When ads come to AI, advertisers gain closer access to our minds :: WRAL.com

The trajectory
feels familiar. Over the past two decades, we have watched digital platforms
follow a predictable arc. They begin by offering free access to accelerate
adoption and build network effects. As market share consolidates and switching
costs rise, monetization becomes imperative. Advertising, often framed
initially as limited or carefully contained, expands over time.

Search engines
began as tools to organize information. Social networks promised connection.
Streaming services once assured viewers that subscription revenue would free
them from commercial interruption. In each case, economic gravity eventually
pulled these platforms toward advertising as a dominant revenue stream.

>> Guest column: Super Bowl ads predict the end of the AI bubble

Even the cultural
signals are revealing. During the recent Super Bowl, Anthropic purchased
multimillion-dollar advertising slots to promote its AI products and to
emphasize that it would not use advertising within its systems. The irony of
advertising to promise an ad-free future was difficult to miss, and foreshadows
that advertising is already a priority for Claude.

I’m doubtful
Anthropic’s commitment will remain permanent. It is good PR for a news cycle or
two. But I’d be amazed if the promise lasts over the long haul. Consumers have
been duped by these promises time and time again.

What makes this
moment different is not the reappearance of advertising. It is the nature of
the information now at stake. Here’s what I mean.

Search engines
learned what we clicked. Social platforms observed what we liked and shared.
Those signals were powerful, but they were still behavioral traces, or external
manifestations of interest.

Large language
models operate at a different layer. They do not merely observe our browsing
patterns; they participate in our thinking. Users confide in them about health
concerns, career anxieties, financial decisions, personal relationships and
half-formed ideas. In “voice mode” (which I use all the time), the interaction
becomes even more intimate. The system receives not only typed queries but
tone, cadence and spontaneous reflection.

In economic terms,
this is not incrementally better data. The shift is orders of magnitude more
significant. We have moved from insight into intention.

That distinction
matters because intent carries far more predictive and persuasive power than
clicks or page views. A conversational AI that understands not only what you
bought last month but what you are uncertain about today occupies a profoundly
asymmetrical position. If that asymmetry is leveraged for advertising, the
ethical landscape shifts.

We prohibit insider
trading because it grants certain market participants privileged knowledge
unavailable to others, distorting fairness and undermining trust. A trader with
inside information can consistently outmaneuver those operating without it. The
system ceases to function as a level playing field.

When advertisers
gain access, directly or indirectly, to the “inside” of our cognitive lives, a
similar imbalance emerges. They are no longer inferring preferences from
surface behavior. They are targeting based on articulated fears,
vulnerabilities, and aspirations. The consumer, unaware of the depth of insight
shaping the message, is placed at a structural disadvantage.

This imbalance does
not affect only individuals. It also reshapes competition. Companies with the
largest marketing budgets will be best positioned to purchase access to these
sophisticated targeting capabilities. Smaller retailers, local businesses, and
emerging brands will struggle to compete in an environment where influence is
calibrated by AI systems trained on intimate user data. The result could be not
only consumer manipulation but further market concentration, reinforcing the
dominance of already powerful firms.

In that sense,
advertising within conversational AI is not merely another monetization tactic.
It crosses an ethical boundary. It transforms a tool designed to assist human
reasoning into a channel through which asymmetrical power can be exercised at
scale.

The pilot rollout
of ads to lower-tier users while preserving ad-free environments for premium
subscribers hints at a broader structural risk. If privacy and neutrality
become features reserved for those who can afford higher subscription tiers, we
may find ourselves constructing a two-tier system of intelligence.

Wealthier
individuals and enterprises would operate within protected, minimally monetized
environments. Lower-income users would access systems funded by advertising,
where conversational outputs could be subtly shaped by commercial incentives.

When AI tools
increasingly mediate access to education, healthcare information, legal
guidance and employment opportunities, such stratification takes on societal
significance. The question is no longer whether an advertisement interrupts
entertainment. It is whether economic status determines the neutrality of the
cognitive tools available to you.

None of this
dismisses the underlying economic challenge. Advanced AI systems are expensive.
Subscription revenue alone may not cover the full costs of continuous model
improvement and infrastructure expansion, especially in a competitive market.

The question,
however, is not whether we will pay. It is how we choose to distribute the
burden and align incentives.

One possibility is
deliberate cross-subsidization. Enterprise clients and high-volume users could
fund broad public access, allowing individuals to use core AI capabilities
without exposure to targeted advertising. Such models already exist in other
industries, where higher-margin segments support universal service. Utilities,
for example, often rely on commercial and industrial customers to stabilize and
offset residential rates, ensuring that essential services remain broadly
accessible. Airlines operate on a similar principle: premium cabin revenue
makes lower economy fares viable for millions of travelers.

We have long
accepted that essential infrastructure should not depend on extracting
disproportionate value from those least able to afford it. When a service
becomes foundational to economic participation, education, or civic life,
fairness demands that its costs be distributed according to capacity to pay,
not according to who is most vulnerable to monetization.

Another approach
would treat user data as a governed asset rather than a byproduct. Data
cooperatives or data trust structures could grant individuals ownership and
control over how their conversational data is used. If data is economically
valuable, then participation should be explicit and compensated, not implicit
and opaque.

More fundamentally,
we may need to consider whether a publicly funded AI infrastructure has a role
to play. When technologies become central to participation in modern life like
roads, electricity or public libraries, we should think of them as basic human
rights. The fees of private utilities are still set by the public for this very
reason. Basic access to the foundations of society must be paramount.

Consider
telecommunications. Access to communication technology should be seen as a
basic human right. We got this right with the landline phones and with the
internet, but we failed with broadband. By law, phone companies had to provide
copper wire to every new home or business without “penalty” pricing for
location, no matter how rural or hard to reach. Those costs were subsidized
within the greater system, for the broader societal good.

In the early days
of the World Wide Web, policymakers chose to maintain open standards rather
than allowing proprietary protocols to dominate. The internet’s core
architecture remained neutral and interoperable, enabling broad participation
and innovation. Later, debates around net neutrality sought to prevent network
providers from discriminating among types of traffic based on commercial
interest and this largely was successful. Everyone must participate in using
the Internet today, as it becomes more and more core to education, healthcare,
banking and entertainment.

But we failed
regarding access to the internet in the age of broadband. As fiber optics and
high speed cellular networks replaced copper-wire telecommunications, private
industry won the day, securing exclusive licenses to wireless spectrum and
loose regulation over population coverage requirements. As a result, less
profitable areas (primarily rural communities and low income urban areas) do
not have adequate access to broadband.

We may now face a
comparable decision regarding AI. If these systems become mediators of
knowledge, opportunity and decision-making, protecting their neutrality may be
as important as protecting the neutrality of the networks that carry our data.
An “AI neutrality” principle that limits direct monetization of intimate user
intent and guarantees baseline protections regardless of subscription tier,
could serve as the modern extension of those earlier commitments.

The software world
is changing, and business models are changing with it. As generative systems
blur the line between tool and collaborator, the economic structures
surrounding them will shape not only corporate earnings but human agency.

We have, in past
technological transitions, allowed monetization logic to outpace ethical
reflection. Only later did we confront the unintended consequences of
surveillance advertising and algorithmic influence. With conversational AI, the
stakes are higher because the layer being monetized lies closer to human
cognition itself.

We are still early
enough to decide differently. If access to intelligence is becoming as
essential as access to the internet once was, then it deserves a framework that
protects users from asymmetric manipulation and preserves fair competition in
the marketplace.

The question before
us is not simply how AI companies will generate revenue. It is whether the most
intimate digital systems we have ever created will operate as neutral
infrastructure or as instruments of commercial persuasion. In answering that
question, we are not merely financing technology. We are defining the ethical
boundaries of the next economic era.