This is the first post in a multi-part series exploring the fast-growing area of Generative AI -assisted coding; GenEng.
Software development is undergoing a profound transformation fueled by rapid advancements in generative AI. Generative Engineering isn’t just an incremental upgrade—it’s redefining how software is built, who builds it, and how fast it can be delivered.
This shift is already well underway at forward-leaning organizations across every sector. Roughly 30% of new code at Google and Microsoft is now AI-generated , Morgan Stanley is using AI to modernize its legacy code , and Hitachi reports that 83% of its developers complete tasks faster with AI coding tools.
As Sravana Karnati, EVP of global technology platforms at Walmart, explains , “We are seeing a huge step change in the way coding and software development is done—and not just as in generating the code, testing it, compiling it, and deploying it, but it’s also our ability to run systems in production.”
For business and technology leaders, understanding the impact of AI-assisted coding is no longer optional. Generative Engineering is changing the fundamentals of software development. Those who embrace it early—and with a clear strategy—will be better equipped to turn software development into a lasting competitive advantage.
The Rise of Generative Engineering
We're witnessing the emergence of increasingly capable AI coding assistants—from basic autocomplete tools to agents capable of building entire applications from natural language prompts. The coding community was among the first to embrace and practically exploit generative AI's potential, pioneering methods that drastically boost productivity.
What began with copying code snippets and error messages into ChatGPT soon advanced to in-IDE solutions like GitHub Copilot’s code suggestions. Now, we’re seeing a new generation of AI agents such as Cursor, Windsurf, Codex and Claude-Code that can reason about system architecture, navigate entire codebases, and make coordinated edits across multiple files—even building full-stack applications from a single prompt.
Generative Engineering goes beyond initial interface generation, excelling at streamlining repetitive and low-priority tasks that affect code quality and maintainability. Iterative UI refinements, based on design updates from tools like Figma, used to take hours but can now be done in minutes with a prompt or even a screenshot. Documentation and README files can be auto-generated with visuals like Mermaid charts, speeding up new team member onboarding. And test suite creation, often a bottleneck, can be accelerated significantly, freeing developers to focus on architecture and solving more complex challenges.
But it is not just low-value toilsome tasks that are in play. The best coders are now using agentic coding tools to build entire new features an order of magnitude faster. A well-written 3-line prompt to an agent can consistently generate 1,000 lines of high-quality code. And the top engineers are spinning up multiple agents in parallel to even further multiply their impact. Engineering leaders looking for 10-20% efficiency improvements are missing the opportunity by a mile.
Generative Engineering represents a strategic evolution of coding practices, but it's also changing the nature and number of traditional engineering roles.
A Leaner Future: Reinventing the Role of the Engineer
As Tim O’Reilly notes in The End of Programming as We Know It , we’re not witnessing the end of engineering, but a shift toward work that’s more strategic and less manual. Engineering will persist, but not all roles as we currently know them will. You’ll still need experienced developers to get apps safely and securely into production. But with Generative Engineering, those experts can ship significantly more features, faster. Some teams are already seeing 20x, 50x—even 100x—increases in feature output compared to traditional manual coding workflows.
This shift also has the potential to democratize app creation—at least at the prototyping stage—by giving non-technical domain experts basic development capabilities. These experts often have a deeper understanding of the problem the app is meant to solve. As a result, ideas can be tested sooner, enabling earlier feedback and faster iteration on both product requirements and the product itself.
Riding the Wave: The Journey Toward (Almost) Full Autonomy
Just as the
automotive industry has developed a taxonomy for levels of autonomous driving
, we believe a similar framework applies to coding. The Generative Engineering automation spectrum can be understood through these progressive levels:
- L0—Luddite: Completely rejects AI assistance for coding, and is skeptical of its value and capabilities.
- L1—Chat-Overflow: Uses AI primarily as a smarter search engine and documentation assistant, replacing Google and Stack Overflow but not yet leveraging AI code generation.
- L2—Copy and Complete: Leverages AI for code completion and generating useful snippets, with less than 20% of code coming from AI sources.
- L3—Feature Editor: Generates larger units of work using AI, building entire features across multiple files with prompts. AI writes over 50% of the code, with the human acting more like an editor—reviewing, modifying, and orchestrating the AI's output.
- L4—Full Agentic ("Vibe Coder"): Represents a state where basically 100% of code is AI-written, with the user relying on all prompts and no direct code manipulation. The human acts purely as a prompt engineer or creative director—and pull request reviewer—describing the desired outcome and letting AI handle implementation details.
Engineers will increasingly need to operate at the higher levels of this spectrum to stay competitive. This framework is a maturity model, and adoption will vary by organization. For example, banks and regulated industries may start at L1–L2, while Y Combinator startups may start at L3–L4 from the outset.
Jumping straight to L4 "vibe coding" isn’t realistic—or advisable—for most teams. A more prudent and sustainable approach involves:
- Coaching engineers to adopt L1 and L2 practices broadly
- Encouraging safe experimentation with L3 in sandboxed environments
- Assigning select pilot teams (such as those working on prototypes or greenfield apps) to explore the frontiers of L4
Enabling teams to navigate these levels and thrive in an AI-assisted future will require deliberate effort: business leaders must foster a culture of experimentation, and engineers must embrace an open mindset.
The Pitfalls of Generative Engineering and Applying the Necessary Guardrails
Implementing Generative Engineering successfully demands a clear-eyed understanding of its limitations—and the proactive implementation of guardrails. Risks to be aware of include:
- Security vulnerabilities: A study accepted by ACM found that nearly a third of generated code snippets may contain security weaknesses.
- Code quality concerns: Without proper supervision, AI-generated code can introduce orphaned functions, unnecessary abstractions, or overly complex implementations.
- Hallucinations: These models can confidently reference non-existent functions, libraries, or APIs—requiring human review to catch.
- Maintainability issues: AI-assisted codebases can sprawl quickly without discipline, growing beyond what teams can realistically maintain and introducing technical debt.
These risks aren’t reasons to avoid Generative Engineering. They’re reasons to evolve your software development lifecycle (SDLC) and coding practices. Generative Engineering tools can yield complete, high-quality solutions much faster—but it does require intentionality and discipline.
One practical approach to applying those guardrails: Treat AI-generated output as a pull request.
Encourage a "lead engineer" mindset at every level. Junior developers, in particular, gain valuable experience by learning to rigorously review and validate AI-generated code. The level of scrutiny should also match the use case. During prototyping, “vibe coding" can speed up iteration. But for production, Generative Engineering is most effective when used with in-IDE tools for targeted, time-saving edits.
While AI output should be reviewed like code from a junior developer, your prompts should be crafted like instructions to a senior dev . Give clear instructions about the outcome you want—including architectural guidance—but leave room for the AI to determine how best to implement it.
Teams should also set up rules for GenEng tools. Engineer and founder Ben Guo aptly refers to these as being part of the “ immune system ” of a codebase; GitHub Copilot has Instructions , Claude Code has Claude.md , and Cursor has Rules . While there’s not (yet) a uniform protocol for these files, the idea is the same: give your coding assistants stylistic guidelines to include in some or all prompts to help keep your codebase in line with expectations. They should be revisited and evolved throughout the life of a repo, similar to keeping a Readme.md or Contributor.md file up to date! Early effort to establish guardrails like this makes it easier to build down the road for coding agents and engineers alike!
This Isn’t Just a Tooling Shift—It’s a Mindset Shift
Adopting Generative Engineering at scale isn’t just a technical shift—it’s a behavioral one. Many organizations are rolling out tools like GitHub Copilot and seeing surface-level adoption. But this early usage often stays at L1. Without deeper adoption, the impact is limited.
Leaders must go beyond basic training to help engineers advance along the Generative Engineering maturity model. This means not only teaching developers how to use the tools effectively, but also addressing the underlying behavioral and psychological shifts required to change how they work.
That kind of transformation doesn’t come from tooltips or one-time sessions. It takes coaching, experimentation, and space for engineers to build trust with these tools in the context of their actual workflows. It’s about giving teams the time, support, and permission to evolve their habits through experience.
Senior engineers play a pivotal role here. They can model best practices, mentor others, and help build confidence in using AI tools responsibly. In turn, this frees them up to focus on higher-order architectural challenges rather than repetitive tasks.
AI coding tools won't deliver massive productivity gains overnight, and not every tool will be right for every tech stack. The most successful organizations will be the ones that adopt thoughtfully—embracing these tools while establishing clear guardrails, investing in meaningful behavior change, and evolving development processes to account for both the promise and the limits of AI assistance.
The real question for business leaders isn't whether to adopt Generative Engineering, but how to do so in a way that balances its undeniable productivity benefits with the discipline required to build sustainable systems. Those who find the right balance between speed and structure will lead—not just in innovation, but in translating it into real business value.
Stay tuned for the next post in this series, where we’ll delve into how to effectively manage the strategic shift toward engineering within your team or organization.
