Egnyte, the $1.5 billion cloud content governance company, has embedded AI coding tools across its global team of more than 350 developers — but not to reduce headcount. Instead, the company continues to hire junior engineers, using AI to accelerate onboarding, deepen codebase understanding, and shorten the path from junior to senior contributor. The approach challenges a dominant 2025 narrative that automation will replace developers, showing instead how enterprises are using AI to scale engineering capacity while keeping humans firmly in the loop.“To have engineers disappear or us not hiring junior engineers doesn't look like the likely outcome,” Amrit Jassal, Egnyte CTO and co-founder, told VentureBeat. “You've got to have people, you're training and doing all types of succession planning. The junior engineer of today is the senior engineer of tomorrow.”How Egnyte coders are using AI — without ceding controlEgnyte — which has more than 22,000 users including NASDAQ, Red Bull, and BuzzFeed — has rolled out Claude Code, Cursor, Augment, and Gemini CLI coding tools across its developer base to support its core business strategies and expand its newer AI offerings like customer-facing copilots and customizable AI agents. Devs use these tools across a variety of tasks, the simplest of which include data retrieval, code comprehension, smart search, and code lookup. Egnyte’s code base has lots of Java code, which uses numerous libraries, each with different versions, Jassal explained. AI tools are great for peer-to-peer programming, helping new users get a lay of the land, or existing users probe into different code repositories. “We have a pretty big code base, right?” Jassal said. “Let's say you're looking at an iOS application, but you're not well versed; you will fire up Google CLI or an Augment, and ask it to discover the code base.”Some Egnyte devs are moving into automatic pull request summaries, which provide simple overviews of code changes that essentially explain the “what,” “how,” and “why” of proposed modifications. “But obviously, any change that's made, we don't want to hear that AI made the change; it has to be that developer made the change,” Jassal pointed out. “I would not trust AI to commit to the production code base.” Commits still pass through human review and security validation, and anything red-flagged is escalated to senior engineers. Devs are warned of the dangers of settling into autopilot mode or blindly trusting code. A model may not have been exposed to, or given enough samples of, certain coding components and infrastructure in its training. Another growing, and closely monitored, use case for AI is unit testing, where code components are run in isolation to ensure they work as intended. “At the end of the day, it is a productivity improvement tool,” he said. “It is really a continuation, it's like any other tool, it's not some magic.”Beyond core engineering, AI is helping other teams collaborate with programmers. Product management, for instance, is using tools like Vercel to bring “demo-worthy” prototypes, rather than just ideas, to devs, who can then move ahead with mock-ups. Or, if UX teams are looking to change certain elements on a dashboard, AI can quickly spin up a handful of options, like different widgets or buttons. “Then you come to engineering with that, and the engineer immediately knows what you really intend to do with it,” Jassal said. Setting expectations, meeting devs where they areHowever, day-to-day activities for all Egnyte engineers, including junior developers, extend beyond just coding. Junior developers are given hands-on tasks across the full development lifecycle to accelerate their growth and experience, Jassal said. For instance, they assist with requirement analysis in early software engineering phases, as well as deployment, productization and post-deployment maintenance.In turn, these activities require “Egnyte-specific tacit knowledge and experience” offered by senior engineers. One clear example of work that sits firmly with senior engineers is authoring architecture notes, as these cut across the platform and require a more holistic, system-level view, Jassal said. “Many of the traditional roadblocks are navigated faster these days with AI; for example, understanding the codebase, dissecting requirements, auto-testing,” he said. “This faster track allows our talented junior hires to progress more quickly and provide higher value to the company sooner.”The company expects a much faster learning curve from junior to mid-level engineers, Jassal said. “It's always the case that people coming straight into the workforce are much more excited about trying new things,” Jassal said. But that has to be colored with reality to temper expectations, he added. On the other hand, some senior engineers may need to be ramped up in their adoption because they’re hesitant or had ho-hum or bad experiences with earlier generation tools. This requires incremental introduction. “The senior people, having been burnt multiple times, bring that perspective,” he said. "So both [types of engineers] play an important role.”Hiring will continue for scale and fresh perspective“In general, I would say it has been really hyped by folks who want to sell you tokens,” Jassal said referring to people who talk about human coders becoming obsolete. "Vibe coding" could be construed in a similar vein: Like others in software development, he prefers the term “AI assisted coding,” wherein programmers have a self-driven loop, generating code, analyzing exceptions, then correcting and scaling. At least in Egnyte’s case, hiring will continue, even if at a slower clip as people become more productive thanks to AI, Jassal said. “We are not just hiring for scale, but to develop the next generation of senior developers and inject fresh perspectives into our development practices,” he said. The takeaway for technical decision-makers is not that AI will eliminate engineering jobs — but that it will reshape how talent is developed. At Egnyte, AI-assisted coding is compressing learning curves and raising expectations, not removing humans from the process. Enterprises that treat AI as a replacement risk hollowing out their future senior talent pipeline; those that treat it as infrastructure can move faster without losing the judgment, creativity, and accountability that only engineers provide.