Ishraq Khan, CEO and Founder of Kodezi Inc – Interview Series

Ishraq Khan, CEO and Founder of Kodezi Inc., is a self-taught coder who began programming at eight and launched his first startup while still in middle school. Born in Dhaka, Bangladesh and later moving to the United States, he built a track record of early entrepreneurship, securing venture funding in high school and scaling a product to more than 100,000 users. His path reflects a focus on independent learning, rapid experimentation, and a drive to build systems that make technology more accessible and powerful for developers.

Kodezi Inc. is the company behind Kodezi OS, an autonomous platform designed to function as an “AI CTO” for engineering teams. It continuously detects and fixes issues, automatically documents systems, generates API specifications, enforces coding standards, and integrates directly into CI/CD pipelines. By transforming codebases into self-healing, self-governing systems, Kodezi helps organizations build software that is more reliable, scalable, and efficient.

You started coding at just eight years old and founded your first startup in middle school. What originally drew you to building software so early, and how did those experiences shape your entrepreneurial mindset?

What pulled me in was control. I moved to the United States as a kid who did not speak English, so the first language I learned fluently was code. It was a space where logic made sense, where I could build something and see it respond instantly. That instant feedback loop became addictive. It taught me how to think, not just how to program.

When I built TeachMeCode in middle school, it was not about starting a company. It was about making learning easier for people like me. But through that, I learned how systems behave, how users respond, and how progress happens line by line. It shaped how I see entrepreneurship today: less about ideas, more about feedback loops, iteration, and resilience.

You were accepted to 40 colleges—including several Ivy League institutions—but chose not to attend. What was the turning point that made you decide building mattered more than waiting?

By the time I finished high school, I had already lived what most people go to college to simulate. I had launched products, pitched investors, managed a team, and solved real problems. I had 40 acceptance letters on my desk, including several Ivy League schools, but I also had something most students did not: momentum.

The bigger risk was slowing down. College would have taught me frameworks for innovation, but I was already running experiments in the real world. I did not want to pause an active system to study how to start one. For me, the classroom became the product itself. Kodezi was the education I wanted.

Kodezi began as an idea when you were still a teenager. How has the company evolved since its inception in 2019, and how did your vision of an “AI CTO” emerge over time?

Kodezi started as an autocorrect for code, a simple idea that debugging could be faster. As we scaled, I realized debugging was not the root problem. The real issue was that codebases never stay still. They evolve, drift, and decay faster than humans can maintain them.

Over time, Kodezi evolved from a product into an operating system, what we now call Kodezi OS, that learns from every bug, test, and commit. The term “AI CTO” emerged naturally. CTOs do not just write code; they maintain architecture, guide decisions, and keep systems alive. That is what Kodezi does, but continuously and autonomously.

Kodezi’s newest model, Chronos, is described as the first AI system built specifically for code debugging—rather than code generation. What fundamental difference does that distinction make for developers?

Because debugging is reality, not imagination. Code generation is about guessing what might work; debugging is about understanding why something failed.

Most AI tools today are prompt-based assistants that react when told. Chronos, on the other hand, is proactive. It remembers previous bugs, understands dependency graphs, runs tests, validates fixes, and refines them until the issue is actually resolved.

That is the distinction that matters. Developers do not want an assistant that talks. They want infrastructure that acts and acts correctly.

The results you’ve shared show Chronos outperforming GPT-4.1 and Claude 4 Opus on bug-fix accuracy. Can you walk us through the dataset and methodology behind those benchmarks?

Our evaluation is empirical, not promotional. Chronos is tested on thousands of real-world debugging cases drawn from public datasets such as SWE-bench, Defects4J, and BugsInPy, along with anonymized enterprise data.

Each benchmark is strict: the model must generate a patch, apply it, and pass all test cases without regressions. No hand-picked examples, no cherry-picking success.

Chronos achieves 67.3 percent fix accuracy and 80.33 percent resolution rate on SWE-bench Lite, while GPT-4.1 and Claude 4.5 stay below 15 percent. The difference is not size; it is specialization. Chronos is trained on debugging itself, on 15 million real debugging sessions, so it does not just pattern-match, it diagnoses.

You’ve described Kodezi as an “AI CTO” that autonomously maintains and evolves a company’s codebase. How close are we to fully self-healing infrastructure in production environments?

Closer than most people think, at least for deterministic systems. Today, Kodezi can autonomously fix many CI or CD failures, test regressions, and runtime errors using contextual data and historical memory.

Fully autonomous production maintenance, where infrastructure diagnoses, heals, and redeploys itself, is emerging. I see it evolving in stages: first inside controlled CI environments, then staging environments, and finally production under human oversight.

We will always keep a human in the loop for creative, architectural, and ethical decisions, but most of the repetitive and error-prone work such as linting, refactoring, and test recovery will soon happen without intervention.

You’ve spoken about systems that “quietly do the right thing.” What does that philosophy mean in the context of AI governance and responsible automation?

To me, “quiet” does not mean silent. It means trustworthy by default. A well-designed AI system should not need to ask for constant input or validation. It should act predictably, transparently, and safely.

Responsible automation means every decision made by the AI is explainable, reversible, and logged. Chronos documents its reasoning and actions: what it changed, why, and how tests validated the fix.

Governance is built into the system itself. No hidden modifications, no black box outcomes. The goal is not for AI to be loud or flashy, but to quietly improve the world beneath the surface where it matters most.

The term “Quiet Tech” is compelling—it suggests technology that’s powerful yet invisible. How do you see this movement reshaping how humans and AI collaborate in engineering?

Quiet Tech is infrastructure that is powerful but invisible. The best technology should not interrupt; it should integrate.

In engineering, that means the tool does not ask “What do you want me to do?” It already knows what needs attention. It sees the broken dependency, patches it, updates the documentation, and moves on.

As AI becomes part of the developer stack, collaboration shifts from command to coexistence. Humans define intent and direction. The AI executes, maintains, and optimizes quietly in the background. That is the next era, where productivity comes not from more interaction but from less friction.

Many developers worry about AI tools replacing them. You’ve argued that automation should free humans to think, not replace them. How does Kodezi embody that balance?

AI will not replace developers. It will replace the drudgery around them. Engineers are not valuable because they type fast; they are valuable because they think clearly.

Kodezi automates the repetitive work that drains focus: debugging, test maintenance, refactoring, documentation. The human layer, creativity, system design, and tradeoff reasoning remain irreplaceable.

In the long run, AI shifts engineering from execution to orchestration. Developers become architects of behavior, not executors of syntax. Kodezi is built to enable that transition, where machines maintain and humans imagine.

You’ve described Kodezi as “living infrastructure.” Looking ahead five years, what might the developer’s role look like in a world where software sustains itself?

In five years, developers will not spend half their time fixing what they built last quarter. Their role will move upstream from reactive maintenance to proactive governance.

Imagine a world where every repository has memory, where your system tracks its own decisions, heals regressions, and evolves with new dependencies automatically. That is living infrastructure.

In that world, developers act more like stewards. They define policies, verify behavior, and design intent. The codebase becomes a living organism that adapts, learns, and sustains itself.

That is what we are building with Kodezi: software that does not just run. It endures.

Thank you for the great interview, readers who wish to learn more should visit Kodezi.