The software world is entering a new era, and some developers are already running ahead.
AI isn’t looming on the horizon. It’s already reshaping how software gets built. By the end of 2025:
- AI agents will likely match junior-to-mid level developers in real-world tasks
- IDE-native copilots will become the default experience for most developers
- We may see an AI model crack the 95th percentile in competitive programming
And that’s just the warm-up.
By the end of 2026, we expect AI to reach parity with senior engineers in many workflows—capable of architecting, debugging, and integrating code across multiple stacks. Some agents may even surpass human performance on mid-range tasks. The implications go far beyond productivity.
This shift isn’t just about new tools. It’s about transformation:
- How software gets built
- What skills matter most
- How developers are hired and evaluated
- What future career paths look like
It’s early and, while we can’t resolve the fine details, we can already see the silhouette of what’s coming. Think of it like spotting an airplane in the sky: even if you can’t make out the exact make and model, you can tell an airliner from a biplane. We’re at that distance. And a new class of developer is starting to take shape.
What is a next-gen developer?
They’re not just using AI. They’re building with it, and thinking beyond it.
Next-gen developers are emerging as a distinct cohort. They move faster, think more holistically, and blend technical execution with product judgment. These developers aren’t defined by job title or seniority, but by how they work.
We’ve identified four key traits that set next-gen developers apart. Each one represents a spectrum of capability, as well as a mindset shift that separates them from the pack.
1. Strong fundamentals
These developers harness AI as a partner, not a crutch. They know how to challenge it, improve it, and build beyond it. This shows up in three areas:
- Understand core CS principles like complexity, data structures, and architectural trade-offs
- Critique and revise AI-generated code through strong code review skills
- Explain their reasoning clearly, focusing on the why, not just the what
2. AI-augmented development skills
Next-gen developers go well beyond autocomplete. They build workflows around AI.
- Confident users of multiple AI tools, particularly AI-assisted IDEs like GitHub Copilot, Cursor, and Windsurf
- Apply AI throughout the SDLC, including testing, debugging, refactoring, and architecture
- Maintain clarity and ownership throughout, even when AI writes the first draft
3. Knowledge of AI concepts
They treat AI as part of the tech stack, not a separate domain.
- Understand prompt engineering, chaining techniques, and model behavior
- Apply concepts like fine-tuning, RAG, and embedding-based retrieval to real use cases
- Build and integrate AI-powered features into applications with confidence
4. Collaboration, taste, and business acumen
They don’t just ship features, they shape products.
- Navigate blurred roles across engineering, product, QA, and design
- Make thoughtful choices around UX, performance, and long-term maintainability
- Prioritize business impact while staying grounded in technical excellence
Where are they now?
The early signs are clear. And measurable.
Roughly 17% of developers are already working in ways that align with what we consider next-gen. They’re not just using AI tools, they’re using them deeply and deliberately, often across the entire development lifecycle.
As we found in our HackerRank 2025 Developer Skills Report, these developers:
- Generate 48% of their code with AI, compared to a 29% average.
- Are significantly more likely to use AI for complex tasks like implementation (+18 points), deployment (+12), and architecture (+12)
- Use multiple AI tools regularly, and are significantly more likely to use Cursor and Claude.
If you place AI along the classic technology adoption bell curve, 17% lines up almost exactly with the innovators (2.5%) and early adopters (13.5%) that typically lead any new adoption. In other words, next-gen developers aren’t outliers. They’re the leading edge of a much larger shift.
And it’s not just the next-gen group that maps cleanly. The remaining developer segments nearly mirror the classic technology adoption curve, from early majority to late majority and laggards.
| Developer segment | Adoption behavior | % developers | Tech adoption role |
| Next-gen | Deep AI integration across the stack | 17% | Innovators + early adopters (16%) |
| Accelerating | Frequent AI use, growing sophistication | 35% | Early majority (34%) |
| Emerging | Inconsistent AI use, still developing workflows | 35% | Late majority (34%) |
| Resisting | Minimal AI use, prefers manual workflows | 13% | Laggards (16%) |
This isn’t a statistical curiosity. It’s a signal.
When a new wave of technology reaches this kind of distribution, it’s no longer hypothetical. It’s happening. And just like in past transformations—mobile, cloud, DevOps—those at the front of the curve aren’t just working differently. They’re pulling away.
Why this matters
The talent market is quietly splitting. The divide isn’t AI or no-AI—it’s mastery.
Right now, most engineering teams include developers from all across the adoption curve. Some are using multiple AI tools daily to architect, debug, and deploy. Others are still cautiously experimenting, or avoiding AI altogether.
As next-gen developers gain speed, confidence, and higher-level capabilities, those teams will start to stretch. And possibly splinter.
If one developer is shipping features with AI-accelerated workflows—rapidly iterating, exploring edge cases, and fine-tuning code quality—while another is still manually testing and Googling for answers, the gap compounds. What starts as a difference in speed becomes a difference in scope. Then quality. Then impact.
Over time, this creates tension:
- In code reviews, where expectations no longer match
- In project planning, where some developers operate at a different tempo
- In team morale, as the gap between contributors grows more visible
This isn’t just about productivity. It’s about alignment. And the further teams drift in how they work, the harder it becomes to collaborate, mentor, and scale.
How to spot one
Next-gen developers are hard to fake—but not hard to find, if you know what to look for.
You won’t identify next-gen developers by looking for a specific job title or scanning for keywords like “AI” on a résumé. Their edge isn’t superficial—it’s behavioral.
The most reliable signals emerge in how they think, how they debug, and how they work with AI as a true extension of their development process.
Look for developers who:
- Review and refine AI output with confidence, not just accept it as-is
- Use multiple AI tools for real-world tasks, including refactoring, debugging, and integration (not just documentation or quick answers)
- Explain their reasoning clearly, especially when working with or around AI-generated code
- Demonstrate strong fundamentals that allow them to spot edge cases, architectural issues, or incomplete logic in AI-written code
- Show product awareness and good taste, thinking in terms of user value, not just feature delivery
These are hard skills to gauge in a traditional coding test. But they’re starting to show up in new formats that mirror how developers work in the real world.
How we’re evaluating for this
At HackerRank, we’ve started developing new assessment formats to surface these behaviors:
- AI Interviewer – Probes a candidate’s reasoning and debugging process, capturing deeper signals than mere correctness
- Code Repositories – Simulate real-world, multi-file tasks that developers can complete with or without AI—mirroring their daily workflow more closely than isolated algorithms ever could
We’re not just testing whether someone can write code. We’re testing whether they can work like a next-gen developer.
What comes next
Next-gen isn’t a title. It’s a trajectory.
Next-gen developer isn’t a certification or a badge. It’s a direction. A pattern of behavior. A set of behaviors that are emerging in response to real, fast-moving change.
And while some developers are further along that curve, most aren’t far behind. They’re learning. Testing. Figuring out what works in their context. Not every environment makes rapid adoption easy, but the desire to evolve is everywhere.
The opportunity now is to support that evolution. To build evaluation methods, workflows, and team cultures that help more developers grow into next-gen ways of working.
Because while companies can accelerate the shift, it’s developers who are showing us where it’s headed.