Verification Before—and After—Generation: Why AI Answers Still Need to Be Checked
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.
Once the research foundation was in place, artificial intelligence became much more useful.
It could compare sources, organize timelines, identify patterns across documents and suggest explanations for why different accounts appeared to conflict.
But that created a different problem: What happens when the AI gives an answer that sounds right?
An obviously poor answer is relatively easy to catch.
A polished answer is harder.
If an AI system produces a clear explanation, uses professional language and connects events in a logical sequence, there is a natural temptation to accept the result and move forward.
But coherence is not confirmation.
For Vallano Media, that led to another rule: Generation did not end the research process.
It triggered another verification process.
A Confident Answer Is Still Just an Answer
Generative AI can produce language that sounds remarkably certain.
That can be useful.
It can also create risk.
A response may contain accurate information, incomplete information, outdated information, unsupported connections or several of those at once.
Those categories are not always obvious from the writing itself.
There is no automatic warning attached to the sentence that happens to be wrong.
That changes how AI-generated material has to be read.
The question cannot simply be: Does this explanation make sense?
The better question is: What evidence would allow us to publish this explanation?
During the FIFA reporting process, useful AI responses were treated as working material rather than finished conclusions.
An answer could suggest a direction.
It could produce a hypothesis.
It could identify something worth investigating.
But before an AI-generated explanation influenced published copy, its important factual claims still had to be traced back to evidence.
Verification Happens at the Claim Level
An AI-generated paragraph can contain several different claims at once.
Imagine a response saying that one organization introduced a proposal, another group opposed it, that opposition caused the proposal to stall, and the controversy demonstrated a larger governance problem.
That may read like one coherent explanation.
It is actually several claims.
Did the organization introduce the proposal?
Did the other group oppose it?
Did that opposition actually cause the proposal to stall?
And is the governance conclusion an established fact or an interpretation?
Each deserves a level of scrutiny appropriate to the claim.
Routine background information may be relatively straightforward to confirm.
Consequential, disputed, causal or unusual claims generally require greater attention.
The first two claims in the example may be factual questions.
The causal claim is more demanding.
The final conclusion may be legitimate analysis, but it should still be presented as analysis rather than established fact.
This is why verification increasingly had to happen at the claim level.
A paragraph can be mostly accurate and still contain one sentence that changes the meaning of the entire story.
That sentence matters.
Sequence Does Not Automatically Mean Cause
Chronology can create another trap.
Event A happens.
Then Event B.
Then Event C.
It is easy to turn that sequence into a story in which A caused B and B caused C.
Sometimes that interpretation is supported.
Sometimes the events simply occurred in that order.
Journalism has to distinguish chronology from causation.
That means asking: Is there evidence that one development actually caused the next?
Did a participant say that it did?
Did independent reporting establish the relationship?
Or are we connecting the events because the sequence looks persuasive?
AI can be useful for turning complicated timelines into understandable narratives.
But that usefulness requires caution.
A compelling narrative becomes a problem when it adds certainty the evidence does not support.
Words such as because, caused, forced, prompted, resulted in and led to therefore deserve extra scrutiny.
A single verb can turn analysis into an unsupported factual claim.
Asking AI to Challenge Its Own Answer
One of the more useful changes in the workflow came when AI stopped being used only to produce answers.
It also began being prompted to challenge them.
After an explanation was generated, the next questions could include: What is weak about this conclusion?
Which claims are not fully supported?
What assumptions are being made?
What alternative explanation fits the same evidence?
Where might the analysis be confusing sequence with causation?
What would a skeptical editor challenge?
What evidence would weaken or disprove this interpretation?
This changed the role of AI.
Instead of functioning only as a generator, it could become part of an adversarial review process.
That did not turn AI into an independent fact-checker.
An AI critique still requires verification and should not be treated as independent evidence.
Its value was in surfacing questions, weaknesses and assumptions that could then be investigated against the evidence.
That introduced useful friction before publication.
And in journalism, friction can be valuable.
The fastest route from prompt to publication is not necessarily the safest one.
The First Answer Was Never Sacred
Another useful habit was refusing to treat the first AI response as authoritative.
The first answer could still be useful.
Sometimes its greatest value was showing how the system was framing the story.
That framing could then be examined.
Was it relying too heavily on one source?
Was it smoothing over uncertainty?
Was it turning a possible explanation into a definite one?
Was it giving too much weight to the most dramatic interpretation?
Was it leaving out an important stakeholder?
Was it treating secondary reporting as though it were primary evidence?
Those questions could produce a better second answer.
Then that answer could be challenged again.
This is not the same as repeatedly prompting AI until it gives the journalist the answer they already wanted.
That would simply automate confirmation bias.
The objective was the opposite: Make the conclusion harder to defend unless the evidence actually supports it.
Verification Sometimes Sends the Reporting Backward
Verification does not always move a story closer to publication.
Sometimes it sends the reporting backward.
An AI-generated claim might expose the need for another source.
A timeline comparison might reveal a date discrepancy.
A causal explanation might fall apart once the underlying reporting is examined.
A quote might need to be traced back to its original context.
In those situations, the next step is not better wording.
It is more reporting.
The process can therefore look less like: Research → AI → Draft → Publish
and more like: Research → AI analysis → verification → additional research → revised analysis → draft → verification again.
That loop may repeat several times.
From a pure speed perspective, that can look inefficient.
From a journalism perspective, that is the safeguard.
AI is useful not only when it helps finish the work faster, but also when it exposes where the work is not finished.
Verification Also Means Looking for What Is Missing
Errors are not limited to false statements.
Omissions can distort a story too.
An AI summary might accurately describe five important facts while leaving out a sixth fact that changes how the first five should be understood.
That means verification has to ask two questions: Is what I am seeing correct?
and: What am I not seeing?
This becomes particularly important in stories involving multiple organizations, competing interests or conflicting accounts.
A response may be technically accurate but still incomplete.
Which perspectives are represented?
Which are absent?
Does one side receive more explanation than another?
Did the summary emphasize conflict while overlooking agreement?
Did it omit a development that complicates the central narrative?
Those are editorial questions.
AI can help surface them.
It cannot resolve them automatically.
Video Courtesy of CrashCourse YouTube Channel
Red-Team Review Became Part of the Workflow
Over time, this adversarial questioning became more systematic.
Vallano Media began using what could be described as an editorial red-team review: deliberately challenging an argument, analysis or draft in an attempt to expose weaknesses before publication.
This is different from formal AI-security or cybersecurity red teaming.
The purpose here was editorial.
The review could look for unsupported factual claims, causal overreach, missing context, weak sourcing, contradictions, wording stronger than the evidence justified and assumptions disguised as conclusions.
The important shift was simple: Do not ask only how to strengthen an argument.
Ask how the argument could fail.
Generative AI naturally encourages forward momentum: Generate → refine → polish → publish.
Red-teaming interrupts that momentum.
It creates a deliberate stage where the objective changes from building the article to trying to break it.
If the argument survives that challenge and the underlying evidence continues to support it, confidence increases.
If it does not, the weakness has been discovered before publication rather than afterward.
Asking AI to Verify Itself Is Not Enough
There is an important distinction between verification and simply asking an AI system: Are you sure?
The system may reconsider its answer.
It may also confidently repeat the same mistake.
Real verification requires returning to evidence outside the generated response.
That may mean opening the original document.
Reading the full article rather than relying on a summary.
Checking the publication date.
Comparing independent reports.
Locating the original quote.
Reviewing the official statement.
Or acknowledging that confirmation is unavailable.
AI can help guide those steps.
It cannot substitute for them.
So the important question is not: Did AI verify the answer?
It is: What evidence ultimately verified the claim?
The Journalist Has to Be Willing to Reject the Output
Perhaps the most important verification skill is also the simplest.
The journalist has to be willing to say: No.
No, that conclusion is too strong.
No, the evidence does not establish causation.
No, that source is not sufficient.
No, those events should not be combined.
No, the answer is plausible but unverified.
No, that does not belong in the article.
This is where subject knowledge, skepticism and editorial judgment become essential.
AI can make suggestions quickly.
The professional decides which suggestions survive.
The goal is not to become better at accepting AI output.
It is to become better at determining which output deserves to survive the editorial process.
Verify Before and After Generation
The title of this chapter contains an important tension.
Verification should happen before generation because AI analysis is only as strong as the evidence being supplied.
But verification also has to happen after generation, because the system can introduce new assumptions, errors or interpretations while processing that evidence.
The workflow therefore becomes: Verify the inputs → generate the analysis → verify the output.
Then repeat whenever necessary.
That cycle became one of the clearest lessons from Vallano Media’s FIFA reporting process.
AI did not reduce the importance of verification.
It created another place where verification had to occur.
What Comes Next
Once the research foundation had been established and AI-generated conclusions had been tested, the next question became much more practical: What was AI actually useful for?
Not theoretically.
Not as a list of possible applications.
But inside the reporting project itself.
Where did it save time?
Where did it help organize information?
Where did it expose weaknesses?
And where did human judgment still have to take over?
Next week, chapter 4 will examine how AI actually assisted the reporting process — and where its role stopped.
Building the Research Foundation: Why Source Quality Comes Before AI Analysis
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.
Before artificial intelligence could help analyze the FIFA story, there was a more basic problem to solve.
What information could actually be trusted?
That question matters in any reporting project, but it becomes especially important when AI enters the workflow.
Artificial intelligence can organize information quickly.
It can compare documents, summarize reporting, identify differences between accounts and generate additional questions.
But if the information going into that process is weak, incomplete or incorrectly understood, faster analysis does not solve the problem.
It can simply produce a more polished version of the wrong answer.
For Vallano Media’s reporting on the FIFA Forward Enterprise proposal, the research therefore had to begin somewhere AI could not replace: the sources themselves.
Primary Sources and Secondary Reporting Serve Different Purposes
One of the first steps was separating primary sources from secondary reporting.
Primary sources included material originating from organizations or people directly involved in the story, including official statements and other first-hand material relevant to the proposal.
Those sources were important because they could establish what an organization had actually said, proposed or published.
But being a primary source does not automatically make a source neutral or complete.
An organization speaks from its own perspective.
An official statement may explain a proposal, defend a decision or respond to criticism without answering every question surrounding the issue.
That is where independent reporting becomes important.
Secondary reporting can provide additional context, outside sourcing and information that may not appear in an organization’s own communications.
The goal was not to declare one category universally superior to the other.
It was to understand what each source could legitimately establish.
An official statement might establish what FIFA said.
Independent reporting might establish what journalists learned through additional sourcing.
Analysis might help explain why those developments mattered.
Those are different functions.
Treating them as interchangeable creates problems.
Video Courtesy of CrashCourse YouTube Channel
AI Was Not the Source
That distinction helped establish one of the most important rules of the reporting process: An AI response was not treated as evidence.
Generative AI can make that boundary surprisingly easy to blur.
Ask a system a complicated question and it may produce a detailed answer containing names, dates, chronology and explanations within seconds.
It can look like research.
But the response itself does not establish that those claims are true.
AI could help locate material that deserved examination, organize information that had already been gathered, compare accounts and generate additional questions.
But when a factual claim mattered to the story, the important question remained: Where did this information actually come from?
The underlying source still had to be identified and evaluated.
AI could participate in source discovery.
It could not substitute for the source.
Source Quality Matters More Than Source Quantity
Having more links does not necessarily mean having better reporting.
A dozen websites can repeat the same original claim.
Twenty articles can ultimately trace back to one source.
Search results can therefore create the appearance of widespread confirmation when several publications are simply repeating information from one another.
That makes source quality more important than source volume.
The important questions were familiar journalistic ones.
Who originally reported the information?
Was there a primary source?
Was another credible organization independently confirming it?
Was an article contributing new reporting or repeating somebody else’s work?
Did the source clearly distinguish verified information from allegation or interpretation?
How close was the source to the event being described?
None of those questions were invented by artificial intelligence.
The value of the workflow was not that it created a new form of journalism.
It applied familiar reporting standards to a new tool.
Building the Timeline
Chronology became another important part of the research foundation.
Complex stories rarely arrive in perfect order.
One report may describe something that happened several days earlier.
An official response may appear after independent reporting.
A later development may change how an earlier event should be understood.
If those events are combined incorrectly, the resulting narrative can imply relationships the evidence does not support.
For the FIFA project, establishing the timeline helped separate: what happened, when it happened and what was known at each stage.
That distinction matters because hindsight can distort reporting.
Information that appears obvious after several developments may not have been available when an earlier decision was made.
Likewise, two events that occurred close together are not automatically connected.
AI could help organize dates and compare accounts, but chronology still had to be checked against the underlying reporting.
AI-assisted summaries can collapse events from different moments into a single narrative if that chronology is not checked carefully.
A reliable timeline became one safeguard against that problem.
Facts, Allegations, Analysis and Speculation Are Not the Same Thing
Another step was determining what level of certainty the evidence actually supported.
Not every sentence in a complicated story carries the same weight.
Some information can be stated as established fact.
Some must be attributed because it represents what an organization or person claims.
Some material comes from reported allegations.
Some conclusions are analysis.
And some possibilities remain speculation.
Those categories cannot be casually blended together.
There is a significant difference between saying: An organization did something.
and saying: A news organization reported that sources said the organization was considering doing something.
Removing that distinction might make a sentence shorter.
It can also make the sentence inaccurate.
AI systems can be useful for condensing information, but condensation can erase important qualifications.
Words such as according to, reported, alleged, proposed, and could may appear minor.
They are not.
Sometimes they are the difference between accurately representing the evidence and overstating it.
Contradictions Are Signals, Not Answers
When two credible sources appeared to conflict, the goal was not to immediately decide that one of them was wrong.
The contradiction itself became a reporting question.
Were the sources describing different time periods?
Were they answering different questions?
Did one report contain newer information?
Was one describing an official position while another relied on independent sourcing?
Could both accounts be accurate within different contexts?
This is an area where AI can be particularly useful.
It can place statements next to one another.
It can surface apparent differences.
It can help formulate questions about those differences.
But an apparent contradiction identified by AI is not automatically a contradiction in reality.
It is a signal to investigate further.
AI can surface the problem.
Reporting still has to resolve it — or acknowledge that the available evidence does not yet allow it to be resolved.
Sometimes the Gap Is the Finding
One of the easiest mistakes in research is feeling compelled to fill every blank.
Journalists naturally want answers.
AI systems are also designed to produce answers.
That combination requires caution.
Sometimes the evidence simply does not answer a question.
Responsible reporting does not require constructing the most plausible explanation.
Sometimes the responsible conclusion is simply: The evidence is incomplete.
During the FIFA research process, gaps in available information were treated as gaps rather than invitations for AI to construct connections between them.
This matters because generative AI can produce plausible explanations.
But plausibility is not evidence.
If two verified facts sit on opposite sides of an information gap, AI may be able to suggest several possible explanations connecting them.
Those possibilities can become questions for further research.
They cannot automatically become facts in the story.
Research Became an Iterative Process
The research did not move neatly from beginning to end.
One source raised another question.
A secondary report led back to primary material.
A timeline exposed something that needed additional research.
An AI comparison surfaced a potential inconsistency.
Verification produced new questions.
The process became cyclical: Find information → evaluate the source → verify the claim → compare the evidence → identify questions → research again.
AI could accelerate parts of that process.
It could not eliminate it.
In fact, some of the most useful AI interactions produced more reporting rather than less.
That may sound counterintuitive.
Much of the promise surrounding AI focuses on efficiency: fewer steps, less time and faster output.
But in journalism, a tool can be valuable precisely because it reveals something that deserves another round of research.
Efficiency does not always mean finishing sooner.
Sometimes it means discovering sooner that the work is not finished.
The Foundation Comes Before the Analysis
By the time AI became useful for deeper analysis of the FIFA story, the basic research structure was already taking shape.
Sources had been separated according to their role.
Important claims could be traced back to evidence.
Chronology had been established.
Potential contradictions had been identified for further examination.
Facts had been distinguished from allegations, reporting and interpretation.
And gaps were allowed to remain gaps when the evidence did not justify filling them.
Only then could AI provide meaningful assistance.
That sequence matters.
If AI analysis comes first, there is a risk that the journalist begins building the research around the system’s initial explanation.
If the evidence comes first, AI can instead be asked to work against a foundation the journalist has already examined.
The difference is subtle but important.
One approach asks AI: What happened?
The other begins with evidence and asks: What should we examine next?
That is a much safer place to begin.
What Comes Next
Building a strong research foundation did not eliminate the risks associated with artificial intelligence.
It made those risks easier to recognize and manage.
The next challenge was deciding how much confidence to place in what AI produced once it began analyzing that material.
That required another principle: Verification does not end when AI enters the workflow.
If anything, it becomes more important.
Next Tuesday, chapter 3 will examine verification before generation — including how Vallano Media challenged AI-assisted conclusions, checked confident answers and prevented analysis from outrunning the evidence.
Why This Case Matters: Journalism, AI and the Work Behind the Story
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 make journalism faster.
That is precisely why journalists need to be careful with it.
AI can help organize research, compare documents, identify questions, summarize complicated material and assist with drafting.
Tasks that once consumed hours can sometimes be completed much more efficiently.
But speed is not the same thing as accuracy.
An AI system can produce an answer that sounds authoritative while overlooking context, confusing chronology, relying on weak sources or presenting an interpretation as fact.
The technology can assist with journalism, but it cannot assume responsibility for what a journalist ultimately publishes.
That distinction became increasingly clear to Vallano Media while researching its two-part series examining the FIFA Forward Enterprise proposal and its aftermath.
What began as a sports story also became a real-world test of how artificial intelligence could fit inside an existing journalistic process without replacing the principles that made that process reliable.
The central question was simple:
What does responsible AI-assisted journalism look like when the journalist remains responsible for the work?
The Story Came First
The project did not begin as an experiment with artificial intelligence.
It began with a story.
The FIFA Forward Enterprise proposal raised questions that could not be answered simply by reading one article or accepting one organization’s explanation.
The reporting required multiple sources, comparisons between claims and documents, attention to chronology and a clear distinction between what could be verified and what remained uncertain.
AI became useful because it could assist with parts of that process.
It could help organize information.
It could surface potential inconsistencies for further investigation.
It could compare material from different sources.
It could generate additional questions.
And it could help structure complicated research before that research became an article.
But throughout the project there was one important boundary:
AI could help investigate the story.
It could not decide what was true.
That still required verification.
Using AI Is Not the Same as Depending on AI
There is an important difference between using artificial intelligence in journalism and handing journalism over to artificial intelligence.
A simplified AI-first approach might look like this:
Choose a topic → ask AI to research it → ask AI to write the article → publish.
That process may be fast.
It is also risky.
A more responsible workflow looks very different:
Question → research → source evaluation → verification → AI-assisted analysis → additional research → drafting → fact-checking → editorial judgment → publication.
Video Courtesy of NBA YouTube Channel
In that model, AI operates inside the journalistic process rather than replacing it.
The purpose of this project is not to argue that journalists should avoid artificial intelligence.
Nor is it to suggest that AI can independently perform journalism simply because it can produce convincing prose.
The more useful question is how journalists can use the technology while preserving the sourcing, verification, skepticism and accountability that journalism already requires.
That is what the FIFA reporting project allowed Vallano Media to examine in practice.
Journalism Already Had a Workflow
Artificial intelligence did not invent the journalistic process.
Long before generative AI became widely available, reporters already had a basic system for determining whether something was ready to publish.
A reporter receives information.
The reporter asks where it came from.
A source is identified.
Additional sources are consulted.
Claims are compared with documents, statistics, direct statements or other evidence.
Contradictions are investigated.
Context is added.
The reporter determines what can be stated as fact, what needs attribution and what cannot yet be supported.
Then the story is edited and reviewed before publication.
The tools have changed repeatedly over journalism’s history.
Reporters moved from notebooks and typewriters to computers, databases, search engines, digital archives, social media and now artificial intelligence.
But the fundamental responsibility did not change with each technological shift.
The journalist still had to determine whether the information was reliable enough to publish.
AI should be treated the same way.
It is another tool inside the workflow.
It can make portions of that workflow dramatically faster, but it does not eliminate the workflow itself.
In fact, because an AI system can generate polished explanations so quickly, the temptation to skip traditional verification may become greater.
A confident answer can look like a researched answer.
Those are not necessarily the same thing.
The Professional Has to Know the Field
This leads to another important lesson from the FIFA project.
AI becomes more useful when the person using it already understands the fundamentals of the work.
A journalist familiar with sourcing can recognize when an answer depends too heavily on secondary reporting.
A journalist who understands attribution can identify when an AI response has turned somebody’s claim into an apparent fact.
A journalist familiar with chronology can notice when separate events have been incorrectly combined.
And a journalist who understands the subject can recognize when an explanation simply does not make sense.
This is why AI literacy alone is not enough.
Someone can become highly skilled at prompting an AI system and still produce unreliable journalism if they do not understand reporting.
The technology does not remove the need for professional knowledge.
It increases the value of that knowledge because the professional must evaluate what the technology produces.
In that sense, one of the most important AI skills may not be knowing what to ask.
It may be knowing when the answer is wrong.
AI Can Play More Than One Role
Another lesson from the reporting process was that AI did not perform one fixed job.
Its role changed depending on what the story required.
At one stage, it could function like a research assistant, helping organize information that had already been gathered.
At another, it could act like a brainstorming partner by suggesting questions that had not yet been considered.
Later, it could help compare claims from different sources.
And during review, AI could be deliberately prompted to challenge the assumptions behind the reporting.
That shift is important.
AI does not always have to help a journalist strengthen an argument.
Sometimes one of its most useful roles is helping expose weaknesses in that argument.
Rather than treating the first AI response as an answer, the response can become the beginning of another round of questioning.
What evidence supports this?
What is missing?
What assumptions are being made?
What would contradict this conclusion?
Is this statement supported by a primary source?
Those questions deliberately slow down a technology whose greatest appeal is speed.
The objective is not to use AI as quickly as possible.
The objective is to use it without allowing speed to outrun verification.
Accountability Never Moved
There was one responsibility that could not be delegated anywhere in the process.
Publication.
AI could recommend wording.
It could summarize research.
It could challenge assumptions.
It could help identify information that deserved further examination.
But when Vallano Media published the FIFA articles, responsibility for those articles belonged to Vallano Media.
If a claim had been wrong, saying that an AI system generated it would not have been an adequate explanation.
That may be the simplest principle in this entire project:
AI can participate in the process.
Accountability stays with the publisher.
Why This Matters Beyond One FIFA Story
The FIFA case is useful because it turns an abstract debate about artificial intelligence into something practical.
Much of the conversation around AI and journalism tends to swing between two extremes.
One side treats AI primarily as a threat to journalism.
The other presents it as a replacement for large parts of the reporting and writing process.
The experience of using AI inside an actual reporting project was more complicated than either position suggests.
AI was useful.
It made research easier to organize.
It helped surface questions.
It helped test assumptions.
It reduced the time required for some tasks.
But none of those benefits removed the need for reporting judgment.
The technology worked best when combined with an existing professional process.
That lesson extends beyond journalism.
Public relations professionals still have to understand audiences, messaging and reputation.
Researchers still have to understand methodology.
Business owners still have to understand their markets.
Analysts still have to understand the information they are interpreting.
AI can increase a professional’s capacity, but it does not automatically create expertise.
Someone who understands the work is in a much better position to use AI effectively because that person knows what should happen before, during and after AI becomes involved.
The Goal Is Not Automation for Its Own Sake
It is tempting to measure AI adoption by how much work can be automated.
That may be the wrong measurement for journalism.
A better question is whether AI improves the quality, speed or depth of the reporting without weakening verification.
If the technology helps organize dozens of sources more efficiently, that can be useful.
If it surfaces a contradiction that deserves further investigation, that can be useful.
If it helps produce a clearer outline, that can be useful.
But if it encourages a reporter to stop checking sources because an answer sounds convincing, the technology has made the workflow worse rather than better.
Efficiency only matters if the finished work remains trustworthy.
That principle became one of the most important lessons of the FIFA project.
The objective was not to remove the journalist from the process.
It was to determine where AI could make the journalist more effective.
A Working System, Not a Perfect One
The workflow examined in this series should not be treated as a universal formula.
Different journalists, publications and subject areas will require different safeguards.
Breaking news will operate differently from long-form analysis.
Investigative reporting will require different levels of documentation from a game recap.
A local newsroom may use AI differently from an independent digital publication.
And the technology itself will continue to change.
What matters is the underlying principle.
AI should be incorporated into a system where its output can be questioned, checked and rejected.
The journalist needs the ability to say:
That answer is useful.
That answer needs verification.
That answer is incomplete.
Or simply:
That answer is wrong.
The process documented here developed through actual reporting rather than theory.
It evolved as problems appeared.
Weak answers led to stronger verification steps.
Overconfident conclusions led to more adversarial questioning.
Gaps in research led to additional sourcing.
In other words, the workflow improved because the technology was not assumed to be reliable by default.
It had to earn its place in the process.
What Comes Next
The next stage is where the discussion becomes more practical.
Before AI could help interpret the FIFA story, the reporting needed a research foundation strong enough to support that analysis.
That meant identifying credible sources, separating primary evidence from secondary reporting, establishing chronology and recognizing where the evidence remained incomplete.
Only then could AI become useful without becoming the source itself.
Chapter 2 will examine how that research foundation was built — and why source quality had to come before AI analysis next Tuesday.
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.
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