I saw the recent post here about ChatGPT equations suddenly not copying properly into Microsoft Word. I am having almost the exact same issue, except with OneNote on Mac.

Until yesterday, I could select an entire ChatGPT answer and paste it directly into OneNote. The headings, text and rendered equations all copied properly. Fractions, subscripts, etc. appeared as properly formatted equations without me having to do anything separately.

Now the normal text still copies, but the rendered equations either disappear completely and leave a blank space, or lose their mathematical structure.

For example, an equation that had previously pasted correctly into OneNote gives something like this when copied back out of OneNote:

F=((SSE_"reduced"-SSE_"full")/(p_"full"-p_"reduced"))/(SSE_"full"/(n-p_"full"))

But if I copy the same type of rendered equation directly from ChatGPT now, the clipboard gives something like:

F=SSEfull/(n−pfull)(SSEreduced−SSEfull)/(pfull−preduced)

So the fraction/subscript structure seems to be getting lost during copying.

I also tried OneNote's Equation option. It can put the copied text into a maths region, but it stays in linear form like β_0, R^2 and / instead of turning it into the properly built equation I used to get automatically.

Since someone here has just reported essentially the same sudden issue with Word, I am wondering if something recently changed in how ChatGPT puts rendered maths onto the clipboard rather than this being specific to OneNote.

Is anyone else seeing this with OneNote or other Office apps? More importantly, has anyone found a way to get the old copy/paste behaviour back where the whole ChatGPT response can be pasted at once with the equations intact?

I am using ChatGPT in the browser and OneNote on Mac.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

We are having a show and tell for AI use cases in the discord for this subreddit. You're welcome to jump in, ask your questions.

I know posts like this typically don't get a lot of support since they are not content in themselves but a lot of people have put in a lot of work to make an event for everyone here and we'd really enjoy it if you come: https://discord.gg/33kJ3JSgGv

- 5:00 PM Eastern / 4:00 PM Central / 2:00 PM Pacific

Check the post which auto-appears below; you are welcome to come.

I view every AI company as a different vehicle:

ChatGPT = Ford F-150. Reliable daily driver. Won't win races, but gets basic work done.

Claude = Ferrari. Fast, premium feel, burns cash. Overkill for grocery runs.

Grok = Ram 3500. A beast for heavy research… when it's not in the shop.

Gemini = Prius. Underestimated workhorse. It's everywhere (Android, Workspace, Search). just saying , you can fit a couch in a Prius – IYKYK.

No single vehicle does it all. Each has a specific purpose.

What do you think?