Co-founder and CEO of Qodo (formerly CodiumAI), an AI-powered code quality platform serving 1M+ developers. Previously founded Visualead (acquired by Alibaba for $50M) and led Alibaba’s Machine Vision Lab. Over 20 years of experience in ML and software engineering from Technion.
Champion of AI Code Quality & Context-Driven Testing
Itamar Friedman has emerged as a leading voice challenging the AI code generation hype, arguing that the industry’s real crisis isn’t code generation—it’s verification and trust. His central thesis: quality is your competitive edge in an AI-assisted world where code generation is becoming commoditized.
Current Work
As CEO of Qodo, Itamar leads development of enterprise-grade AI code review and testing tools. The platform has achieved significant scale: 1M+ developers, 1M+ pull requests quarterly, 50,000+ tests generated daily. Qodo raised $51M total funding (2023 $11M seed, 2024 $40M Series A).
His focus areas include:
- Context Engine - Aggregating logs, git history, and PR comments to give AI models richer understanding of codebases
- Autonomous Testing - AI-generated test suites that build developer trust in AI-generated code
- Code Review Automation - Achieving 2x productivity improvement through intelligent, context-aware reviews
- Open Source - Leading PR-Agent, an open-source tool for automated pull request analysis
He writes actively on Medium and the Qodo blog, covering AI code quality, testing methodologies, and production challenges.
Background
Visualead (2012-2017) - Co-founder/CTO of QR code and image scanning company. Acquired by Alibaba Group for estimated $50M. Developed technology for secure image scanning and P2P transactions.
Alibaba Group (2017-2021) - Director of Machine Vision Lab in Israel. Led teams developing ML applications used by millions.
Qodo (2022-present) - Co-founded to address the critical gap between AI adoption and code quality trust.
Studied at Technion - Israel Institute of Technology. Winner of multiple ML and programming competitions.
Philosophy on AI Code Quality
Itamar’s approach reframes the industry’s understanding of AI-assisted development:
The trust problem isn’t model quality - Developers hesitate because AI lacks deep contextual understanding of system architecture, business logic, and technical debt. Solution: Context Engine that aggregates critical information.
The real crisis is velocity without safety - AI helps developers ship more PRs faster, but this increases total bugs (higher volume, not higher density). Traditional manual review doesn’t scale. Solution: Autonomous testing and intelligent review.
Context is the new currency - Better context unlocks better AI outputs. Not just feeding code to models, but providing logs, historical PRs, team conventions, and domain rules.
Key Articles & Media
Published Articles:
- Software 3.0 — the era of intelligent software development - Defining the next era beyond neural nets
- Building code generation that makes sense for the enterprise
- Qodo’s $50M to Accelerate Quality of Software Development with AI
- Tandem Coding with my Agent
Podcasts & Interviews:
- Software Engineering Radio - Automated Testing with Generative AI
- Latent Space Podcast - Debugging the Internet with AI agents
- Not Another CEO Podcast
About Qodo
Qodo provides autonomous test generation (Qodo Cover), AI code review (Qodo Merge), and the Context Engine for intelligent code understanding. The platform addresses the gap where 82% of developers use AI assistants but 76% don’t fully trust AI-generated code (2025 State of AI Code Quality Report).
Conference Appearance
Event: AI Engineering Code Summit 2025 Date: November 20, 2025 Time: 12:00 PM Session: Testing with AI: The State of AI Code Quality—Hype vs. Reality
Itamar presented findings from Qodo’s 2025 Developer Survey, challenging the industry’s focus on code generation speed over verification. His central argument: organizations shipping more code faster with AI need comprehensive testing and review infrastructure to maintain quality. He introduced Qodo’s Context Engine and demonstrated how context-aware AI achieves 2x productivity gains in code review.
Key Insights
“76% of developers don’t fully trust AI-generated code. They don’t trust the context that the LLM has.”
“The crisis is you are getting more tasks being done. You have more bugs because there are more quantity of PRs, not because the PRs themselves are more buggy.”
“Quality is your competitive edge. AI code review delivers 2x productive gain.”