{"slug": "technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens", "title": "Technical Debt Didn’t Disappear. We Just Started Paying for It in Tokens.", "summary": "A developer argues that large language models and AI agents do not eliminate technical debt but merely shift its cost from engineering headcount to token consumption. As systems grow complex, architectural entropy causes the cost of each new feature to increase exponentially, whether measured in salaries or compute tokens.", "body_md": "One of the most common claims about LLMs and AI agents is that they'll dramatically reduce the cost of software development. Instead of hiring more engineers, you can simply assign a task to a model and have it completed in minutes.\n\nI think there's a lot of truth to that.\n\nBut there's one important caveat.\n\nFor years, the growth pattern of many large software projects looked something like this:\n\nTechnical debt → slower development → larger engineering teams → more communication overhead → more regressions → even more technical debt.\n\nAs systems became more complex, companies hired more engineers. But every additional team increased coordination costs, introduced more communication paths, and gradually slowed development.\n\nTechnical debt never disappeared.\n\nCompanies simply paid for it with more people.\n\nLLMs don't eliminate the problem.\n\nThey change the price of one of the resources.\n\nInstead of hiring five more engineers, a company can now \"hire\" thousands of times more compute.\n\nWhen a model struggles, we increase the context window, add more agent loops, switch to a larger model, or simply regenerate the solution multiple times.\n\nThis can preserve development velocity – for a while.\n\nBut the underlying problem remains.\n\n**LLMs don't reduce system complexity.**\n\nI'd put it this way:\n\nWe used to pay technical debt with people. Now we can pay for it with tokens. The debt hasn't gone away – it has simply changed currency.\n\nSeveral factors push us toward the same outcome.\n\nAs projects grow, models need more files, dependencies, business rules, and historical context to make correct changes.\n\nEventually, the cost of solving a task isn't driven by the task itself.\n\nIt's driven by the cost of understanding the system.\n\nLLMs perform remarkably well in clean, well-structured codebases. But do many enterprise systems really meet these standards?\n\nWhen a project is full of massive services, circular dependencies, hidden side effects, and years of architectural compromises, the model has to spend far more computation simply figuring out what's happening.\n\nAt the same time, the probability of mistakes increases.\n\nEven if code is generated in seconds, it still has to be reviewed, tested, and safely integrated. If the architecture is poor, the cost of verification starts growing faster than the speed of generation.\n\nThat's why generating code faster doesn't automatically mean shipping software faster.\n\nIf a system lacks modularity and strong test coverage, AI can introduce regressions even faster than humans.\n\nThe reason is simple. It produces much more code per unit of time.\n\nThe more changes you make to a complex system, the greater the chance that something breaks.\n\nThis is where the AI era differs from previous generations.\n\nBefore, companies were constrained by hiring budgets and the number of available engineers.\n\nToday, the constraint is less visible.\n\nInstead of expanding the team, organizations gradually increase spending on inference, larger context windows, agent loops, repeated generations, automated testing, and additional validation.\n\nFrom the outside, productivity appears to remain high. In reality, the cost of each additional feature quietly increases.\n\nThe problem hasn't disappeared. It's simply better disguised.\n\nThe real scarcity in the AI era\n\nThe most interesting conclusion has little to do with token costs.\n\nIt's **architectural entropy.**\n\nLLMs scale code production incredibly well.\n\nThey do very little to reduce the complexity of the system itself.\n\nAnd once a system becomes too complex for humans to understand, it gradually becomes too complex for AI as well.\n\nThat's when the cost of every new change starts growing – not linearly, but exponentially.\n\nAt that point, it doesn't matter whether you're measuring costs in developer salaries or millions of burned tokens.\n\nI'd summarize it like this:\n\nAny technical debt that once required linear growth in engineering headcount will now first require linear growth in token consumption – and eventually lead to the same outcome: an exponential increase in the cost of change. The only difference is that the limiting factor shifts from the number of engineers to the amount of compute required to understand the system.\n\nIf this hypothesis is correct, then the biggest competitive advantage in the AI era won't be generating code faster.\n\nIt will be maintaining an architecture that remains simple enough for both humans and AI to understand.\n\nThat, for MHO more than raw coding speed, may become the true bottleneck of software development over the next decade.", "url": "https://wpnews.pro/news/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens", "canonical_source": "https://dev.to/samuel_akopyan_e902195a96/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens-22ml", "published_at": "2026-07-21 15:08:18+00:00", "updated_at": "2026-07-21 15:22:53.402710+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "developer-tools"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens", "markdown": "https://wpnews.pro/news/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens.md", "text": "https://wpnews.pro/news/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens.txt", "jsonld": "https://wpnews.pro/news/technical-debt-didnt-disappear-we-just-started-paying-for-it-in-tokens.jsonld"}}