Why Generative AI Forgets, Drifts, and Sometimes Lies With Confidence

TL;DR;

Why Your Assistant Changed and No One Told You

You sit down to pick up an old project with your AI assistant. Same prompt, same goal—but the assistant behaves differently. It forgets a pattern it once used. It shifts tone. It reorganizes your carefully designed structure. And worst of all? It does it confidently. This isn’t just model drift: you and your assistant are out of sync.

Just as Word silently changes formatting or a CRM displays incorrect numbers due to delayed warehouse updates, AI bewilders us because we expected it to behave like traditional software. But generative AI isn't software—it's something entirely different. Unless we begin to understand and monitor these shifts, it will continue to catch us off guard.

It’s not fair to expect everyday users to track these shifts ourselves—but right now, no one else is doing it either. So until this is fixed at the design level, awareness is our first line of defense.

The Illusion of Stability

Most people assume AI should behave like traditional software: consistent, predictable, and always running the same code. In software, we call this being stateless—it means each time you run it, the result depends on the input you give it, not what happened before.

But memory-enabled generative models like ChatGPT aren’t stateless. They’re more like collaborators who can remember patterns, preferences, and previous conversations—and sometimes, that memory bleeds into new work in unexpected ways.

Worse still, there is no way to tell what is happening. There’s no "memory last updated" timestamp, no changelog, no version tag. That leaves users guessing—not because the system is trying to mislead, but because the interface doesn’t reflect what’s happening.

Memory Isn’t Instant—And It Feels Like a Bad Data Refresh

OpenAI quietly notes that memory updates "may not happen immediately." Almost all of us missed this crucial statement, but those familiar with enterprise systems understand its implications. It's similar to saving changes in your CRM, running a report, and seeing outdated data—simply because the data warehouse hasn't refreshed yet. You haven't made any mistakes. The system just lags behind reality.

Same thing happens here. You tell your assistant “remember this,” and then immediately ask a follow-up question that assumes the update landed. Sometimes it works. Sometimes it doesn’t. And unless you know to wait—or reframe—you won’t catch the drift. This isn’t a hallucination. It’s a timing-based state mismatch, and it’s happening behind the curtain.

The AI Race Condition

This is a classic race condition: the memory update and prompt execution happen too close together. If the assistant reads its memory before the update finishes, you'll get an inconsistent response.

Here's an example: you tell the assistant "Remember I live in Chicago." Then you immediately ask for restaurant recommendations. If the memory hasn't fully updated yet, the AI might suggest places in New York or wherever it previously thought you lived. From your perspective, it seems like the model ignored your instruction. In reality, the system simply raced against itself—and lost.

Temporal Context Drift

Here's where things get more human. Imagine you're working on Project X. Then you spend two days on Projects A through D. When you return to X, you expect the assistant to behave like before—but it doesn't. While its memory remains the same, its learned behavior has shifted. Recent interactions have shaped its tone, assumptions, and formatting preferences.

This phenomenon is called Temporal Context Drift. It's not a memory storage failure—it's the result of contextual entanglement between sessions. Sometimes this works in your favor. Perhaps Projects A through D introduced new policy decisions or formatting standards that should apply to X. The assistant, like any human, wouldn't know to maintain old patterns unless explicitly told. But when these context shifts happen unintentionally—and catch users off guard—they create inconsistency.

Most users remain unaware of this behavior, and even fewer understand how to manage it.

Why This Is So Dangerous

Because the model doesn’t warn you. It doesn’t say, “Hey, I’ve picked up new habit recently.” It doesn’t offer a diff between your current context and the last time you used that prompt. It just acts.

And because the assistant responds with confidence, we trust it. We use its outputs in real work: code, publications, sales copy, contracts. When that output diverges from what we expected—without any visible cause—we don’t just lose accuracy. We lose trust.

This Isn’t Just an AI Problem—Humans Do It Too

When work is delegated across multiple people and revisited later, similar issues arise. Context gets lost, assumptions change, and formatting becomes inconsistent. Like when one intern takes over from another—breaking the established patterns.

The frustrating part? We readily accept this behavior from humans but expect AI to function like rule-based software. Instead, AI acts more like a disorganized team member who doesn't keep a shared schedule.

What We Need

Field Guide for Humans Working with Memory-Enabled AI

• 🧭 Reintroduce your project — Don’t assume your assistant remembers how things worked last week.
• 🕒 Give memory time to settle — Memory updates are not always instant.
• 🧠 Ask it what it remembers — Seriously. This often reveals drift.
• 🧪 Test your prompts regularly — Especially before using AI output in real work.
• 🧍 Treat AI like a flexible intern — Smart, helpful, but not omniscient.

This is a two-way street: AI systems need better tools to help humans understand when and why drift occurs, while humans must accept that flexibility inherently brings ambiguity. If we want assistants that evolve alongside us, we need clear ways to navigate that evolution.

Here’s what would help:

• Memory changelogs (even if humans won’t always love them)
• Version-controlled behaviors
• Context freezing or branching
• Assistant self-awareness (“You’ve changed topics since last time. Want to reset?”)
• A damn user manual

Let's be honest: a memory changelog could make people uncomfortable. It wouldn't just show what the assistant remembered—it would reveal what you changed, forgot, or contradicted. That's a tough mirror to face.

This highlights the core problem we keep seeing: an impedance mismatch between human expectations and AI reality. When you don't understand how the system behaves, you'll use the wrong tool. This leads to real harm—especially when you believe you're delegating to a consistent assistant.

Until the systems catch up, our responsibility is to keep building the manual. Because someone has to.

And we're not done yet.

This piece is part of a developing Red Hat Creative series on human-AI collaboration breakdowns. If it made you uneasy, good—it was supposed to. More to come on system transparency, trust recovery, and how we close the impedance gap—one expectation at a time.