Across the broader search community and specifically at SMX Advanced last week, the phrase “Delphic costs ” was mentioned multiple times. But while many are referencing it, few outside of those speakers seem to grasp the strategic implications of this emerging concept fully.
Delphic costs refer to the hidden costs incurred when users are shielded from suboptimal options by an oracle-like system, such as an AI assistant that answers questions directly. These systems are designed to reduce friction, but in doing so, they often bypass traditional publishers, brands, and websites entirely.
This isn’t just a semantic shift—it’s a profound structural change in how value is distributed in digital ecosystems. This article aims to unpack Delphic costs and explain why understanding them is now a business imperative.
If your business depends on discoverability, this shift deserves more than a passing mention. It demands rethinking how you structure content, create value, and earn presence in AI-driven experiences.
The Premise
In classical economics, search theory models how individuals or businesses expend time and effort to find the best possible option, whether it’s a product, partner, or piece of information. But much of that effort is being eliminated in today’s AI-powered landscape.
In traditional search ecosystems, publishers, affiliates, and review sites acted as informational brokers, collecting, evaluating, and repackaging product or service data to help users make informed decisions. They added a layer of trust, interpretation, or aggregation between the raw source (the seller or manufacturer) and the buyer.
Search engines like Google functioned as meta-intermediaries, routing users to those brokers.
But now, AI-powered systems are collapsing that stack. Instead of routing users to review sites or affiliate roundups, they extract and synthesize the value those intermediaries provided—and deliver it directly to the user in zero-click summaries.
In effect, the AI becomes the broker of brokers, eliminating not just friction for the user—but also removing a revenue-generating step for publishers who once thrived on that friction.
This shift reconfigures the value chain:
- From Search Engine → Publisher → User
- To AI System → User, with the publisher disintermediated and often uncredited.
AI engines, large language models, and synthesized search results now act as zero-friction intermediaries, delivering answers instantly, often without any need to browse or click. This effectively bypasses traditional content sources and incurs what is now recognized as the Delphic cost—the lost opportunity for those who are no longer part of the user’s decision journey.
What Are Delphic Costs?
Delphic Costs are an emergent phenomenon—a real-world consequence that arises when classical search theory (which values optimal matches and friction reduction) intersects with modern AI systems that remove user effort entirely. Modern researchers build on this by applying it to web search behavior, extending the idea of economic search into cognitive, interactional, and time-based costs that users incur while seeking information.
This is the essence of the framework Andrei Broder and Preston McAfee introduced in their 2023 paper, “Delphic Costs and Benefits in Web Search.” In it, they describe Delphic Costs as the hidden price organizations pay when they’re quietly removed from the discovery process, not due to low quality, but because the system found a more efficient way to deliver answers.
Their framework signals a critical shift—from merely routing users to resolving their intent, which aligns perfectly with the rising trend we’re now observing: the publisher fallout from AI systems becoming the dominant intermediary.
They argue that user satisfaction with search engines depends not on rankings alone, but on the experience of Delphic costs and benefits on the path to completing a task. This framing supports my emphasis on friction reduction as a strategic imperative.
Some companies are losing traffic not because they did something wrong but because AI realized they were never truly necessary.
Key insights from Broder and McAfee’s 2023 paper that reinforce this concept:
- Searchers carry hidden non‑monetary costs—time, cognitive load, and interaction effort.
- Delphic utility matters more than traditional precision metrics: user satisfaction is tied more to reduced friction than to exact match rankings.
- Reducing Delphic Costs has shaped search evolution: the move from IR to rich UI and now to LLMs was driven by minimizing user effort.
- Integrated actions (e.g., direct bookings from results) show how AI removes multi-step friction, eliminating the need to visit the original source.
Who Pays the Delphic Cost?
In the emerging AI-mediated ecosystem, the Delphic cost is borne by those disintermediated—their roles are made invisible, and their value is captured upstream. This includes publishers, review sites, content creators, and even brands that once stood between a search query and a decision.
These entities:
- Invested in content to reduce search friction for users
- Relied on traffic, visibility, and trust to monetize that role—via ads, affiliates, or brand equity
- Were rewarded under the old system for helping users evaluate options
But now, AI agents act as oracles, preempting the user’s need to explore. They extract the informational value, withhold the click, and collapse the buyer journey into a single synthesized output.
These entities are now paying the price—the Delphic cost—for a new set of realities:
- They ranked due to early visibility, not lasting authority
- They captured traffic with shallow or derivative content
- They’re quietly replaced in AI answers by someone—or something—more complete, credible, or contextually relevant
In other words, the cost isn’t just being unseen. It’s now being unnecessary.
Revisiting Broder’s Search Intent Taxonomy
If you are a student of SEO, the name Andrei Broder may ring a bell. In 2002, Andrei Broder, while at IBM, introduced a landmark taxonomy of web search intent, classifying queries into three core types:
- Navigational – finding a specific site or brand
- Informational – learning about a topic or concept
- Transactional – completing an action (e.g., buying or signing up)
For over twenty years, this framework guided website structure, content creation, and SEO execution. It shaped the SERP, user expectations, and strategy.
The irony? The very framework that helped us map user intent is now being used against us.
AI doesn’t ignore Broder’s taxonomy—it accelerates it. It fulfills intent more efficiently. Informational intent is often satisfied before the user even considers clicking.
So perhaps Broder wasn’t just right. He was even more right than we realized.
Mapping the Connection: Search Theory → Delphic Consequences
| Search Concept | AI-Era Behavior | Delphic Cost Trigger |
|---|---|---|
| Search friction creates value for intermediaries | AI removes friction via synthesized answers | Brands built on visibility are bypassed by instant responses |
| Broder’s “Informational” queries create discovery funnels | AI short-circuits discovery by delivering resolved intent in place | If you’re not the final answer, you’re not surfaced |
| Visibility ≠ Authority | AI prioritizes trust, clarity, and structure | Content built for SEO, not substance, is demoted or excluded |
| Intermediaries win by reducing effort | AI becomes the dominant intermediary | Traditional publishers, blogs, and product sites are disintermediated |
The Hollow Content Trap: Adjacent but Not Authoritative
In an attempt to “capture” the funnel, many B2B and e-commerce companies flood the web with shallow adjacent content, including glossaries, topic pages, and basic definitions, hoping to rank for broader informational queries. The faulty yet straightforward logic is that they must want my ultra-wiz-bang cloud computing ecosystem if they are interested in cloud computing. Or, more commonly, how can we super-tune a webpage to get a featured snippet and drive them to a page suffed with dozens of non-relevant ads and autoplay videos to fund our generally failing tech publication?
But most of it is regurgitated, redundant, and devoid of insight.
Examples:
- B2B: Cybersecurity firms are defining “What is a firewall?” with no context or link to their unique capabilities.
- E-commerce: Retailers listing vague specs like “600 watts” or “ABS plastic,” offering no guidance on suitability, fit, or decision factors.
AI can summarize that kind of content faster, better, and from more trusted sources.
You’re not being cited because you’re not needed.
The system is trained to skip friction. If your content doesn’t clarify, differentiate, or reduce user effort, it’s ignored. These costs reflect what happens when the web’s “truth-givers” are algorithmically re-evaluated and deemed unnecessary.
Erosion of the Pageview and Attention Economy
For publishers and content marketers, this signals the collapse of a familiar economy. The old web rewarded those who could capture attention through SEO, content velocity, or emotional hooks.
You’re not losing traffic—you’re being removed as an unnecessary step.
Delphic Costs flip that model.
Today’s AI engines reward those who reduce friction and resolve intent instantly. The winners are answer engines, not publishing platforms. Zero-click answers replace multi-page funnels. Structured data replaces scrolling. Authority replaces engagement hacks.
Even once-viable utility sites like those offering to help you figure out “What time is it in Tokyo,” “USD to EUR conversions,” or “meters to feet,” are being quietly eliminated. Google doesn’t need to scrape a page to answer those questions. It can detect your IP, determine your local time, and calculate a simple plus/minus time zone. Currency and measurement conversions require only licensed feeds and trivial logic. The user gets an instant, accurate answer with no clicks required.
Was Google wrong to do this? Or did it simply make the obvious economic and user experience choice by delivering answers at near-zero marginal cost while preserving ad real estate and user satisfaction?
This isn’t just a convenience—it’s a quiet disintermediation. You’re not losing traffic. You’re being removed as an unnecessary step.
This is answer-level commoditization. When the value of your content becomes low enough and the cost to replicate becomes negligible, the system optimizes you out. That’s the essence of Delphic cost alignment: if the answer can be synthesized instantly, the cost of attention, effort, and loyalty becomes too high to justify.
Strategic Imperative: Make Friction Reduction Your Business Model
This isn’t just a content or SEO issue. It’s a business strategy issue.
- If your onboarding is complex, AI will summarize you and route around you.
- AI will compare others and omit you if your product selection lacks clarity.
- If your answers require effort, AI will rewrite them—and take credit.
In the age of AI-mediated discovery, friction reduction is not a UX best practice—it’s a survival strategy.
Every function—marketing, product, operations—must now ask:
- Are we the fastest path to clarity?
- Are we indispensable to the resolution?
- Are we still part of the user’s intent fulfillment or just a remnant of their search journey?
From Intent to Resolution — and the Cost of Being Skipped
Broder helped us understand what users wanted. AI shows us what happens when someone else gives it to them faster.
Delphic Costs are not algorithmic punishment—they’re algorithmic clarity.
They emerge when you no longer need to fulfill the user’s intent. And they remind us that in this new paradigm, visibility isn’t enough.
To survive, you must be indispensable to the resolution.
Side Note:
On a personal note, I’ve been researching Search Theory since 1995, when I was introduced to the concept while defending my thesis at an academic conference in Athens, Greece. Since then, and more so now, I have been focusing on how digital technologies reduce friction in international trade. Back then, I predicted the internet would transform global commerce by directly connecting buyers and sellers. Today, with AI, we are witnessing another leap forward, making connections even easier and eliminating inefficiencies. Check out my What is old is new again post.
