⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex
SubscribeSign in
Latent Space: The AI Engineer Podcast
⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex
1
1×
0:00
Current time: 0:00 / Total time: -27:10
-27:10
Audio playback is not supported on your browser. Please upgrade.
⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex
Nov 19, 2025
1
Share
Transcript
Alex Lieberman and Arman Hezarkani, co-founders of Tenex, reveal how they’re revolutionizing software consulting by compensating AI engineers for output rather than hours—enabling some engineers to earn over $1 million annually while delivering 10x productivity gains. Their company represents a fundamental rethinking of knowledge work compensation in the age of AI agents, where traditional hourly billing models perversely incentivize slower work even as AI tools enable unprecedented speed.
The Genesis: From 90% Downsizing to 10x Output The story behind 10X begins with Arman’s previous company, Parthian, where he was forced to downsize his engineering team by 90%. Rather than collapse, Arman re-architected the entire product and engineering process to be AI-first—and discovered that production-ready software output increased 10x despite the massive headcount reduction. This counterintuitive result exposed a fundamental misalignment: engineers compensated by the hour are disincentivized from leveraging AI to work faster, even when the technology enables dramatic productivity gains. Alex, who had invested in Parthian, initially didn’t believe the numbers until Arman walked him through why LLMs have made such a profound impact specifically on engineering as knowledge work.
The Economic Model: Story Points Over Hours 10X’s core innovation is compensating engineers based on story points—units of completed, quality output—rather than hours worked. This creates direct economic incentives for engineers to adopt every new AI tool, optimize their workflows, and maximize throughput. The company expects multiple engineers to earn over $1 million in cash compensation next year purely from story point earnings. To prevent gaming the system, they hire for two profiles: engineers who are “long-term selfish” (understanding that inflating story points will destroy client relationships) and those who genuinely love writing code and working with smart people. They also employ technical strategists incentivized on client retention (NRR) who serve as the final quality gate before any engineering plan reaches a client.
Impressive Builds: From Retail AI to App Store Hits The results speak for themselves. In one project, 10X built a computer vision system for retail cameras that provides heat maps, queue detection, shelf stocking analysis, and theft detection—creating early prototypes in just two weeks for work that previously took quarters. They built Snapback Sports’ mobile trivia app in one month, which hit 20th globally on the App Store. In a sales context, an engineer spent four hours building a working prototype of a fitness influencer’s AI health coach app after the prospect initially said no—immediately moving 10X to the top of their vendor list. These examples demonstrate how AI-enabled speed fundamentally changes sales motions and product development timelines.
The Interview Process: Unreasonably Difficult Take-Homes Despite concerns that AI would make take-home assessments obsolete, 10X still uses them—but makes them “unreasonably difficult.” About 50% of candidates don’t even respond, but those who complete the challenge demonstrate the caliber needed. The interview process is remarkably short: two calls before the take-home, review, then one or two final meetings—completable in as little as a week. A signature question: “If you had infinite resources to build an AI that could replace either of us on this call, what would be the first major bottleneck?” The sophisticated answer isn’t just “model intelligence” or “context length”—it’s controlling entropy, the accumulating error rate that derails autonomous agents over time.
The Limiting Factor: Human Capital, Not Technology Despite being an AI-first company, 10X’s primary constraint is human capital—finding and hiring enough exceptional engineers fast enough, then matching them with the right processes to maintain delivery quality as they scale. The company has ambitions beyond consulting to build their own technology, but for the foreseeable future, recruiting remains the bottleneck. This reveals an important insight about the AI era: even as technology enables unprecedented leverage, the constraint shifts to finding people who can harness that leverage effectively.
Full Video Episode
Timestamps
00:00:00 Introduction and Meeting the 10X Co-founders
00:01:29 The 10X Moment: From Hourly Billing to Output-Based Compensation
00:04:44 The Economic Model Behind 10X
00:05:42 Story Points and Measuring Engineering Output
00:08:41 Impressive Client Projects and Rapid Prototyping
00:12:22 The 10X Tech Stack: TypeScript and High Structure
00:13:21 AI Coding Tools: The Daily Evolution
00:15:05 Human Capital as the Limiting Factor
00:16:02 The Unreasonably Difficult Interview Process
00:17:14 Entropy and Context Engineering: The Future of AI Agents
00:23:28 The MCP Debate and AI Industry Sociology
00:26:01 Consulting, Digital Transformation, and Conference Insights
Discussion about this episode
CommentsRestacks
Latent Space: The AI Engineer Podcast
The podcast by and for AI Engineers! In 2025, over 10 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0.
We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al.
Full show notes always on https://latent.space
Sponsorship and business inquiries: [email protected]
The podcast by and for AI Engineers! In 2025, over 10 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0.
We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al.
Full show notes always on https://latent.space
Sponsorship and business inquiries: [email protected]
Subscribe
Listen on
Substack App
Apple Podcasts
Spotify
RSS Feed
Recent Episodes
Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk
Oct 8
Academia is for Ambition — Alex Zhang, MIT
Oct 2
Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week
Sep 30
Claude Code’s Next Era — Thariq Shihipar, Anthropic
Sep 29
OpenRouter: from Seed to Stripe — with OpenRouter’s Alex Atallah & AMP’s Anjney Midha
Sep 25
Runway’s WorldPrompt and the Engineering of Real-Time Worlds
Sep 25 • Richard MacManus
🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)
Sep 23 • RJ Honicky
🔬 An Oscar, Two Asteroids, and the Algorithm in Your sklearn: John Platt on AI for Science
Sep 22 • Brandon Anderson
Ready for more?
Subscribe
© 2026 Latent.Space · Privacy ∙ Terms ∙ Collection notice
Start your SubstackGet the app
Substack is the home for great culture