AI in End-to-End Software Development: Are We Trading Off Mentorship and Talent Growth for Productivity?
Over the past few decades, global economic growth has brought with it a troubling side effect: a sharp rise in wealth inequality. The share of wealth owned by the top 0.01% climbed from under 8% in 1995 to nearly 12% by 2022. Yet throughout this period—especially since the rise of the internet, access to knowledge served as a counterbalance. For millions of people, including myself, opportunity did not come from privilege but from the ability to learn, unlearn, and relearn. As a young graduate from a middle-class family, I could enter a corporate environment, learn from more experienced colleagues, and rise through merit.
Throughout my career in technical leadership, I benefited immensely from mentors and, in turn, took great satisfaction and pride in guiding junior engineers. These cycles of knowledge transfer have long powered the IT industry’s growth and helped distribute its economic rewards. But my recent hands-on experience using AI for full-stack software development has left me wondering: Are we on the brink of a shift that accelerates both wealth and intellectual inequality?
And if so, what does that mean for the next generation entering the field?
The Traditional Model of Software Development
A typical software project involves many specialized, interdependent roles:
Startups may compress these roles, but each is still a distinct discipline. The coordination required across them introduces friction, variation in quality, and—inevitably—missed deadlines —sometimes culminating in the industry’s infamous "Code-Red's", when all hell breaks lose and you're at the brink of losing a customer! But it also creates something invaluable: learning. Junior developers observe senior ones, teams mature together, and collective expertise compounds over time.
My Experience: AI as a One-Person Delivery Team
My recent experiments using AI to build a real-world application end-to-end from ground-up have been nothing short of transformative. Below is a summary of my direct observations:
1. Surprisingly strong UI/UX generation - As someone with a deeply technical, “left-brained” background, I've always had to rely on a UI/UX expert for front-end design, even in smaller startup engagements. Yet with simple natural-language instructions, AI produced clean, functional user interfaces, not just the design but the actual implementation using the latest UI technologies in use. The productivity gains here alone were stunning.
2. True iterative development, not just one-off code generation - AI has been capable of generating boilerplate code, comprising of even complex logic for sometime now. One pleasant surprise has been how well it managed incremental changes, maintaining context across iterations, and its ability to handle multiple production releases with minimal friction. With the right tooling, it behaved like a reliable development partner—an assistant who remembered everything. An example of an incremental change: "screen xyz lists the abc documents but does not allow these documents to be downloaded. Let's implement the ability to download the documents from this screen....". Tying back to the above UI/UX example, I didn't need to specify where to place the download icon, or what the icon should be and the AI was able to make sensible decisions by itself. So that was a great to see!
3. Requirements: still a critical skill AI works best when given clear, structured requirements. Domain modeling, explicit user journeys, and well-articulated software requirements dramatically improved the quality of the output. Far from eliminating this skill, AI amplifies its importance.
4. Architecture still matters—immensely Questions like multi-tenancy, pagination, role-based access, asynchronous workloads, and general system boundaries help define the expected software system far more reliably than letting AI figure it out eventually through many iterations. Proper architectural framing makes the AI’s job drastically easier. As an example, in the document download example I mentioned above, I didn't initially specify that the documents should go into a cloud bucket that is specific to an environment. So it simply implemented functionality that works in development that works off local storage, but if this functionality went into production, the documents would disappear from the ephemeral storage when the containers were restarted as part of auto-scaling! On the second iteration when I mentioned using cloud buckets, it implemented a single cloud bucket that was shared across environments - ouch! Funny that the AI proudly asserted the ability to access documents submitted in the development environment from production as a feature! So being able to ask the right architectural questions and providing a clearer specification with awareness of key architectural concerns as well as a proper design/architecture reviews of what is actually implemented is essential.
5. The déjà vu of old mistakes—but fixed instantly The generated code reminded me of my early consulting days working with teams of junior developers. AI made the same mistakes junior developers do: missing pagination, inefficient loops, unnecessary synchronous operations, overly frequent refresh intervals.
But here’s the difference: In the old world, finding and fixing these issues involved JIRA tickets, project managers, dev leads, QA cycles, and deployment pipelines. Days or weeks of coordination. With AI, I spotted an issue, described it, the model understood the feedback, implemented the fix, and I deployed the correction—in minutes.
This was both exhilarating and unsettling. My entire history of debugging development teams became a personal accelerator. I could offload decades of learned patterns into prompt templates and bypass the messy process of teaching humans those lessons over months or years.
Recommended by LinkedIn
And that is where the tension lies.
The Trade-Off: Productivity vs. the Talent Pipeline
Following are my biggest insights from the experience:
1. AI can collapse specialized roles into one person
Tasks once handled by architects, UI/UX designers, developers, testers, and DevOps can now be orchestrated by a single experienced practitioner. With natural-language instructions, AI generates production-grade UIs, iterates on requirements, fixes defects, and handles deployments with remarkable consistency.
2. Experience becomes the ultimate multiplier
AI still makes the same mistakes junior developers do—pagination issues, inefficient logic, or poor architectural choices—but an experienced engineer can correct them in minutes, or provide sufficient prompt templating upfront to avoid them altogether. Some of the skills only gained by experience – ability to understand a customer’s needs and translate them into software requirements, ability to understand architecture considerations and technology landscape is still a huge asset to obtain the right outcome from AI. Decades of accumulated judgment now translate directly into speed and quality. This creates a new form of intellectual leverage that benefits the already-skilled disproportionately. My impression is that seasoned technical personnel with great customer interaction skills will be the ones best positioned to leverage this technology. The famous book, Mythical Man Month from Fred Brooks, originated the concept of the 10x programmer which highlights the productivity variance across developers. I could easily see 100x or 1000x using AI with the right background experience.
3. The mentorship ladder is breaking
Traditional talent development—junior engineers learning from seniors through real-world cycles of debugging, testing, and iteration—is being short-circuited. As AI improves, fewer humans will participate in these learning loops. We risk a future where only a small cohort of experts can effectively wield AI, widening both knowledge inequality and wealth inequality.
Where Do We Go From Here?
AI has given us astonishing leverage. It empowers individuals to produce what once required large, coordinated teams. But unless we rethink how we develop talent, we risk creating a future where the ladder I climbed—the one built on mentorship, apprenticeship, and gradual mastery—no longer exists.
The gains are real. The risks are real. We must not treat this transition as purely a productivity story. It is, at its core, a societal one.
The coming decade will test whether we can harness AI to broaden opportunity—or whether we allow it to concentrate knowledge, power, and wealth in ways that reshape the industry far more profoundly than any technological shift before it.
Good insights Chandika! What toolset did you use?
Commenting for exposure to my network. Good Stuff. Cheers.
Very insightful article, and it aligns closely with what I’ve seen while bringing AI, GenAI, and agentic AI into software testing workflows in recent years. AI is already taking over many engineering tasks, but it still depends heavily on the guidance, domain expertise, and guardrails provided by experienced professionals. In many ways, the mentoring we once gave to juniors is now also shifting toward mentoring AI. Even though AI will learn fast and reduce some traditional mentoring needs, the fast-evolving nature of the IT industry means continuous human oversight will still be essential. And in certain areas, especially those requiring context, empathy, and human-centered thinking, our mentoring may even surpass what AI can internalize. Ultimately, I believe our roles will evolve, but human judgment, empathy, and real-world experience will remain indispensable in shaping sustainable and meaningful innovation
Excellent article Chandika! Currently the seasoned professionals seem to be benefitting from Sonnet like models disproportionately and looks like they are creating a social division. However, these models will evolve fast. The models will evolve in the next few years to avoid the entry level developer mistakes and empower the clever rookies to be pros and thereby levelling the field again. The ultimate divider will not be the experience but the level of imagination and aptitude which has always been the case, but AI will amplify this.