Arman Hezarkhani is the co-founder and managing partner of Tenex, the AI engineering and transformation company he founded with Morning Brew co-founder Alex Lieberman. He treats AI adoption as an organizational design challenge: better tools change little unless compensation, delivery practices, and hiring reward people for using them effectively.
Hezarkhani studied electrical and computer engineering at Carnegie Mellon, launched an education-technology startup through the AlphaLab accelerator, and taught computer science at Carnegie Mellon. He worked on Google’s education, cloud, and AI initiatives before founding Ahez Consulting. He subsequently founded and led Parthean, a financial-education company designed to give people practical guidance when they needed it. Lieberman backed Parthean before becoming Hezarkhani’s co-founder at Tenex.
- Outcome-based engineering compensation. Tenex bills clients for accepted story points and pays engineers a base plus additional compensation for completed work. Hezarkhani argues that this gives engineers a direct financial incentive to adopt productivity-enhancing AI, unlike conventional salaries and uncertain startup equity. His explanation of the model connects compensation directly to accepted deliverables.
- Quality safeguards for performance incentives. Strategists define requirements and maintain client relationships; engineers develop architecture, estimate tickets, and implement solutions. Internal review, strategist oversight, and client acceptance counter inflated estimates, rushed work, and incentives that reward output without quality.
- Production AI under real constraints. Hezarkhani’s client work includes automated moderation for billboard advertising and retail computer-vision systems. For retailers operating low-power devices, his team used model optimization and quantization to support multiple capabilities, including traffic heat mapping, queue detection, and theft detection.
- Agent-assisted knowledge work. His Claude Code playbook applies coordinated subagents to competitive-intelligence research, extending agent-based workflows beyond software development to nontechnical business users.
- Proof-of-work hiring. Hezarkhani favors demonstrated engineering ability, demanding technical evaluation, and consequential problems over pedigree alone. His account of Tenex’s hiring approach reflects his conviction that AI amplifies both strong technical judgment and weak execution.
