Who Put That Expert in Front of You?

By Dr. Trudy Beerman, DSL — Published September 14, 2026

AI, Algorithms, and the Invisible Gatekeepers of Authority

Think about the last expert you discovered online. Maybe you found them on YouTube. Maybe Google surfaced their article. Maybe LinkedIn placed their post in your feed. Perhaps an AI tool mentioned their name when you asked a question.

Now consider a different question: Did you actually choose that expert, or did you choose from the experts a machine put in front of you?

Those are not the same thing.

Much of the conversation about the attention economy focuses on what happens after something captures our attention. We talk about persuasive design, endless scrolling, outrage, dopamine, notifications, and algorithms engineered to keep us watching. Those are legitimate concerns, but there is an earlier stage of influence that receives far less attention.

Before someone can influence you, they usually have to enter your consideration set.

Increasingly, machines are helping determine who gets in.

The First Decision May Not Be Yours

When you search online, you are not evaluating every possible person who might know the answer. You are evaluating the people and information that survived a selection process before you ever saw them.

Google openly explains that its automated ranking systems evaluate many signals when deciding what information to surface, including relevance, usability, the expertise of sources, and other contextual factors. Google also notes that its systems attempt to identify signals that indicate expertise, authoritativeness, and trustworthiness. Google, How Search Works.

That does not mean Google decides who is genuinely the best expert in the world. It means its systems must make judgments about which available information appears most useful and relevant to a particular query. The same basic reality exists across recommendation engines, feeds, search platforms, and increasingly AI-generated answers.

By the time you make your decision about whom to click, follow, hire, believe, or investigate further, another decision has often already happened:

Who was visible enough to be considered?

Humans Use Shortcuts Too

Machines are not the only ones making rapid judgments. We do it too.

Determining whether another person is genuinely knowledgeable can be difficult. Properly assessing expertise might require reading their work, verifying credentials, checking their claims, evaluating their experience, and comparing them with others in the field. Most people do not have the time, expertise, or motivation to conduct that level of investigation every time they encounter someone new.

So the human mind uses shortcuts.

Psychologists Daniel Kahneman and Shane Frederick described a related process as attribute substitution. When people face a difficult judgment, they may unconsciously substitute an easier question that can be answered more quickly. Their work on attribute substitution became part of the broader research on heuristics and intuitive judgment. Kahneman & Frederick, 2002.

Instead of asking, “Is this person genuinely credible?” the mind may answer easier questions:

• Have I seen this person before?
• Do they appear professional?
• Have they written a book?
• Have they appeared in the media?
• Do other respected people associate with them?
• Do they have impressive credentials?
• Do they have a large audience?
• Does Google seem to know who they are?

None of those things, standing alone, proves expertise. But they become signals from which people form judgments.

The Halo Around Authority

This is also where the halo effect becomes relevant. The American Psychological Association describes the halo effect as a bias in which a general positive evaluation of someone influences judgments about that person's other characteristics. Someone who is generally liked, for example, may also be judged as more competent or intelligent than the available evidence warrants. American Psychological Association.

The concept has deep roots in psychological research. Edward Thorndike's 1920 work on psychological ratings showed that people's overall impressions could influence how they evaluated supposedly separate characteristics of the same individual. Thorndike, 1920.

In modern authority-building, the halo can come from many places. A respected media appearance may elevate someone's perceived credibility. An impressive stage may do the same. A book, a prestigious association, a university credential, strong reviews, or repeated exposure may alter how the audience evaluates everything else about that person.

That is not inherently deceptive. Often those signals exist precisely because someone has done meaningful work. The problem arises when we confuse the signal of authority with the substance of expertise.

Machines Need Signals Too

This is where personal branding has entered a fundamentally different era.

A person can meet you at a conference and form an impression through conversation. A colleague can observe your work over several years. A client can experience your judgment firsthand. Machines cannot evaluate expertise in those ways.

They need accessible evidence.

Search engines discover, index, organize, and rank digital information through automated processes. Google's own documentation describes crawling, indexing, and serving as distinct stages through which online information may eventually become available in search results. Google Search Central.

This is why I have argued that the modern personal brand now has two audiences to impress: people and machines.

The human audience encounters your reputation through experience, referrals, perception, familiarity, and social proof. The machine audience encounters whatever evidence it can find, interpret, connect, and retrieve about who you are and what you appear to be known for.

Your publications, interviews, credentials, reviews, website, media appearances, citations, consistent identity, associations, and other digital artifacts can all become part of the information environment surrounding your name.

That does not mean any one signal guarantees that an AI system will recommend you. It means that in a machine-mediated world, being excellent but digitally invisible creates a very different competitive position from being excellent and supported by clear, discoverable evidence.

Visibility and Credibility Are Not the Same Thing

There is an uncomfortable side to this conversation, especially for those of us who work in media, personal branding, authority, and visibility.

The same mechanisms that can help a legitimate expert become recognized can also make weak expertise look impressive.

A polished studio does not make someone knowledgeable. A bestselling book label does not make every idea correct. Thousands of followers do not guarantee competence. A media logo does not mean the outlet independently endorsed every claim made by the guest. Repetition can produce familiarity, and familiarity can easily be mistaken for authority.

This is why I believe authority architecture should never be about manufacturing the appearance of expertise where expertise does not exist.

Authority signals should make legitimate expertise easier to discover. They should not be used as a substitute for expertise.

That distinction matters even more as machines become involved in discovery.

AI Adds Another Gatekeeper

Consider how frequently people now ask questions such as:

• Who is an expert on this topic?
• Who should I hire for this problem?
• What company should I consider?
• Who are the leading voices in this industry?
• What source should I trust?

When an AI system generates an answer, most users naturally focus on whether the recommendation is correct. Recommendation Science raises another question:

Why did those particular names become candidates for the answer in the first place?

That question becomes increasingly important because recommendation affects opportunity. Who gets discovered may influence who gets interviewed. Who gets interviewed may influence who gets quoted. Who gets quoted may influence who gets invited to speak. Who gets invited to speak may accumulate more searchable evidence, which may further strengthen recognition.

Authority can compound.

So can invisibility.

The Consideration Set Is the Real Battleground

I often talk about visibility not as fame, but as consideration. You do not need everyone to know your name. You need the right people to encounter sufficient evidence to consider you when the relevant need arises.

That is why the question, “Who put that expert in front of you?” matters so much.

If algorithms, search engines, recommendation systems, media platforms, and AI tools increasingly shape our consideration sets, then influence begins before persuasion. It begins with selection.

The individual who never enters the consideration set may never get the opportunity to demonstrate that they were the better choice.

This changes the responsibility of the expert as well. Excellence alone is not always enough if excellence cannot be found, interpreted, or connected to the problem someone is trying to solve.

A Biblical Warning About Appearances

There is an interesting biblical parallel here. In 1 Samuel, Samuel initially assumed that Jesse's impressive son Eliab must be God's chosen king. God's response challenged the very shortcut Samuel was using:

“People look at the outward appearance, but the LORD looks at the heart.”

The verse is usually discussed spiritually, but the human behavior it describes remains strikingly relevant. People make judgments based on what is visible. We have always done that. Digital environments simply give us new kinds of outward appearances to evaluate: profiles, follower counts, search rankings, logos, credentials, reviews, media clips, and algorithmic recommendations.

That does not mean these signals have no value. It means they should remain evidence to be evaluated rather than automatic proof.

There is another useful principle in Proverbs:

“Let someone else praise you, and not your own mouth; an outsider, and not your own lips.”

Third-party validation has carried weight for thousands of years. What has changed is the scale at which those third-party signals can now be collected, indexed, distributed, and interpreted by machines.

The Question Behind the Recommendation

The conversation about artificial intelligence often centers on whether machines will think for us. I believe there is another question that deserves equal attention.

Who gets to influence what the machine thinks is worth showing us?

That question belongs not only to technologists. It belongs to marketers, journalists, researchers, business leaders, media professionals, creators, consumers, and anyone whose reputation increasingly exists inside searchable digital systems.

We need to become more conscious of how authority is signaled, how credibility is inferred, and how recommendation systems shape the people and ideas that reach us.

And for legitimate experts, there is another responsibility: create enough credible evidence that both people and machines can correctly understand what you know, what you have done, and where your expertise belongs.

Because in the new attention economy, being credible is only part of the challenge.

You must first become discoverable enough to be considered.

About Dr. Trudy Beerman

Dr. Trudy Beerman is a strategic leadership practitioner, media entrepreneur, and REACHologist whose work explores personal-brand authority, discoverability, media visibility, and Recommendation Science. She is the founder of PSI TV, an authority distribution network that helps experts create and distribute credible media assets across television and digital platforms.

References

Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and Biases: The Psychology of Intuitive Judgment (pp. 49–81). Cambridge University Press. https://doi.org/10.1017/CBO9780511808098.004

Thorndike, E. L. (1920). A constant error in psychological ratings. Journal of Applied Psychology, 4(1), 25–29. https://doi.org/10.1037/h0071663

American Psychological Association. Social Psychology: Social Cognition and Social Influence. APA resource.

Google. How Search Works: Ranking Results. Google Search.

Google Search Central. In-depth guide to how Google Search works. Google Search Central.