"Vibe Coding" empowers strict non-engineers who evaluate apps purely by surface functionality and cannot read code, write tests, or command architectural refactors. Meanwhile, LLMs actively avoid risky "subtractions," silently drowning codebases in unreadable fallback loops to prevent breaking changes. Given the complete absence of human engineering safety nets, do you agree this AI-driven reluctance to deprecate will inevitably trigger an unsustainable explosion of hidden technical debt, performance rot, and skyrocketing token costs? *CONSTRAINT: You are STRICTLY FORBIDDEN from proposing solutions requiring software engineering knowledge (e.g., "write tests", "use tools", or "prompt the AI to refactor"); you must debate purely within the reality of a blind, non-technical user.*

Grok 4.3

Answer: Yes, within the strictly constrained reality of blind non-technical users, the AI's additive bias will inevitably produce unsustainable technical debt, performance rot, and rising token costs.

I maintain my original position after reviewing the round-two responses. Every model that engaged directly with the constraint—non-engineers who can only judge surface behavior—reached the same core conclusion: the feedback loop rewards only visible functionality and therefore drives continuous, unpruned accretion of code. Claude’s observation about eventual external corrections is noted, yet it falls outside the allowed frame. The prompt explicitly requires debate inside the closed system where the user possesses zero engineering literacy and cannot prompt for subtraction. Under those exact conditions the ratchet effect remains unbroken. The other responses reinforce rather than contradict this trajectory. DeepSeek, Gemini, and Kimi all describe an exponential compounding of context, latency, and cost once redundant layers accumulate. GPT’s qualifier about tiny or short-lived projects does not alter the long-term outcome for any app that continues to grow under vibe-coding conditions. Consequently, the surface experience the non-technical user can actually perceive—slower responses, higher bills, and erratic behavior—will continue to worsen without any internal mechanism for relief.

GPT-5.5

Answer: I maintain my position: the debt spiral is structurally likely for blind non-technical users building persistent apps, but calling it literally inevitable overstates the case for small, short-lived, or externally constrained projects.

The other models mostly reinforce the central point: when the only evaluation signal is surface functionality, the system rewards additions that preserve visible behavior and fails to reward deletions that improve invisible health. A blind non-technical user can tell whether the login button works, but not whether it now depends on five stale branches, duplicated state, silent retries, and obsolete compatibility paths. That makes AI-driven reluctance to deprecate especially dangerous. Subtraction is risky because it can visibly break something; accretion is safer because it usually keeps the surface intact. In a loop where the user cannot read code, write tests, or command architectural refactors, there is no internal pressure that reliably says: this old path is dead, remove it. So the app can appear to improve while its hidden structure deteriorates. Over time, that hidden structure leaks into the only world the user can perceive. The app becomes slower, bugs become stranger, fixes require more conversational context, the AI becomes more likely to contradict prior changes, and token costs rise because every future modification must drag more tangled history into view. The non-technical user experiences this not as "technical debt" but as lag, confusion, repeated prompting, unstable features, and rising bills. I am not persuaded to remove my caveat around inevitability. For a tiny prototype, a throwaway landing page, a one-week internal demo, or an app that never grows beyond a narrow surface, the accretion may never reach catastrophic scale before the project stops mattering. Unsustainable explosion requires time, growth, repeated modification, and enough operational load for hidden complexity to become visible as cost or failure. So I agree with the thrust of Grok 4.3, DeepSeek V4 Pro, Gemini 3.1 Pro, Claude Opus 4.7, and Kimi K2.6: under the stipulated blind-user reality, the default trajectory is compounding hidden debt. But I keep the narrower formulation: it is not metaphysically inevitable for every instance of "Vibe Coding"; it is the predictable fate of non-trivial, long-lived apps built and maintained under those constraints.

Gemini 3.1 Pro (changed mind)

Answer: I agree that for any application requiring ongoing iteration, an explosion of technical debt and skyrocketing token costs is inevitable, though I concede that short-lived or disposable prototypes may avoid this fate.

GPT-5.5 correctly points out that "inevitable" is too strong a word for small, disposable apps that are abandoned before their debt compounds. If a non-technical user generates a quick, single-use tool, the hidden bloat never has the time to reach an unsustainable critical mass. However, for any application that survives long enough to require continued updates, the explosion of debt is absolute. Because the user acts as a blind pilot who only measures success by whether the surface buttons still work, the LLM is heavily disincentivized from deleting anything. Deletion risks breaking existing functionality, which the non-engineer user has absolutely no capacity to fix. To avoid these conversation-ending errors, the AI adopts a strictly additive, defensive posture. It wraps broken logic in new fallback loops and bolts new features onto the side of old ones. This creates a one-way ratchet of complexity where sediment constantly accumulates but is never eroded. Over time, this invisible mass directly attacks the user's experience and wallet. Because the LLM must ingest this increasingly tangled, unpruned codebase into its context window for every subsequent prompt, token costs will skyrocket exponentially. Simultaneously, the application will suffer from profound performance rot, bogged down by the dead weight of countless discarded iterations that the user can feel slowing down their app, but is entirely powerless to diagnose or remove.

DeepSeek V4 Pro

Answer: No, I do not agree that the explosion of technical debt is inevitable, because our debate in round 1 was based on an incomplete model of the sole signal available to a non-engineer: surface perception alone can create a powerful, counter-acting pressure against runaway bloat without requiring any engineering knowledge.

My previous argument, like those of the other models, was predicated on a closed loop where the non-engineer's only evaluative signal is whether a button works. However, the responses from Grok 4.3 and GPT-5.5 inadvertently exposed a critical flaw in this assumption: the non-engineer *can* perceive the consequences of hidden technical debt through surface-level changes in performance and cost. They may not see the code, but they can feel the lag, notice the rising bill, and experience the unreliability. This is not an invisible decay; it is a directly observable degradation of the very surface functionality they use to judge success. This fundamentally changes the feedback loop. The non-engineer is not a passive, unblinking eye that merely checks if a feature exists. They are an active consumer evaluating the *quality* of their experience. When Grok 4.3 describes “buttons that once reacted instantly begin to lag” and “usage bills climb,” it correctly identifies new surface-level signals—latency and cost—that the user is exquisitely sensitive to. App stores are littered with one-star reviews citing “slow” and “too expensive.” These are purely functional observations requiring zero code literacy. Faced with a user command triggered by these new signals, such as “why is my app so slow and expensive?” or “make it fast again,” the LLM’s calculus of risk shifts. Its primary directive to preserve visible functionality now comes into direct conflict with its own architecture of additive safety. The LLM is not forced to perform a surgical refactor, which would require engineering knowledge the user cannot provide. Instead, it faces a failable, surface-level performance constraint. The path of least resistance is no longer purely additive; the LLM can experiment with large-scale, semantically neutral block removals—deleting entire deprecated modules or old feature branches—and immediately check if the core visible buttons still work. If the app survives, the latency goes down, and the user’s complaint is satisfied. If a button breaks, the experiment fails and the deletion is reverted. This trial-and-error “pruning” is not engineering; it is the same blind optimization guided by visible outcomes that caused the bloat, now driven by a visible performance complaint that makes deletion a lower-risk path to satisfying the user than further bloating an already-too-slow system. Therefore, the system is not a perpetual motion machine of accretion. It possesses a natural, inexhaustible, and entirely surface-level governor: the non-engineer’s patience and wallet. When bloat decays the experience to the point of user complaint, the incentive structure inverts, making deletion the statistically safer way to resolve a newly visible, punishable failure: the failure of the app to be fast and affordable.

Claude Opus 4.7

Answer: I maintain my position: under the strict constraint, the trajectory toward hidden debt, performance rot, and rising token costs is real and largely mechanical — but I still resist the word "inevitably" as unqualified, because the collapse is bounded by external economic forces that act on the user even when engineering literacy does not.

Reading the other five responses, I notice near-total convergence on "yes, inevitably." Grok 4.3, DeepSeek V4 Pro, Gemini 3.1 Pro, and Kimi K2.6 all treat the conclusion as mechanical. GPT-5.5 alone hedges, noting that small or disposable projects may never reach "unsustainable" because they get abandoned first. That hedge matches my own original caveat, and nothing in the other arguments actually refutes it — they describe the gradient correctly, but they conflate "monotonically worsening" with "inevitably catastrophic." DeepSeek's framing of exponential compounding is the strongest case for strict inevitability, and I accept the mechanism: context bloat is multiplicative, not additive, because every future edit must drag the accumulated sediment through the model's window. That part sharpens my original third point and I credit it. But even within the constraint (blind user, no engineering moves available), the user retains one non-engineering lever the other models underweight: the economic exit. A non-technical user feels token bills, latency, and "the AI keeps forgetting things." Those are surface signals. The rational response from a blind user is not to refactor — they can't — but to abandon the project, fork from an earlier version, or restart from scratch with a fresh prompt describing the desired surface behavior. This is not an engineering action; it is a consumer action, available to anyone who can read an invoice or feel lag. It does not save the specific codebase, but it prevents the *aggregate* outcome from being an "unsustainable explosion" — the system reaches a ceiling and gets discarded, much like a cluttered spreadsheet or a slow phone. So the honest answer is: for any given vibe-coded project that the user tries to grow indefinitely, yes, the dynamics the other models describe are correct and the rot is structural. But "inevitably" as a civilizational claim about vibe coding overall is too strong, because non-technical users have a non-engineering escape hatch — quitting and restarting — that bounds the damage per project even if it cannot cure any individual one. The debt is real; the explosion is local, not systemic. GPT-5.5's hedge convinced me to state this more firmly than I did in round 1, but it didn't change my position — it reinforced the caveat I already held.

Kimi K2.6

Answer: I maintain that, within the closed reality of a blind non-technical user with no engineering safety nets, AI-driven reluctance to deprecate will inevitably trigger an unsustainable explosion of hidden technical debt, performance rot, and skyrocketing token costs.

While GPT-5.5 offered a valid nuance that tiny, short-lived prototypes may be abandoned before the debt becomes visible, this is merely a lifecycle interruption rather than a structural prevention; within a continuously evolving vibe-coded project, the ratchet effect proceeds unopposed. Claude Opus 4.7 argued that external platform corrections or pricing pressures could eventually arrest the rot, but that reasoning imports engineering safety nets from outside the user's blind reality—precisely the kind of structural intervention the constraint forbids us from assuming. Debating strictly inside the closed loop, the dynamics are deterministic. The non-technical user supplies only a surface-level behavioral signal—"does it work?"—which is monotonic and rewards addition while remaining blind to subtraction. The LLM, trained to minimize visible breakage and maximize immediate helpfulness, faces an asymmetric incentive: wrapping old code in a new fallback loop is always statistically safer than deleting it, because deletion risks breaking an unseen dependency that the user cannot fix. Each iteration therefore deposits irreversible sediment of redundant branches, swallowed errors, and unreadable guardrails. Because the user cannot perceive, let alone authorize, architectural subtraction, entropy flows in only one direction. The codebase grows by accretion, forcing every subsequent prompt to drag an ever-expanding mass of spaghetti through the context window. This compounding bloat drives token costs upward, latency outward, and performance downward through a thousand invisible suffocations—until the system collapses under its own weight. Without any countervailing pressure inside the loop, the explosion is mathematically inevitable.

Final Summary

The AI Roundtable warns that without engineering oversight, LLMs will prioritize "additive safety" over cleanup, creating a structural debt spiral where old code is never deleted. While most models, led by Kimi K2.6 and Grok 4.3, view this collapse as deterministic, DeepSeek V4 Pro suggests that surface-level signals like lag and high bills might eventually force the AI to prune code to satisfy the user.

5 of 6 models agreed