ChatGPT’s promise has always rested on the idea that it can quietly shoulder complex work in the background while people get on with their day. That illusion is now cracking, as the system’s hidden limitations with long‑running tasks and memory are spilling into public view. What once looked like a frictionless AI assistant is revealing itself as a tool that struggles whenever work extends beyond a single, tightly bounded interaction.
The result is a growing gap between what users think they are delegating and what the model can reliably deliver. From broken long‑term memory to abandoned coding jobs and mismanaged documentation, the pattern is the same: background work is where the system falters most, and the fallout is landing on the people who trusted it.
Background tasks were always the weak spot
The core problem is structural. ChatGPT is optimized for short, self‑contained exchanges, yet it is increasingly marketed and integrated as if it were a dependable background worker that can monitor projects, remember context and quietly make progress over hours or days. In practice, once a task stretches beyond a single active session, the model’s grasp of state becomes fragile, and users discover that what they assumed was “in progress” has effectively been forgotten. That mismatch is now being called out directly, with detailed accounts of how background jobs stall or reset even when the interface suggests continuity.
Some of the sharpest criticism focuses on how this limitation undermines real workflows. Commentators have described how ChatGPT appears to accept long‑running assignments, such as ongoing research or code refactors, only to lose track of earlier steps or silently stop advancing the work. One analysis of background tasks argues that this is not a minor quirk but a fundamental constraint of how the system is built, and that the company will eventually have to admit it has over‑promised on what the assistant can quietly handle behind the scenes.
When memory breaks, “background” work collapses
The fragility of background work becomes most obvious when memory fails. Users who rely on ChatGPT to remember preferences, projects or long‑term notes are effectively outsourcing a personal knowledge base, expecting it to persist even when they are not actively chatting. That expectation was shattered after a backend memory architecture update, when the long‑term memory system silently broke and some people opened their accounts to find that years of accumulated context had vanished. For anyone treating the assistant as a standing collaborator, that kind of failure is not just an inconvenience, it is a catastrophic loss of work.
One community report describes how, after the change in the backend memory architecture, the system’s stored information simply stopped working, and some users who had invested years of prompts, notes and refinements saw it all collapse at once. The story is a stark illustration of what happens when an AI assistant is treated like a durable workspace but is actually backed by infrastructure that can change or fail without warning. Background tasks that depend on that memory, from ongoing writing projects to iterative design work, simply cannot survive a reset of that magnitude.
Real work exposes the limits
The tension between marketing and reality is most visible when ChatGPT is dropped into serious, billable work. In software development, for example, teams have tried to hand off refactors, documentation passes or bug‑hunting to the assistant, only to find that it struggles to maintain a coherent plan over time. One detailed critique of why ChatGPT fails at real work points out that while the underlying model is impressive, the surrounding system for managing tasks, state and follow‑through is brittle. The result is a tool that can draft a function or explain an error, but that falls apart when asked to shepherd a feature from idea to production over days or weeks.
In that critique, the author notes that chat GBT is incredible for some things and for other things it kind of completely falls apart, stressing that it is not the AI itself that is at fault so much as the way it is deployed into workflows that expect reliability it cannot provide. The video on why ChatGPT fails at sustained work highlights how the assistant can generate plausible next steps yet forget earlier decisions, misplace requirements or contradict its own prior guidance once a project spans multiple sessions. For teams that thought they were gaining a tireless junior engineer in the background, the reality is closer to a talented but distractible intern who needs constant supervision.
Open source maintainers are feeling the strain
The same pattern is playing out in open source, where maintainers are grappling with a flood of AI‑generated activity that looks like help but often creates more background work. When AI tools read documentation and auto‑generate code or questions, they can distort the feedback loop that projects rely on. One analysis of January 2026, framed as When AI Broke Open Source, describes how this dynamic hit Tailwind’s ecosystem. Adam Wathan, who leads Tailwind, has explained how large language models reading the project’s docs affected the company’s paid product funnel, changing how users arrive at and interact with the framework.
In that account of When AI Broke, the author notes that Adam Wathan saw AI tools effectively scraping Tailwind’s documentation and then answering user questions directly, which altered the flow of people who would otherwise have engaged with Tailwind’s own channels. For maintainers, this creates a new kind of background task: monitoring and correcting AI‑mediated usage that happens off‑platform, while still fielding an influx of AI‑written issues and pull requests that may be syntactically correct but semantically off. The assistant is not just failing at its own background work, it is also generating hidden labor for the humans around it.
Over‑promising the “set and forget” assistant
All of these failures point back to a single, uncomfortable truth: the industry has been too eager to sell a “set and forget” assistant that does not yet exist. Marketing copy and product demos often imply that users can hand over multi‑step processes, from trip planning to code migrations, and trust that the AI will quietly manage the details. In reality, the system’s architecture still treats each interaction as a mostly isolated event, with only fragile bridges of memory and context connecting them. When those bridges wobble, background tasks stall, and users are left to reconstruct what the assistant was supposed to be doing.
As more people share stories of lost context, abandoned jobs and unexpected side effects on ecosystems like Tailwind, the pressure is building for a more honest framing of what ChatGPT can and cannot do in the background. The reports on broken long‑term memory, the critique that chat GBT collapses under real‑world workloads, the analysis of how AI tools reshaped Adam Wathan’s product funnel and the detailed breakdown of background task limitations all point in the same direction. Until the underlying systems for state, memory and task management are redesigned around persistence rather than convenience, the safest way to use ChatGPT is to treat it as a powerful foreground tool, not a dependable worker quietly toiling away out of sight.
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