As we navigate through the current AI revolution in software development, one thing is glaringly apparent: Both the infrastructure and application layers are experiencing a surge in demand. AI and other trends are dramatically increasing the amount of code being developed, meaning there is more code than ever that needs to be reviewed and verified before it’s put into production.

That’s catching the attention of huge tech players like GitHub, AWS, Google and OpenAI, as well as startups who see a huge opportunity here. We see promise in this emerging space, which is why we’re excited to announce our investment in Baz*, the company that helps engineers and AI agents to build better code. Here’s some context on our outlook, and more thoughts about the market opportunity.

The tech stack is being re-written

• With AI tools becoming mainstream for more and more business and consumer applications, more developers are being tasked with adapting and, often, rewriting their tech stack.
• We’re seeing an unprecedented increase in code generation. This surge creates a proportional need for both upstream and downstream support. The more code AI produces, the more there is to host, review, verify, and refine.
• The TAM for tools that can handle this volume, we believe, is expanding at a breakneck pace.

A cycle of errors amplifies tech debts

• With better evaluation and reflection criteria, the inefficiencies of AI in real-world scenarios are becoming more apparent.
• Each cycle of fixing AI-generated code opens up new avenues for software that can predict and prevent these errors, making the market for advanced debugging and quality assurance tools increasingly exciting.
• The more developers rely on AI, the more they’ll need tools to manage the resultant complexity, pushing the TAM for these solutions skyward.

Educating the masses

• The knowledge gap between what AI can do and what developers need to know to use it effectively is widening. This educational need, as subtly pointed out in discussions around AI coding, isn’t just about training; it’s about tools that democratize AI coding benefits for all skill levels.
• Educational platforms combined with AI-assisted code review tools represent a burgeoning market segment, exponentially increasing the TAM as more individuals and companies seek to upskill their workforce in this new paradigm.

Implementing human oversight

• The shift towards more agentic AI in production is a clear indicator of market growth. On the other end, tools that ensure that agentic capabilities run with necessary human oversight are becoming essential.
• For example, this convergence of AI and human expertise in the review process creates a new space, one that’s growing as AI becomes more integrated into software development workflows.

Each inefficiency uncovered by AI generates opportunities for more tools. As AI coding becomes integral to production systems, new areas are emerging as critical components in every developer’s toolkit. The software giants are racing to lead the charge, proving that the future lies in the tools that make AI coding smarter, faster, and more reliable. This is a clear signal to the market: A migration is happening, and developer adaptation is coming.

Enter Baz

Baz’s two founders, Guy Eisenkot and Nimrod Kor, are both veterans of Bridgecrew*, a previous Battery investment where I was also a co-founder. There, along with co-founder Idan Tendler and Guy Eisenkot, we created a popular open-source tool for IaC Checkov. Now, Guy and Nimrod have decided to embark on a new journey to cater to the people we were always obsessed with making happy and more productive: developers.

Baz is handling the source control management gaps: Developers encounter constant friction with concepts like rebasing, merging, and conflict resolution—tasks that should be straightforward, but instead often lead to hours of lost productivity. These frustrations stem from a fundamental limitation: Today’s version-control systems treat code as static text rather than as dynamic, interconnected systems.

Baz is starting with code review, a critical bottleneck for teams, to lay the foundation for something far greater. By combining code and application behaviors, Baz evaluates how code changes impact running services, endpoints and APIs. This first step not only addresses the immediate inefficiencies in reviewing and merging code but also paves the way for a transformative vision: a coding system that truly understands code AST (abstract syntax trees, which are special data structures used in coding).

The promise, I feel, is enormous. By evolving version control to incorporate code comprehension, Baz can eliminate the guesswork and friction that slow developers today. Imagine a system that doesn’t just flag conflicts but explains their root cause, predicts downstream issues, and offers inline code edits.

The beginning: Enhancing the code-review interface

AI code generation positively impacts delivery pace but might also create fatigue in the review process due to trust issues with generated code. Baz is improving review velocity, release speed, and production uptime by reordering the review process using the order code function relationship, instead of doing it alphabetically. With Baz, code changes are correlated back to runtime metrics, and every change is automatically reviewed by its AI to analyze potential breaking changes, enforce coding standards, and improve code testing coverage.

Want to improve your review experience? Give it a try: https://baz.co/