Author(s): Dr. Brian Scott Glassman

Originally published on Towards AI.

The perspectives, abilities, traits, and knowledge required to be an AI‑Native Software Developer

How to Become an AI‑Native Software Developer

Software development is undergoing a seismic shift driven by AI coding tools, and skilled AI‑native software developers represent the core of this future. This article offers the next generation of developers my best insights into becoming lead developers in an AI‑first, agent‑driven software landscape. As a corporate AI technology strategist, a daily AI coding practitioner, former college professor at NYU, former VP of Engineering managing software development for over 20 years, I aim for these insights to be clear and grounded. My hope is to motivate developers to up skill and meet this next monumental shift.

The article is structured to flow naturally. It begins by explaining three new perspectives an AI‑native developer should adopt, as these drive high‑level behaviors. It then transitions to three abilities that should be developed through learning and practice with AI‑coding tools. Next, it discusses three natural traits that give some developers an advantage over others. The article then outlines five key areas of knowledge that AI‑native developers should deepen. Finally, it delivers a clear warning to hiring managers and VPs that two areas of knowledge and skill are no longer relevant in an AI‑native software development organization. Let’s start with the core perspectives that an AI‑native developer should adopt.

Key Perspectives of AI‑Native Developers

To prepare yourself for a shift to an AI‑native coding approach, you must first internalize the following three perspectives. While one could argue that many other perspectives also matter, I have found these to be the most important for reorienting from an Agile‑based philosophical mindset to an AI‑native development perspective.

Shift Your Thinking From Writing Code to Leading AI Agent Teams

It is vitally important to realize that you are no longer acting as an individual contributor. Instead, you should internalize that you are a virtual department head who directs multiple AI agents through all the aspects of software development for your projects. Hence, your role is to break up the project into clear phases and tasks, assign them to AI agents, and ensure the AI team produces high-quality results. Think of yourself as a manager of a department of AI agents where you establish clear processes, structure, well-defined responsibilities, measurable outcomes, and ongoing monitoring of resource use such as token consumption and quality of their work products.

Build Codebases That AI Can Easily Understand and Evolve

You must shift your thinking that you are writing code for execution and possibly other people to read to realizing you are writing code so AI systems can read, understand, and change it, with very little effort. Hence, your codebase should be simple for AI models to work with at both the individual file level and the overall system level. Ask yourself: if an AI were reading this file quickly with minimal attention to detail, what would be clear and what might be confusing? Once you accept that AI is now the primary reader and modifier of your code, the priorities of a codebase change. High-level context must be obvious without deep inspection. Project-level markdown documents should explain the codebase’s form and function. File-level headers should clearly state the core functions and relationships. Directory structure, variable naming conventions, and comments should be very obvious and descriptive. The goal is not documentation for its own sake, but reducing cognitive load so an AI can quickly comprehend and confidently make edits.

A New Mindset: Designing Processes Instead of Performing Them

Second, you must no longer focus on carrying out individual tasks yourself. Instead, think and act like a task designer who directs AI agents through clear processes, guidelines, best practices, reference examples, explicit rules, and unmistakable prompts. The faster you realize that you are not doing the work, the AI team is, and that your role is to guide it accurately, the faster you will gain massive productivity. For example, when designing a user interface, first outline the steps required to achieve a strong design result, then create opportunities to evaluate results and insert your input to creatively guide the outputs and produce a superior UI.

Abilities to Practice and Develop for AI-Native Developers

With the mindsets adopted, next, you install an AI CLI (Claude Code, Cursor, Codex CLI, Gemini CLI) or install one as an extension in your IDE. Then practice the abilities below until you feel relatively comfortable with each. After that, make a plan to improve each ability as you perform your software development tasks.

Learn How to Manage Many AI Tasks Without Losing Control

Your first new ability should be high-quality multi-tasking. You are no longer limited to working on one task at a time. AI agents give you the ability to run multiple tasks in parallel, and your new ability is to manage this concurrency effectively. This means keeping track of progress, inputs, and outputs across several AI-driven tasks at once. You may be concurrently evaluating architecture trade-offs in one command-line session, guiding code generation in another, and running QA checks in a third. The goal is not to be busy or maximize your output, but to remain in control without having too much idle time.

Build the Ability to Move Fast Without Getting Stuck

Your second ability is rapid solution generation and quick implementation. You must avoid over-focusing on a single problem or chasing the most elegant solution at the cost of progress. In an AI-driven workflow, this leads to lost productivity. Thus, your new ability is to quickly generate possible solutions with the help of AI, implement them, test them, and evaluate results. If a solution does not work after a brief debugging effort, the correct response is to pivot to an alternative approach. The priority is to deliver a high-quality outcome efficiently while maintaining momentum, experimenting quickly, and moving away from unproductive paths without hesitation.

Learn How to Think Like an AI Agent and Predict Its Outputs

Your third ability is learning to think like the AI models you use. Each AI model has its own strengths, limitations, and behavior patterns. As you gain experience with a specific model, you will begin to anticipate how it responds to tasks and instructions of increasing complexity and adjust your tasks and instructions accordingly. It is also critical to recognize that different AI models are better suited to different types of work, such as lightweight models for structured edits and more advanced models for system architecture planning and complex reasoning.

Natural Talents and Traits of AI-Native Developers

Unfortunately, AI‑native programming is challenging, arguably more challenging than traditional programming, and some individuals are more suited for it than others due to their natural abilities. This section will give individuals the chance to see whether they are up to the task.

Long Periods of High‑Intensity Thinking

Running multiple AI agents concurrently, quickly reviewing results, making adjustments, identifying patterns, and analyzing outcomes all require a high level of intelligence and the ability to learn quickly. In my experience, many developers are smart and perform well in this area. However, if your preferred work style is to avoid engaging in five to ten hours of sustained, complex thinking each day, an AI‑native developer role may not be a good fit for you.

Naturally Highly Organized

AI systems today rely heavily on deep project context and well‑defined processes. If you tend to avoid cleaning up code, dislike writing documentation, or resist setting up and maintaining processes in favor of flexibility, or avoiding work that feels unproductive, then AI‑native development is likely not for you. AI‑native development requires highly organized thought processes, careful planning, and the willingness to invest in upfront organizational work to effectively guide AI agents in their tasks. This is a core personality trait; you either have it or you don’t, and those who do will see significant success!

Strong Willingness to Experiment

AI capabilities continue to advance rapidly, with significant jumps in performance occurring monthly. If you are naturally curious and compelled to experiment and continuously improve your methods, you have a significant advantage. Organizations increasingly need AI‑native developers who are willing to refine and improve their practices on a daily basis. However, if you view individuals who deviate from standard workflows to run one‑hour experiments or spend time after work trying new approaches as inefficient or problematic, you may not be a good fit for an AI‑native development role.

Required Knowledge Areas

AI‑coding tools and the seismic shift they are creating make some traditional knowledge areas far less valuable, while elevating other areas to critical importance. This shift is also introducing entirely new areas of expertise that developers must understand. Below are the key knowledge areas you must master to perform at the highest level in AI‑native software development.

System Architecture and Its Trade‑Offs

AI‑native developers must possess deep system architecture knowledge and understand the trade‑offs between different architectures and their configurations. This knowledge allows you to guide architectural planning and decision‑making for a software solution rather than blindly deferring to AI suggestions, which I have found can be highly biased. When creating software solutions, early architectural choices have a major influence on which features can be implemented and how much performance can be extracted from the system. For this reason, architectural decisions and the underlying knowledge should not be deferred to AI.

Knowledge Across Multiple Technical Domains

AI‑native development favors broad technical knowledge over deep specialization in a single area. Comfort moving between frontend, backend, data stores, DevOps, databases, and APIs allows you to guide AI agents as they naturally cross boundaries. Developers with cross‑domain fluency maintain momentum, avoid bottlenecks caused by narrow expertise, and are significantly more valuable in the AI‑native era.

Knowledge Beyond AI Prompt Engineering

Modern AI prompt engineering is far more than writing a clever prompt. It now involves designing processes, methods, and guidance for AI systems to follow. This includes defining rules, structure, documentation, and validation steps that AI agents can consistently apply. Clear, coherent instructions across planning, implementation, testing, and maintenance are what keep AI‑driven work reliable, and developing this capability requires significant reading, practice, and ingenuity.

Knowledge of How to Optimize AI Token Usage

In AI‑native development, tokens are a real operating cost. Effective AI developers design workflows that minimize token waste while preserving output quality. Knowing when to use lightweight models, when to escalate complexity, and how to efficiently scope tasks in terms of token usage is now part of everyday engineering judgment. This is a cutting‑edge capability that will become increasingly important as organizations begin actively monitoring AI token budgets.

Skills Becoming Obsolete

It may seem weird, but some areas of knowledge or areas of expertise will be phased out as AI‑Native software development becomes the norm. This author believes the following will no longer be important:

Hand Coding Skills are Soon to Be Outdated

The ability to write machine code is already obsolete, and manually writing low-level memory allocation code is similarly outdated. As technology advances, developers naturally move up levels of abstraction, since working with finer implementation details wastes time and effort. The same shift is now occurring with AI tools and hand-written code. While you may be able to write a software function manually, an AI model can typically do it faster, with fewer errors, and run validation checks. As a result, managers who insist on traditional coding exams during interviews must remove this outdated approach and instead focus on evaluating a candidate’s ability to effectively manage and validate code produced through AI-coding tools.

Agile Will Soon Be Obsolete

I have written a detailed article explaining why Agile is not compatible with AI‑native software development. In short, applying Agile processes to teams of AI‑native developers, who can operate at 100 to 1,000 times the speed of traditional hand‑coding teams, renders many Agile rituals, tasks, and associated skills obsolete. Without going into excessive detail here, Agile’s relevance is rapidly diminishing. Managers who continue to test for or emphasize Agile‑specific practices when interviewing for AI‑native developer roles should strongly consider updating their evaluation criteria.

Summary

In closing, this article covered four major areas essential to becoming an AI‑native software developer. First, it outlined the core mindset shifts required to move from writing code to leading AI agent teams. Second, it described the practical abilities developers must practice to operate effectively with AI‑coding tools. Third, it examined the natural traits that determine who is best suited for AI‑native work. Finally, it detailed the key knowledge areas developers must master while also identifying legacy skills that are rapidly becoming obsolete. It is strongly hoped you take these to heart and push yourself into the next major evolution in software development.

About the Author

Dr. Brian Scott Glassman is an AI strategy and implementation expert who is VP at AInspire Technology Consulting. He writes for Forbes, and other publications and does consulting for Fortune 500 organizations.

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Published via Towards AI