Leaning into the AI revolution of code development

AI tools don't replace 20 years of experience — they amplify it. Here's how I'm using AI-augmented workflows to ship higher quality software at a pace that would have been impossible two years ago.

The experienced developer advantage

There's a common misconception that AI coding tools are primarily for beginners — a crutch for people who don't know what they're doing. I've found the opposite to be true. The more you know, the more dangerous these tools become in the best possible way.

When I pair with AI, I'm not asking it to think for me. I'm asking it to execute faster on decisions I've already made. I know what the architecture should look like. I know where the edge cases hide. I know which patterns will cause pain at scale and which ones won't. AI handles the boilerplate, the syntax, the tedious parts — and I handle the judgment.

The result? I'm shipping features in hours that used to take days. Not because the code is worse. Because I'm spending my time on the parts that actually matter.

Quality goes up, not down

The biggest surprise hasn't been speed — it's been quality. When I use AI to generate a first pass of a component or an API route, I'm not just accepting whatever it outputs. I'm reviewing it with the same critical eye I'd apply to a junior developer's PR. But unlike a junior developer, the AI doesn't get defensive when I rewrite half of it.

What this means in practice:

  • More consistent patterns — AI tools are excellent at maintaining consistency across a codebase when you give them the right context
  • Better test coverage — I actually write more tests now because generating test scaffolding takes seconds instead of minutes
  • Fewer typos and copy-paste errors — the mundane mistakes that slip through when you're manually typing the same pattern for the 50th time
  • More time for architecture — when implementation takes less time, I spend more time thinking about whether I'm building the right thing

How I actually use it

My workflow at BisonLab looks something like this:

  1. Architecture and design — this is still 100% me. I sketch the data model, plan the API surface, decide on the component structure. AI is terrible at this because it doesn't understand the business context.

  2. Implementation — this is where AI shines. I describe what I need, provide the relevant context about existing patterns in the codebase, and let the AI generate the initial implementation. Then I review, adjust, and iterate.

  3. Testing — AI generates test scaffolding. I fill in the edge cases it missed (and it always misses some). The coverage ends up higher than it would have been if I were writing everything from scratch.

  4. Code review — I review AI-generated code the same way I'd review any PR. The difference is that the feedback loop is instantaneous.

// This is the kind of pattern where AI excels:
// I describe the business rule, it generates the implementation,
// and I verify it handles the edge cases correctly.
async function getLocateTickets(companyId: string, filters: TicketFilters) {
  const where: Prisma.TicketWhereInput = {
    companyId, // Always scoped to tenant
    ...(filters.status && { status: filters.status }),
    ...(filters.dateRange && {
      createdAt: {
        gte: filters.dateRange.start,
        lte: filters.dateRange.end,
      },
    }),
  }

  return prisma.ticket.findMany({
    where,
    orderBy: { createdAt: 'desc' },
    include: { assignee: true, location: true },
  })
}

The real risk isn't using AI — it's ignoring it

Every week I see developers who refuse to use AI tools because they think it's cheating, or it'll make them lazy, or the code isn't good enough. Meanwhile, I'm shipping an entire SaaS product as a solo developer with a level of polish and reliability that would have required a small team three years ago.

The developers who will thrive in the next decade aren't the ones who can type the fastest or memorize the most APIs. They're the ones who can leverage AI as a force multiplier while bringing the judgment, experience, and product thinking that no model can replicate.

I'm all in on this. BisonLab wouldn't be possible without it.