There was a time when seeing something with our own eyes was enough to believe it.
A photograph was considered evidence.
A recorded conversation could settle an argument.
A video could expose wrongdoing, establish someone’s presence at a particular location, or change public opinion overnight.
Of course, photographs and videos could always be manipulated. People could stage events, edit recordings, and present misleading information.
But artificial intelligence has changed something fundamental.
We are approaching a world where producing convincing evidence may become easier than proving whether that evidence is genuine. That is AI Deepfakes.
And I think we are seriously underestimating what that means.
On October 15, 2026, representatives from OpenAI and Meta warned a U.S. judicial committee that increasingly sophisticated AI-generated audio and video could make it harder for courts to determine whether digital evidence is authentic.
According to Reuters’ reporting, the judiciary has been examining these concerns for several years.
The problem is not yet widespread in American courts. Of 931 judges responding to a survey, only 15 had encountered cases involving challenges to evidence on the grounds that it was a deepfake.
Nevertheless, the discussion caught my attention.
Because if courts are beginning to confront the possibility that visual and audio evidence can no longer be evaluated in quite the same way, imagine what this could mean for ordinary people and businesses.
I believe we are entering an uncomfortable new chapter of the digital age.
We Have Made Content Creation Easier. We Have Also Made Deception Cheaper.
I work extensively with artificial intelligence, and I am genuinely excited about what generative AI makes possible.
Someone who cannot afford a professional design team can now create impressive visuals.
A small business can produce marketing materials without investing heavily in production.
An educator can develop explanations, illustrations, and learning resources much faster.
Creative opportunities are becoming available to people who previously lacked the money, equipment, or technical expertise to pursue them.
I consider this a positive development.
But technology does not distinguish automatically between a person trying to create something useful and someone trying to deceive others.
The same capabilities that help a small entrepreneur create a promotional video can help a malicious actor fabricate a public statement.
A synthetic voice can make content more accessible to people who struggle with reading.
It can also be used to impersonate someone.
An AI-generated photograph can make a story visually engaging.
It can also falsely place a person at an event they never attended.
The technology is not inherently dishonest.
The challenge is that dishonesty can now be produced at a scale and level of sophistication that earlier generations never had to confront.
This is where my concern begins.
The Most Dangerous AI Deepfakes May Be the One Nobody Questions
When people discuss deepfakes, they often imagine a sensational video involving a politician, celebrity, or public figure.
But I think some of the most serious consequences may emerge from much smaller incidents.
Imagine receiving a voice message that sounds exactly like your company’s managing director.
They instruct the finance department to make an urgent payment.
Or imagine a fabricated recording of a business owner making an offensive statement.
The recording spreads across social media.
Customers begin leaving.
Business partners become concerned.
Employees demand explanations.
By the time someone establishes that the recording was fabricated, the damage may already be substantial.
I am not suggesting every company will face such an incident.
But the possibility changes how organizations should think about identity, communication, and trust.
In the past, organizations often treated familiar voices, recognizable faces, or apparently authentic documents as strong indicators of legitimacy.
Increasingly, those indicators may not be sufficient on their own.
That is a profound change.
And it reaches far beyond cybersecurity.
There Is Another Problem: Real Evidence Can Now Be Dismissed as Fake
This is the part I find particularly disturbing.
We spend considerable time discussing fake content being accepted as real.
But what happens when real content is dismissed as artificial intelligence?
Imagine a genuine recording that exposes misconduct.
The person involved simply responds:
“That is AI-generated.”
The public becomes confused.
Some people believe the recording.
Others dismiss it.
Even when the evidence is authentic, the existence of sophisticated generative AI provides a convenient alternative explanation.
Researchers have discussed a related phenomenon known as the liar’s dividend: the ability to exploit public awareness of deepfakes to cast doubt on genuine evidence.
In my view, this may become one of the most damaging long-term consequences of synthetic media.
AI can make false things look true, but it can also make true things easier to deny.
That creates a difficult environment for journalism, law enforcement, public institutions, and individuals seeking accountability.
We may reach a point where a person’s response to uncomfortable evidence is no longer to explain what happened, but simply to question whether the evidence exists in an authentic form.
Think about how much harder it becomes to establish responsibility in that environment.
Technology Is Trying to Solve the Problem. But There Is No Perfect Solution.
Technology companies understand that trust is becoming a serious challenge.
On October 5, OpenAI updated its work on content provenance, expanding efforts to help identify the origins of AI-generated material.
Its approach includes Content Credentials, digital watermarking, and verification tools.
Other organizations, including Google and Adobe, are also working on technologies that help distinguish certain kinds of synthetic or modified content.
These are important developments.
But there is a limitation.
A watermark can sometimes help identify content generated by a particular system.
Metadata can provide information about the origin and editing history of a file.
Yet neither guarantees that everything shown in the content is factually true.
A genuine photograph can still be misleading.
A real video can omit crucial context.
An authentic recording can be selectively edited.
And provenance information may be missing, incomplete, or lost when media is processed or shared through different systems.
The Coalition for Content Provenance and Authenticity makes an important distinction between documenting a file’s history and establishing whether the events depicted are truthful.
This makes sense to me.
In engineering, confirming that a measurement came from a particular instrument is not the same as proving that the instrument measured the right thing under the right conditions.
We must be careful not to confuse verification of origin with verification of truth.
That distinction will become increasingly important.
I Think We Are Moving Toward a Verification Economy
Here is one of my observations.
For the past two decades, digital platforms have rewarded the ability to create and distribute information quickly.
Businesses want more content.
Creators want more visibility.
Media organizations compete for attention.
Technology companies build tools that make publishing easier.
Generative AI has dramatically accelerated this process.
But when everyone can produce professional-looking articles, advertisements, photographs, and videos, the economic value of content production itself may begin to change.
The scarce resource may no longer be the ability to create convincing information.
It may become the ability to establish which information deserves to be trusted.
I call this the shift from a content economy toward a verification economy.
Not because content will disappear or lose all its value.
But because the institutions capable of establishing credibility may become increasingly important.
Think about the implications.
Journalists may need stronger verification procedures.
Businesses may need authenticated communication channels.
Financial institutions may have to reconsider identity verification methods.
Educational institutions may need better ways to establish whether submitted work genuinely demonstrates a student’s knowledge.
Researchers may need stronger processes for validating scientific findings.
And ordinary people may increasingly ask for independent confirmation before believing something important.
This is not necessarily a bad development.
A healthier digital environment would benefit from stronger verification.
But the transition could be difficult.
India Needs to Take This Problem Seriously
I am particularly interested in what this means for India.
We have an enormous digital population, growing adoption of AI, and extensive use of messaging applications, digital payments, and online communication.
At the same time, India is linguistically and culturally diverse.
Synthetic media can be created in different languages and presented in ways that appear familiar to particular communities.
A fraudulent message does not need to reach millions of people to cause harm.
Sometimes, reaching a single individual at the right moment is enough.
Imagine an elderly person receiving an apparently urgent call from a family member.
Or a business owner receiving what seems to be a legitimate instruction from a trusted client.
The question is no longer simply whether the communication sounds convincing.
It is whether we have established a reliable way of confirming the person’s identity and the legitimacy of the request.
I believe digital awareness programs will need to evolve.
Teaching people not to click suspicious links remains useful.
But we may also need to teach them that familiar voices and recognizable faces can no longer be treated as definitive proof of identity.
What I Would Suggest to Business Leaders
If I were advising an organization on this issue, I would begin with a simple question:
Which important decisions in your business currently depend on believing that a message, voice, document, or video is genuine?
Consider payment approvals.
Consider confidential information.
Consider recruitment and identity checks.
Consider instructions supposedly coming from senior management.
Consider public statements that could affect the company’s reputation.
The objective is not to distrust every employee or customer.
That would create an unhealthy organization.
The objective is to recognize situations where the cost of impersonation is high and introduce an independent verification process.
For example, a high-value payment instruction could require confirmation through a previously established and separate communication channel.
An unexpected request involving confidential information could require additional authorization.
An organization facing a suspicious public recording could preserve the original material, examine its provenance, and communicate transparently rather than responding to rumours with more rumours.
These are not glamorous AI solutions.
They are operational safeguards.
And this is where my experience with process excellence influences my thinking.
A technology problem cannot always be solved by introducing another technology.
Sometimes the most effective response is redesigning the decision process around the new risk.
The Biggest Consequence Could Be Psychological
There is another dimension that deserves attention.
What happens to society when people become uncertain about everything they see?
Not just political news.
Not just advertising.
But ordinary photographs, recordings, statements, and personal interactions.
Trust is an invisible part of how society functions.
We trust that documents represent real transactions.
We trust that the person speaking to us is who they claim to be.
We trust institutions to evaluate evidence.
We trust that at least some shared facts can be established even when people disagree about their meaning.
If those assumptions weaken, everyday interactions become more difficult.
Verification takes time.
Uncertainty increases costs.
And people can become vulnerable either to believing falsehoods or to rejecting inconvenient truths.
I don’t believe society will necessarily lose the ability to distinguish reality from fabrication.
But we may need to invest much more effort in preserving that distinction.
Final Thoughts: The Future Needs More Than Intelligent Machines
We often measure progress in artificial intelligence through benchmarks, model capabilities, reasoning performance, and automation.
Those are important achievements.
But I think we need another measure of progress.
Can we make our digital environment more trustworthy as our ability to generate information becomes more powerful?
I don’t want a future where every photograph requires suspicion.
I don’t want businesses operating in a permanent state of uncertainty.
And I certainly don’t want genuine evidence to become easier to dismiss simply because artificial intelligence exists.
As someone who works with AI, I remain optimistic about the technology.
But optimism should not prevent us from recognizing the problems we are creating.
For years, we celebrated how the internet made information accessible.
Then we celebrated how AI made information easier to produce.
Perhaps the next challenge is learning how to establish what deserves our confidence.
Because in a world where almost anything can be convincingly generated, the ability to create may become ordinary, while the ability to prove becomes extraordinary.
And I believe that shift will influence far more than the AI industry.
It will influence how we conduct business, establish accountability, and decide whom to trust. However, the way we do business is changing continuously due to geopolitical scenarios. Read Middle East Escalation 2026: Are Business Prepared? to know whether your business is ready.
— Jaideep Parashar
Founder & Director, ReThynk AI Innovation and Research Pvt. Ltd.
If you have any question, then you can reach out to my LinkedIn Profile!