Conceptual illustration representing the modern "Build vs. Buy" dilemma. The image shows three paths: a "Buy" path (vendor building), a "Build" path (disciplined construction), and a dangerous third "AI Build First" path crumbling into a pit of technical debt with a stopwatch, symbolizing the "15-minute fix that costs 15 months."

Build vs. Buy Isn’t Dead—It Just Got a Third Way to Fail

December 15, 2025 - By John Mecke

Why ‘Build First, Decide Later’ Is the New Technical Debt Trap for Early-Stage SaaS

VentureBeat published an article last week with a provocative claim: “Build vs. buy is dead—AI just killed it.” The author argues that AI has collapsed the cost of building so dramatically that the old framework is obsolete. Instead of choosing between building or buying, companies should now “build something lightweight with AI” to understand what they need, then decide.

It sounds reasonable. It sounds modern. And it’s a strategic trap that will destroy early-stage SaaS companies that fall for it.

I’ve seen this movie before. In the 1990s, I watched CASE tools make the same promises and devastate companies that believed them. As Executive Director of Professional Services at KnowledgeWare and later Group VP of Business Development at Sterling Software—where I led over $300M in acquisitions including Texas Instruments Software, Synon, and Cayenne—I had a front-row seat to what happens when “everyone can be a programmer” meets production reality.

The VentureBeat article isn’t wrong about the technology. It’s wrong about the strategy.

The 15-Minute Bug Fix That Will Cost You 15 Months

The VentureBeat piece opens with a triumphant anecdote: A non-technical customer experience team member noticed a bug in Slack, opened Cursor, described the fix in plain English, and submitted a pull request that engineering merged. “Just 15 minutes after that complaint popped up in Slack, the fix was live in production.”

The author celebrates that this person “isn’t technical in the slightest” and “couldn’t tell you the difference between Python and JavaScript.” They present this as the future of software development.

I present it as a time bomb.

Here’s what wasn’t mentioned: Who will debug that code when it creates an edge case six months from now? Who documented what was changed and why? What tests were written? What architectural implications weren’t considered because the person making the change didn’t know the difference between Python and JavaScript?

The New Stack documented exactly what happens after six months of this approach in their piece “Vibe Coding, Six Months Later: The Honeymoon’s Over”:

“Vibey commits might look charming in a screenshot, but when your team has to trace logic across ten ad hoc files at 2 a.m., it stops feeling playful real fast.”

That 15-minute fix will become a 15-hour debugging session when something downstream breaks. Then a 15-day refactoring project when the pattern has been repeated across your codebase. Then a 15-month nightmare when you realize your “AI-native” development approach has created a system no one can maintain.

The Semantic Sleight of Hand: “Prototype” vs. “Build”

The VentureBeat framework sounds sensible: “Build something lightweight with AI. Use it to understand what you actually need. Then decide whether to buy.”

But notice the linguistic trick. The article uses “build” when it means “prototype.” These are not the same thing.

A prototype is disposable by design. It exists to learn, then gets thrown away. Building is the creation of maintainable, scalable, production-quality systems that must survive contact with real users, real data, and real edge cases.

The problem is that prototypes have a nasty habit of becoming production systems. Under deadline pressure, “let’s just clean this up a little” becomes “we’ll refactor it later” becomes “this is now load-bearing code that no one understands.”

The New Stack observed this pattern repeatedly:

“Teams that leaned too heavily on vibe coding found themselves rebuilding core systems late in the process, paying back technical debt they’d ignored early on.”

The VentureBeat framework assumes disciplined organizations will maintain clear boundaries between exploration and production. But the entire premise of the article is that non-technical people are now “building.” These are precisely the people least equipped to recognize when a prototype has crossed the line into production-critical code.

The Cargo Cult Paradox

The VentureBeat article offers a compelling critique of current AI adoption. The author invokes physicist Richard Feynman’s concept of “cargo cult science“—the Pacific Islanders who built fake airstrips hoping planes would return. Companies are “buying AI tools without asking if they meaningfully change how work gets done.”

It’s a good critique. But the author’s proposed solution has the exact same flaw.

The cargo cult problem isn’t about buying vs. building. It’s about acting without understanding. The Islanders didn’t fail because they built airstrips instead of buying them. They failed because they didn’t understand what made planes land.

If a company lacks the technical expertise to evaluate whether a vendor’s solution solves their problem, they definitely lack the expertise to evaluate whether their own AI-generated code solves their problem. The cargo cult just relocates from procurement to engineering.

The CX team member who can’t tell Python from JavaScript isn’t going to recognize when the AI has generated a SQL injection vulnerability, a race condition, or a memory leak. They’re building cargo cult airstrips in code.

The Ghost of CASE Tools Past

The promises being made about AI-assisted development are nearly identical to those made about Computer-Aided Software Engineering (CASE) tools in the late 1980s and early 1990s:

CASE Tools Promise (1990)	AI/Vibe Coding Promise (2025)
Visual modeling makes everyone a programmer	Natural language prompts make everyone a programmer
Eliminate mid-level developers	AI functions as mid-level engineers
Automatic code generation from specifications	Automatic code generation from prompts
Speed to prototype	What took weeks now takes hours
Democratization of software development	Building accessible to everyone

I watched this movie in real time. KnowledgeWare reached a market cap of over $1 billion on these promises. Companies eliminated senior engineers because the tools would generate the code. Then the generated code proved unmaintainable. Then the architectural shifts of client-server and web development exposed the brittleness. Then the companies that had traded engineering capability for tool dependency found themselves unable to adapt.

The specific tools failed not because the technology was bad, but because the fundamental premise was flawed: that you can separate “what to build” from “how to build it” without consequences.

The hard part of software development was never typing code. It was understanding the problem deeply enough to make good architectural decisions. CASE tools tried to skip that step. AI-assisted development is trying to skip the same step. The results will be similar.

The Real Question: Who Owns the Architecture?

“Build vs. buy” was always a proxy for a deeper question: who makes the architectural decisions that determine maintainability?

When you buy a solution, the vendor owns the architecture. You’re constrained by their decisions, but you’re also protected by them. You get their accumulated wisdom about edge cases, their security patches, their scaling solutions, their documentation.

When you build a solution with proper engineering discipline, your team owns the architecture. You bear the maintenance burden, but you also have complete control and deep understanding.

When non-technical people “build” with AI, no one owns the architecture.

Dependencies become invisible. Integration points become undocumented. Security considerations become afterthoughts. The code works until it doesn’t, and when it doesn’t, no one understands why.

The VentureBeat article celebrates that CX can now bypass engineering. The strategic question no one is asking: who will maintain what CX built when the original person moves on, or when the AI model changes, or when the business requirements evolve?

The answer is usually “engineering, eventually, at 10x the cost.”

The 80/20 Mirage

The VentureBeat article claims that AI “handles 80% of the problems that used to require a sprint planning meeting and two weeks of engineering time.” This framing reveals a fundamental misunderstanding of software development economics.

In software, the first 80% takes 20% of the time. The last 20%—the edge cases, the error handling, the security considerations, the performance optimization, the documentation—takes the other 80%.

AI is excellent at the first 80%. It can generate boilerplate, implement common patterns, and produce working prototypes at remarkable speed. But that was never the hard part.

The hard part is knowing which 80% matters. It’s recognizing that the “simple fix” touches a critical code path. It’s understanding that the generated code works perfectly except for the edge case your largest customer will hit. It’s maintaining the system two years later when the original context is lost and the AI model has been updated three times.

Claiming AI solves “80% of problems” is like claiming a nail gun does “80% of carpentry.” Technically true, strategically misleading.

What Actually Killed Build vs. Buy

Here’s where I’ll give the VentureBeat article partial credit: the old “build vs. buy” framework is genuinely obsolete. But not because AI collapsed the cost of building.

What actually killed build vs. buy is the API economy and composable architectures. Modern software isn’t built or bought—it’s assembled from services, integrated through APIs, and orchestrated through platforms.

The new framework isn’t “build first to understand.” It’s “compose strategically, understand deeply, build only what differentiates.”

This requires more architectural sophistication, not less. It requires understanding how services interact, where to place boundaries, what to own vs. rent. It’s the opposite of “let’s have CX write some code to see what we need.”

The Contrarian Opportunity

While your competitors embrace “build first, decide later” and accumulate invisible technical debt, you have an opportunity to position for the inevitable reckoning.

The 12-18 Month Window

Companies that drink the “everyone can build” Kool-Aid will ship fast initially. Their demos will look impressive. Their time-to-feature metrics will beat yours.

Then the technical debt starts compounding. Bug reports increase. Refactoring becomes mandatory. Senior engineers spend more time cleaning up AI-generated messes than building new features. Customer churn accelerates as reliability degrades.

Your disciplined approach will look “slow” initially. It’s not slow—it’s building on solid foundations while competitors build on sand.

Use AI for Intelligence, Not Engineering

The right use of AI-assisted “building” is for competitive intelligence and validation, not production systems:

Prototype competitor features to understand their complexity and inform your roadmap.

Build throwaway demos for customer discovery calls—but never ship them.

Validate technical feasibility before committing sprint capacity.

Make better buy decisions because you understand the problem space from having prototyped it.

The key discipline: everything vibe-coded gets deleted after learnings are extracted. Never shipped. Never merged. Never maintained.

Position as the Adult in the Room

Enterprise customers increasingly recognize that “move fast and break things” is incompatible with their security, compliance, and reliability requirements. While competitors chase AI hype, position your company for the buyers who care about:

Documented, auditable code with clear ownership

Security practices that go beyond “the AI generated it”

Architecture that scales without requiring complete rewrites

Long-term maintainability over short-term velocity

The “AI-native” competitors will capture the customers who want fast and cheap. You capture the customers who want fast and reliable. Those are different markets, and yours is more valuable.

The Strategic Bottom Line

Build vs. buy isn’t dead. It’s been replaced by something more complex: a three-way tradeoff between building, buying, and composing, with AI as a tool for exploration within that framework—not a replacement for the framework itself.

The VentureBeat article is right that the old binary choice is obsolete. But “build first with AI to understand” isn’t the new framework. It’s a recipe for creating systems that no one understands.

The companies that will win in the next decade are those who use AI to accelerate understanding while maintaining engineering discipline. They’ll prototype rapidly to learn, then build deliberately to ship. They’ll recognize that the 15-minute fix is a learning tool, not a development methodology.

The companies that will struggle are those who confuse prototyping velocity with engineering capability. They’ll ship fast and accumulate debt faster. They’ll celebrate the CX team member who fixed a bug in 15 minutes while ignoring the senior engineers spending 15 hours cleaning up the mess.

I’ve watched this cycle before. The tools change, but the pattern doesn’t. Technology that promises to eliminate the need to understand what you’re building has never worked at scale, and it won’t this time either.

Build vs. buy isn’t dead. Understanding vs. hoping is the real divide. And hope, as they say, is not a strategy.

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John Mecke is the Managing Director of DevelopmentCorporate LLC, an M&A advisory and strategic consulting firm specializing in early-stage SaaS companies. With over 30 years of enterprise software experience, including executive roles at KnowledgeWare and Sterling Software where he led over $300M in acquisitions, he helps pre-seed and seed-stage CEOs navigate competitive landscapes and exit strategies. He writes regularly about SaaS strategy, AI’s impact on software markets, and lessons from technology history at developmentcorporate.com.