in the most compute intensive market of all time.
Welcome to the era of abundant intelligence. We can't wait to build with you!