AI Makes Mistakes Faster, Too

AI makes it possible to accomplish things incredibly fast.

That is one of the biggest reasons people are excited about it. It can write, summarize, research, organize, code, analyze, generate ideas, create images, clean up data, draft emails, build workflows, and automate repetitive tasks in a fraction of the time it would take a person.

But there is another side to that speed that does not get talked about enough.

AI makes it possible to accomplish things incredibly fast. Unfortunately, that includes mistakes.

There is an old saying: “To err is human; to really screw things up requires a computer.”

I think we may need to update that for AI.

Because AI does not just execute quickly. It can make assumptions, write code, change files, create content, update data, trigger workflows, and move on to the next step before you have even realized it misunderstood what you wanted.

That is where acceleration becomes dangerous.

The danger is not simply that AI can be wrong. We already know AI can be wrong. The real danger is that AI can be wrong confidently, quickly, repeatedly, and at scale.

A person might misunderstand an instruction and make one mistake. AI can misunderstand an instruction and make that same mistake hundreds or thousands of times before anyone notices.

That distinction matters.

Speed is only useful when the direction is correct. If you are driving toward the right destination, going faster is great. If you are going the wrong direction, speed just gets you lost sooner. AI has the same problem. When it understands the task, it can be amazing. When it misunderstands the task, it can create a much bigger mess than a slower system would have created.

And because AI often feels productive, it can give us a false sense of progress.

It is generating output. It is completing tasks. It is checking boxes. It is moving forward.

But moving forward is not the same thing as moving in the right direction.

That is why the way we give AI instructions matters so much.

For years, people have been trained to think about computers in a certain way. Traditional software usually does exactly what it is programmed to do. If you click a button, it performs a defined action. If you run a script, it follows the instructions in that script. If there is a mistake, it is usually because the software was built incorrectly, the data was bad, or the user did something unexpected.

AI is different.

AI can interpret. It can infer. It can fill in blanks. It can decide that your instruction probably means something you did not explicitly say. Sometimes that is incredibly useful. Sometimes that is the problem.

If I say, “Clean up these customer records,” a human employee might stop and ask, “What do you mean by clean up?”

Do I mean remove duplicate records?

Do I mean update missing information?

Do I mean correct formatting issues?

Do I mean delete old customers?

Do I mean merge contacts with the same email address?

Do I mean mark inactive customers?

Do I mean standardize phone numbers and addresses?

Do I mean remove people who have not purchased recently?

Those are all different interpretations of the phrase “clean up these customer records.” Some are harmless. Some are destructive. Some might be appropriate in one business and completely unacceptable in another.

A careful employee might ask clarifying questions before taking action, especially if the work affects important business data.

AI may not.

If the instruction sounds complete enough, the AI may fill in the missing details itself and start working. And if it has permission to modify the records directly, it may do exactly that.

Now multiply that by hundreds or thousands of customer records.

The problem is not necessarily that AI made one mistake. The problem is that AI can make the same mistake at machine speed.

That is a very different kind of risk.

If a person accidentally deletes one record, you can probably recover. If AI deletes 5,000 records because it interpreted “clean up” as “remove inactive contacts,” you have a bigger problem. If AI merges customers incorrectly because two people share a name, you may have corrupted your data. If AI updates statuses, notes, categories, or purchase histories based on a bad assumption, you may not even notice the issue until it affects billing, sales, customer service, or reporting.

AI is not dangerous because it is “intelligent.” It is dangerous when we give it vague authority over important systems.

That distinction is important.

There is a big difference between asking AI to draft a paragraph and asking AI to update a database.

There is a big difference between asking AI to suggest code and asking AI to modify your production application.

There is a big difference between asking AI to analyze a spreadsheet and asking AI to rewrite the spreadsheet.

There is a big difference between asking AI for recommendations and giving AI permission to execute those recommendations automatically.

The more power we give it, the more precise our instructions need to be.

I see the same issue in development all the time.

Give AI an incomplete instruction and it might modify one file. That file creates an error, so it modifies another file. Then it updates a dependency. Then it changes a configuration. Then it adjusts the database schema. Then it changes something else because the previous change created a new problem.

Five minutes later, you are troubleshooting an issue that started because of an assumption in the very first prompt.

This is one of the most important lessons when using AI for technical work: AI can create cascading consequences.

A small misunderstanding at the beginning of a task can ripple through an entire project. And because AI can move so quickly, you may not realize how many things changed until after the fact.

For example, let’s say I ask AI to “fix the login issue.”

That sounds simple. But what does “fix” mean?

Does it mean identify the bug and explain it?

Does it mean update the login form?

Does it mean modify authentication logic?

Does it mean change session handling?

Does it mean reset password behavior?

Does it mean adjust database fields?

Does it mean change security rules?

Does it mean bypass something temporarily just to make the error go away?

If the AI has access to the codebase and permission to make edits, “fix the login issue” is a risky instruction. It gives the AI a goal, but not enough boundaries.

A developer may know that login touches authentication, sessions, cookies, redirects, validation, error handling, user roles, password hashing, tokens, and the database. AI may understand that too, but that does not mean it knows what should or should not be changed in this specific project.

Without constraints, it may make a broad change when a narrow change was needed.

And sometimes the change “works” in the sense that the original error disappears, but it introduces a new security issue, breaks another workflow, or creates inconsistent behavior somewhere else.

That is why speed can be misleading.

AI can produce a fix quickly, but fast is not the same as correct. It can produce a lot of code quickly, but more code is not always better code. It can make a system appear functional while quietly introducing technical debt.

The same is true outside of development.

If I ask AI to “improve these product descriptions,” that sounds harmless. But what counts as improvement?

Should it make them more persuasive?

More accurate?

More concise?

More SEO-friendly?

More casual?

More technical?

Should it preserve specific claims?

Should it avoid changing product specifications?

Should it maintain compliance language?

Should it use the same tone across all descriptions?

Should it flag unclear details instead of inventing them?

If the AI rewrites 500 product descriptions and adds benefits that are not actually true, now I have a business problem. If it changes measurements, warranties, ingredients, restrictions, legal disclaimers, shipping details, or compatibility information, now I may have a customer service or legal problem.

Again, the issue is not just that AI can make things up. The issue is that AI can make things up consistently across a large amount of content before anyone catches it.

The same concern applies to marketing, finance, operations, customer support, HR, legal, and almost every area where AI is being used.

If AI summarizes customer complaints incorrectly, I may make the wrong business decision.

If AI categorizes support tickets incorrectly, customers may get sent to the wrong team.

If AI drafts responses without enough oversight, it may promise something the business cannot deliver.

If AI analyzes sales data with the wrong assumption, it may produce a confident but misleading report.

If AI screens resumes based on vague criteria, it may filter out good candidates.

If AI updates internal documentation incorrectly, the team may start following bad instructions.

If AI generates social media posts based on outdated information, it may publish something inaccurate.

All of these errors used to require more time and more human effort. AI reduces that friction. That is the benefit. But reduced friction also means mistakes can travel farther before they are stopped.

That is why faster execution requires better instructions.

The better the tool, the more important the direction.

When we give AI work to do, especially work that affects real systems, real data, real customers, or real decisions, we need to be much more deliberate about the prompt.

A vague instruction may be fine when I am brainstorming ideas. If I ask AI for “ten ideas for a blog post,” the risk is low. If the ideas are bad, I ignore them. No real damage is done.

But if I ask AI to “update all customer records,” the risk is completely different.

The amount of precision needed should match the amount of impact the task can have.

Before giving AI permission to execute something important, I want to be clear about a few things.

What exactly are we changing?

This sounds obvious, but it is often skipped.

If the task is to update customer records, which fields should be changed? Name? Email? Phone? Address? Status? Notes? Tags? Purchase history?

If the task is to edit a document, what sections are in scope? Is the AI allowed to rewrite the entire thing, or only fix grammar? Should it preserve quotes? Should it preserve technical terminology?

If the task is to modify code, which file or feature is involved? Is it allowed to refactor related code, or should it make the smallest possible change?

Specificity matters because AI will often try to be helpful. And “helpful” can become “I changed more than you asked.”

What are we not changing?

This is just as important.

People often tell AI what they want done, but they do not tell AI what to leave alone.

Do not change pricing.

Do not remove records.

Do not alter customer IDs.

Do not rewrite legal language.

Do not modify database structure.

Do not change public APIs.

Do not update dependencies.

Do not change the tone.

Do not invent missing information.

Do not publish anything without review.

These boundaries are not optional when the work matters.

AI needs constraints. Without constraints, it may optimize for completion. It may decide that the fastest path to accomplishing the goal is to change things that should not be changed.

What assumptions should AI stop and ask about?

This may be the most important habit.

AI is often very good at continuing. It is not always good at stopping.

If there is missing information, I want it to ask.

If there are multiple possible interpretations, I want it to ask.

If a decision could affect a lot of data, I want it to ask.

If it encounters conflicting information, I want it to ask.

If a change would require touching more than the expected scope, I want it to ask.

This is one of the best ways to reduce risk. Build the stopping points into the instruction.

For example:

“If any record is ambiguous, do not guess. Flag it for review.”

“If a requested change requires modifying more than one file, explain why before making the additional changes.”

“If you find duplicate customer records but are not at least 95% confident they refer to the same person, do not merge them.”

“If product details are missing, do not invent them. Add a note that the information needs to be confirmed.”

“If you are unsure whether a customer should be marked inactive, leave the record unchanged and list it separately.”

That kind of instruction changes the AI’s behavior. It tells the system that stopping is part of the task. Asking is not failure. Guessing is not preferred.

What does success look like?

AI needs a clear target.

“Make this better” is not a clear target.

“Clean this up” is not a clear target.

“Fix this” is not a clear target.

“Improve the website” is not a clear target.

Those instructions may work in casual situations, but they are not enough for important execution.

Success should be defined in a way that can be checked.

For writing, success might mean: shorter sentences, clearer structure, same facts, same voice, no new claims, and a summary at the end.

For data cleanup, success might mean: standardized phone number formatting, duplicate records flagged but not merged, missing emails listed separately, and no deleted records.

For code, success might mean: the specific error is resolved, tests pass, no unrelated files are changed, and the change is limited to the affected function.

For marketing, success might mean: draft three options, preserve the offer, do not mention discounts, maintain a professional tone, and include a call to action.

The clearer the definition of success, the less room AI has to invent its own.

When should it ask permission before continuing?

This is where many people get into trouble.

AI should not always be allowed to keep going.

There are moments where it should pause and get approval. That is especially true when the next step involves irreversible or large-scale changes.

Ask permission before deleting anything.

Ask permission before overwriting files.

Ask permission before sending emails.

Ask permission before publishing content.

Ask permission before modifying live data.

Ask permission before changing more files than expected.

Ask permission before applying a change across hundreds or thousands of records.

Ask permission before making assumptions about missing facts.

Ask permission before restructuring something that was only supposed to be lightly edited.

This matters because AI does not always understand the real-world significance of an action. A deletion may appear to be a normal cleanup step. A rewrite may appear to be a normal improvement. A merge may appear to be a normal data operation. But in context, that action may carry serious consequences.

Permission points slow things down slightly, but they protect against the exact kind of acceleration that causes damage.

And that is really the balance we need to strike.

I am not against AI acceleration. Far from it. AI acceleration is fantastic when it is moving in the right direction.

It can save enormous amounts of time. It can reduce tedious work. It can help small teams do more. It can help individuals work faster. It can turn rough ideas into drafts. It can help debug problems. It can analyze information quickly. It can create starting points that would have taken hours to produce manually.

But the more powerful the tool becomes, the more thoughtful we need to be about how we use it.

A bicycle going the wrong direction is a problem.

A car going the wrong direction is a bigger problem.

A rocket going the wrong direction is a disaster.

AI gives ordinary people and businesses rocket-level acceleration in certain areas. That is exciting, but it also means direction, safeguards, and review matter more than ever.

One of the simplest questions I like to ask before using AI on something important is this:

If it misunderstands this instruction, how much can it change before I notice?

That question cuts through the hype.

Can it change one paragraph before I review it?

Can it change one file?

Can it change one customer record?

Can it change an entire spreadsheet?

Can it change the database?

Can it change the website?

Can it email customers?

Can it publish publicly?

Can it affect billing?

Can it impact the entire system?

The answer should determine how much freedom the AI gets.

If the worst-case scenario is a bad paragraph, I may not need many safeguards. I can read the paragraph and reject it.

If the worst-case scenario is a broken production system, I need a very different process.

If the worst-case scenario is corrupted customer data, I need review, backups, tests, limited permissions, and clear instructions.

If the worst-case scenario is publishing inaccurate information to customers, I need approval before anything goes live.

AI should not get the same level of trust for every task.

A low-risk brainstorming task is not the same as a high-risk execution task. Treating them the same is how problems happen.

This is also why I think people need to be careful with the phrase “AI agent.”

The idea of an AI agent is appealing. Give it a goal and let it work through the steps. That can be useful. But the more autonomous the agent is, the more important it becomes to define boundaries.

What tools can it use?

What systems can it access?

What data can it modify?

What actions require approval?

How will changes be logged?

How can changes be reversed?

What happens when the AI is uncertain?

What happens when it encounters conflicting instructions?

What happens when the task expands beyond the original scope?

Autonomy without boundaries is not efficiency. It is risk.

The goal should not be to slow AI down unnecessarily. The goal should be to put guardrails around the speed.

One practical way to do this is to separate planning from execution.

Instead of saying, “Clean up these records,” I can say, “Review these records and propose a cleanup plan. Do not make any changes yet.”

Instead of saying, “Fix this code,” I can say, “Identify the likely cause of this issue and list the files you believe need to be changed. Do not edit anything yet.”

Instead of saying, “Rewrite this page,” I can say, “Suggest improvements to this page while preserving all factual claims. Wait for approval before rewriting.”

That extra step can prevent a lot of problems. It gives me the benefit of AI’s analysis without immediately handing over execution.

Another good habit is asking AI to show its assumptions.

For example:

“Before you begin, list any assumptions you are making.”

“If any assumptions are required, ask me to confirm them.”

“Separate confirmed facts from guesses.”

“Tell me what information is missing.”

This is useful because assumptions are often where the mistakes begin.

The AI may assume that inactive customers should be deleted. It may assume that duplicate names are duplicate people. It may assume that a missing field should be filled with a likely value. It may assume that a bug should be fixed by changing the most obvious file. It may assume that a vague instruction gives it permission to restructure everything.

If those assumptions are visible before execution, I can correct them before they turn into changes.

I also like limiting scope.

“Only review the first 20 records.”

“Only change this section.”

“Only update formatting, not content.”

“Only modify this function.”

“Only generate a draft; do not publish.”

“Only flag issues; do not fix them yet.”

Scope limits are powerful because they reduce the blast radius. If something goes wrong, it affects a small area instead of the whole system.

That phrase, “blast radius,” is useful when thinking about AI.

How far can the damage spread if the AI gets it wrong?

A small blast radius means the AI can work with more freedom. A large blast radius means we need more control.

Backups matter too.

If AI is going to change files, data, or content, I want a way back. Version control, database backups, change logs, export files, revision history — these are not just technical details. They are safety nets.

AI can move fast. Recovery needs to be possible.

If I cannot easily undo what AI does, I should be much more cautious about letting it do the work directly.

Review matters as well.

There is a temptation to assume that because AI sounds polished, the work is good. That is a mistake.

AI can produce bad work in a professional tone. It can write incorrect information clearly. It can create code that looks reasonable but fails in edge cases. It can generate summaries that sound balanced but omit important details. It can confidently present assumptions as facts.

Polish is not proof.

The output still needs to be checked, especially when the work affects customers, money, legal obligations, security, public communication, or core business operations.

This does not mean every AI output needs a slow, painful review process. The level of review should match the level of risk.

A brainstormed headline? Quick review.

A customer email campaign? More careful review.

A database update? Very careful review.

A production code change? Testing and version control.

A legal or compliance document? Human expert review.

Risk should determine process.

This is also why I think businesses need to train people not just on how to use AI, but how to supervise AI.

There is a difference.

Using AI means knowing how to prompt it.

Supervising AI means knowing how to define the task, limit authority, evaluate output, catch assumptions, require approvals, and prevent unintended consequences.

That skill is going to become more important.

As AI gets integrated into more tools, people may not even think of it as “using AI.” It will just be built into the software. A button will say “optimize,” “clean up,” “summarize,” “fix,” “enhance,” or “automate.”

Those buttons will be convenient. They will also be dangerous if people do not understand what they actually do.

Any feature that modifies data, content, code, customer communication, or business processes needs to be treated with care.

The key question is always the same:

What happens if it misunderstands?

If the answer is “nothing serious,” enjoy the speed.

If the answer is “it could create a major problem,” slow down, clarify, limit, review, and require permission.

AI is not magic. It is a tool. A very powerful tool, but still a tool.

And powerful tools amplify intent.

If I give it clear instructions, good context, proper constraints, and sensible checkpoints, it can help me move much faster.

If I give it vague instructions, broad permissions, and no review, it can help me create a mess much faster.

That is the tradeoff.

The future of AI is not just about who can use it the most. It is about who can use it well. The people and businesses that get the most value from AI will not necessarily be the ones who automate everything blindly. They will be the ones who know where AI should move fast, where it should slow down, and where a human needs to stay in control.

I want the acceleration. I want the productivity. I want the leverage.

But I do not want speed without direction.

So before I let AI execute something important, I want to know:

What exactly am I asking it to do?

What should it avoid doing?

What assumptions should it not make?

What should it ask me before changing?

What does success look like?

How will I review the output?

How can I undo the change if necessary?

How much can it change before I notice?

Those questions may seem like extra work, but they are far less work than cleaning up a large-scale AI mistake after the fact.

Humans make mistakes.

AI makes mistakes too.

The difference is that AI may be able to make yours a whole lot faster.