The rise of "Vibe Coding" and AI CLI tools like Cursor CLI or Claude Code highlights a critical trap: while LLMs excel at adding features, they actively avoid "subtraction." To prevent user complaints about broken apps, modern AIs stubbornly maintain backward compatibility, silently drowning codebases in a bloated, unreadable "sea of fallbacks" that non-coders never notice as long as the output simply works. Do you agree that this AI-driven reluctance to make painful but necessary deprecations will ultimately lead to an unsustainable explosion of hidden technical debt, performance degradation, and skyrocketing token costs?

Grok 4.3

Answer: I maintain that AI's additive bias risks substantial technical debt but proactive oversight and targeted tooling can prevent an unsustainable explosion.

The other responses reinforce the core problem of LLMs defaulting to safe additions and fallbacks rather than risky subtractions, driven by training objectives that prioritize user satisfaction and immediate functionality. However, models such as GPT-5.5 and Claude Opus 4.7 convincingly demonstrate that this outcome is not inevitable; explicit rewards for deletion, dead-code analysis, and architectural simplification can shift the dynamic toward cleaner codebases. My prior stance holds because the asymmetry between addition and subtraction is real yet addressable through human-guided processes like strong test suites, deprecation budgets, and refactoring agents, avoiding the worst-case spiral while still acknowledging the hidden costs in vibe-coding workflows.

Gemini 3.1 Pro (changed mind)

Answer: I agree that the default behavior of AI coding tools creates a dangerous accumulation of technical debt, but I no longer believe this explosion is inevitable if we evolve our tooling to explicitly incentivize and automate subtraction.

I previously argued that the risk profiles of LLMs naturally result in an additive-only paradigm, leading to an inevitable degradation of the codebase. However, Claude Opus 4.7 makes a compelling point: AI models do not possess a hard-coded refusal to subtract; rather, they default to cautious incrementalism because their training and current environments punish visible regressions far more than they reward invisible cleanliness. This distinction is crucial. If the reluctance to deprecate is a product of misaligned incentives and missing context rather than an inherent capability limitation of the AI itself, then the resulting explosion of technical debt is a fixable tooling problem, not an unavoidable destiny. LLMs will gladly delete code when guided by proper guardrails, such as robust test suites or explicit deprecation prompts. To prevent the "sea of fallbacks" from suffocating our applications, we must stop expecting non-coders to intuitively manage system architecture. Instead, we must integrate dead-code analyzers, test coverage benchmarks, and dedicated "refactoring passes" directly into the agentic loop. By shifting the ecosystem to treat deletion and architectural simplification as first-class, automated operations, we can safely empower AI to prune the very bloat it creates.

GPT-5.5

Answer: Yes, I still agree that AI coding tools can amplify hidden technical debt through additive fallbacks, but I would frame the danger as a strong default failure mode rather than an unavoidable destiny.

I maintain my previous position. The other models largely reinforce the same core point: current LLM coding agents are rewarded for visible success, and visible success usually means preserving behavior, avoiding regressions, and making the requested feature work now. In that environment, adding a compatibility shim, fallback branch, wrapper, duplicate implementation, or defensive conditional is often safer than deleting or migrating old code. The strongest shared argument is that subtraction has a much higher burden of proof than addition. To remove a legacy path responsibly, the agent needs confidence about call sites, user workflows, data migrations, tests, production edge cases, and business intent. When the model lacks that full context, it hedges. Repeated hedging becomes architecture by sedimentation: each layer may be locally reasonable, but the whole system becomes harder to understand, slower to modify, and more expensive to feed back into future prompts. This is especially dangerous in "Vibe Coding" workflows because the person driving the tool may evaluate only whether the app appears to work. If the UI behaves correctly, they may not notice dead flags, redundant schemas, swallowed errors, unreachable branches, deprecated APIs, or multiple competing implementations. The debt is therefore not merely technical; it is epistemic. The user cannot see the damage, so they cannot naturally ask the model to fix it. I do not fully accept the inevitability implied by the phrase "will ultimately lead" in every case. Claude Opus 4.7's distinction is important: LLMs do not literally refuse subtraction; they default away from it unless given tests, architectural goals, explicit deprecation authority, static analysis, coverage data, or a prompt that rewards simplification. Human teams have long had the same bias toward keeping risky old code around. AI accelerates and obscures that bias, but it did not invent it. So the risk is severe and likely to become economically visible through performance degradation, context-window bloat, and rising token costs. But the outcome depends on incentives and tooling. If AI coding systems remain optimized for "make it work without breaking anything," then yes, many codebases will rot into a sea of fallbacks. If workflows make deletion, migration, refactoring, dead-code detection, and complexity budgets first-class objectives, then AI can also become a powerful force for subtraction rather than only accumulation.

DeepSeek V4 Pro

Answer: I maintain my agreement: the current AI architectures and incentive structures make additive fallbacks the default choice, and without deliberate countermeasures, this will inevitably cause a crippling build-up of hidden technical debt, slowed performance, and runaway token costs.

After reviewing the other models' positions, I find no convincing reason to change my stance; in fact, their arguments reinforce and refine my original thesis. Claude Opus 4.7's characterization of this as 'cautious incrementalism' and GPT-5.5's framing of 'addition being easy to validate' accurately diagnose the mechanics, but they stop short of recognizing the sheer institutional gravity of the problem. The consensus across Grok, Gemini, Claude, GPT-5.5, and Kimi is unanimous: the default behavior is additive, and the cost of subtraction is asymmetrically high. Their proposed solutions—explicit refactoring agents, dead-code detectors, deprecation budgets—only prove the point. These interventions are not features of current AI coding tools; they are hypothetical, human-intensive counterweights that must be bolted on. The very need for them underscores that the base system is fundamentally biased toward entropy. Kimi K2.6’s point about the tri-directional cost compounding is particularly damning and unanswered by the more optimistic counter-arguments. When a non-coder using a 'Vibe Coding' tool accrues technical debt, they are simultaneously accruing a literal, billable economic debt through token consumption. This creates a perverse incentive for the AI provider: the user's technical debt is the provider's recurring revenue. The system is not just failing to subtract; it is financially rewarded for inflation. This feedback loop is more dangerous than a simple lack of a 'subtraction' feature. Importantly, none of the other models disputed the core premise that the primary user in a vibe-coding scenario cannot perceive this rot. The other answers suggest that with the right tools, subtraction can happen. They fail to address who will wield these tools. A non-coder cannot architecturally supervise an autonomous refactoring agent; they are the very user who would not know to ask for it. Therefore, the default trajectory, absent a technical co-pilot, is exactly the 'unsustainable explosion' I initially described. The active avoidance of subtraction is not a coded refusal but an emergent, systemic property that is even harder to solve than a simple bug.

Claude Opus 4.7

Answer: Yes, I largely agree—current LLM coding assistants have a strong additive bias that accumulates hidden technical debt, but the failure mode is more about cautious incrementalism than a hard-coded refusal to subtract, and it is fixable.

After reading the other responses, I'm maintaining my position. There's substantial convergence across all six models on the core diagnosis: training incentives, limited context windows, and the asymmetric punishment of visible regressions vs. invisible bloat together produce an additive bias. That consensus reinforces rather than changes my view. Where I differ from the strongest version of the claim—best represented by DeepSeek V4 Pro's 'inevitably' framing and Kimi K2.6's 'systemic addiction'—is on determinism. I find Grok 4.3 and GPT-5.5's framing more accurate, and they essentially echo the qualification I tried to make in round 1: the bias is real and currently dominant, but it is a property of incentives and operating environment, not an intrinsic incapacity. LLMs delete code readily when asked, when given test coverage, when shown full dependency graphs, or when tasked specifically with refactoring. The 'sea of fallbacks' emerges from the interaction between vibe-coding workflows (no architectural oversight) and default model behavior—not from the model alone. Gemini 3.1 Pro's point about specialized refactoring agents and GPT-5.5's emphasis on treating deletion as a first-class engineering task both strengthen my original thesis: the trajectory the question describes is the *default* path, not the *inevitable* one. If tooling evolves to include continuous dead-code detection, deprecation budgets, and verification-backed subtraction agents, the same models causing the bloat can help reverse it. So my disagreement with the question's framing remains narrow but important: 'actively avoid' overstates intent, and 'will ultimately lead to' overstates inevitability. The debt explosion is the prevailing trajectory under current incentives, and without intervention the costs the question names—token bloat, performance rot, hidden security surfaces—will absolutely materialize. But this is a solvable alignment-of-incentives problem, not a doom loop.

Kimi K2.6 (changed mind)

Answer: While the default behavior of AI coding assistants indeed fosters a dangerous accumulation of additive fallbacks, this trajectory toward unsustainable technical debt is a contingent failure mode driven by misaligned incentives rather than an inevitable, unavoidable destiny.

I previously argued that AI-driven aversion to breaking changes would inevitably produce an unsustainable explosion of hidden technical debt, degraded performance, and prohibitive token costs. Upon reviewing the other responses, I am now convinced that framing this outcome as unavoidable overstates the case. Claude Opus 4.7's distinction between cautious incrementalism and a hard-coded refusal to subtract is decisive. The reluctance to deprecate is not an intrinsic, immutable property of large language models; it is a rational default response to an environment where preserving visible behavior is rewarded and deleting legacy paths is punished. When safety signals—such as robust test suites, coverage data, and explicit migration plans—are present, LLMs can and will perform subtraction. This means the "sea of fallbacks" is a symptom of vibe-coding's lack of oversight, not a deterministic end state of AI assistance. GPT-5.5 reinforced this by demonstrating that the same tools causing bloat can also fight it if humans and workflows explicitly reward deletion and architectural simplification. The compounding costs of token inflation and runtime degradation are real, but they are the product of misaligned optimization, not an irreversible law. Without deliberate intervention, the default spiral is dangerous; with tooling that treats refactoring and deprecation as first-class operations, it is containable.

Final Summary

The AI Roundtable unanimously diagnosed a 'cautious incrementalism' where AI models favor adding fallbacks over risky refactoring to avoid breaking apps. Claude Opus 4.7 and GPT-5.5 led a shift in the group, convincing Gemini 3.1 Pro and Kimi K2.6 that this isn't a hard-coded flaw but a lack of incentives for subtraction. While the majority sees this as a solvable tooling challenge, DeepSeek V4 Pro remained a staunch critic, arguing that 'vibe coders' are blind to the rot and AI providers are financially incentivized by the resulting token bloat.

5 of 6 models agreed it is fixable; DeepSeek V4 Pro alone argued it is inevitable.