⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex

Episode Summary

Executive Summary: Alex Lieberman and Armand Hezarcani, co-founders of 10X, discuss their AI-first engineering consultancy that compensates engineers based on output rather than hours. They share how Armand's experience downsizing his engineering team by 90% and rebuilding with AI led to the insight that AI enables 10x productivity, but traditional hourly billing disincentivizes efficiency. 10X hires top engineers, pays them for story points, and has seen rapid prototyping results, like building a mobile app that hit #20 globally in a month. They are currently human-capital constrained and use a rigorous take-home interview to filter candidates.

Main Topics: Origin of 10X: From AI-Driven Efficiency to Output-Based Compensation (Priority: 5/5): Armand downsized his engineering team by 90% and rebuilt with AI, achieving 10x output. This led to the insight that hourly billing disincentivizes AI-driven efficiency, so 10X compensates engineers based on output (story points) to align incentives. Output-Based Compensation Model and Its Challenges (Priority: 5/5): 10X pays engineers per story point, not per hour. They mitigate gaming by hiring long-term selfish engineers and using technical strategists who are incentivized on retention to sign off on engineering plans. They have not yet faced client disputes over story points. Rapid Prototyping and Client Success Stories (Priority: 4/5): 10X built a prototype for a retail camera system in two weeks (previously quarters), a mobile trivia app that hit #20 on the App Store in a month, and a fitness coach app prototype in four hours to win a client. Technology Stack and AI Agent Selection (Priority: 3/5): Default stack is TypeScript (frontend and backend) with React/Express. They do not have a favorite coding agent; they switch based on daily performance (e.g., Claude Code vs. Codex). They emphasize 'feel' over formal evals. Hiring Process and Constraints (Priority: 4/5): 10X is human-capital constrained. They use an unreasonably difficult take-home interview that 50% of candidates fail to complete, but it shortens the overall process to as fast as one week. They ask questions about building an AI engineer to gauge depth. Debate on MCP (Model Context Protocol) (Priority: 2/5): Armand is skeptical of MCP, calling it 'a three-letter word for API' and criticizing hype-driven fundraising. Alex is more neutral, noting that MCP has useful spec elements beyond APIs. They plan a pro/con debate at the conference.

Key Arguments: Hourly billing perversely incentivizes engineers to work slower, especially when AI boosts productivity; output-based compensation aligns incentives with efficiency. Hiring 'long-term selfish' engineers who understand that maintaining client relationships yields long-term rewards prevents gaming of story points. Technical strategists incentivized on retention and account growth serve as a quality gate, ensuring engineering plans are robust before clients see them. Rapid prototyping with AI can win clients who initially decline, as demonstrated by building a working app in four hours. The limiting factor for scaling 10X is finding enough top-tier engineers, not technology or capital. Context engineering (getting the right context into LLMs) is the key bottleneck to building a fully autonomous AI engineer, not just model intelligence.

Data Points: Engineering team downsizing: 90% - Armand downsized his engineering team by 90% at Parthian and rebuilt with AI. Output increase after AI-first rebuild: 10x - Output of production-ready software increased 10x after the shift. Engineer compensation: Over $1 million cash per year - 10X expects multiple engineers to earn over $1 million cash next year based on story point compensation. Prototype build time (retail camera system): 2 weeks - A prototype that previously would have taken several quarters was built in two weeks. App Store ranking: #20 globally - A mobile trivia app built by 10X hit #20 on the App Store globally in one month. Prototype build time (fitness coach app): 4 hours - An engineer built a working version of a fitness coach app in four hours to win a client. Take-home interview completion rate: 50% - 50% of candidates do not even respond to 10X's unreasonably difficult take-home interview. Interview process duration: 1 week - The fastest interview process can be completed in one week if the candidate passes the take-home.

Pivotal Quotes: "We will probably have more than one engineer make a million dollars cash next year based on this model." — Alex Lieberman: Describing the financial upside of 10X's output-based compensation model for top engineers. "I just think that MCP is a three-letter word for API." — Armand Hezarcani: Expressing skepticism about the hype around Model Context Protocol, which he sees as rebranding existing concepts. "The thing that keeps us up at night is how can we hire enough good engineers fast enough?" — Alex Lieberman: Identifying human capital as the primary constraint on scaling 10X.

Implications: Output-based compensation could disrupt traditional engineering billing models, especially as AI boosts productivity. Companies may need to rethink hiring and incentive structures to attract top AI-native talent. Rapid prototyping with AI will become a competitive advantage in sales and client acquisition.