Tenex CEO Alex Lieberman and Director of Engineering CJ Hess discuss how their applied AI partner helps Fortune 2000 companies transition from AI apps to AI-native operations. They share insights on output-based compensation for engineers, the state of enterprise AI adoption, and the challenges of deploying agents for non-engineering work.
Summarized by Podsumo
Tenex uses a story-point-based compensation model where engineers are paid based on output, with top earners making millions per year and a 4x variance between highest and lowest paid.
Only 5% of enterprises have deployed AI agents for non-engineering work in production, highlighting a massive gap in enterprise AI adoption.
Tenex has seen a roughly 2x increase in engineer output over the past year due to model improvements, particularly with Opus 5.5.
The company is bearish on vertical-specific AI solutions (like Harvey for lawyers), arguing that frontier models and one-of-one software expectations will challenge their margins.
Enterprises struggle with permissioning and RBAC as employees adopt general-purpose LLMs like Claude and Codex, creating a 'nightmare for CIOs and CISOs'.
"You can't automate a process that does not exist. — CJ Hess"
"Our view is our model isn't for everyone, but for the person who basically wants to go long themselves, who has this incredible foundation in software engineering but also is constantly pushing themselves to leverage the frontier, there's no better way to earn. — Alex Lieberman"
"When you have 40 people that are all roughly doing the same function, but they're all doing it a little bit of a different way, it's way harder to build an agent that's proactively doing that in the background. — CJ Hess"