One frustrating reality of AI assistants is that they can go from genius to clown from one day to the next. Yesterday, you had found the perfect workflow or the Holy Grail of prompts. Everything worked like magic. Today, the same tool with the same setup suddenly fails completely. The AI is ignoring your instructions. It is hallucinating facts all over the place. It gives you a lazy, short summary when you asked for a deep dive. It forgets important information halfway through the process. In other words: The results are garbage.
You start to wonder: Did I forget how to prompt? Did I change something?
The truth is: It’s (probably) not you. In this article, I will explain five major reasons why your assistant suddenly seems lobotomized. And we will talk about ways to deal with it.
Sometimes, there are easy ways to get your AI back on track. Sometimes, there aren’t.
Invisible changes
The first category of “dumber” AI is the most frustrating because it is completely invisible. There are no settings you changed, and there is no notification from the vendor saying, “We are currently running at lower capacity. Your AI is stupid now. Sorry not sorry.”
These changes are often driven by the challenging reality of running these massive AI models. Data centers have bottlenecks. Electricity costs money. When millions of users log in at once, vendors have to make compromises to keep the service running without going bankrupt or crashing the servers.
Another reason for changes behind the scenes can be liability: The vendors use safeguards to make sure their AI doesn’t get them into legal trouble. Some of these safety features can be adjusted on the fly and can have unintended consequences.
Let’s see in more detail how that affects you and what you can do about it.
1. Quantization: Your AI is a “low-res” version
If you have ever felt that your AI is brilliant in the morning but sluggish and dense by 3:00 PM, you might be interacting with a more heavily quantized version of the model during peak hours. Quantization is a technique used to make massive AI models use less resources.
Big cloud vendors like OpenAI, Google, and Anthropic probably use similar techniques dynamically. I’m not aware of any official acknowledgment. But it would be weird if they didn’t use it.
What can you do?
Unfortunately, vendors are not transparent about this. There is no “High Res” switch in the settings as far as I have seen. Your best bet is to identify if the “stupidity” is time-dependent. If the AI is failing at a complex task, try again at a non-peak time (like late evening or early morning) to see if you get the full-resolution brain back. But it could also be that your AI vendor has to deal with a lot of additional traffic, because of a new model release, a discount on their pricing tiers, a public relations push etc. In that case, you are pretty much out of luck.
2. Smaller context window: Your AI has “short-term memory loss”
You are in the middle of a long, productive session. Suddenly, the AI forgets a rule you established at the very beginning. Or, you upload a 50-page PDF, and the AI answers questions based only on the first five pages and the last five pages, completely ignoring everything else (a phenomenon researchers call “Lost in the Middle”).
To the user, this feels like the AI has become stupid or lazy. In reality, it has just been given a smaller workspace.
The important factor in this case is the “context window”: It is effectively the AI’s short-term memory and defines how much text – i.e. the conversation itself, uploaded documents, system instructions – the AI can “see” at any given moment.
Modern models boast massive context windows. Google Gemini, for example, advertises a context window of up to one million tokens (roughly 750,000 words). In theory, you could paste entire novels into the chat, and the AI should remember every detail.
But processing these massive context windows is incredibly expensive for the vendor. It requires exponential amounts of computing power. A prompt that is twice as long doesn’t cost twice as much to run, but significantly more.
Because of this, vendors may silently throttle the effective context window during times of high traffic. Again: There’s no official acknowledgment of this behavior. But smart people have done tests and found compelling evidence.
What can you do?
If you suspect your context window is shrinking, treat your AI sessions like a goldfish’s memory:
- Keep chats short: Don’t let a single conversation drag on for days.
- Restart frequently: If the AI starts hallucinating or forgetting rules, don’t argue with it. Start a fresh chat.
- Re-state the context: Repeat key instructions and information. That is of course even more important If you start a new chat out of necessity.
3. The “safety tax”: Your AI refuses to do a seemingly harmless task
You are working on a crime novel and ask the AI to describe a bank heist scene. Or you are writing code for a security audit and need a script to test a firewall. Yesterday, the AI was a helpful collaborator. Today, it suddenly refuses to answer. Instead of a draft, you get a lecture about how it “cannot assist with harmful or illegal activities,” even though your request is completely fictional or legitimate.
To you, it feels like the AI has been lobotomized or turned into an over-sensitive nanny. It seems to understand the context less than it did before.
The reason for this is rarely a change in the model itself, but an update to the “safety layers” that sit on top of it. Vendors are under immense pressure to prevent their tools from being used for harm (or generating bad PR). To do this, they use invisible “system prompts” as well as safety classifiers that intercept your request before the model even processes it.
A tweak made overnight to prevent real-world harm can easily result in “false positives,” blocking legitimate creative writing or technical work. The model isn’t really dumber. It’s just been muzzled.
What can you do?
When you hit a safety refusal, you are fighting a filter, not the intelligence of the model.
- Contextualize heavily: Explicitly state the benign context. Start your prompt with “I am writing a fictional story about…” or “This is for an authorized security educational course.” Be warned though that these kind of workarounds are often also disabled on purpose.
- Avoid trigger words: A filter might look for specific keywords. Try to use synonyms or describe the action more abstractly.
- Challenge the refusal: Sometimes, simply replying with “This is for a fictional context, please proceed” works, as the model’s own reasoning might override the initial safety reflex in the conversation history. But if it doesn’t work immediately, stop trying.
- Try again in a new chat: As frustrating as it is, sometimes it’s better to start over. Because of the non-deterministic nature of modern AI models, they can react differently to the same prompt when asked again without the negative presence of the initial refusal. Ideally, combine some of the other tips (context, trigger words) in this fresh chat right away.
Visible changes
While the previous constraints happen in the dark, this next category happens in plain sight. These are changes you can actually see in the user interface (if you know where to look). They often involve the specific model version you are using.
4. The stealth downgrade: You are suddenly using the “mini” model
You open your favorite AI app to write a complex strategy document. You type in a detailed prompt. You are pleasantly surprised: The answer comes back instantly! But then you realize: The content is shallow and it lacks the depth you are used to. Or the AI didn’t get what you actually wanted.
You more closely at the screen and realize: You aren’t using the advanced “Pro” model anymore. The selector has silently switched to “Flash” or “Mini” or something similar.
This is the “stealth downgrade.” Unlike the invisible changes above, this one happens right in the user interface, but it is easy to miss if you aren’t paying attention.
Vendors like to employ defaults to push users toward smaller, cheaper models. If you used the “Fast” model for a quick question yesterday, the interface might use that model again today. Sometimes, free-tier users are automatically switched to the cheaper model during high-traffic periods or after a long chat session without a way to switch back.
In some cases, users have reported that the interface simply defaults to the cheaper model every time they start a new chat, forcing them to manually select the “smart” model every single time. It is a small friction that can save the vendor massive amounts of compute.
What can you do?
- Check the selector: Build a habit of looking at the model name before you type your prompt.
- Watch for speed: If the AI is generating text suspiciously fast, you are likely on a “turbo” or “flash” model. Pause and check the settings.
- Force the switch: If you catch it mid-conversation, switch the model immediately (if the interface allows changing models mid-chat) or start over with the correct model selected.
5. Model updates: The brand new AI isn’t actually better
It’s an exciting day: Your favorite AI vendor announces a major model update. “Version 5.0” is finally here! You rush to the app, expecting it to be smarter, faster, and more creative than the previous version.
But after using it, you are quickly dismayed: The prose is dry and robotic. It refuses to take risks. It misunderstands instructions that the old model handled perfectly.
This was a major point of contention during the transition from GPT-4o to GPT-5. While the new model might have scored higher on academic benchmarks, many users found it “dumber” for creative writing and nuanced reasoning.
Why does this happen? “Newer” doesn’t always mean “better for you.” A new version can be more advanced at many tasks and still be worse at some others. If “some others” is what you use it for every day, the upgrade becomes an instant downgrade. For example: If the vendor focused on improving coding or math capabilities for the new version, the model’s creative writing skills might degrade as a side effect.
Furthermore, every model has a “personality.” A new model is effectively a different brain. The prompting tricks you’ve learned over time for the old model might not work that well anymore with the new one.
What can you do?
- Don’t delete your old prompts: But be ready to rewrite them. A new model can require a new style of instruction. Maybe you have to give more context. Maybe you have to be more explicit.
- Look for the “Legacy” switch: Some vendors allow you to select older versions. If the new model isn’t working for you, switch back. But be aware that old models won’t be kept around forever.
- Give it time: Sometimes, the initial release of a model is rough, and the vendor will “patch” it over the following weeks. GPT-5 already received several updates since its launch.
The human element
6. User error: Is it actually your fault?
When people complain about an AI suddenly underperforming the knee-jerk reaction on sites like Reddit is often: “It’s a skill issue. You just don’t know how to prompt properly.”
And, yes, sometimes this could be true. It’s human nature to become more complacent (dare I say: lazy) over time.
When we first start using a powerful AI tool, we tend to be careful. We write detailed prompts. We provide context. But as we keep seeing how “smart” the AI is, we might starting to slack off. We write shorter instructions. We assume the AI “knows what we mean” because it got it right the last ten times. And doesn’t it remember old chats now anyway?
So maybe you let your prompting skills slide a bit. That can also make your workflow more fragile: If the vendor makes a minor update or change, those minimum viable prompts might suddenly not be good enough anymore. Maybe the model now requires the explicit instructions you stopped giving it weeks ago.
What can you do?
- Audit your prompts: Look at the prompts you wrote when you were getting great results. Are your current prompts just as detailed? If not, go back to basics.
- Don’t assume context: Treat every new chat like you are hiring a new freelancer who knows nothing about your project. Over-explain. The “memory” features of ChatGPT, Gemini, and others are more finicky than they might seem at first.
The ultimate solution: Run your own AI
If reading this list made you frustrated about how much is out of your control, there is one fix. It is the only way to opt out of the quantization, the shrinking context windows, the stealth updates, the sudden changes brought on by a new model generation.
The solution is: Stop renting your AI. Start owning it.
That’s generally called “Local AI”. This term refers to downloading an AI model (like Llama, Qwen, Mistral) and running it directly on your own computer or server. Because this model lives on your own hard drive, you have several advantages:
- No one but you can update it: If you like how it behaves today, it will behave exactly the same way in five years.
- No one changes the model without your knowledge: You decide which model, which version, which quantization.
- No one can shrink your context: You define all parameters based on your own hardware and needs.
- Privacy is absolute: Your data never leaves your machine.
- No pesky safety features if you don’t want to: There are many open AI models that answer pretty much any question and fulfill any task.
“Local AI” sounds technical, and it is. But many tools make this increasingly easy, even for non-developers. You need some interest, patience, and motivation. But you don’t need a degree in Computer Science.
I will cover how to set this up in a future article. Make sure you are subscribed to Smart Content Report’s newsletter so you don’t miss it.
