Agentic AI Will Reshape Web Ad Economics

“For at least the last at least 30 years, the business model of the internet has been advertising,” says Matthew Prince, Cloudflare CEO. “It’s not the entire business model of the internet, but it’s really driven all of the growth of the web.”

So what happens now that artificial intelligence traffic for training, inference and agentic operations begins to dominate web traffic?

Already, automated traffic has now passed human traffic. “Five years from now, we think that automated traffic will be 1,000 times human traffic,” he says. 

“The challenge of that is, if you have 1,000 times more traffic, someone’s got to pay for the infrastructure to power that,” says Prince. “That’s going to require bandwidth, that’s going to require servers, that’s going to require a lot of things.”

The traditional model of how to pay for that, which was advertising, doesn’t work for bots because they do not click on ads, which destroys the monetization mechanism. 

So the issue is how content providers will create new revenue mechanisms for bot traffic, since ads do not work. 

For that matter, it is not clear how subscription or commerce revenues will be affected, either. 

Traditional web	AI/agentic web
Human is the "customer"	Human may never visit
Page view creates advertising opportunity	Bot request may create no ad impression
Search crawler is economically valuable because it sends traffic	AI crawler can consume content without sending traffic
More traffic generally = more revenue	More bot traffic can = more bandwidth/compute/security cost
SEO means getting a high search ranking	AEO means getting selected/cited by an AI
Affiliate click produces revenue	AI agent may bypass the affiliate link
E-commerce wants customer on its site	Agent may choose product and potentially transact elsewhere
Content is given away in exchange for distribution	Content increasingly becomes a licensable input

All that suggests we might have to invent new ways of generating revenue beyond advertising, almost all of which might involve some form of payment for content. 

Model	How it works	Economic logic
Annual licensing	AI company pays publisher fixed fee	Similar to syndication
Pay-per-crawl	Payment for each page/request	Metered consumption
Pay-per-answer	Payment when content contributes to an answer	Closer to value created
Revenue share	Publisher gets share of AI subscription/ad revenue	Aligns incentives
Referral/affiliate	AI sends user to publisher	Preserves old model
Transaction fee	Website earns money when agent completes transaction	Potentially much larger
API access	AI accesses structured proprietary data	Turns website into data provider

It remains to be seen whether licensing regimes can replace lost advertising revenues, though. Commerce revenues should help, but it is not unreasonable to suggest the new business models might not be as lucrative as the older ad-based models. 

As we have seen in other businesses disrupted by the internet, such as music, subscriptions and events might become more important. In most other cases, it is easier to see how agentic commerce revenues might well be a bigger opportunity. 

Web firm type	Old primary economic engine	AI-era pressure	Likely new revenue
News publisher	Ads + subscriptions	AI answers substitute for clicks	AI licenses + subscriptions + events
Reference/data site	Ads	AI extracts information	Data/API licensing
UGC platform	Ads + engagement	AI absorbs user-generated knowledge	AI licensing + transactions
E-commerce	Product margin + ads	AI becomes shopping interface	Agent transactions + APIs + sponsored placement
Travel site	Ads + booking commissions	Agent bypasses comparison site	Agent booking commissions
Review site	Ads + affiliate	AI summarizes reviews	Licensing + affiliate/transaction fees
SaaS/web app	Subscription	Agent performs tasks without UI	API/agent usage fees
Search engine	Advertising	AI answer reduces external clicks	AI advertising + transactions
Social platform	Ads	AI consumes content without users	Licensing + commerce
Cloud/CDN/security provider	Infrastructure fees	Huge AI bot volume	Bot management + AI traffic infrastructure
Marketplace	Seller fees/ads	Agent becomes buyer interface	Transaction fees + agent APIs

And to the extent that advertising value shifts, it might shift in the direction of payments that optimize a supplier’s visibility in the candidate set or actual purchasing behavior. When an agent is searching hotels in a city with certain requirements, payment might take the form of paid placements to enhance inclusion, ranking, then selection and booking. 

Previously the scarce asset was supplying an audience. In the agentic AI era, value might shift to  proprietary information, trusted data, transaction capability and permission to act.

That might be an easier transition for commerce-oriented sellers, compared to content suppliers dependent on human visitors and advertising.

Is California's Antitrust Lawsuit Against Paramount Skydance Near Resolution?

Is the California lawsuit trying to block the Paramount Skydance merger with Warner Brothers Discovery about to be settled? One might hope so, though some interests might not believe they benefit.

Given the “nuclear option” Paramount could exercise in moving its studios out of Los Angeles to another state, the city and state would benefit from keeping a key firm in a key industry. Paramount itself would prefer not to incur further delay and costs.

Sure, key competitors might prefer continued litigation. But both parties to the lawsuit have big reasons to settle. Paramount wants to execute on its business vision. California wants to keep a key firm in a key industry from moving.

So far, even though much actual production already has left California, studio headquarters and production lots remain based in the Los Angeles area. That matters as video content remains a vital part of the region's economy.  

source: Associated Press

The antitrust argument has been that the merger would give the merged company too much power in the video content value chain. That seems at least questionable. 

One high-level example is the oft-cited observation that If a consumer pays $100 at the box office:

• $45–50 ultimately remains with the theater

• $50–55 goes to the film distributor/studio.

Looking only at the content part of that example, owners of studios with distribution rights represent a bit more than half of revenue shared for exhibition, while theater owners get a bit less than half. But that isn’t the full story of theatrical exhibition revenues and profits. 

Movies represent huge risk, as it is not unusual for the top 10 movies distributed in a year to earn as much as 54 percent of all revenue. In other words, a small number of titles generate disproportionate revenue, which is why we see so many “existing franchise” titles issued. 

source: Statista

Still, it can be argued that talent (actors) and production workers receive about 20 percent of movie revenues; theaters perhaps the same and other participants also in the 20-percent range, with studios generally getting 35 percent of revenue in the broader video value chain that includes streaming and linear TV elements.  

The adage that movie theaters make their profits on popcorn is largely correct. Ticket revenue is shared with the studio, but concessions largely belong to the theater.

Theater chain AMC Entertainment in 2025 generated $2.65 billion of admissions revenue and $1.67 billion of food-and-beverage revenue, for example, according to data filed with the Securities and Exchange Commission by AMC Entertainment. 

And studio gross revenue is not profit, as production, talent, marketing costs, financing and overhead consume up to 90 percent of gross revenue, leaving perhaps five percent to 10 percent as expected profit, overall. 

Theatrical $100	Approximate share
Theater	$45–50
Studio/distributor gross	$50–55
— production/talent	~20–25
— marketing/distribution	~10–15
— studio overhead/financing	~5–10
— studio economic profit	~5–10

In the broader value chain that includes video streaming and linear TV, other elements come into play, such as advertising and subscription revenues. 

Warner Brothers Discovery in 2025 reported revenues of about $17.66 billion, composed of :

• $6.33 billion advertising revenue

• $9.82 billion distribution revenue (paid by streaming and linear networks)

• $1.20 billion content revenue. 

Content itself was a relatively small part of total revenue. The point might be that revenue shares are highly distributed, even if studios and a class of participants might claim the largest single shares in the value chain. 

Even more complicating is the fact that participants often participate in multiple parts of the value chain. Netflix and Disney provide the best examples. 

Model	Content ownership	Distribution ownership	Main economic advantage
Independent producer	Yes	No	Creative/IP specialization
Traditional studio	Yes	Partial	IP + multiple distribution windows
Cable network	Some	Yes	Audience + advertising/carriage
Pure streaming distributor	No/limited	Yes	Customer relationship + scale
Netflix-style integrated platform	Yes	Yes	Content + audience + data + global scale
Fully integrated Disney-like model	Yes	Yes	Multiple windows + franchises

The point is that the video content value chain is quite complex, with lots of participants and lots of distributed revenue streams. It is not so clear that any single role exercises monopoly-style control of the whole value chain. Content matters, but so does distribution, in any of the key segments (theatrical release, linear TV or streaming.

What Will AI Do to Content Market Suppliers?

Critics of language model copyright protection often make the argument that such content production by artificial intelligence harms existing content market suppliers.

But some of us might note that this also is a trend and theme quite familiar to all digital content processes and their impact on existing content business models. One example is the impact of AI summaries on search traffic volume for content suppliers.  

Phenomenon / study	What is happening	Evidence of substitution / economic impact	Implication for profitability
Google AI Overviews / AI search	Search engines increasingly answer questions directly rather than simply providing links	Reuters Institute/Chartbeat data show Google organic-search traffic to 2,500+ sites fell 33% globally and 38% in the U.S. between Nov. 2024 and Nov. 2025. Publishers expect search traffic to fall another ~43% over three years. (reutersinstitute.politics.ox.ac.uk)	Negative. Less referral traffic means fewer ad impressions, subscriptions and affiliate conversions. Particularly damaging to publishers dependent on search.
Wikipedia + Google AI Overviews	AI summaries can satisfy the user's information need without a click	A 2026 causal study of 161,382 article-language pairs found Google AI Overview exposure reduced English-Wikipedia traffic by about 15%, with larger effects for topics where short answers are sufficient. (arXiv)	Strong evidence of actual content substitution, rather than merely correlation.
French publishers / Google AI summaries	Publishers argue AI-generated summaries are replacing visits to original articles	The French press association says regulator Arcom data indicate AI summaries have produced a 33–38% decline in traffic to media sites. The association is seeking competition action and compensation. (Reuters)	Potentially serious threat to advertising-supported journalism; also creates pressure for AI licensing revenue.
LLMs as news destinations	Consumers increasingly obtain news directly from chatbots	Reuters Institute reports that weekly generative-AI use in six markets rose from 18% to 34% between 2024 and 2025. Its 2026 research finds a growing, though still minority, use of chatbots for news. (reutersinstitute.politics.ox.ac.uk)	Potentially more important over time: AI moves from being a distribution intermediary to being the destination.
Publisher AI production	Publishers increasingly use AI for back-office automation, newsgathering, coding and content production	In the 2026 Reuters Institute survey, 97% of publishers regarded back-end AI automation as important; 82% cited newsgathering and 81% coding/product development. But only 44% said AI initiatives were showing promising results, versus 42% calling results limited. (reutersinstitute.politics.ox.ac.uk)	Positive cost effect, but so far not a demonstrated profitability windfall.
AI actually increasing publisher content volume?	One might expect near-zero-cost generation to create enormous increases in articles	A study of large publishers finds no evidence that publishers increased text volume following GenAI adoption. Instead they increased rich content, advertising and targeting technologies. (arXiv)	Important counterexample to the simple "AI = infinite content" thesis. Professional publishers may be recognizing that additional generic text has little economic value.
AI and publisher traffic	LLM bots consume publisher content while potentially sending fewer readers back	The same study finds a moderate decline in publisher traffic after August 2024. Interestingly, publishers that blocked GenAI bots subsequently experienced 23% lower total traffic and 14% lower real-user traffic than comparable publishers that did not block them. (arXiv)	Shows the relationship is complicated: AI can be both a threat and a discovery mechanism.
AI-assisted social-media creation	AI makes it much cheaper to create posts, comments and other social content	A controlled experiment with 680 U.S. participants found some AI tools increased engagement and content volume, but also reduced perceived quality/authenticity and generated negative spillovers in conversations. (PubMed)	Supply explosion is real, but more content does not necessarily mean more economic value.
AI-generated social posts	AI can produce content that competes directly with human-created material	A 2025 study found GPT-4-generated social-media posts could outperform human-written posts in engagement; another cross-platform study examines comparable performance on Facebook, Instagram and X. (ScienceDirect)	Potentially disruptive to the labor economics of content creation, especially routine marketing/PR content.
AI disclosure / authenticity	Consumers don't necessarily value AI-created material as much as human-created material	A 2026 study found labeling content as AI-generated or AI-enhanced reduced affective and behavioral engagement relative to human-created content, especially for emotional content. (DOI)	Creates a possible scarcity premium for human/original content as AI content becomes abundant.
AI use in newspapers	AI-generated material is already entering professional media	An audit of 186,000 articles from 1,500 U.S. newspapers estimated about 9% were partially or fully AI-generated in summer 2025. (arXiv)	Demonstrates that substitution of human content production is already occurring, particularly in smaller/local outlets.
AI licensing	Publishers are attempting to turn substitution into a new revenue stream	Reuters Institute found 36% of publishers expected licensing income from technology/AI companies to become significant. Brookings describes AI licensing as a new layer of the media economics historically dominated by search platforms. (reutersinstitute.politics.ox.ac.uk)	Could partially offset lost advertising/search revenue, but licensing revenue is not yet comparable to the scale of displaced traffic.
Premium vs. commodity content	AI has much greater ability to substitute for routine informational content than differentiated reporting	Reuters Institute finds subscription/membership-oriented publishers with strong direct traffic have a clearer path to profitability, while advertising-dependent publishers are much more worried about AI search. (reutersinstitute.politics.ox.ac.uk)	Suggests bifurcation: commodity information gets cheaper; original reporting, brands, personalities and communities become more valuable.

Much of the argument about regulating artificial intelligence training and output has to do with the efficiency with which computers work, compared to biological limitations humans have doing the same things.

That might strike some of us as an odd argument. Of course, the real issue is not about how humans or machines learn, or even how quickly they can produce original new work. 

The issue, as often is the case, is about the effect on markets for content. And that is what the authors of a new paper suggest is the case. 

source: Tuhin Chakrabarty, Xinyue Liu , Jane C. Ginsburg, Paramveer Dhillon 

Comparing best-selling books with no AI content to books with light AI content or wholly-AI produced on Amazon, the authors suggest the AI books are having an impact on non-AI book sales and revenues. 

 

source: Tuhin Chakrabarty, Xinyue Liu , Jane C. Ginsburg, Paramveer Dhillon 

When a human author reads hundreds of mystery novels, internalizes their mechanics, and writes a new mystery novel using those structural lessons, copyright law views this entirely as lawful inspiration and learning. 

Supporters of applying stricter copyright rules to AI model content typically are based on the argument that humans are relatively slow learners, while computers are fast. 

Just as a human author synthesizes everything they have ever read to draft a novel, an AI synthesizes the patterns learned from its training data to generate new text.

Critics essentially argue AI models should not receive the same level of protection because they are “too efficient,” which is a new argument in the copyright domain. 

If copyright law were applied with strict, absolute functional consistency, the legal outcomes for human and AI generation would look remarkably similar:

• Process Equivalence: Both human brains and AI models consume existing works to extract abstract patterns, rules of grammar, and stylistic conventions.

• Output Evaluation: If an AI generates a completely novel story that merely employs general tropes and stylistic patterns learned during training (without plagiarizing specific passages), a consistent legal framework would view it the same way it views a human-written work.

Proponents of AI model “freedom to create” argue that training is inherently transformative. The AI is not being trained to reproduce the books it reads; it is learning how language works. So that is fair use.

Critics argue AI should not be protected in the same way humans are because the machines are so much more efficient.

Dimension	Human Authors	AI Models
Legal Status of "Reading"	Lawful (cognitive processing falls outside copyright).	Contested (involves digital copying; subject to ongoing fair use litigation).
Legal Status of Output	Protected, provided it avoids literal copying or plagiarism.	Contested, with questions regarding authorship, originality, and market substitution.

The point is that AI-produced content will probably have a similar impact to existing content suppliers as we have seen with both digital and internet content markets. 

There will be some amount of disruption; severe disruption in at least some instances. 

Consider what happened to business-to-business content businesses such as specialized trade media. 

Historically, trade journals relied on a print-centric, monopoly-like model where niche B2B advertisers had virtually no other way to reach specialized professional audiences. But what happened was more than a shift from physical media to online and digital formats. 

Advertising budgets massively migrated away from print. In 1995, specialized print trade journals commanded nearly the entirety of B2B advertising budgets. 

By the late 2000s, digital channels achieved parity, and today, digital and online formats capture the vast majority of B2B marketing spend.

Year	Legacy Print Share (%)	Digital Online Share (%)
1995	98%	2%
2000	88%	12%
2005	65%	35%
2010	35%	65%
2015	15%	85%
2020	8%	92%
2026	4%	96%

AI-enabled changes might have a range of effects, some quite negative for legacy content providers but also some positive changes as well for others. 

Business/content type	Effect of AI content abundance	Likely long-term economics
Commodity news	Very high substitution	Worse
Weather, sports scores, financial quotes, basic facts	Very high substitution	Much worse
SEO articles / "10 best..." content	Very high substitution	Worse
Generic marketing copy	High substitution	Lower labor cost, potentially higher margins
Social-media posts	High substitution	More supply; declining value per post
Local routine journalism	High production substitution	Lower costs but potentially weaker differentiation
Original investigative journalism	Low direct substitution	Scarcer and potentially more valuable
Celebrity/personality content	Low-to-moderate	Human identity becomes an asset
Video/entertainment	Moderate initially	More content, but attention remains scarce
Strong media brands	Moderate substitution, strong defensive value	Potentially resilient
Communities / memberships	Low substitution	Potentially increasingly valuable
Proprietary data/research	Low substitution	Potentially more valuable
Human-authored expertise/authenticity	Potentially negative supply effect but positive scarcity effect	Could command a premium