Artificial intelligence has become one of those topics where the loudest opinions tend to be the least useful.
On one side, you have people dismissing AI-assisted development as a factory for slop. In that view, AI-generated code is insecure, unstable, poorly understood, and destined to collapse under its own technical debt. The term “vibe coding” gets used as shorthand for the entire category: someone types a vague request, accepts whatever comes back, and ships a product they do not understand.
On the other side, you have people talking as if software development has already been solved. The pitch is that anyone can describe an idea in plain English and an AI agent will produce a complete, production-ready system. No technical judgment required. No architecture needed. No real understanding of security, maintainability, deployment, data modeling, or user experience.
Both extremes miss what is actually happening.
AI-assisted development is neither a toy nor a replacement for engineering discipline. It is a major shift in how software gets built. It is one of the most powerful tools developers have received in a long time, probably the most important workflow change since modern IDEs, integrated debuggers, package ecosystems, and cloud development environments became normal. But like every powerful tool, it amplifies the person using it.
In the hands of a careless developer, AI can absolutely produce bad software faster than before.
In the hands of a strong developer, it can compress weeks of work into days, unlock faster iteration, improve exploration, and make a single person capable of delivering at a level that previously required a much larger team.
That distinction matters.
The question is not whether AI is good or bad for development. The better question is: what kind of developer does AI reward?
The Debate Is Too Binary
Software people love binary debates. Static typing versus dynamic typing. Monoliths versus microservices. Native apps versus web apps. Tabs versus spaces. The AI debate has fallen into the same pattern, but the stakes are higher because this one touches career identity.
For many developers, writing code is not just a job function. It is the craft. It is the thing they spent years learning, refining, and taking pride in. So when a tool appears that can generate a large amount of code in seconds, the reaction is understandable. Some people see leverage. Some people see a threat. Some people see both.
The problem is that the debate often treats AI-generated code as if it exists outside of any development process.
That is not how real software teams work.
On a serious project, code does not become production software just because someone typed it. Code moves through context, review, testing, architecture, deployment, monitoring, and maintenance. It has to fit into an existing system. It has to honor business rules. It has to align with security requirements. It has to be readable by the next person who touches it.
If AI output bypasses all of that, then yes, the result can be dangerous.
But the same was true before AI.
There have always been developers who copied code from a forum, a blog post, or Stack Overflow without understanding it. There have always been people who pasted a snippet into an application, saw the immediate error go away, and moved on without asking what the code actually did. Sometimes that worked. Sometimes it introduced subtle bugs. Sometimes it created security issues.
That behavior was never good engineering. AI did not invent it. AI just made it easier to do at a larger scale.
That is what I consider true vibe coding: not the use of AI itself, but the absence of professional review, ownership, and understanding.
AI Is Already Normal
It is tempting to talk about AI-assisted development as something still on the edge of the industry, but that is no longer accurate.
The 2025 Stack Overflow Developer Survey showed that developers are already using AI tools and agents in meaningful numbers, even while many remain skeptical of their accuracy. The 2025 DORA research from Google Cloud found broad adoption among software professionals, with respondents reporting meaningful productivity gains and a positive effect on code quality, while still treating AI as something that requires human judgment. GitHub’s 2025 Octoverse report points in the same direction: AI, agents, and typed languages are changing how developers work and what toolchains they prefer.
The exact numbers will change every year. The direction is already clear.
AI is becoming part of the baseline expectation for modern development work.
That does not mean every company has figured out how to use it well. Many have not. Some teams are experimenting carefully. Some are letting developers use AI quietly because policy has not caught up. Some organizations are blocking tools out of concern for data leakage, licensing, compliance, or quality control. Those concerns are legitimate, especially in regulated environments.
But the broader movement is not going away.
The practical reality is this: if two equally skilled developers are working on comparable problems, and one has learned how to use AI effectively while the other refuses to use it at all, the AI-assisted developer is going to have a major productivity advantage.
Not because the AI is smarter than the developer.
Because the developer has more leverage.
The Senior Developer Analogy
The best way I can describe working with AI is this: it feels like leading a team of extremely fast developers.
That may sound strange if you have only used AI as autocomplete or as a chatbot. But when you work with coding agents inside a real project, the experience is surprisingly close to technical leadership.
You provide direction. You give context. You define constraints. You review output. You identify architectural drift. You ask for revisions. You decide what is acceptable. You keep the work aligned with the broader product.
That is not very different from leading a team.
Anyone who has worked as a senior developer, team lead, or project lead understands this. On a real team, not every engineer has the same experience level, the same taste, the same understanding of the domain, or the same instinct for maintainability. A major part of leadership is creating enough structure that different people can contribute without pulling the codebase in five different directions.
You write standards. You establish patterns. You review pull requests. You explain why one approach fits the system better than another. You catch security issues. You push back on duplication. You notice when a feature solves the immediate problem but creates a long-term maintenance problem.
Working with AI requires the same muscles.
The difference is speed.
An AI agent can produce a large first pass very quickly. It can inspect files, follow patterns, wire up a feature, write tests, update documentation, and propose changes at a pace that feels unnatural compared to traditional development. That speed is the value. It is also the risk.
If you do not review the work, AI lets you make mistakes faster.
If you do review the work, AI lets you iterate faster than traditional development cycles allow.
That is why I do not think experienced developers should be afraid of AI. The work does not become less senior. In many ways, it becomes more senior. You spend less time typing every line yourself and more time making decisions about architecture, quality, behavior, trade-offs, and direction.
You are still responsible for the result.
The Developer Is Still Accountable
One of the worst habits AI can create is emotional distance from the code.
When a developer writes code manually, they usually feel ownership over it. When AI generates code, some people treat it as something separate from themselves: “the AI did that.” That mindset is dangerous.
If it goes into your codebase, it is your code.
That is the standard I apply to AI-generated work. I do not care whether I typed every line by hand, asked an AI agent to scaffold the first version, copied a pattern from documentation, or adapted an example from another project. If I commit it, I own it.
That means I need to understand it. I need to be able to explain it. I need to know how it behaves in failure modes. I need to know what assumptions it makes. I need to know whether it fits the architecture. I need to be comfortable supporting it later.
This is not new.
Before AI, I was already careful with code samples from outside sources. If I found an answer online, I rarely copied it directly into a project. I would read it, understand it, and then rewrite it in the style of the codebase I was working in. Sometimes the rewritten version was functionally identical. That was fine. The act of rewriting forced me to understand the implementation and made the final code feel native to the project.
AI does not change that standard.
If anything, it makes the standard more important.
AI can produce code that looks convincing. It can use the right vocabulary. It can create files that compile. It can make confident architectural choices. Sometimes those choices are good. Sometimes they are subtly wrong. Sometimes they solve the narrow prompt while ignoring a constraint that was obvious to the human but missing from the context.
That is why the developer cannot become a passive approver.
The developer has to remain the technical authority.
Good AI Output Requires Good Inputs
One of the biggest mistakes developers make with AI is treating prompting as a one-off request.
“Build this feature.”
Sometimes that works for a small task. But for serious development, the quality of the output depends heavily on the context you give the agent and the structure you create around it.
At the beginning of a project, I spend real time setting up agent instructions and project documentation. That includes:
- architectural patterns
- coding style expectations
- naming conventions
- security requirements
- testing expectations
- deployment assumptions
- preferred libraries and frameworks
- areas of the codebase that should not be changed casually
- examples of good local patterns
That work matters.
It is the same thing I would do for a developer joining a team. I would not expect a new engineer to walk into an unfamiliar codebase and magically infer every standard, business rule, historical decision, and architectural preference. I would give them documentation. I would point them to examples. I would explain where flexibility is allowed and where consistency matters.
AI needs the same guidance.
The better the instructions, the better the first pass. The clearer the project structure, the better the agent can follow it. The more consistent the existing codebase, the easier it is for AI to extend it without introducing strange variation.
This is one reason AI can expose weak engineering systems.
If a codebase has no coherent patterns, AI will not magically create them. It may amplify the inconsistency that already exists. If a team has no testing discipline, AI-generated changes may appear to work while breaking behavior nobody has captured. If architecture decisions live only in one person’s head, the agent will not know them unless that person supplies the context.
AI rewards clarity.
That should be a wake-up call for teams.
The First Pass Is Not the Finish Line
One of the most impressive things about modern AI coding tools is how often they can produce a workable first implementation.
For many features, especially in a well-structured codebase, the first pass can be surprisingly close. The files are in the right place. The naming mostly fits. The UI is wired up. The data flow makes sense. Tests may even cover the core behavior.
That does not mean the work is done.
My typical workflow is iterative. I might ask the agent to implement a feature, then review the result and ask for another pass:
- simplify this integration
- remove this duplication
- align this with an existing helper
- make this component more reusable
- reduce the amount of state
- tighten the validation
- improve the error handling
- add coverage for this edge case
- avoid introducing this dependency
- move this logic closer to the domain model
That back-and-forth is where a lot of the value is.
The old development cycle often made iteration expensive. If a feature took a week to build, there was pressure to accept the first version that worked. With AI, you can explore alternatives much faster. You can ask for a simpler version. You can compare implementation strategies. You can have the agent identify risks in its own approach. You can ask it to write tests, then ask another tool or another agent to critique those tests.
That does not remove the need for judgment. It gives judgment more room to operate.
This is why I think AI fits naturally with agile development when used well. The value of agile was never ceremonies. It was fast feedback, small increments, and the ability to adjust based on what you learn. AI accelerates that loop.
The danger is confusing speed with completion.
Fast iteration is valuable only if each iteration is reviewed with care.
The Real Risk Is Lazy Development
The anti-AI argument often focuses on bad output, but I think that frames the problem incorrectly.
The deeper risk is lazy development.
Lazy development existed before AI. It looked like copying code without understanding it. It looked like ignoring compiler warnings. It looked like shipping without tests. It looked like patching symptoms instead of understanding causes. It looked like adding one more special case to an already fragile system because it was faster than fixing the design.
AI can make lazy development more productive in the short term and more damaging in the long term.
That is a real concern.
If a developer does not understand authentication, AI will not make them good at authentication. It may help them produce an authentication flow that appears to work. That can be worse. If a developer does not understand concurrency, data consistency, browser security, permissions, or deployment boundaries, AI can generate code that masks the gap until production exposes it.
But the answer is not to reject AI.
The answer is to raise the standard for how AI is used.
Code generated with AI should still go through review. It should still be tested. It should still be evaluated against the architecture. Security-sensitive work should receive extra scrutiny. Major changes should be broken down into understandable pieces. Developers should be able to explain what changed and why.
The same engineering discipline still applies.
AI does not eliminate the need for standards. It makes standards more important.
AI Is a Force Multiplier, Not a Moral Category
There is a strange moral tone in some AI discussions, as if using AI is inherently less authentic than writing everything manually.
I do not buy that.
Developers have always used tools to avoid unnecessary manual work. We use compilers, package managers, frameworks, IDEs, linters, refactoring tools, scaffolding tools, database migration tools, infrastructure templates, CI/CD pipelines, and cloud services. Nobody serious argues that using a debugger means you are not a real developer.
AI is another tool in that progression, but it is a more flexible one.
It can help write code. It can explain unfamiliar code. It can generate tests. It can summarize a diff. It can inspect logs. It can help debug an issue. It can draft documentation. It can convert repetitive patterns. It can identify likely edge cases. It can act as a reviewer. It can help explore a new library. It can generate throwaway prototypes that clarify whether an idea is worth pursuing.
The value is not just that it writes code faster.
The value is that it reduces friction across the development process.
That matters because software development is full of friction. Context switching. Boilerplate. Repetitive wiring. Searching through unfamiliar APIs. Remembering exact syntax. Writing the first draft of documentation. Creating test data. Reading long logs. Comparing two approaches. Explaining a legacy module you have not touched in years.
AI helps with all of that.
For an experienced developer, those are not replacements for expertise. They are accelerants.
It Has Changed My Own Development Process
I have spent most of my career working in the Microsoft .NET ecosystem. At this point, a lot of the core framework patterns, libraries, and implementation approaches are deeply familiar. I have strong opinions about how I like code structured because I have had to live with software long after the first release.
That matters to me.
I want to be able to return to a project years later and still understand it. I want the code to feel intentional. I want the structure to make the next change easier, not harder. I want patterns to be consistent enough that a developer can infer where new work belongs. That has always made me picky about implementation details, even when two approaches are functionally equivalent.
AI has not made me less picky.
It has made that pickiness more useful.
There are times when an AI agent produces an approach that is close to what I would have written, but slightly different in a useful way. Maybe it uses a newer framework feature. Maybe it avoids a habit I have carried forward from older versions of the stack. Maybe it suggests an abstraction that I would not have reached for on the first pass. Maybe it shows a cleaner way to express a test case or isolate a piece of behavior.
That does not mean I accept the output blindly. It means the tool gives me more options to evaluate.
This is one of the underappreciated benefits of AI for experienced developers. It is not only about saving time on code you already know how to write. It is also about surfacing alternatives. A good developer can look at those alternatives and decide whether they improve the system or merely add novelty.
That distinction is important.
Novelty is not architecture. Different is not automatically better. But neither is familiar automatically better. After 20 years, it is easy to develop reliable patterns that serve you well, but it is also possible to keep using an approach because it is the one you already know. AI gives you a second set of eyes with broad exposure to patterns across languages, frameworks, and eras of development.
The developer still has to decide what belongs in the codebase.
But having more credible options on the table is valuable.
The Market Will Not Ignore This
This is the part of the conversation many developers do not want to talk about.
AI-assisted development is not just a personal productivity tool. It changes the economics of software teams.
If one strong developer using AI can do the work that previously required several developers, companies will eventually adjust around that reality. Maybe not cleanly. Maybe not immediately. But it will happen.
On the right kind of project, I would rather have one highly capable developer with a well-configured AI workflow than five traditional developers working without that leverage. That is not because people do not matter. It is because speed of iteration matters enormously, and coordination has a real cost.
I do not think every development job disappears. I do think the market contracts in certain areas. The work that is easiest to specify, easiest to test, and easiest to review will be increasingly automated or handled by smaller teams. Routine CRUD work, straightforward integrations, basic UI scaffolding, documentation updates, simple bug fixes, and many repetitive refactors are already becoming faster with agents.
That does not mean software gets easier overall.
It means the bottleneck moves.
When implementation becomes faster, the valuable constraint becomes deciding what to build, how it should behave, how it fits the business, how it should feel to users, how it should be secured, and how it should evolve. The developer who can only wait for a ticket and turn it into code is less differentiated than before.
The developer who can understand the business problem, shape the product, design the system, use AI to accelerate execution, review the output, and own the result becomes much more valuable.
That is a different profile.
It favors people who are multidisciplinary.
The Rise of the Multidisciplinary Developer
For years, there has been a certain prestige around deep specialization. The developer who knows the internals of a framework. The backend expert who can optimize a query plan by instinct. The frontend engineer who understands rendering behavior in detail. The infrastructure specialist who can diagnose a networking issue from a few log lines.
Those skills still matter. Deep technical understanding is not going away.
But the market is going to reward a broader shape of developer more than it did before.
The strongest AI-assisted developers will not just be code writers. They will be people who can connect disciplines:
- engineering
- product thinking
- design judgment
- user experience
- business process
- security awareness
- technical writing
- testing strategy
- operational thinking
That combination matters because AI can help fill in technical details, but it still needs direction. The person giving direction has to know what good looks like.
If you are building a product, the code is only one part of the outcome. Does the workflow make sense? Does the interface communicate clearly? Does the feature solve the right problem? Does it reduce friction for the user? Does it support the business model? Does it create operational burden? Does it introduce risk?
AI can help answer pieces of those questions, but it cannot replace ownership of the product judgment.
This is why I think developers looking toward 2027 and beyond should invest beyond code. Learn more about the business side of the systems you build. Learn how users think. Learn why conversion matters. Learn basic design principles. Learn how support teams experience software. Learn how sales teams describe value. Learn how executives think about risk, timelines, and return on investment.
The more context you can hold, the more valuable AI becomes in your hands.
AI Will Also Change Team Structure
Traditional software teams are often built around concurrency. If the backlog is large, add more developers. More developers means more work can happen at the same time.
That model still has value, but it comes with coordination cost. More people means more communication, more review, more meetings, more handoffs, more variation in implementation style, and more risk that different parts of the system evolve in conflicting directions.
AI changes the balance between concurrency and iteration.
A single strong developer with AI can move through a problem quickly, keeping the whole system in their head while still producing code at a pace that previously required multiple contributors. That can be more effective than splitting the work across a larger group, especially in early product development or in systems where cohesion matters.
This is one reason I would often rather work with a well-configured AI coding agent than add several more developers to a small project.
That does not mean teams are obsolete.
It means teams need to be more intentional.
Five strong developers who all know how to use AI well can be incredibly powerful. But five developers using AI without shared standards can create chaos quickly. The role of technical leadership becomes even more important, not less. Someone still has to define the architecture, keep the work coherent, establish review standards, and decide when speed is creating hidden cost.
In other words, AI does not remove the need for leadership.
It increases the impact of leadership.
The Future: Agents as Specialized Teammates
I do believe we are moving toward a world where non-technical people can build more software than they can today.
That is not fantasy. The direction is obvious. Tools are getting better at planning, editing, testing, running commands, inspecting failures, and iterating. Agent systems are becoming more capable. The concept of multiple specialized agents working together is already visible: one agent implements, another reviews, another tests, another checks security, another evaluates usability, another verifies documentation.
Over time, those systems will improve.
Eventually, a non-technical founder or operator may be able to describe a workflow and have a specialized agent setup produce a usable internal tool or application end-to-end. That will happen more often as the tools mature, especially for well-understood patterns.
But we should be honest about where we are now.
Current AI systems can produce impressive results, but they still need guardrails. They still need context. They still make mistakes. They still struggle with ambiguous requirements. They can miss security implications. They can overfit to the prompt. They can solve the wrong problem elegantly.
For now, the best results still come from pairing AI with a developer who has enough experience to guide, review, and correct it.
That is not a small caveat. That is the core of the current opportunity.
AI gives experienced developers leverage before it gives everyone else full autonomy.
What Developers Should Do Now
If you are a developer who has been hesitant to adopt AI, my advice is direct: start now.
Not because every AI tool is perfect. Not because every workflow is mature. Not because you should trust output blindly. You should start because the skill of using AI well is itself becoming part of software development.
You do not need to turn your entire workflow over to agents on day one. Start with lower-risk use cases:
- ask AI to explain unfamiliar code
- generate tests for code you already understand
- summarize a large diff
- draft documentation
- find duplication in a module
- propose refactoring options
- help debug an error after you have inspected it yourself
- scaffold a feature behind a review process
Then build discipline around the workflow:
- review every change
- ask why the agent chose an approach
- compare alternatives
- run the tests
- inspect security-sensitive code manually
- keep project instructions updated
- document patterns the agent should follow
- reject output that does not fit the codebase
The goal is not to become dependent on AI.
The goal is to become effective with it.
There is a major difference.
What Businesses Should Do Now
Businesses also need to be careful not to misread the moment.
AI can reduce development cost, but it is not a magic replacement for engineering judgment. The worst version of AI adoption is giving powerful tools to teams without standards, security guidance, review processes, or clarity about acceptable use.
Organizations should be asking:
- What tools are approved?
- What data can and cannot be shared?
- How are AI-generated changes reviewed?
- Where is human approval required?
- What types of work are safe for agentic workflows?
- How are security and compliance handled?
- How do we measure quality, not just speed?
- How do we train developers to use AI responsibly?
The companies that win with AI will not simply be the ones that buy licenses. They will be the ones that adapt their engineering process around faster iteration while keeping quality controls intact.
That is the balance.
Move faster, but do not remove accountability.
The Bottom Line
AI-assisted development is not a passing trend. It is becoming part of the standard development toolkit.
The developers who dismiss it entirely are not protecting the craft. They are falling behind the direction of the industry. The developers who trust it blindly are not embracing the future. They are repeating old mistakes with faster tools.
The middle path is the professional one.
Use AI aggressively. Review it rigorously. Give it context. Build guardrails. Iterate quickly. Own the result.
That is where the real productivity gain lives.
For developers, the message is simple: learn to use AI well, because the expectation is changing.
For businesses, the message is just as important: AI does not eliminate the need for senior technical judgment. It makes that judgment more valuable.
Software development is not becoming less strategic. It is becoming more leveraged.
And in a leveraged environment, the best developers are not the ones who type the most code by hand. They are the ones who can turn judgment, context, and tools into reliable software faster than anyone else.
Trying to figure out how AI-assisted development fits into your product, engineering team, or technology roadmap? Get in touch. I help organizations use modern development practices without losing the discipline that keeps software maintainable.