AI Output Is Not Evidence
By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
One of the easiest mistakes to make with artificial intelligence is also one of the most dangerous.
The answer looks finished.
The language sounds confident.
The explanation makes sense.
So you believe it.
That is exactly when a journalist needs to slow down.
AI can be useful for finding patterns, organizing information, generating questions, comparing claims and helping a journalist think through a story.
But none of those things make the AI-generated answer proof.
That distinction has become one of the most important rules in my own work:
AI output is not evidence
If an AI system gives me a fact, I still need to verify it.
If it gives me a quote, I need to find the original source.
If it gives me a statistic, I need to trace it back to the organization or dataset that produced it.
If it tells me that something happened, I need evidence that it actually happened.
The AI response may help me know where to look.
It may even point me directly toward legitimate evidence.
But the answer itself does not replace the proof.
Confidence Is Not Verification
Generative AI has a particular ability that makes this problem more difficult.
It can sound certain.
A wrong answer may not look wrong.
It may arrive in clean sentences with a logical explanation and enough detail to make the reader think the system must know what it is talking about.
That creates a dangerous shortcut.
The journalist stops asking:
“Is this true?”
And starts asking:
“Does this sound true?”
Those are not the same question.
Journalism cannot operate on plausibility alone.
A statement can be reasonable and still be wrong.
A date can look right and still be wrong.
A statistic can sound familiar and still be invented, outdated or taken out of context.
A quote can sound exactly like something a person would say and still never have been said.
That is why confidence cannot be treated as verification.
The Evidence Has to Exist Beyond the AI Answer
When AI gives me useful information, one of my next questions is usually:
Where is the evidence?
That may mean an official report, a government database, a court document, a transcript, a recording, a reputable news report or a historical archive.
The form will depend on the story.
What matters is that I can inspect the underlying evidence independently rather than treating the AI-generated answer itself as proof.
That distinction becomes even more important when the claim is central to the story.
The more consequential the claim, the more carefully I want to examine what supports it.
An AI system can point toward evidence.
It can summarize evidence.
It can help compare evidence.
But the evidence still has to withstand scrutiny on its own.
A Citation Is an Invitation to Verify
AI systems create another temptation.
They can provide citations.
A citation may look complete.
Author.
Title.
Publication.
Date.
Maybe even a link.
That still does not mean the work is done.
The source may not exist.
It may exist but not say what the AI claims it says.
The citation may point to something outdated.
Important context may have been omitted.
Details from several sources may have been mixed together.
That means I cannot verify a claim simply by seeing a citation attached to it.
I have to open the source.
Read it.
Confirm the relevant material.
Check the date.
Understand the context.
And decide whether the evidence actually supports the statement I want to make.
A citation is not verification.
It is an invitation to verify.
Verification Changes the Question
Early in my AI use, I was much more likely to ask:
“What is the answer?”
Now I am more likely to ask:
“What evidence supports this answer?”
That small change has made a significant difference in how I work.
The first question invites the system to produce information.
The second forces me to think about the information differently.
What would prove this?
Who would know?
Where would the original record exist?
Is there a primary source?
Does another independent source confirm it?
Is the information current?
Am I looking at the original claim or somebody repeating it?
Those questions move the process away from information generation and toward verification.
And that is where journalism has to live.
Primary Does Not Mean Infallible
When a primary source exists, I usually want to see it.
If a government agency publishes the data, I want the agency’s report.
If a university announces something, I want the university’s documentation.
If a sports organization changes a rule, I want the official rulebook or announcement.
If a company reports financial results, I want the filing or official release.
But there is an important distinction.
Primary does not mean infallible.
A company can emphasize information that benefits the company.
A government agency can provide an incomplete picture.
A university can frame an announcement in its own interest.
A court filing represents a party’s legal position, not necessarily an established fact.
Getting closer to the original record does not eliminate the need for judgment.
The source still has to be evaluated.
Secondary reporting matters too.
Good journalism can provide context, expertise, independent confirmation and scrutiny that the original source does not provide.
So my preference for primary evidence is not a rule that says primary sources are always better or always correct.
It means I want to understand where the claim originated before deciding what it proves.
Two Sources Do Not Automatically Mean Two Confirmations
Another verification trap appears when the same claim shows up in multiple places.
It is tempting to think:
Two sources say it, so it must be confirmed.
Not necessarily.
Both sources may be repeating the same original report.
One may have copied the other.
Both may rely on the same press release.
Both may be citing the same anonymous source.
AI can make this especially tricky because it may summarize several webpages that appear to agree when all of them ultimately trace back to one original claim.
So verification is not simply about counting links.
It is about understanding provenance.
Where did the information originate?
Are the confirmations actually independent?
What evidence does each source possess?
Even two genuinely independent sources can be wrong.
Independence increases confidence.
It does not eliminate the need to evaluate the quality of the evidence itself.
Verification Is More Than Fact-Checking
Verification is often described as fact-checking.
That is part of it.
But verification is broader.
A sentence can contain technically correct facts and still create a misleading impression.
A statistic may be accurate but missing context.
A quote may be authentic but selectively presented.
An AI-generated summary may include the facts while flattening uncertainty or leaving out an important qualification.
So verification also means asking:
What is missing?
What would change the interpretation?
What assumptions are hidden inside this claim?
Does the evidence support the conclusion being drawn from it?
That is where verification overlaps with editorial judgment.
Sometimes the Answer Is: We Do Not Know Yet
Verification does not always produce certainty.
Sometimes the records are incomplete.
Sometimes sources disagree.
Sometimes a developing event is changing faster than the available evidence can keep up.
Sometimes the most accurate conclusion is:
We do not know yet.
That is not a failure of journalism.
Pretending to know would be the failure.
This is especially important during breaking news.
Waiting until every uncertainty disappears is not always possible or desirable.
The public may have a legitimate need for information before the full picture is known.
The responsibility then is different.
Publish what the evidence supports.
Clearly distinguish confirmed information from what remains uncertain.
Attribute claims.
Avoid presenting speculation as fact.
Update the reporting when stronger evidence becomes available.
Verification is not always a switch between “publish” and “do not publish.”
Sometimes it determines what can responsibly be published right now.
AI Can Help With Verification — Carefully
There is an irony in all of this.
The same technology that creates new verification problems can also help with verification.
AI can help surface claims that may need checking.
It can compare documents, identify apparent inconsistencies, generate questions and challenge an assumption.
Those uses can be valuable.
But there are two boundaries I try to maintain.
First, AI does not decide which claims deserve the deepest scrutiny.
That remains an editorial judgment.
A journalist has to decide which claims are central, disputed, risky or consequential enough to require more work.
Second, AI cannot certify its own answer.
If an AI system produces a claim, asking that same system whether the claim is correct is not independent verification.
AI can assist the verification process.
It cannot become the final authority within that process.
The Verification Record Matters
One of the principles I keep returning to is that the evidence trail matters more than the appearance of certainty.
Someone can speculate that an article was produced with AI.
Someone can run the article through an AI detector.
Even if someone could perfectly determine whether AI was involved, that still would not establish whether the journalism itself was accurate.
Those are different questions.
Accuracy depends on evidence.
What sources were used?
What claims were checked?
What evidence supports the central statements?
What uncertainty remained?
What was changed or removed during the editorial process?
Those questions tell me much more about the quality of the journalism than simply asking whether AI touched the work.
The important question is not:
“Was AI involved?”
The better question is:
“Can the journalism be verified?”
A Real Workflow Needs a Verification Stage
This is where verification connects directly to the workflow I described in Chapter 2.
A workflow is not complete because it produces a draft.
A responsible workflow needs a verification stage.
In my own work, that often means stopping after the draft and examining the factual claims.
Which statements came from firsthand reporting?
Which came from primary documents?
Which came from secondary reporting?
Which originated as AI suggestions?
Which require another source?
Which are interpretations rather than established facts?
Which statements sound stronger than the evidence actually allows?
That pause matters.
It separates production from publication.
AI can make it easier to generate material quickly.
Verification is where I decide whether that material deserves to become journalism.
When Verification Gives the Time Back
There is something else worth acknowledging.
Verification takes time.
Sometimes AI saves time on one part of the process and verification gives some or all of that time right back.
Sometimes verification takes longer than the drafting.
That is not necessarily a failure of the workflow.
It may be the workflow doing exactly what it is supposed to do.
If a claim takes 20 minutes to verify, those 20 minutes are part of producing trustworthy work.
This is why I do not measure AI productivity only by how quickly a draft appears.
The better question is whether AI helps me move through the entire process without lowering the standard.
If the draft arrives faster but verification becomes weaker, I have gained very little.
If AI helps me spend less time on mechanical work and more time examining the evidence, that is a much more meaningful improvement.
The Journalist Has to Be Willing to Let Go
Sometimes verification produces an answer nobody wants after spending time on a story:
The evidence is not strong enough.
Maybe a sentence has to go.
Maybe an entire section has to go.
Maybe the central claim has to be rewritten.
Maybe the story has to wait.
And sometimes, after all that work, the story should not be published at all.
That can be difficult.
The draft exists.
Time has already been invested.
The headline may be good.
A graphic may even be finished.
None of those things turn weak evidence into strong evidence.
At the same time, uncertainty does not always require abandoning the entire story.
Sometimes the responsible decision is to publish only the portion that can be supported and clearly explain what remains unknown.
The principle is the same either way:
The evidence determines what survives.
Not the amount of work already invested.
Not how convincing the draft sounds.
And not how badly I want the story to be true.
Trust Is Built Before Publication
Readers usually see the finished article.
They do not see the searches.
The discarded sources.
The rewritten paragraphs.
The claims that were removed.
The moments when the journalist stopped and said:
I cannot prove this.
Or:
We do not know this yet.
Those invisible decisions are part of what makes the final product trustworthy.
AI does not change that.
If anything, those decisions become more important because plausible information has become easier to produce.
The harder task is deciding what deserves to survive.
That is why verification cannot be treated as an optional final check.
It has to be built into the process.
Evidence Before Confidence
AI can be fast.
It can be useful.
It can be impressive.
It can also be wrong.
The journalist’s job is not to be impressed by the answer.
The journalist’s job is to determine what the evidence supports.
That means asking where the information came from.
Opening the source.
Checking the context.
Looking for independent confirmation when appropriate.
Understanding uncertainty.
And being willing to reject or qualify a claim when the evidence does not support certainty.
AI can help generate and challenge information.
But journalism begins when the information survives verification.
That is the line I keep returning to:
AI output is not evidence.
The AI answer may point toward the evidence.
The evidence still has to speak for itself.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
From Doing Everything Myself to Directing the Workflow
By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
For most of my career, writing meant doing nearly everything myself.
Research the story.
Find the sources.
Take the notes.
Organize the information.
Write the draft.
Rewrite the draft.
Check the facts.
Edit the copy.
Come up with the headline.
Prepare the article for publication.
Then start all over again with the next story.
That process was familiar because it was the way I had always worked.
It was also exhausting.
Journalists often wear several hats, especially when they work independently or in smaller organizations.
A reporter may also become the researcher, editor, publisher, social-media producer and, depending on the project, even the person creating graphics.
For a long time, I simply accepted that as part of the job.
Then artificial intelligence changed the way I thought about the work.
Not because AI could suddenly do everything for me.
It could not.
What changed was that I began to realize I no longer had to perform every part of the process in exactly the same way.
I could direct the workflow.
That was a much bigger change than simply learning how to use a chatbot.
The First Stage: Asking Questions
My early use of artificial intelligence was not sophisticated.
Like many people, I started by asking questions.
I treated AI almost like a conversational search tool.
Ask something.
Get an answer.
Ask another question.
Move on.
There was nothing particularly strategic about it.
At that point, I was using AI.
I was not yet working with AI.
That distinction would become important.
The real change began when I stopped thinking only about individual questions and started thinking about the entire process.
What happens before I write?
What happens after I write?
Where do mistakes usually occur?
Which parts take the most time?
Which parts require judgment?
Which parts are repetitive?
Which parts could benefit from another set of eyes?
Once I began thinking that way, AI stopped being only a question-and-answer tool and started becoming part of a workflow.
The Work Did Not Disappear
It is easy to describe AI as a labor-saving technology.
Sometimes it is.
But that description can be misleading.
The work did not disappear.
The work changed.
Instead of spending all of my time manually performing every task, I increasingly spent time deciding what needed to happen, what AI could assist with and what still required my direct attention.
That sounds subtle.
It is not.
Consider research.
A traditional workflow might involve opening a series of tabs, reading articles, taking notes and trying to identify connections manually.
An AI-assisted workflow might help organize documents, compare information or identify questions more quickly.
But somebody still has to decide which sources are trustworthy.
Somebody still has to notice when important context is missing.
Somebody still has to recognize when two sources appear to contradict each other.
And somebody still has to decide what belongs in the final story.
That person is still me.
AI can reduce some forms of manual work while creating another responsibility: supervision.
From Doing Everything to Directing the Workflow
This is the part I did not understand when I first started using AI.
I thought the main benefit would be speed.
Write faster.
Research faster.
Edit faster.
Those benefits are real.
But the deeper change has been learning how to direct the work.
The shift was not from working to supervising.
It was from performing every task manually to deciding which tasks I should still perform, which AI could assist with and where human approval had to remain.
One workflow may be focused on research.
Another may challenge the argument.
Another may examine factual claims.
Another may help transform a completed article into a different format.
Another may help evaluate whether the final product still sounds like me.
Some of those workflows are simple and manually triggered.
Others are more structured.
Not every useful AI workflow needs to be autonomous.
The point is not to remove the human from the process.
The point is to build a process where the human knows when to step in.
I Did Not Start With a Master Plan
The strange part is that this happened gradually.
I did not sit down one morning and announce that I was going to build a collection of AI-assisted workflows.
Much of it came from trial and error.
I encountered a problem.
I tried something.
Sometimes it worked.
Sometimes it did not.
I needed a better way to verify information.
A workflow developed.
I needed a way to challenge weak arguments.
Another workflow developed.
I needed to repurpose an article without losing its meaning.
Another process emerged.
Some workflows worked immediately.
Others had to be changed.
Some became more useful after repeated use.
Some probably were not worth keeping.
Over time, those individual solutions began to form a system.
That is when I started realizing that I was no longer simply using AI.
I was designing ways to work with it.
The Journalist Becomes the Editor of the Output
Traditional journalism has always involved editing.
The difference now is that the journalist may also have to edit what the machine produces.
That means more than correcting grammar.
It means questioning assumptions.
Rejecting weak suggestions.
Checking sources.
Restructuring an answer.
Recognizing when the system has misunderstood the assignment.
Sometimes the most important thing I do is tell the AI:
No.
That is wrong.
That is unsupported.
That does not sound like me.
That is not the question I asked.
Try again.
Those moments matter because they reveal something that gets lost when AI is described as automatic.
The process is often interactive.
The final result may emerge from several rounds of challenge, correction and refinement.
That makes the human role different from traditional writing, but not less important.
In some situations, it can become more demanding because the journalist must supervise both the journalism and the AI-assisted process.
Direction Requires Knowing What Good Looks Like
There is another problem with telling people to simply “use AI.”
You have to know what you are trying to produce.
AI cannot compensate for having no standards.
If I do not know what a strong news story looks like, I cannot reliably judge whether AI has helped create one.
If I do not understand verification, I may not recognize an unsupported claim.
If I do not understand my own voice, I may allow AI to replace it with generic language.
Delegation only works if the journalist still understands the task well enough to judge the result.
That matters because over-delegation carries its own risk.
If journalists stop practicing the underlying skills of research, verification, writing and editing, they may become less capable of recognizing when AI has produced weak work.
AI can increase capability.
It does not automatically provide judgment.
The better I understand journalism, the better I can direct AI inside a journalistic workflow.
A Different Kind of Productivity
Before I began using AI seriously, productivity often meant working harder or working longer.
There were only so many hours available.
If an article took three hours, producing another article often meant finding another three hours.
AI changes that equation, but not as simply as the hype sometimes suggests.
The goal is not to produce as much content as possible.
The goal is to use saved time intelligently.
The most useful question is not:
“How much faster can AI make me?”
It is:
“What should I do with the time AI gives back?”
If AI helps reduce the mechanical parts of a task, I can spend more time checking the work.
I can challenge the argument.
I can examine another source.
I can rethink the headline.
I can decide that a story is not ready.
That is a much healthier definition of productivity than simply publishing more.
Speed only matters if the quality survives.
When the Workflow Fails
Directing AI-assisted work also means accepting that the workflow will sometimes fail.
AI may misunderstand the task.
It may give me information that looks useful but is not.
It may repeat itself.
It may introduce language that sounds polished but weakens the point.
It may send research in the wrong direction.
Those moments are not exceptions to the process.
They are part of the process.
The important questions are simple:
Did I catch the problem?
Do I understand why it happened?
Do I need to change the workflow?
That is how the system improves.
Not because AI becomes perfect.
Because the human process around it becomes better.
Building the System While Using It
One of the unusual things about my experience is that I built many of these workflows before I knew what to call them.
I was solving problems.
I was creating repeatable steps.
I was assigning specific roles to AI.
I was deciding where human approval had to remain.
Only later did I realize that what I had been building resembled structured AI workflows and, in some cases, agent-like systems.
Most of those systems were not fully autonomous.
They were usually manually triggered and specialized.
That distinction matters.
Not every useful AI workflow needs to operate independently.
Sometimes the value comes from building a repeatable process that helps the human work more consistently.
I had not learned the terminology first and then applied it.
I developed the practice first and learned the terminology afterward.
That may be one reason the systems felt natural.
They were built around real problems rather than abstract concepts.
The Managerial Shift
This change also affected how I thought about my role inside Vallano Media.
I was no longer simply writing articles.
I was increasingly managing a publishing system.
Research needed direction.
Verification needed standards.
Drafting needed oversight.
Graphics needed review.
Publishing needed coordination.
AI could assist with many of those functions, but I remained the person deciding how the pieces fit together.
That is where the shift from writer to director became clear.
The word “director” does not mean standing above the work.
It means coordinating it.
The work still has to be understood.
The standards still have to be maintained.
The final decision still has to be made.
But I no longer have to perform every task manually in order to remain in control.
What Other Journalists Can Learn From This
The lesson is not that every journalist needs dozens of workflows.
The lesson is simpler.
Start by examining the work you already do.
Where do you spend the most time?
Where do mistakes occur?
Which tasks are repetitive?
Which tasks require judgment?
Which tasks could be assisted without handing away editorial control?
Then test one small process.
Do not automate the entire newsroom.
Do not start with the highest-risk task.
Pick something manageable.
Maybe it is organizing notes.
Maybe it is generating questions to consider.
Maybe it is comparing two versions of a draft.
Maybe it is asking AI to challenge an argument before publication.
Then examine what happened.
Did it help?
Did it save time?
Did it introduce problems?
What still required human judgment?
That is how a workflow begins.
The Work Is Still Mine
AI has changed how I work.
It has not changed who is responsible for the work.
That was the lesson of Chapter 1.
Chapter 2 adds another layer.
Responsibility does not require doing everything manually.
Control does not mean refusing assistance.
The journalist can delegate parts of the process without delegating accountability.
That is the shift I am still learning to make.
From doing every task myself to directing a workflow.
From asking AI isolated questions to designing repeatable processes.
From thinking only like a writer to thinking more like an editor, manager and publisher.
The technology did not remove me from the process.
It changed where I add the most value.
And increasingly, that value comes from knowing what to direct, what to question, what to verify and when to say no.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Chapter 1: The Journalist Is Still Responsible
By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Artificial intelligence can help a journalist work faster.
It can organize information, summarize documents, compare sources, identify gaps in an argument, suggest questions, assist with editing and challenge assumptions the journalist may have overlooked.
But there is one thing it cannot do.
It cannot take responsibility for the journalism.
That responsibility remains human.
The journalist remains responsible for the work they produce.
Editors remain responsible for the decisions they make.
News organizations remain responsible for the systems they approve, the standards they set and the safeguards they require.
AI can become part of the process without becoming accountable for the result.
That distinction sounds simple.
In practice, it is not.
If an AI system produces a paragraph that eventually appears in an article and that paragraph contains an error, who is responsible?
If AI summarizes a document incorrectly and nobody catches it, who owns the mistake?
If a newsroom introduces an AI tool into its workflow without adequate safeguards, can responsibility simply be pushed down to the individual journalist?
These questions become more important as AI systems move deeper into journalism.
The answer cannot be that the technology is responsible.
Accountability still belongs to the people and organizations using it.
The Temptation of Speed
One of the most attractive things about artificial intelligence is speed.
Tasks that once required substantial manual work can sometimes be completed much faster with AI assistance.
Information can be organized quickly.
Long documents can be examined.
Ideas can be compared.
A first draft can take shape faster than it might have before.
For journalists working under deadlines, that can be incredibly useful.
But speed can create another danger.
The faster information appears, the easier it can become to accept it without examining it closely.
A polished answer can feel authoritative even when the underlying information is incomplete, misleading or wrong.
And an AI system does not have to invent a fact to cause problems.
It can omit crucial context.
It can emphasize the wrong detail.
It can flatten uncertainty.
It can steer attention toward one interpretation while neglecting another.
That is why human judgment becomes more important, not less.
AI does not eliminate the need for verification.
It increases the importance of knowing what needs to be verified and why.
Polished Does Not Mean Correct
One of the first lessons anyone working seriously with generative AI learns is that confident language does not guarantee accurate information.
An AI system can produce an answer that sounds professional, complete and perfectly reasonable.
It can also be wrong.
That creates a particular problem for journalism because credibility depends on the accuracy of what gets published.
A spelling mistake can be embarrassing.
A factual mistake can damage trust.
An unsupported accusation can create much more serious consequences.
The danger is not simply that AI makes mistakes.
Human beings make mistakes too.
The difference is that generative AI can produce those mistakes quickly and present them in language that already looks finished.
That means journalists cannot judge AI output by how polished it appears.
They have to judge it by the evidence behind it.
AI Output Is Not Evidence
This is one of the rules I have increasingly adopted in my own work: AI output is not evidence.
If an AI system tells me something happened, that does not make the statement true.
If it provides a date, statistic, historical claim or quotation, that information still has to be traced back to reliable evidence.
AI can help point me toward something worth investigating.
It can help organize what I find.
It can help compare multiple pieces of information.
But an AI-generated claim should never be treated as verified simply because the system produced it.
That distinction has changed the way I work.
Instead of asking only, “What is the answer?” I increasingly ask:
“What evidence supports the answer?”
Those are very different questions.
The first can produce information.
The second begins the verification process.
Human Oversight Has to Mean Something
There is a phrase that appears constantly in discussions about artificial intelligence:
Human in the loop.
It sounds reassuring.
But simply putting a human somewhere in the workflow does not automatically make the process responsible.
A journalist can still accept an AI-generated summary without checking it.
An editor can still approve a story too quickly.
A newsroom can still adopt an AI system without understanding its limitations.
Human oversight only works when the human is actually verifying, questioning and exercising judgment.
Otherwise, the human becomes little more than a rubber stamp.
The goal should not simply be to keep a person somewhere in the process.
The goal should be to make sure that person still has the time, authority and responsibility to challenge what the technology produces.
The Human Has to Push Back
Another lesson I have learned is that AI becomes more useful when I stop treating it as something that should agree with me.
If I present an argument and ask an AI system whether it is good, I may receive a helpful answer.
But I often learn more by asking it to attack the argument.
What am I missing?
What assumptions am I making?
What would a skeptical reader challenge?
What evidence would weaken my conclusion?
What alternative explanation should I consider?
That is where AI can become useful as an adversarial partner rather than simply a writing assistant.
But there is another layer to this.
The AI’s criticism also has to be challenged.
It can identify a weakness that does not actually exist.
It can suggest an alternative interpretation that is unsupported.
It can overcorrect.
So the journalist has to evaluate both the original argument and the AI’s response to it.
The human remains responsible for deciding what survives.
AI Is Not Just Another Spellcheck
Journalists have always used technology.
Search engines changed research.
Digital recorders changed interviews.
Transcription software changed note-taking.
Content-management systems changed publishing.
Spellcheck changed editing.
So why should generative AI be treated differently?
The difference is not that AI is the first technology journalists have ever used.
It is that generative AI can produce outputs that resemble reasoning, interpretation and writing.
It does not merely correct a misspelled word.
It can suggest an argument.
It can summarize a document.
It can rewrite a paragraph.
It can compare competing explanations.
It can recommend what information appears most important.
That gives it greater potential to influence editorial judgment.
And the closer technology moves toward those judgment-heavy parts of journalism, the more carefully journalists need to understand what the system is doing.
Judgment Cannot Be Automated Away
Journalism is filled with decisions that go beyond arranging facts.
Which facts matter most?
Which source is credible?
Which detail belongs near the top of the story?
Which claim requires additional verification?
When is there enough evidence to publish?
When should publication wait?
What context does the audience need?
Those decisions involve judgment.
AI may help inform that judgment, but it cannot remove human responsibility for the decision.
That becomes especially important when a story involves uncertainty.
Real reporting is rarely as clean as the completed article makes it appear.
Sources may disagree.
Documents may be incomplete.
Eyewitness accounts may conflict.
Statistics may require context.
People may have incentives to describe the same event differently.
A journalist has to work through those contradictions and decide what can responsibly be said.
AI can assist with that process.
It cannot own the decision.
Responsibility Is Bigger Than a Byline
I often think about this in very simple terms.
If my name appears on an article, I am responsible for what is in it.
But responsibility in journalism extends beyond the byline.
Editors, producers, researchers and others may shape journalism without their names appearing prominently on the final product.
Whether my name appears on the work or I contribute behind the scenes, I am still responsible for the decisions I make in producing it.
And organizations have responsibilities too.
A newsroom cannot introduce AI tools, encourage their use and then pretend every consequence belongs only to the reporter.
Management decisions matter.
Training matters.
Editorial standards matter.
The design of the workflow matters.
Responsibility is shared, but it does not disappear.
What That Means for This Book
Artificial intelligence will play a significant role in the creation of this book.
It will help me organize research.
It will challenge arguments.
It will help examine drafts.
It may suggest language that ultimately survives into the finished manuscript.
But it will not receive editorial authority over the work.
I will decide what stays.
I will decide what goes.
I will decide whether the argument reflects the evidence.
And I will be responsible for what is published.
If an AI system produces something inaccurate, misleading, unsupported or simply not good enough, I cannot defend the final result by saying:
“The AI told me.”
That is not an editorial defense.
A Different Kind of Partnership
None of this makes AI unimportant.
Quite the opposite.
Used carefully, it can become one of the most useful tools available to a modern journalist.
But the most productive relationship may not be one in which the journalist asks AI to do the thinking.
It may be one in which AI helps the journalist think harder.
Challenge this.
Compare these sources.
Find the weakness.
Show me another interpretation.
What am I overlooking?
Where does this argument fail?
Those kinds of questions turn AI from a shortcut into something closer to an intellectual sparring partner.
And that may ultimately be one of its most valuable roles in journalism.
Responsibility Remains Human
The tools journalists use have always changed.
The responsibilities underneath the profession have changed much less.
Verify the information.
Understand the evidence.
Provide context.
Be fair to the people involved.
Correct mistakes.
Do not publish something simply because it sounds convincing.
Artificial intelligence does not erase those responsibilities.
It makes them more visible.
The journalist may have a new assistant.
The newsroom may have faster tools.
The organization may be able to accomplish more in less time.
But when journalism reaches the public and someone asks, “Can I trust this?” the answer cannot be:
“The AI said it was right.”
The answer has to come from the work behind the story.
And responsibility for that work remains human.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
The Game Is Everywhere: Who Is Left to Cover It?
By Cheval John | Vallano Media
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
High school and small-college sports are easier to watch, track and discuss than they were a generation ago.
But as traditional newsrooms continue cutting sports staffs, more content does not necessarily mean more independent journalism.
On a Friday night, the stadium can still be full.
Parents check scores on their phones.
Players post highlights before leaving the parking lot.
Coaches communicate directly with their communities.
Fans who cannot attend may be able to stream the game from home.
The underlying activity has not disappeared.
High school sports recorded 8,415,684 participation entries during the 2025-26 school year, another all-time high and an increase of 149,440 from the previous year.
The National Federation of State High School Associations compiles those figures through its member state associations, making the total a broad measure of participation across American high school athletics.
There are also more ways to follow those athletes than previous generations had.
Scores update quickly. Highlights circulate across social platforms.
Schools can produce broadcasts themselves.
Streaming has made thousands of games available beyond the communities where they are played.
The games are increasingly visible.
Yet another part of the local sports ecosystem is shrinking.
The independent reporter in the press box.
The games remain. Many of the jobs do not.
The change became particularly visible again this fall.
On Oct. 1, multiple USA TODAY Co. sports staffers publicly announced that their positions had been eliminated.
Akeem Glaspie had been covering Butler basketball.
Michael Chavez said the company eliminated his position covering Mississippi high school sports.
Sherlon Christie, a senior trending editor, announced his departure after 22 years with the company.
The full scope of the cuts was not immediately clear.
Only weeks earlier, McClatchy eliminated a majority of the positions in the Lexington Herald-Leader newsroom.
The cuts included three sports reporters, according to Kentucky Public Radio.
Those departures matter for reasons that extend beyond the number of jobs lost.
For generations, the local sports reporter was part of the infrastructure surrounding a community’s teams.
The reporter knew the schools.
The coaches.
The administrators.
The rivalries.
The history behind why one game mattered more than another.
Over years, that person might watch an athlete become a coach, an assistant become a head coach or a struggling program become a contender.
That kind of institutional knowledge is difficult to replace with a scoreboard.
This is bigger than the sports department
What is happening in press boxes reflects a much larger transformation in local journalism.
Nearly 40 percent of local U.S. newspapers have disappeared since 2005, according to Northwestern University’s Medill Local News Initiative.
More than 270,000 newspaper jobs have been lost over that period, a decline of more than 75 percent.
Across journalism occupations more broadly, employment fell a little more than 7 percent from 2023 to 2024.
Digital publishing has not simply recreated the economics that supported those newsrooms.
Medill examined web traffic at 100 of the country’s largest newspaper sites and found that their average monthly pageviews declined by more than 45 percent over four years.
Only 11 of the 100 recorded growth during that period.
At the same time, local journalism itself has not disappeared.
In 2005, newspapers employed 71 percent of U.S. reporters.
By 2024, that share had fallen to 29 percent, while digital media and broadcast reporting each accounted for roughly a quarter of journalism employment.
That distinction matters.
The story is not simply that journalism is disappearing.
Where journalism happens is changing.
More content is not the same thing as more journalism
That becomes particularly important in sports.
A high school can livestream its football game.
That is useful.
A university can produce an ESPN+ broadcast.
That is useful too.
A coach can post highlights.
A parent can upload video.
A recruiting service can track prospects.
An independent creator can operate a podcast, newsletter or YouTube channel.
A school can distribute its own interviews, statistics and behind-the-scenes content.
Each can add something valuable to the sports-media ecosystem.
But they do not necessarily perform the same function.
A team’s social account represents the team.
An athletic department’s media operation represents the institution.
A recruiting service serves a particular audience.
A streaming platform primarily distributes the competition.
Independent journalism serves another purpose.
Someone still needs to ask why a decision was made.
Someone needs to provide context when something changes.
Someone needs to verify conflicting accounts rather than merely repeat them.
Someone needs to cover athletes, coaches and institutions as more than statistics, recruiting rankings or highlight clips.
And sometimes the most important story has little to do with the final score.
Streaming solved one problem while exposing another
Small-college sports provides one of the clearest examples of the tension.
Technology solved much of the old distribution problem.
Sam Houston offers a useful case study.
In 2008, Jason Barfield was leading the operation that became Bearkat Sports Network, which was streaming more than 60 Sam Houston sporting events annually.
In 2016, he began transitioning live-event production toward ESPN.
The operation continued to grow.
By 2024, Sam Houston said Bearkat Sports Network was responsible for nearly 80 ESPN+ productions each year, along with more than 50 live audio productions, press conferences, podcasts and studio programming.
That is genuine progress.
A game produced in Huntsville, Texas, no longer has to remain primarily a Huntsville experience.
But Bearkat Sports Network and ESPN serve different purposes from The Huntsville Item or an independent journalist covering Sam Houston.
One can show the game.
Another may need to report independently on the institution, its decisions and the people around the program.
Those roles can coexist.
Ideally, they should.
The danger comes when growth in the first category creates the impression that the second is no longer necessary.
Video Courtesy of ESPN College Football YouTube Channel
The patchwork is replacing the old model
The future of local sports media is unlikely to belong to one type of organization.
Traditional newspapers remain part of it.
So do television and radio.
But coverage increasingly can come from independent digital publications, newsletters, podcasts, YouTube channels, photographers, school broadcasts, university media operations and individual creators.
There are advantages to that fragmentation.
A reporter who loses a newspaper position no longer needs another newspaper to grant permission to keep reporting.
A broadcaster can create a podcast.
A photographer can build a direct relationship with an audience.
A journalist with deep knowledge of one conference, school or region can build a specialized publication around an audience that a larger outlet considers too small.
The barriers to publishing have fallen dramatically.
The cost of sustained original reporting has not fallen nearly as far.
Travel costs money.
Covering teams every week takes time.
Building sources can take years.
Public-records work requires patience and expertise.
Attending games, practices, press conferences, school-board meetings and community events requires somebody to show up.
Someone still has to fund that work.
That is the unresolved problem.
AI makes the distinction even sharper
Artificial intelligence adds another layer to the economic challenge.
Routine sports information is particularly easy to summarize: Scores. Schedules. Standings. Statistics.
Basic game results.
A reader increasingly may be able to obtain those answers without visiting the publication that originally gathered or organized the information.
Medill identified the integration of generative AI into search engines as an additional challenge for publishers, noting that users can increasingly encounter summarized information before reaching the underlying source.
The decline of local journalism began long before generative AI, so AI should not be treated as the cause of that decades-long contraction.
It is another pressure on an already disrupted business model.
That makes commodity information harder to build a business around.
But it may make another category of journalism more valuable: information that does not exist until someone reports it.
AI can summarize a press conference once a recording, transcript or report exists.
It can search documents, compare information and identify patterns.
It can help journalists organize research, test claims and work more efficiently.
But it does not eliminate the need for the sourcing and firsthand reporting that put new information into the record.
Local knowledge that has never entered that record still has to be sourced, verified and reported.
The easier existing information becomes to summarize and redistribute, the more valuable original sourcing, verification and local context can become.
That creates an opportunity, too
The decline of the old model does not necessarily mean the end of local sports journalism.
It may mean its business structure has to change.
Independent publishers could become part of the answer.
So could memberships.
Subscriptions.
Sponsorships.
Nonprofit newsrooms.
Local radio partnerships.
Newsletters.
Podcasts.
Specialized digital publications.
No single model is guaranteed to replace what newspapers once supported.
But the migration of journalism employment beyond newspapers demonstrates that the profession itself is already adapting.
Broadcast and digital media now employ substantial shares of U.S. journalists even as newspaper employment continues to contract.
The challenge is ensuring that the new structure can financially support enough people to do original reporting consistently.
There is still something to cover
The audience has not vanished.
More than 8.4 million high school sports participation entries were recorded during the 2025-26 school year.
Thousands of schools stream events.
College programs outside the largest conferences can place games on national digital platforms.
Athletes can communicate directly with fans.
Communities can follow teams from almost anywhere.
Technology has made the competition dramatically easier to see.
That is worth celebrating.
But visibility alone does not create journalism.
A community can have livestreams, highlights, statistics, podcasts and hundreds of social-media posts about its teams while still losing something important if nobody is being paid to independently follow those teams week after week.
The future therefore presents two different questions.
The first has largely been answered: How do we make the games easier to see?
Technology solved much of that problem.
The harder question remains: Who is going to keep showing up to report what happens around them?
Because the game can be everywhere.
That does not automatically mean the journalism will be.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
I’m Taking the Nonfiction Writing Challenge Again-This Time With AI
By Cheval John | Vallano Media November 1st, 2026
This article was created with the assistance of artificial intelligence.
The final version was reviewed, edited and fact-checked by the author
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
Twelve years ago, I participated in a nonfiction writing challenge.
Generative AI was not part of my writing process.
There was no ChatGPT sitting beside me, no AI assistant helping me organize research and no system I could ask to challenge an argument before I published it.
The process was more traditional: research, notes, writing, rewriting and editing.
Now it is 2026, and I am doing the challenge again.
This time, the blank page has company.
For the next 30 days, I will be working on a new book, Journalist and AI — Volume 1, while documenting what happens when artificial intelligence becomes part of the nonfiction writing process.
This is not an experiment in having AI write a book for me.
It is an experiment in what happens when a writer uses AI as a tool while remaining responsible for the research, reporting, verification, judgment, writing and final product.
That distinction matters.
Twelve Years Later
A lot has changed since 2014.
Writers already had search engines, digital publishing tools, social media and numerous technologies that had transformed how information was gathered and distributed.
But mainstream writers did not have the kind of easily accessible generative AI systems available today.
Now I can use AI to help organize research, examine competing ideas, identify weaknesses in an argument, compare information and assist with editing.
But that does not automatically make the writing better.
It does not automatically make the research accurate.
AI can accelerate good work.
It can also accelerate bad work.
It can help organize information more quickly, but it can also produce convincing errors, flatten a writer’s voice or reinforce a weak assumption if nobody challenges it.
That is part of what I want to explore over the next 30 days.
Why Do This Again?
I have already written books.
So this challenge is not about proving that I can finish one.
I am returning to it because the writing environment has changed so much that I want to experience the difference for myself.
Where does AI actually save time?
Where does it improve the work, and where does it create new problems?
Most importantly, can AI help a writer become more productive without sacrificing accuracy, judgment, transparency or individual voice?
Those questions are part of the reason Journalist and AI exists.
Instead of discussing them only in theory, I am going to put them to the test while writing the book itself.
This Is Not a Perfect Comparison
There is one thing I want to make clear from the beginning.
This is not a controlled experiment between 2014 and 2026.
AI is not the only thing that has changed since 2014.
I have changed too.
I have another 12 years of experience as a writer, publisher and researcher.
Publishing technology has changed.
The projects are different.
The way I organize information has changed.
So if I work faster in 2026 than I did in 2014, I cannot simply say AI caused the entire difference.
AI is an important variable, but it is not the only one.
That limitation is part of the experiment, and I do not intend to hide it.
What I can document is how AI affects the process I use today.
How AI Will Be Used
Artificial intelligence will be part of this project, but it will have defined roles.
I will use it to help organize ideas, analyze information, identify questions, compare sources, examine arguments and assist with editing.
I will also use it as an adversarial partner.
One of the most useful habits I have developed while working with AI is asking a different kind of question.
Instead of only asking, “Is this good?” I often ask:
“What is wrong with this?”
I use AI to red-team ideas and drafts — to search for weak arguments, missing context, unsupported assumptions and alternative interpretations.
But the AI’s criticism is not automatically correct either.
I still have to evaluate it.
The same principle applies to research.
AI can help me find patterns, organize information and work through material faster than I could manually.
But an AI-generated statement is not evidence.
If something matters to the book, it still has to be verified against reliable sources.
What AI Will Not Do
AI will not receive final editorial authority over this project.
I decide what belongs in the book.
I decide whether an argument accurately reflects the evidence.
I decide whether the writing sounds like me.
And if an AI system produces something inaccurate, misleading, unsupported or simply not good enough, I am responsible for catching it.
A system capable of producing polished language quickly can also produce polished mistakes quickly.
Speed cannot become a substitute for judgment.
What Readers Are Going to See
For the next month, I want to make parts of this process visible.
There will be successes.
There will probably be mistakes.
There will be AI suggestions that improve the work and others that I reject.
There may be research that changes the direction of a chapter.
There may be sections that get rewritten completely because the first approach does not work.
And if AI wastes time, gives me weak information, sends me in the wrong direction or produces writing that I throw away, that belongs in the record too.
The goal is not to create the illusion that writing with AI is effortless.
The goal is to document what actually happens.
I will not be publishing every chapter of the book as I write it.
Readers will instead see selected parts of the process, observations from the experiment and what I learn along the way.
At the same time, Vallano Media will continue its regular Tuesday and Thursday publishing schedule, with additional articles published when news, reporting or other projects warrant it.
The book challenge will happen alongside that work rather than replace it.
My Rule for the Next 30 Days
The Write Nonfiction in November challenge does not require me to work on the project every single day.
That is a rule I am adding for myself.
From November 1 through November 30, I will work on Journalist and AI — Volume 1 every day.
Some days may produce a large amount of writing.
Other days may be spent revising, verifying information, reorganizing a chapter or questioning an argument that is not working.
The point is not to chase an arbitrary daily word count.
The point is to keep moving the manuscript forward.
Thirty Days
By November 30, I want more than a word count.
I want a complete first manuscript of Journalist and AI — Volume 1.
But I also want a record of how we got there.
How much did AI accelerate the process?
Where did human judgment remain indispensable?
Did greater speed allow more time for verification and deeper thinking, or did it simply encourage more production?
Did AI help preserve my voice, or did I sometimes have to push back against its tendency to make everything sound polished and generic?
And after 30 days, will I view the relationship between journalists and artificial intelligence differently than I do today?
I do not know all of those answers yet.
That is exactly why I am doing this.
In 2014, I participated in a nonfiction writing challenge using the tools available to me at the time.
Twelve years later, I am returning in a completely different technological environment.
The technology has changed.
The responsibility of the writer has not.
For the next 30 days, you are going to see the process.
Graphics created with AI-assisted design tools.
Visual concepts, editorial direction, composition, and final creative decisions were developed by Vallano Media.
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