This is the first installment of Where We Stand: Position, Proximity, and Power in AI Safety.
I am guilty of nearly everything I am about to warn against.
I get lazy and write an ambiguous prompt. I leave out context. I barely think through what I want before asking AI to produce it.
And often, the output is great.
Sometimes I get back something so useful that it feels as if I did more thinking than I actually did. The system takes a half-formed idea and turns it into an organized plan, a polished draft, or a solution I can immediately use.
That is not a failure. It is one of the most valuable things about generative AI.
These systems are getting better at interpreting incomplete instructions and predicting what we probably mean. As that improves, we will be able to accomplish more with less effort. People who struggle with writing, organization, language, or getting started may benefit enormously.
Still, I think it is worth noticing what happens when the system gets good enough to fill in all the gaps for us.
Every time AI frames the problem, chooses the structure, writes the first draft, and supplies the conclusion, we skip some of the mental work that would otherwise happen along the way. Sometimes skipping that work is the entire point. Sometimes it is how we save time.
But if we skip it every time, certain skills may begin to atrophy.
That does not mean we should stop using AI. It means we should occasionally pause and do something ourselves, even when the tool could do it faster. Write the paragraph. Sketch the plan. Work through the problem. Sit with the blank page for a few minutes.
Not because using AI is cheating, but because judgment needs exercise.
Know when ambiguity is useful
Advice about prompting often focuses on specificity. Define the role, provide context, describe the format, list the constraints, and explain exactly what you want.
That advice is useful when you need a predictable result. It is not a rule for every interaction.
An ambiguous prompt can be productive when you are brainstorming or exploring. It gives the system room to surprise you. You may discover an interpretation or direction you would not have considered on your own.
The issue is not ambiguity itself. The issue is whether the task can safely absorb it.
If you are generating names for a project, an unexpected answer may be interesting. If you are asking an AI to send an email, change a record, spend money, or advise you on something consequential, ambiguity carries more risk.
Before prompting, it helps to decide what kind of task this is.
Are you exploring, drafting, deciding, or acting?
Exploration can tolerate uncertainty. Action usually requires clearer boundaries.
If I ask an AI to “handle my trip,” it might reasonably interpret that as researching flights, comparing hotels, checking my calendar, or booking a ticket. The phrase feels obvious to me because I know what I meant. The system has to infer it.
A better instruction might be: compare three flights within this budget, but do not book anything.
That still saves time. It also keeps the decision where I intended it to stay.
Do some of the thinking before you prompt
One of the easiest ways to preserve your own judgment is to give yourself a short head start.
Before asking AI to write something, jot down what you think. Before asking it to solve a problem, make your own rough attempt. Before asking for a recommendation, identify the factors that matter to you.
This does not need to take long. Even a few minutes can change the interaction.
When you begin with your own position, AI becomes something you can compare your thinking against. You can see where it adds value, where it challenges you, and where it pulls the work in a direction you do not agree with.
When the AI always goes first, its framing can quietly become your framing.
This matters because the first plausible answer often shapes everything that follows. Once a structure appears on the page, it is easy to edit within it rather than question whether it was the right structure in the first place.
I do not think every email, summary, or routine task deserves deep independent thought. Offloading low-value work is part of the benefit. The point is to keep practicing the skills you want to retain.
If writing matters to you, do some writing before you prompt.
If analysis matters to you, sometimes form a view before asking for one.
If a decision matters to you, decide what you value before asking the system what it recommends.
The NIST Generative AI Profile includes human overreliance among the risks organizations should consider. From the user’s perspective, the practical concern is not that AI will suddenly eliminate our ability to think. It is that convenience can gradually change which parts of thinking we continue to practice.
Give it the context it needs
AI often works better with context. That does not mean it needs every piece of context you have.
If you want help summarizing a document, the system may not need names, account numbers, addresses, or client information. If you want help with your schedule, it may need your availability without needing the details of every calendar event.
Before uploading something, ask:
Does the AI need all of this to complete the task?
Often, the answer is no.
You can remove identifying details, replace names with placeholders, share an excerpt instead of a complete file, or describe the situation without uploading the original material.
It is also worth reviewing which services your AI tools can access. An integration that was convenient months ago may no longer be necessary. Disconnecting unused accounts reduces the amount of information any one system can reach.
The NIST Privacy Framework treats privacy as more than preventing a data breach. Data use can also affect autonomy, reputation, opportunity, and trust.
For an individual user, the practical lesson is simple: share what the task requires, not everything the system is willing to accept.
Check the things that matter
AI does not have to be perfect to be useful.
If you are brainstorming or looking for a starting point, a strange suggestion may be harmless or even helpful. The calculation changes when the answer will influence your health, finances, work, legal obligations, security, or reputation.
A polished response can still contain a false date, an invented quotation, a faulty calculation, or a source that does not support the claim attached to it.
Verification should rise with the stakes.
For something important, open the sources. Check the publication dates. Confirm names and quotations. Recalculate the numbers. Test the code somewhere safe. If the decision requires professional judgment, talk to a qualified person.
Pay particular attention to citations. Sometimes an AI will invent a reference. Other times it will give you a real source that says something different from what the AI claims. Always ask for links, click them, and read the material. Is this really what you are looking for? Is it helpful? Did the model only give you things to substantiate your claims?
Fluency makes an answer easier to accept. It does not make the answer more accurate.
Use the output as material for your judgment, not as a substitute for it.
Keep control of what the system can do
There is a meaningful difference between an AI that advises you and one that acts for you.
A chatbot can suggest an email. An agent may be able to send it. A planning tool can compare products. An agent may be able to buy one. A coding assistant can recommend a change. An agent may be able to edit the files and deploy them.
These capabilities can save real time. They can also turn a small misunderstanding into an immediate action.
When an AI can send messages, spend money, change records, or access other services, use settings that limit its reach.
Preview actions before they are completed. Confirm the recipient, price, destination, and scope. Set spending limits. Restrict which accounts and tools the system can access. Review its activity history.
Start with small, reversible tasks. See how the system behaves before giving it more authority.
An AI agent may also read webpages, emails, or documents containing instructions that did not come from you. OWASP’s guidance for large language model applications identifies prompt injection and excessive agency as important security risks.
In plain language, the system can encounter content that tries to manipulate what it does. Narrow permissions limit the consequences if it follows the wrong instruction.
You do not need to avoid AI agents. Just do not give them access to everything simply because the setup screen makes it easy.
Make sure there is a way back
Before using AI for something important, ask whether the result can be corrected. What is the worst that could happen if it got something wrong?
Keep an original copy of documents before allowing an AI to revise them. Use version history. Test generated code before it reaches a live system. Review cancellation policies before allowing an assistant to make a reservation or purchase.
Know how to disconnect the tool from your accounts. If it can take actions for you, check whether it keeps a record of what it did.
Planning for recovery is not pessimistic. We already do this with technologies we trust. We save documents, back up photos, review bank statements, and keep spare keys.
Those habits do not keep us from using the tools. They make the tools easier to trust.
This is not all on the user
There are limits to what any individual can do.
A user cannot audit a model they cannot inspect. They cannot create a rollback feature the product does not provide. They cannot turn a buried disclosure into meaningful consent.
Companies still have a responsibility to provide safe defaults, understandable explanations, limited permissions, accessible controls, reliable recovery, and human support.
The NIST AI Risk Management Framework treats AI risk as an organizational responsibility involving governance, assessment, measurement, and continuous management. That responsibility should not be pushed onto the person at the keyboard. Let’s keep pressure on the companies that produce these models, the thought leaders in the space and the legislators that write policies.
Good product design and thoughtful user habits reinforce each other. We need both.
Take a moment
Before you ask AI to do something that matters, pause for a moment.
Ask yourself:
Have I thought about what I actually want?
What information does the system need?
Am I exploring, drafting, deciding, or acting?
How will I check the result?
Can I undo what happens next?
Then, every so often, do the task yourself.
Write something without asking AI to begin it. Work through a problem before requesting the answer. Make a decision before asking for a recommendation. And, every once in a while, do the task without AI. Write a report or an essay without it, look through the Excel formulas yourself. Make sure your brain doesn’t atrophy.
You may still prefer what the AI produces. Often, I do.
The point is not to prove that you can outperform the tool. The point is to make sure the tool is extending abilities you still possess.
AI is going to get better at predicting what we mean. That will be useful, convenient, and sometimes remarkable.
Our job is to make sure convenience does not quietly become dependence, and that assistance does not replace the pause in which our own judgment forms.
Next: The Subject, When AI Is About You
Anthralytic is a social impact and evaluation studio creating software and offering consulting to help mission-driven teams do what they do well, even better.
