AI-Powered Development: Navigating the Build vs. Buy Decision

I've watched the build vs. buy debate play out across executive teams and engineering standups for over three decades. The arguments are almost ritualistic at this point. Business team wants to deploy a new product. Technology team looks at the price tag, looks at the requirements, and inevitably says: "We can build something faster, cheaper, better. It'll be customized to exactly what we need."

And now, with the advent of AI and large language model-driven development, this confidence has reached unprecedented levels. The average engineer can now produce products and services that, even a year or two ago, weren't imaginable in the hands of a small team.

A 2024 Google study found developers using AI completed tasks 21% faster. Analyses from Faros AI and Opsera showed code merges happening 50% faster and lead time to production decreasing by 55%. The productivity gains are real and documented. What's also documented, though less discussed - a rigorous 2025 study found experienced developers working on complex, mature codebases were actually 19% slower with AI assistance, even as they reported feeling 20% faster - so the jury's still out on exactly how well founded this newly discovered confidence really is.

Regardless of some of the conflicting research, these aren't incremental improvements. They're transformational. And they're changing how every technology leader approaches the classic build vs. buy decision.

But here's what I've learned from watching too many of these decisions play out - the initial build is rarely where companies stumble. It's everything that comes after.

The Seductive Speed of AI-Assisted Building

Let me be clear about something - the productivity gains from AI-assisted development are real. When 78% of developers report actively using AI tools in their development process - with over half using them daily - we're not talking about a fringe technology anymore. This is mainstream, and the capabilities are genuinely impressive.

In my own day-to-day work, AI-assisted development has unlocked my ability to turn product ideas into working code in record time - whether that's a new public-facing app or back-end code used to automate and accelerate a highly manual process, I can deliver faster and solve complex customer challenges more completely.

I've seen teams leverage these tools to prototype in days what would have taken weeks or months. The explosive capability changes that have occurred with AI-driven, LLM-based code development aren't hype. They're documented, measured, and increasingly difficult to ignore when you're making resource allocation decisions.

The problem isn't that AI-assisted development doesn't work. The problem is that it works so well in the short term that it creates a dangerous illusion about the long term.

Over my career, I've experienced the push and pull between the technology, product team, and the business team countless times.  I've found myself responsible for each one at some point in my career, so I can confidently represent this issue from the varied  perspectives of each stakeholder.

A business representative wants to deploy a new product or internal tool. The technology team evaluates it, sees the price, and makes their pitch - "we can build it ourselves". And when given the opportunity, they generally will build something. It will generally work. It'll probably be over budget and take longer to deploy than expected, but eventually, it ships.

What nobody thinks about in that moment of triumph is - who maintains this thing for the next five years, especially when the issue it solves is no longer in the limelight of executive priority?

The Maintenance Reality That AI Doesn't Change

Here's a number that should give every technology leader pause - maintenance operations consume approximately 67% of the total software development lifecycle cost. Let me repeat that...

Two-thirds of what you'll spend on a piece of software happens after you've built it.

Regular updates, bug fixes, and security patches typically account for 15-25% of the overall development cost annually. That's not a one-time expense. That's every year, for as long as you're running that software. Bug fixes. Security patches. Updates for changing environments. Feature enhancements based on user feedback. The work never stops.

AI-assisted development can absolutely help you build faster. But it doesn't change the fundamental economics of software maintenance. If anything, early research suggests it might make things worse. GitClear's analysis of 211 million lines of code found that AI-generated code has a 41% higher churn rate compared to human-written code, meaning more of it gets revised or reverted within two weeks of being written.

This is the core tension that every build vs. buy decision ignores at its peril - speed and sustainability are fundamentally different metrics. Optimizing for one doesn't automatically optimize for the other. In fact, they often work against each other.

There still no solution for the old adage, of Cheap, Fast, or Good - Pick 2.  The laws of physics still hold true.

The Hidden Costs Nobody Budgets For

When you buy a commercial product, you're not just buying code. You're buying the whole package - an organization whose entire business model depends on maintaining that code, updating it for new environments, patching security vulnerabilities, and ensuring compliance with evolving regulations. You're buying their subject matter expertise, their dedicated security team, their compliance certifications, and their 24/7 support operations.

Yes, you're also buying vendor lock-in. That's real, and it's worth factoring into your decision. But here's what's often missed - when you build internally, or even when you onboard an open source solution - you're not avoiding lock-in. You're just trading one form of dependency for another. Instead of being locked to a vendor's roadmap, you're locked to your own team's capacity, institutional knowledge, and continued employment.

When you build internally, you're taking on all of those maintenance responsibilities yourself. And the tradeoffs that occur when you go on your own, when you don't have an organization whose main business is producing and maintaining those products and services, compound over time.

Consider what happens when the developer who built your internal tool moves to a different company. Or consider the open source library that's critical to your infrastructure - maintained by one volunteer in Nebraska, as the famous XKCD comic illustrates.

Two-thirds of software project costs stem from ongoing maintenance, and that work becomes exponentially harder when institutional knowledge walks out the door - whether that door belongs to your employee or an unpaid open source maintainer. Cross-training, documentation, knowledge transfer - these aren't optional extras. They're survival requirements for any internally-built system that needs to operate beyond a single developer's tenure. 

The Compliance and Security Blind Spot

Here's an area where the build vs. buy calculus has become even more treacherous in the AI era, compliance and security expertise. And this is where I'll start giving you the framework for how to think about these decisions - not just why they matter.

AI can generate code. What it cannot do is replace the nuanced, specialized knowledge required to ensure that code meets regulatory standards, handles sensitive data appropriately, and maintains security against evolving threats. Organizations routinely underestimate the compliance overhead of AI-assisted development - from maintaining audit trails that satisfy regulators, to ensuring AI-generated code doesn't introduce vulnerabilities or violate data handling requirements. These aren't afterthoughts - they're fundamental requirements that demand specialized expertise.

The lack of subject matter expertise around compliance and security requirements is particularly dangerous for products that aren't in the core competency of an organization. If you're a fintech company and you decide to build your own authentication system because AI makes it "easy," you're assuming responsibility for a domain where regulatory requirements change constantly, where security threats evolve daily, and where a single breach can result in catastrophic business consequences.

Commercial vendors in specialized domains have dedicated teams tracking regulatory changes, implementing security updates, and maintaining compliance certifications. They do this because it's their core business. Can your internal team, juggling multiple priorities and projects, match that level of specialized focus? The answer to that question should guide your decision - and I'll give you the specific questions to ask in the framework below.

The Junior Developer Risk

Industry observations suggest that junior developers often see the highest productivity gains from AI assistance. This makes intuitive sense - AI coding assistants help less experienced developers punch above their weight, completing tasks they might have struggled with otherwise.

But this creates a subtle trap. When junior developers can rapidly generate sophisticated-looking code with AI assistance, they may bypass critical learning experiences. They accept suggestions without fully understanding the underlying architecture. They build systems that work in isolation but violate organizational security principles or architectural standards.

Senior developers, by contrast, tend to benefit differently - less from raw code generation and more from staying in flow and automating routine work. The implication is important, experienced developers bring judgment that AI doesn't replace. They know when a suggestion violates best practices, when a pattern is inappropriate for a specific context, when a quick fix creates long-term technical debt.

Build decisions made by AI-empowered junior developers without adequate senior oversight are build decisions made without the institutional knowledge and judgment that separates functional code from maintainable, secure, compliant systems.

Rethinking the Decision Framework

I'm not arguing against building. There are absolutely scenarios where internal development is the right choice. Proprietary competitive advantages, proprietary data, unique integration requirements, control over roadmap and features - these are legitimate reasons to build.

What I am arguing against is letting AI-assisted development speed tip the scales without accounting for the unchanged realities of software ownership.

Before green-lighting any build decision, ask these questions:

Who maintains this in year two, year three, year five? The developer who built it may have moved on. The AI tool that helped create it will have evolved. Who owns the ongoing responsibility, and do they have the expertise to handle it?

What's our actual total cost of ownership? Initial development is typically only 40-50% of total lifecycle cost. Have you budgeted 15-25% annually for maintenance? Have you accounted for training, integration, hosting, and eventual upgrades?

Do we have the specialized expertise this domain requires? If the product involves compliance, security, or specialized industry knowledge, do we have internal experts who can match what a dedicated vendor provides? Or are we hoping our generalist developers can figure it out?

Does this align with our core business focus? Every hour your team spends building and maintaining internal tools is an hour not spent on your actual product or service. What's the opportunity cost of diverting engineering resources from infrastructure maintenance instead of revenue-generating work?

What's our exit strategy if this doesn't work? If the internal build fails to meet needs or becomes unmaintainable, what's the cost to migrate to a commercial solution? Sometimes starting with buy and proving the business case is lower risk than building first.

The Balanced Path Forward

AI-assisted development is a fantastic tool. I use it. My teams use it. The productivity benefits are real, and ignoring them would be foolish.

But tools are not strategies. The same AI capabilities that let you build faster also let commercial vendors build faster. They're using these tools too, while also bringing dedicated maintenance teams, specialized compliance expertise, and economies of scale you can't replicate internally.

The best approach is a balanced one. Embrace AI to speed development where you have genuine competitive advantage or unique requirements that commercial products can't address. Buy for commodity functions, specialized compliance domains, and anything that requires sustained maintenance you're not positioned to provide.

And for everything in between? Do the honest math. Not just the initial development cost, but the five-year total cost of ownership. Not just "can we build this," but "should we own this, and can we sustain it?"

AI has changed how fast we can build. It hasn't changed the fundamental economics of software ownership. The organizations that thrive will be the ones that leverage new capabilities while respecting enduring realities.