Explaining "AI Dumbing Down" (AI Degradation)

Recently, many members of my community have told me: models are getting stronger, yet they keep encountering "dumbing down" (降智).

For example, the same task worked fine yesterday, but today it starts missing requirements or talking nonsense. When this happens, most people's first reaction is: "It's been dumbed down again."

But behind "dumbing down" could be different issues: the model was swapped, the task became harder, context was lost, server rate limiting, and so on. Let me give you a simple rundown today.

In general, "dumbing down" usually falls into the following categories.

1. Model downgrade

Many people use various relay/proxy stations. I've warned before: you can't easily verify how your data is stored and used, and there is risk of data leakage and resale.

Among these, silently downgrading the model is a common trick. The page might say it's using Opus or GPT, but you have no idea what is actually being called behind the scenes.

So use official direct connections whenever possible. When using a relay, verify model settings and call logs, and use the same material as a baseline for critical tasks. Don't just trust the name on the page.

Official sources also have model fallbacks. For example, Fable 5 used to fall back to Opus 4.8. But generally, the official side won't do it "secretly" and will clearly inform you.

2. Reasoning level

The same model, with different reasoning intensity, can perform differently on complex tasks, and speed and token consumption also change.

Model and reasoning level should be considered separately; you don't need to set every task to the highest level. My suggestion: for most users, the default "high" is usually sufficient. For complex problems or bugs you can't fix repeatedly, try a higher setting.

Also, model releases may adjust the default reasoning level; if you don't pay attention, task differences can be large.

3. New version regressed on your tasks

Generally, a new model version improves overall capability, but it's not guaranteed to do better on every specific task.

For example, a new version might be better at coding but no longer fits your writing requirements. Shortened responses, erroneously rejected normal requests, and actually wrong answers should be judged separately.

Keep a few tasks you do regularly and re-test after upgrades. If the old model does well on your tasks, there's no need to force using the latest model.

4. Tasks became harder

Many people have the feeling: it used to go smoothly, why do new projects and new tasks keep failing?

As your AI usage abilities improve, the tasks you hand to the model may also become more complex.

For instance, earlier you used a weak model to process weekly reports and reply to emails. Now you ask a weak model to do front-end design and complex logic, so it seems to make errors.

For complex tasks, you can try stronger models, but you also need to break down steps, clearly state requirements and constraints, and check incrementally. Don't expect that switching to a new model will automatically fill in the information you didn't provide.

5. Context too large or polluted

Many models now support 1M context, but support doesn't mean that performance is better when context reaches a million tokens.

Once context is nearly full, automatic compression triggers, and many key details may be forgotten.

So try to use a single window for a single functional requirement. In software engineering there's a term called "modular" development; I think in the AI era, your conversations should also be modular.

I've always advised all community members to think in terms of projects. Open different windows for different tasks; each window is an independent module requirement.

6. Instructions, memory, or account settings interfering

Codex has a local auto-memory feature, off by default. When enabled, it reduces repeated instructions, but if erroneous or outdated information is saved, it can interfere with new tasks.

From the very beginning, I taught project-based thinking in my community, like writing, personal websites, weekly reports, each with its own project-level workflow and rules.

But as your AI capabilities improve, many old usage patterns and prompts may become ineffective. Once you rely on local memory, it can also be a source of interference and "dumbing down".

Using project-based thinking avoids this problem; you don't even need to rely on global memory.

7. Tool and access environment mismatch

Many people may encounter this: I ask it to analyze a WeChat official account or scrape X (Twitter) information, a seemingly simple task, but it fails.

Some tasks are stuck on login state, access permissions, anti-scraping, or tool configuration; the material is never obtained.

A headless browser just doesn't have a visible interface; it doesn't mean anonymous, nor does it mean it can't log in. Only changing a User-Agent doesn't guarantee solving access issues either.

This layer essentially involves platform risk-control strategy and technical choices, which may need human-specific designation.

8. Model overload and server-side concurrent rate limiting

Many people often encounter sudden slowness or model overload, especially when opening multiple windows, which can trigger server rate limiting.

So I previously said that two $100 Codex accounts feel better than one $200 account.

Especially during resets and new model releases, global concurrent usage spikes, naturally causing model overload and server rate limiting.

I see many people sharing accounts with others. You think you opened only one window, but others are using it concurrently at the same time.

I have personally tested: during resets and new model releases, avoid important projects. Otherwise you may waste many tokens and get poor results.

9. System prompt updates or bugs

Sometimes, even without a major model release, the experience is bad. This could be because the model wasn't updated, but the server may have adjusted system prompts or has a bug.

These can also directly cause apparent "dumbing down".

Same advice: avoid high-concurrency use for important tasks. Once you notice something wrong, pause, or switch to another agent.

I recommend not relying on a single vendor's agent. Use two or more vendors separately to handle emergencies later.

10. Remember to update and iterate global constraint rules

I have a habit now: every time a major model version is released, I check the global AGENTS.md, verify it, make necessary modifications, and share it with my community members.

The model's capabilities, execution habits, autonomy, boundaries, etc., may all change. Rules that worked well before may no longer be suitable in the new version.

My local projects involve writing, coding, music, video, etc. I personally test each new version. Put general requirements in the global file, keep specific workflows in the respective project, and modify them as needed. Don't pile all rules into the global file.

That's all for now; I'll add more later if I think of anything. Hope this gives you some reference.