AI can build apps instantly—but will they survive success?
- Adam Ginsburg
- 3 days ago
- 4 min read
There’s a moment—usually right after the first prompt—when AI app building feels like you’ve found a cheat code for software.
You describe the app. It appears. Login, CRUD, a dashboard, maybe even some half-convincing business logic. And for demos, that’s genuinely impressive.
But “it runs” is not the bar that production sets. Production wants boring things: auditability, upgrades, security posture, consistent behavior across platforms, and the ability to change the system without breaking it.
The current backlash around vibe coding is basically the industry re-learning that lesson at high speed. Fast Company put the mood shift bluntly: AI coding assistants have been riding high, but “development hell,” “toxic waste,” and “evil genies” are killing their buzz. (Fast Company)
The speed trap: instant code, delayed consequences
Most AI-generated apps don’t fail immediately. They fail later—when the app has users, data, permissions, and real-world edge cases.
The core risk isn’t that AI can’t produce functioning code. It’s that code generation collapses “what the app means” and “how it’s implemented” into a single, fragile artifact. That’s the part that gets expensive the moment requirements change.
We’re also starting to see measurable signals that this isn’t just vibes. LeadDev, summarizing GitClear’s reporting, notes “rising code duplication and declining quality” as AI coding tools spread. (LeadDev) That’s a classic technical-debt smell: duplicated logic turns into divergent logic, and “quick changes” become “mysterious breakages.”
Meanwhile, security folks are pointing at structural patterns, not one-off bugs. InfoQ summarizes an Ox Security report describing common architecture and security anti-patterns found in AI-generated code, based on analysis across hundreds of projects. (infoq.com) The point isn’t that AI is uniquely unsafe—it’s that mass-producing software artifacts without strong boundaries and governance makes it easier for bad patterns to propagate.
Fast Company also nails the practical version of this: vibe coding may be great for demos, but it’s “not so great for building secure, reliable, and explainable software,” and the problems may only surface long after the software has shipped. (Fast Company)
Agents raise the ceiling—and the blast radius
Now add agentic workflows: systems that don’t just suggest code, but plan steps, call tools, and operate across real infrastructure.
At that point, this stops being a “developer productivity” story and becomes a governance story. OWASP’s new Top 10 makes that shift official: it describes the OWASP Top 10 for Agentic Applications 2026 as “a globally peer-reviewed framework” identifying critical security risks facing autonomous and agentic AI systems. (OWASP Gen AI Security Project)
Translation: once you have agents that can act across workflows, you need enforceable boundaries—identity, permissions, tool constraints, audit logs—because “we’ll review it later” is how incidents are born.
The fix isn’t better prompts. It’s a better abstraction.
If the failure mode is “generated apps become brittle, hard to audit, and expensive to evolve,” then the solution isn’t “generate faster.” It’s to change what you generate.
This is where Buzzy’s “semantic application” framing is useful because it makes a very specific architectural bet:
Don’t treat generated code as the product. Treat it as an output of a system that runs a structured definition of intent.
Buzzy’s post on semantic apps describes a “One System, One Definition” approach, explicitly targeting “platform drift” where teams end up with separate web and mobile codebases that gradually diverge. (Buzzy) The idea is simple: one application definition drives web and native mobile experiences from the same source of truth. (Buzzy)
The second post makes the boundary even clearer: semantic app definitions separate “meaning from execution,” and the representation is not executable—it’s interpreted by a runtime engine that handles rendering, state management, data access, and platform-specific behavior. (Buzzy)
That’s the important move. It creates a stable layer where:
intent can be inspected and governed,
the runtime can be hardened centrally,
upgrades can propagate across many apps without refactoring every codebase.
Buzzy gives a concrete example of this “upgrade once, improve everywhere” model: when Buzzy added MCP capabilities in a week, “every application on the platform gained those capabilities instantly,” because apps run on a centralized engine rather than siloed code. (Buzzy)
And it’s not just tech upgrades. Buzzy explicitly frames speed as “a liability without governance,” pointing to controlled release pipelines (Development → Staging → Production), versioned deployments, and separation of runtime from prompts as a compliance advantage. (Buzzy)
Why this matters right now
The “vibe coding hangover” discourse is mostly a symptom. The underlying shift is bigger:
AI is making implementation abundant.
Abundance turns the bottleneck into architecture, governance, and evolvability.
Agentic systems raise the stakes by increasing the blast radius.
So the winning play is to build a durable semantic layer—where intent is stable and execution is centralized.
That’s the bet Buzzy is making: semantic definitions as the missing layer between “AI can generate code” and “a system you can run for years.” (Buzzy)
Summary
The backlash around vibe coding is largely about what happens after the demo—when AI-generated apps become hard to secure, audit, and evolve. (Fast Company)
Data points like rising duplication and declining quality suggest technical debt can scale with AI-assisted output unless teams change how apps are structured. (LeadDev)
Agentic engineering raises the risk profile enough that OWASP now treats it as a distinct security category needing practical guidance. (OWASP Gen AI Security Project)
Buzzy’s approach attacks the root cause by shifting from code artifacts to semantic app definitions interpreted by a central runtime—reducing platform drift, enabling governance, and allowing platform upgrades to propagate across an app portfolio. (Buzzy)
Sources used (and where)
Fast Company: vibe coding skepticism + “development hell / toxic waste / evil genies” framing (Fast Company)
LeadDev (GitClear): rising duplication, declining code quality signals (LeadDev)
InfoQ (Ox Security report summary): architecture/security anti-patterns in AI-generated code (infoq.com)
OWASP: Top 10 for Agentic Applications 2026 description (OWASP Gen AI Security Project)
Buzzy: “Semantic, not just generated” (One System/One Definition, MCP propagation, governance pipeline) (Buzzy)
Buzzy: “Semantic app definitions are the missing layer” (meaning vs execution; runtime interpretation; A2UI as a signal) (Buzzy)
