Artificial Intelligence is no longer a side experiment in programming. What was once a novelty, using AI assistants to autocomplete code or debug snippets, has now become a baseline expectation in the industry. The conversation is shifting from if we use AI in development to how we use it responsibly.

One of the most visible examples of this shift came from Coinbase. CEO Brian Armstrong recently mandated that engineers onboard AI coding assistants such as GitHub Copilot and Cursor within one week. Those who failed to comply without a valid reason were ultimately let go. To some, that move looked harsh or even reckless. To others, it was a signal of where programming is headed: fast, AI-augmented, and uncompromising in its expectations.

At WAM DevTech, we believe this moment reflects a bigger truth: AI in programming will help more than it will hurt if we use it wisely.

The Coinbase Example: Bold, Not Reckless

The Coinbase mandate was not about firing developers on a whim. It was about culture and pace. Armstrong’s requirement was simple: adapt quickly to new AI workflows or risk being left behind. Those who complied discovered that AI-generated code was not just efficient. It reshaped how they approached their work.

Within months, Coinbase reported that 40% of its daily code was being generated by AI, with a goal of reaching nearly 50% by the end of the quarter. Numbers like these highlight not just productivity gains, but the reality that coding with AI is no longer optional in some organizations.

Was the move aggressive? Yes. But reckless? Not necessarily. It forced a conversation every tech leader must now face: how do we integrate AI into our workflows without losing the fundamentals of good engineering?

Why I Believe AI Will Help More Than Hurt

From my own experience, AI in programming is not a threat. It is a multiplier. When my team at WAM DevTech began adopting AI tools, the results were not just measurable in faster delivery times. Developers found the work more enjoyable, less bogged down by repetitive tasks, and more focused on solving meaningful problems.

Here is where I see the biggest benefits:

• Efficiency with guardrails — AI accelerates routine coding and boilerplate, freeing developers to spend more time on architecture, problem-solving, and optimization.
• A training accelerator — For junior developers, unchecked AI use can be dangerous, leading to bad habits or shallow understanding. But when paired with proper mentoring, AI becomes a turbocharged training tool. Juniors can see working code faster, ask better questions, and learn by comparison.
• Knowledge transfer — Senior developers can use AI as a teaching aid, demonstrating not just what works but why. It enhances the mentoring process instead of replacing it.
• Focus on problem-solving — By taking the grunt work off the table, AI enables teams to focus on higher-order thinking, such as design, architecture, integration, and scalability.

Generational Strengths: Vibe Coding Meets AI

AI does not replace human intuition. It amplifies it. A recent TechRadar article highlighted how older developers often excel at “vibe coding.” That phrase may sound casual, but it describes something critical: an intuitive grasp of patterns built over years of experience.

Where a junior might see a block of code, a seasoned developer sees a pattern, a smell, or an architectural choice. AI fits perfectly into this equation. It can handle the routine and repetitive, while human experience guides the design and ensures long-term maintainability.

Far from sidelining older coders, AI could make their intuition even more valuable. Instead of being stuck writing boilerplate, they can focus on where their judgment makes the biggest impact.

Real-World Proof: AI in Action at WAM DevTech

This is not just theory. We have seen it in practice. At WAM DevTech, we helped an organization migrate from Microsoft SQL Server to PostgreSQL. On the surface, this kind of database migration is a massive undertaking, often requiring manual conversion of stored procedures, queries, and business logic.

But in this case, AI transformed the process. With AI-assisted code conversion, we accelerated the migration dramatically. The results were not just about time savings. They translated directly into cost efficiency and scalability. PostgreSQL ended up costing roughly a third of what SQL Server had, and the organization gained flexibility for the future.

You can read more about that project in detail here: SQL Server to PostgreSQL Migration: Cost & Scalability.

This experience showed us something important. AI is not just a productivity booster for writing new code. It is also a game-changer for modernizing legacy systems, where repetitive conversions and transformations often consume the bulk of the effort.

The Danger of Misuse

That does not mean AI is without risks. Misused, it can be just as dangerous as any other shortcut.

• For juniors — If they rely on AI without guidance, they risk never learning the fundamentals. AI might provide working code, but without understanding why it works, developers will not grow into problem-solvers.
• For companies — Using AI purely as a cost-cutting measure, as some firms have done by reducing staff, is shortsighted. AI does not replace expertise. Without experienced engineers to guide it, you risk creating fragile systems nobody fully understands.
• For teams — Over-reliance can create complacency. Developers may become too quick to accept AI output without proper review, leading to subtle bugs or vulnerabilities creeping in.

The key is balance. AI should be an accelerator, not a replacement. It should free humans to do more valuable work, not excuse leaders from investing in training and mentorship.

The New Baseline: Coding Smarter, Not Less

We are past the point of asking whether AI belongs in the software development toolkit. It does. The question now is how we integrate it responsibly.

AI Coding Tools Shaping the Future

The rapid adoption of AI in programming is not happening in isolation. Several tools are now becoming standard across the industry, each designed to serve a specific purpose:

• GitHub Copilot — An AI pair programmer built on OpenAI Codex. It suggests code snippets, functions, and even full methods in real time, helping developers move faster on routine tasks.
• Cursor — A next-generation code editor with built-in AI integration. It goes beyond autocompletion by enabling developers to refactor, explain, or generate new code directly within their editor.
• Tabnine — An AI assistant focused on code completion that learns from your own codebase to provide context-aware suggestions tailored to your project.
• Amazon CodeWhisperer — An AI tool from AWS designed to integrate seamlessly into cloud development workflows. It generates code recommendations and security scans while supporting multiple programming languages.
• Replit Ghostwriter — Built into the Replit platform, it helps developers and students alike by providing instant code suggestions and explanations in a collaborative environment.

These tools show that AI is not just one product or one mandate. It is becoming an ecosystem of assistants that reduce the repetitive work of coding while enhancing the ability of teams to build, maintain, and modernize systems.

Closing Thoughts: The Developer's Future

AI will not replace developers. But developers who embrace AI will outpace those who do not.

The future of programming lies in this partnership. Leaders like Brian Armstrong may push aggressively, but the underlying message is one we all must consider: adaptation is not optional. The real challenge is not whether AI will hurt us. It is whether we will use it wisely enough to let it help.

At WAM DevTech, we see this every day. Whether it is modernizing legacy systems, automating manual workflows, or mentoring teams into new ways of working, AI is a tool that, when used thoughtfully, moves businesses forward without disruption. That is the kind of future we believe in: steady, smarter, and more connected than ever before.