The AI Augmented JavaScript Developer and How to 10x Your Output Without Losing the Skills That Keep You Employed

There is a strange contradiction happening in JavaScript development right now. Developers are writing less code than ever and working harder than ever. That does not make sense until you understand what the job has actually become.

In February 2026, the conversation is dominated by fear. Software company stocks dropped 20 to 25 percent in a single week after Anthropic launched Claude Cowork plugins that can automate entire enterprise workflows. CEOs are publicly saying AI will displace half of entry level white collar jobs within five years. Layoff announcements keep coming, with roughly 55,000 tech jobs cut in 2025 alone, and companies increasingly citing AI as the reason.

But here is the part nobody puts in the headlines. The developers who actually use AI well are not getting replaced. They are getting promoted. They are shipping features in two days that used to take two weeks. They are handling the workload of three people without burning out. They are solving problems they would not have touched a year ago because the activation energy was too high.

The gap between developers who treat AI as a threat and developers who treat AI as a multiplier is becoming the single biggest factor in career outcomes. Not framework knowledge. Not years of experience. Not which company is on your resume. Whether you can effectively work with AI tools is now the dividing line between developers who thrive and developers who get left behind.

This is not a theoretical guide about what AI might do someday. This is a practical, detailed breakdown of how JavaScript developers are actually using AI tools right now to dramatically increase their output while keeping the fundamental skills that make them valuable. Because that second part matters just as much as the first.

Why Most Developers Are Using AI Wrong and Why It Is Costing Them

Let me describe the most common AI workflow I see among JavaScript developers. They open their editor. They start writing a component. They get stuck or bored. They open ChatGPT or Claude and type something like "write me a React component that shows a list of users with pagination." They copy the result. They paste it into their project. They spend twenty minutes fixing the things that do not quite work. They repeat this for the next component.

This is not AI augmented development. This is AI assisted copy and paste. And it is actually making these developers worse at their jobs.

Here is why. When you outsource the thinking to AI and only handle the fixing, you are practicing the lowest value part of development. You are becoming a human compiler, translating AI output into working code through trial and error. You are not building mental models of how systems work. You are not developing the architectural intuition that separates a mid level developer from a senior developer. You are getting faster at producing code while getting slower at understanding it.

The developers who use AI effectively do something fundamentally different. They think first, then use AI to execute faster. They maintain the cognitive work of deciding what to build, how to structure it, and why certain tradeoffs make sense. They hand off the mechanical work of actually typing out the implementation. The thinking stays human. The typing becomes automated.

This distinction sounds subtle but it changes everything about how you interact with AI tools, what you get from them, and whether you become more valuable or less valuable over time.

The Real Daily Workflow of an AI Augmented JavaScript Developer

Let me walk through what an AI augmented workday actually looks like in practice. Not the marketing version where everything is magical. The real version where AI is incredibly useful for some things and completely useless for others.

Morning. Planning and architecture. This is where AI helps least and human thinking matters most. You are reading requirements, understanding business context, thinking about how a new feature fits into the existing system, considering edge cases, and making structural decisions. AI cannot do this well because it does not know your codebase history, your team's conventions, your users' actual behavior, or the political dynamics behind why this feature exists.

What AI can do during planning is help you think through options. You can describe your situation and ask it to outline three different approaches with tradeoffs for each. This is not outsourcing the decision. It is using AI as a thinking partner that helps you consider angles you might miss. But you make the call.

Mid morning. Implementation. This is where AI shines the most. You have already decided what to build and how to structure it. Now you need to write the actual code. Here, AI can save you enormous amounts of time.

But the key is how you prompt it. Instead of saying "write me a user list component," you give it the full context it needs to produce something actually useful. You tell it the component hierarchy you have decided on. You specify the data shape coming from your API. You describe the error states you need to handle. You mention the existing patterns in your codebase that this component should follow. You define the edge cases you thought about during planning.

The more context you provide up front, the less fixing you do afterward. A five minute investment in writing a detailed prompt saves thirty minutes of debugging AI output that almost works but does not quite fit your project.

Afternoon. Code review and testing. This is where AI becomes a surprisingly effective pair programmer. You can paste a function you wrote and ask it to find bugs, suggest improvements, or identify edge cases you missed. You can describe a tricky piece of logic and ask it to generate test cases, including the weird edge cases that humans tend to forget about.

The pattern I see among the best developers is that they use AI for a "second pass" on everything. They write the code themselves or with AI assistance, then use a separate AI conversation to review it critically. The fresh context means the AI catches things the developer overlooked while deep in implementation mode.

End of day. Documentation and communication. This is the most underrated use of AI for developers. Writing pull request descriptions, documenting architectural decisions, drafting messages to stakeholders explaining technical tradeoffs in nontechnical language, creating onboarding docs for new team members. Most developers hate this work and do it poorly. AI is excellent at it, especially when you give it the technical details and tell it who the audience is.

Developers who use AI for communication consistently get better performance reviews. Not because the communication is fancier, but because it actually exists. The pull requests have descriptions. The architecture decisions are documented. The stakeholders understand what is happening. This is the kind of invisible work that gets people promoted, and AI makes it almost free.

Prompt Patterns That Actually Work for JavaScript Development

The term "prompt engineering" sounds fancy but really it just means learning how to communicate clearly with an AI tool. The developers who get the best results from AI are not using secret techniques. They are being specific, providing context, and setting clear constraints.

Here are the patterns that consistently produce good results.

The context first pattern. Before asking AI to write anything, give it a paragraph describing your project, your tech stack, your coding conventions, and the specific problem you are solving. This sounds like extra work but it dramatically improves output quality.

Instead of "write a function to validate email addresses," try something like this: "I am working on a Next.js application with TypeScript. We use Zod for validation schemas and our convention is to create reusable validators in a shared validators directory. I need an email validation function that checks format, length under 254 characters, and rejects plus addressing because our billing system does not support it. It should return a typed result object with either the validated email or specific error messages."

The second prompt produces code that actually fits your project. The first produces generic code you spend twenty minutes adapting.

The incremental refinement pattern. Do not try to get perfect output in one prompt. Start with the structure, review it, then ask for specific changes. "Add error handling for network failures." "Now add loading state with a skeleton component." "Refactor the data transformation into a separate utility function." Each step is small, reviewable, and controllable.

This mirrors how experienced developers actually work. Nobody writes a perfect component in one pass. You build the basic structure, then layer in complexity. Using AI the same way produces dramatically better results than trying to specify everything upfront.

The explain then implement pattern. Before asking AI to write code, ask it to explain its approach first. "How would you structure a shopping cart that needs to persist across browser sessions, handle concurrent tab updates, and sync with the server when the user goes online?" Read the explanation. Disagree with parts if needed. Then say "good, now implement it using that approach." You stay in control of the design while AI handles the implementation.

The existing code pattern. Paste a piece of your existing code and ask AI to write new code that follows the same patterns. "Here is how we handle API calls in this project. Write a new API function for the notifications endpoint following the same pattern." This produces code that is consistent with your codebase instead of code that introduces new conventions nobody agreed on.

AI for Debugging JavaScript and Why It Changes Everything

Debugging is one of the areas where AI creates the biggest time savings, especially for the kind of bugs that are not conceptually hard but take forever to find.

Consider this scenario. Your React component renders correctly on the first load but breaks when you navigate away and come back. The data is stale. You have been staring at it for forty minutes. You could continue staring, or you could paste the relevant code into an AI tool and describe the symptom.

Nine times out of ten, the AI will immediately spot the issue. A missing cleanup function in a useEffect. A stale closure capturing an old state value. A query that is not invalidated on route change. These are patterns that AI has seen thousands of times and recognizes instantly, while a human developer buried in the specific context of their code can miss them for hours.

The important distinction is between using AI to find bugs and using AI to understand bugs. The developers who grow their skills do both. They let AI identify the problem quickly, but then they take a moment to understand why the bug happened and what they should watch out for in the future. This turns every debugging session into a learning opportunity instead of just a time sink.

If you want a deeper framework for systematic debugging that works alongside AI tools, I wrote about a structured debugging approach that gives you a repeatable process for finding bugs efficiently regardless of whether you are using AI assistance or not.

AI is also exceptional at debugging performance issues. Paste a component that renders slowly and ask "why is this rendering more often than it should?" The AI will trace through the dependency arrays, identify unnecessary rerenders, spot missing memoization, and explain exactly what triggers each render cycle. This kind of analysis takes a senior developer minutes and a mid level developer hours. AI does it in seconds.

AI for Testing and How to Get Real Coverage Without Writing Every Test by Hand

Testing is perhaps the most transformative area for AI augmented development. Not because AI writes perfect tests, but because it eliminates the main reason most JavaScript projects are undertested: the sheer tedium of writing test boilerplate.

Here is how this works in practice. You write a utility function for formatting currency values. Instead of spending fifteen minutes writing test cases, you paste the function into an AI tool and say "generate comprehensive test cases for this function, including edge cases for negative values, zero, very large numbers, different locales, and invalid inputs." In thirty seconds you have twenty test cases that would have taken you fifteen minutes to write manually, and they include edge cases you probably would not have thought of.

But here is where it gets really valuable. AI generates test cases that expose bugs you did not know existed. You wrote the function thinking about normal inputs. The AI tests what happens with NaN, undefined, Infinity, negative zero, strings that look like numbers, and numbers with more decimal places than your format string can handle. Half of these edge cases will pass. The other half reveal real bugs that would have reached production.

The workflow that works best is to write the implementation yourself, use AI to generate an extensive test suite, run the tests, fix the failures that represent real bugs, and then add any additional test cases for business logic that the AI would not know about. Your test coverage goes from "we have a few happy path tests" to "we have comprehensive coverage including edge cases" with minimal extra effort.

For integration testing, the approach is slightly different. AI is excellent at generating the boilerplate of setting up test environments, mocking API responses, and simulating user interactions. But the assertions, what you are actually checking for, should come from your understanding of the business requirements. AI does not know that a specific validation message matters or that a particular redirect needs to happen after a certain action. You define the what, AI generates the how.

If you are preparing for interviews and need to understand the full testing landscape, I put together a comprehensive JavaScript testing guide covering everything from unit tests to Playwright, including the real questions companies ask.

Code Review of AI Generated Code and Why This Is a Critical Skill

Here is something that keeps me up at night. A growing number of developers are committing AI generated code that they do not fully understand. Not because they are lazy, but because the code looks right, the tests pass, and the deadline is tomorrow.

This is how technical debt accumulates at AI speed.

AI generated code has specific failure patterns that you need to learn to recognize. Understanding these patterns is becoming one of the most important skills a JavaScript developer can have.

Plausible but incorrect logic. AI is excellent at generating code that looks correct and handles the obvious cases but fails subtly on edge cases. It might write a date comparison that works for most dates but breaks across daylight saving time boundaries. It might write a sorting function that works perfectly until it encounters null values in the array. The code passes a quick visual review and even passes basic tests, but it has a bug that only shows up in production under specific conditions.

Security blind spots. AI tools frequently generate code with security vulnerabilities that are not immediately obvious. An API route that does not validate input types. A query builder that is vulnerable to injection because the AI used string concatenation instead of parameterized queries. An authentication check that verifies the token exists but does not validate its signature. These are exactly the kinds of issues that create real security risks in production applications, and they are easy to miss in a quick review.

Outdated patterns. AI models are trained on code from across the entire history of JavaScript. They might generate class components when your project uses functional components. They might use var instead of const. They might implement a custom debounce function when your project already has lodash installed. They might use deprecated APIs that still work but will break in the next major version of your framework.

Overengineered solutions. AI tends to generate "complete" solutions that handle cases your application will never encounter. A simple form validation turns into a 200 line abstraction with plugin architecture. A straightforward API call gets wrapped in three layers of error handling with custom retry strategies. The code works, but it is dramatically more complex than it needs to be, and that complexity has real maintenance costs.

The skill of reviewing AI generated code is different from reviewing human written code. With human code, you are looking for logic errors, style inconsistencies, and missing edge cases. With AI code, you are also looking for plausibility traps, security holes, unnecessary complexity, and patterns that do not match your project's conventions. Developing this skill requires deliberate practice and a healthy skepticism of code that looks too clean.

The Skill Degradation Problem and How to Prevent It

Let me be honest about something uncomfortable. If you use AI as a crutch instead of a tool, your fundamental programming skills will deteriorate. This is not speculation. It is already happening.

I see developers who cannot write a basic array transformation without asking AI for help. Developers who do not remember how async/await error handling works because they always let AI write the try/catch blocks. Developers who cannot debug a simple issue because they have never had to trace through code manually, they just paste the error into AI and apply whatever fix it suggests.

These developers are building a career on a foundation that does not belong to them. The moment AI tools are unavailable, or the moment they face a problem that AI cannot solve well (which happens more often than you think), they are completely stuck.

Here is how to prevent this while still getting the speed benefits of AI.

Write the first version yourself, then use AI to improve it. Instead of asking AI to write code from scratch, write your own implementation first, even if it is rough. Then ask AI to review it, suggest improvements, and optimize it. This way you are exercising your programming muscles while still getting the benefit of AI feedback. Your skills stay sharp because you are still doing the thinking work.

Do regular "unplugged" sessions. Once or twice a week, work for a few hours without any AI assistance. No Copilot suggestions. No Claude. No ChatGPT. Just you and the code. This feels slow and frustrating at first, which is exactly the point. If it feels hard, that means your unassisted skills are atrophying and you need these sessions even more.

Understand every line you commit. This is the most important rule. If AI generated a piece of code and you cannot explain what every line does and why it is written that way, do not commit it. Read through it. Ask AI to explain the parts you do not understand. Modify it until you genuinely understand every decision. This slows you down slightly but prevents the gradual erosion of comprehension that makes developers fragile.

Learn from AI suggestions instead of just applying them. When AI suggests a better way to write something, do not just accept the change. Understand why it is better. If AI rewrites your useEffect to use a cleanup function you forgot, learn why cleanup functions matter and when they are necessary. Each AI interaction should leave you slightly more knowledgeable, not slightly more dependent.

The goal is to become a developer who is excellent with AI and excellent without it. That combination is what makes you genuinely layoff proof, because you can operate at AI augmented speed when the tools are available and at fully competent human speed when they are not.

AI for Learning New Technologies Faster

One of the most underappreciated uses of AI is as a personalized learning tool. When you need to pick up a new library, framework, or concept, AI can compress weeks of learning into days.

Traditional learning looks like this: read the documentation, follow a tutorial, build a toy project, struggle through error messages, search Stack Overflow, watch a video, and eventually develop enough understanding to use the technology productively. This takes weeks for anything nontrivial.

AI augmented learning looks like this: tell AI what you already know, what you need to learn, and what you are trying to build. Ask it to explain the core concepts in terms of what you already understand. Ask it to show you how common patterns in the new technology map to equivalent patterns in technologies you already know. Then build your actual project with AI guidance, asking questions whenever you hit something you do not understand.

For example, if you know React well and need to learn Svelte for a new project, you can ask AI to explain Svelte's reactivity model by comparing it to React's useState and useEffect. You can ask it to translate a specific React component you understand into Svelte syntax and explain every difference. You can ask it to identify the ten most common "React developer learning Svelte" mistakes and how to avoid them.

This approach works because it builds on your existing mental models instead of starting from scratch. And because AI can answer follow up questions instantly, you never get stuck on a confusing concept waiting for someone to respond to your Stack Overflow question.

The developers who leverage this learning acceleration are picking up new technologies at a pace that was simply not possible two years ago. When your company decides to adopt a new tool, being the developer who can become productive with it in a week instead of a month is enormously valuable.

AI for Refactoring Legacy JavaScript Code

Every JavaScript developer eventually encounters legacy code that needs to be modernized. A five year old Express API using callbacks. A React class component with 800 lines and twelve lifecycle methods. A utility file full of clever one liners that nobody understands anymore.

AI is remarkably good at this kind of work, and the reason is interesting. Legacy code refactoring requires pattern recognition (identifying what the old code does) and pattern application (rewriting it using modern conventions). Both of these are tasks where AI excels.

Here is a practical workflow for AI assisted refactoring.

Start by having AI analyze the existing code. Paste a legacy module and ask it to explain what every function does, identify any bugs or security issues, and map out the dependencies between functions. This gives you a clear picture of what you are working with before you change anything.

Then refactor incrementally. Do not ask AI to rewrite the entire module at once. Take one function at a time. Ask AI to rewrite it using modern patterns while maintaining the exact same behavior. Review the output carefully. Run existing tests (if they exist) to verify nothing broke. Move to the next function.

For the especially tricky parts, ask AI to write tests for the legacy code before you refactor it. This gives you a safety net. If the tests pass before and after the refactor, you can be confident the behavior is preserved.

The key insight is that AI makes refactoring less risky, not risk free. You still need to understand what the code does and verify that the refactored version does the same thing. AI handles the mechanical translation. You handle the verification and the judgment calls about what "modern" actually means for your specific project.

How AI Changes the JavaScript Interview Landscape

The rise of AI tools is fundamentally changing what companies look for when hiring JavaScript developers. If you are currently job searching or planning to look for a new position, understanding this shift is critical.

Companies are increasingly less interested in whether you can write a sorting algorithm from memory or implement a binary tree on a whiteboard. They are more interested in whether you can design systems, evaluate tradeoffs, review code critically, and communicate technical decisions clearly. These are exactly the skills that AI cannot do well and that become more important when AI handles the routine coding.

In practical terms, this means system design interviews are becoming more important relative to algorithmic coding challenges. You might still get a coding problem, but the interviewer is watching how you approach it, how you break it down, how you handle edge cases, and how you communicate your thinking, not just whether your solution compiles.

Some companies are now explicitly allowing or even encouraging candidates to use AI tools during technical interviews. They want to see how you work with AI as a partner. Do you blindly accept its output? Do you verify and adapt? Do you give it good context? Do you catch its mistakes? Your AI collaboration skills are now part of the evaluation.

The developers who will do best in this new interview landscape are those who combine strong fundamentals (you understand what the code does and why) with effective AI collaboration (you can produce high quality work faster with AI assistance). Neither alone is sufficient. Both together are extremely powerful.

Building an AI Augmented Development Environment

Your development environment matters more than you might think. The difference between a well configured AI augmented setup and a basic one can be an hour or more per day in saved time.

The foundation is your IDE. Tools like Cursor, Windsurf, and VS Code with GitHub Copilot integrate AI directly into your coding flow. Inline completions, chat panels that see your current file, and the ability to reference other files in your project make AI assistance seamless rather than requiring constant context switching between your editor and a separate chat window.

But the IDE is just the beginning. The real productivity gains come from building a personal prompt library. Over time, you develop prompts that work well for your specific needs. A prompt for generating React components that match your project's patterns. A prompt for writing API route handlers with your team's error handling conventions. A prompt for creating comprehensive test suites. Save these prompts somewhere accessible. They are reusable tools that get better as you refine them.

Context management is another critical piece. AI tools produce dramatically better output when they have relevant context about your project. Some developers maintain a markdown file describing their project architecture, coding conventions, and key decisions. When starting an AI conversation, they paste this context document first. It takes ten seconds and saves minutes of explaining the same things over and over.

For a detailed comparison of the major AI coding tools including real performance data and honest assessments, I wrote about my experience testing every major option so you can make an informed choice about what works for your workflow.

The Economics of AI Augmented Development and What It Means for Your Career

Let me put some numbers on this, because the financial implications are significant.

A developer working without AI assistance might produce a well tested feature every three to four days. The same developer using AI effectively might produce the same quality feature every one to two days. That is a 2x to 3x increase in output. Some developers report even higher multiples for certain types of work, particularly boilerplate heavy tasks like building CRUD interfaces, writing test suites, and creating API documentation.

Now think about what this means from an employer's perspective. If one developer with AI tools can do the work that previously required two or three developers, the math on team size changes dramatically. This is exactly why companies are restructuring teams and, in some cases, reducing headcount. It is not that AI is replacing developers entirely. It is that AI augmented developers need fewer colleagues to deliver the same amount of work.

The career implication is clear. If you are not using AI tools effectively, you are competing at a significant disadvantage against developers who are. Your output looks lower relative to your peers. Your velocity looks slower. And when the next round of layoffs happens, the developers who produce the most value relative to their cost are the ones who stay.

But here is the flip side that makes this encouraging. Companies are willing to pay a premium for developers who can leverage AI effectively. If you can do the work of three people, you can negotiate compensation that reflects that multiplied value. The developers who master AI augmented workflows are not just more productive. They are more valuable in a very concrete, financial sense.

When you sit down for salary negotiations, the ability to articulate your AI augmented productivity is becoming a real differentiator. If you want to prepare for those conversations, I wrote about specific tactics and scripts for JavaScript developer salary negotiations that account for the new reality of AI augmented value.

What AI Cannot Do and Why That List Is Your Career Insurance

Despite everything I have described, there is a long and important list of things AI is genuinely bad at. Understanding this list is just as important as understanding what AI can do, because these are the skills that will keep you employed and valuable for the foreseeable future.

Understanding business context. AI does not know why your company chose to build feature A instead of feature B. It does not know that the VP of Sales promised a specific capability to a key customer by next quarter. It does not know that your team tried a similar approach last year and it failed for organizational reasons. This kind of context drives real technical decisions and AI simply does not have access to it.

Navigating ambiguity. Real requirements are vague, contradictory, and constantly changing. "Make it faster" does not specify what "it" is, what "faster" means, or what tradeoffs are acceptable. AI needs clear specifications to produce good output. The work of turning vague human needs into clear technical specifications is fundamentally human work that requires empathy, communication skills, and judgment.

Long term system thinking. AI optimizes for the immediate task. It does not consider that the shortcut it suggests will make next quarter's migration impossible. It does not know that the database schema it proposes will become a bottleneck when your user base grows by 10x. Thinking about second and third order consequences over months and years requires a mental model of the entire system and its trajectory that AI does not possess.

Interpersonal skills. Convincing a skeptical product manager that a technical refactor is worth the investment. Mentoring a junior developer through a difficult problem without just giving them the answer. Navigating a disagreement with another team about API ownership. Communicating a project delay to stakeholders in a way that maintains trust. These skills are invisible in your code but visible in your career trajectory, and AI cannot do any of them.

Taste and judgment. Knowing when the code is "good enough" versus when it needs more polish. Knowing which technical debt to accept and which to fix immediately. Knowing when a framework is the right choice for a project and when it is overkill. These judgment calls are built from years of experience and pattern recognition across many projects, and they are what make senior developers genuinely senior.

If you look at this list, it maps almost perfectly to the skills that determine whether someone is a senior developer or a mid level developer. AI is accelerating the commoditization of implementation skills while increasing the premium on thinking skills. The developers who invest in the second category are building careers that become more valuable over time, not less.

A Practical 30 Day Plan to Become an AI Augmented Developer

If you are not currently using AI tools in your development workflow, or if you are using them but not effectively, here is a practical plan to get up to speed.

Week one. Establish the foundation. Set up an AI coding tool in your IDE. Use it for code completion and see how it feels. Start using a conversational AI tool (Claude or ChatGPT) as a pair programmer for at least one task per day. Focus on giving good context and being specific in your prompts. Do not worry about speed yet. Just get comfortable with the interaction pattern.

Week two. Develop your review skills. Start using AI to generate code for real tasks, but review every line critically before committing. Keep track of the mistakes AI makes. What patterns does it get wrong? What security issues does it miss? What conventions does it violate? This builds your AI code review intuition, which is one of the most valuable skills you can develop.

Week three. Expand to testing and documentation. Use AI to generate test suites for your existing code. Use it to write pull request descriptions, documentation, and technical explanations. These are the areas where AI provides the most value with the least risk, because the output is easier to verify and the cost of mistakes is lower.

Week four. Optimize your workflow. By now you have a sense of what works and what does not. Build your personal prompt library. Create a context document for your project. Establish a routine where AI augmentation is natural rather than forced. Start measuring your output and notice where the biggest time savings are happening.

After thirty days, you should be noticeably more productive and have a clear understanding of where AI helps, where it hurts, and how to tell the difference. From there, it is a matter of continuous refinement as the tools improve and your skills develop.

The Future of JavaScript Development Is Human Plus Machine

Let me close with a prediction that I am quite confident about. The JavaScript developer of 2027 and beyond will not be a person who writes code all day. And they will not be a person who prompts AI all day. They will be a person who thinks about problems, designs solutions, orchestrates AI tools to implement those solutions, reviews the output critically, and communicates the results to humans.

The "10x developer" was always somewhat of a myth. One person cannot literally do ten times the work of another through coding speed alone. But an AI augmented developer who thinks clearly, communicates well, and uses tools effectively absolutely can produce ten times the value of a developer who is fighting the tools or ignoring them entirely.

The fear that AI will replace JavaScript developers is understandable but ultimately misdirected. AI is replacing tasks, not people. The people who defined their value by the tasks (writing boilerplate, implementing standard patterns, doing repetitive coding) are in trouble. The people who defined their value by the thinking (understanding problems, designing solutions, making judgment calls, mentoring others) are in the strongest position they have ever been in.

The question is not whether to use AI. That ship has sailed. The question is whether you will use it in a way that makes you more capable over time, or in a way that makes you more dependent. Choose the first option, invest in the human skills that AI amplifies rather than replaces, and you will not just survive the AI transformation of software development. You will lead it.