OpenAI Launches an Ad Platform: A Business Selling to the Poor Under the Guise of the Affluent

Original Author: Kaori

Original Editor: Sleepy

Sam Altman once referred to advertising as ChatGPT's "last resort."

For a long time, this statement represented a form of restraint. OpenAI continued to package itself as a research company, an infrastructure company, a company trying to make AI capabilities accessible to everyone. Advertising, the most familiar monetization method of the old internet, was treated as a backup plan.

But the advertising plan became a priority much faster than expected.

On May 5, OpenAI launched the self-service advertising platform Ads Manager, allowing advertisers to place ads on ChatGPT directly or through agencies like Dentsu, Omnicom, Publicis, and WPP. This came less than three months after the initial ad pilot was launched on February 9.

The platform is still in its testing phase, but the direction is clear. ChatGPT is no longer just a conversational product; it has become ad inventory. OpenAI aims to achieve $2.5 billion in advertising revenue by 2026 and push that figure to $100 billion by 2030.

With a user base of 900 million, ChatGPT is finding the free route increasingly difficult to sustain.

Burning Billions Annually, Relying on Ads to Recover Costs

OpenAI is growing at a pace so rapid that it's hard for traditional internet companies to find a benchmark for comparison.

But it's also burning through cash quickly.

HSBC analysts estimated in late 2025 that OpenAI could still face a funding gap of up to $207 billion by 2030. Its spending on cloud and AI infrastructure could reach $792 billion between the second half of 2025 and 2030, with long-term computing commitments potentially nearing $1.4 trillion by 2033.

These figures explain why they are moving into the advertising business.

Subscription revenue proves users are willing to pay, but it struggles to cover the inference costs for all free users. Enterprise APIs can generate cash flow, but face price wars and model commoditization. Capital financing can extend the runway, but it dilutes equity and pushes higher valuation pressures back onto the company.

Advertising is the fastest source of non-dilutive revenue. It doesn't require free users to pay, doesn't need to re-educate the market, and is easier to pitch to investors.

According to Reuters, OpenAI's ad pilot generated over $100 million in annualized revenue within six weeks. The ads are only shown to free and Go plan users, do not affect ChatGPT's generated responses, and do not share user data with marketers.

Leaving aside user privacy concerns for now, this strategy hides a more fundamental problem.

Ads are sold to free users, but advertisers are looking for paying users

ChatGPT has 900 million weekly active users, with approximately 50 million paid subscribers, a free-to-paid conversion rate of less than 6%. Since ads are only shown to free users, OpenAI's entire ad inventory comes from that 94% who are unwilling to pay.

The problem is that advertisers willing to spend a minimum of $50,000 aren't usually selling goods to individual consumers. The decision-makers for high-ticket items like enterprise software, SaaS tools, and B2B services are most likely ChatGPT's paying users. They spend $20 to $200 a month for more powerful models and larger context windows, and their screens will never see an ad.

Beyond this audience mismatch, there is a deeper issue: even if ads successfully reach free users, how much advertising value can their actual usage scenarios support?

High Intent Doesn't Equal High Conversion

OpenAI's advertising narrative is built on a core assumption: ChatGPT users enter the dialogue with genuine intent, and ad placements in this high-intent context command a premium.

This assumption is only half true.

For the past two decades, brands have prioritized the search box because it represents intent. A user searching for a hotel likely intends to book a room; searching for corporate tax software likely indicates a pending purchase; searching for the best noise-canceling headphones means the user is already at the decision-making threshold for a purchase.

Google built its advertising empire on this principle. With the advent of ChatGPT, users can delegate the entire decision-making process to AI. This is both more enticing and more intimidating for advertisers. The enticing part is that ChatGPT sees the entire narrative of the need; it not only knows what the user wants to buy but also why they want to buy it that way. The intimidating part is that if AI directly provides the answer, users might not even look at the search results page anymore.

But "help me buy a pair of running shoes" and "help me write an email" are two completely different intents. The former is a consumption scenario, the latter a productivity scenario. In everyday ChatGPT usage, the latter makes up a much larger portion than the former. Users come here to write, translate, edit code, create proposals, organize their thoughts – activities that are high-frequency but don't naturally correspond to purchasing goods.

This directly depresses advertising performance metrics. Advertisers are willing to pay a premium for high-certainty purchase intent. Google search ads are expensive because users often enter the search box with clear intentions to buy, compare, book, or place an order. Meta ads are cheaper, but it possesses social profiles and vast amounts of conversion data, using algorithms to repeatedly filter low-intent users into potential consumers.

ChatGPT is stuck in the middle. It resembles a demand gateway more than social media, but it's harder to gauge commercial intent than search. It's more private than search, yet attribution is more difficult. It can solve user problems, but it doesn't necessarily generate ad clicks.

This is also why OpenAI's move from CPM (cost per mille/impression) to CPC (cost per click) is more than just a product upgrade. Advertisers aren't willing to pay based on the "next-generation search gateway" narrative over the long term. They ultimately ask, who did this click come from? Where did the conversion happen? How much budget should be shifted from Google, Meta, and TikTok to ChatGPT?

Category fit is also a problem. Low-risk categories like home goods, travel, education, and software tools can be tested first, but high-margin categories are often heavily regulated, such as finance, healthcare, insurance, and recruitment. Once ChatGPT places ads in these areas, the platform bears risks not just for ad performance, but also for misinformation, discrimination, and compliance.

Google's approach serves as a mirror. In the first quarter of 2026, Google's search ad revenue was $77.25 billion. Yet even so, Google is still very cautious about integrating ads into AI Mode and AI Overviews, and the standalone Gemini app has not formally run ads.

OpenAI's expansion into advertising is exploring a broader business model for the entire large model track.

OpenAI needs to make users feel that AI is intimate enough while convincing advertisers that there is sufficient commercial intent here. If this balance is lost, ChatGPT risks losing both sides simultaneously: users will see it as impure, and advertisers will find it unconvertible.

But the changes brought by advertising don't stop there; it's also reshaping how brands compete.

The Focus of GEO is Shifting

For the past year, brands have been anxious about whether they would disappear from AI-generated answers. The market packaged this as GEO, but it's essentially not a new concept; it's just the old SEO anxiety repackaged for the AI era.

OpenAI's launch of Ads Manager perfectly taps into this anxiety, but it also changes the direction of that anxiety.

In the ad-free era, the core question of GEO was "how to get into the AI's context." Brands competed to be cited by the model through product documentation, media coverage, third-party reviews, and community discussions. It was a battle of information quality and data structure.

With the ad platform online, precise traffic can be directly purchased. Brands no longer rely solely on organic citations. However, the competitive focus hasn't simply reverted to the traditional "buy more impressions." Instead, it has shifted from "how to get into the AI's answer" to "what does the AI think of my product?"

The reason is simple: after seeing an ad, the most natural next step for a user is to ask the AI, "Is this product actually any good?" The AI's answer then becomes the true conversion gate. Advertisers can buy impressions, but they can't buy a favorable review from the AI. If the AI gives a negative assessment based on public data, every dollar spent on ads accelerates user churn instead of driving conversion.

This means brands need to build a positive reputation within the AI's evaluation system. The quality of the product itself, the density of user reviews, the coverage of third-party tests – these signals that AI can read will likely determine conversion effectiveness more than the ad placements themselves.

GEO is moving from "entering the context" to "winning the evaluation." This is another trend worth watching following the launch of OpenAI's new advertising platform.

Not Running Ads is the Most Expensive Ad in 2026

Speaking of OpenAI, we must mention its nemesis, Anthropic, which is pursuing a completely different "advertising model."

On February 4, 2026, two days before the Super Bowl, Anthropic published a blog post stating that Claude will never run ads. No sponsored links. No third-party integrations.

That statement itself was an expensive ad.

Super Bowl ads aren't cheap. By spending heavily to tell users it doesn't sell ads, Anthropic is essentially using advertising to buy a brand perception of being ad-free.

Being ad-free is never just a moral stance; it's also a business positioning. It tells enterprise clients, professional users, and users in highly sensitive scenarios that Claude's answers won't be influenced by advertisers, Claude's product direction won't be optimized around ad inventory, and Claude's revenue comes from the money *you* pay.

The effect was immediate. Claude's ranking on the US App Store climbed steadily from 42nd place at the beginning of the year. On February 28, following OpenAI signing a Pentagon contract and triggering the "QuitGPT" movement, Claude topped the US App Store's free apps chart for the first time, surpassing ChatGPT. Free active users grew by 60%, daily registrations quadrupled, and paid users doubled within a week.

Anthropic's revenue structure is completely different from OpenAI's: over 80% comes from enterprise clients, with annualized recurring revenue surging from about $9 billion to $19 billion. Enterprise tools like Claude Code and Cowork have already contributed at least $1 billion in revenue. Anthropic doesn't need the advertising value of free users; it needs the trust premium from enterprise clients who are assured their data won't be used for advertising.

In this context, "no ads" is a precise business decision. By forgoing advertising revenue, it strengthens the trust barrier with enterprise clients, thereby supporting higher subscription pricing.

However, "no ads" is not an eternal virtue.

Data from the Stanford AI Index shows that the cost of achieving performance equivalent to GPT-3.5 dropped 280-fold in two years, from $20 per million tokens in November 2022 to $0.07 per million tokens in October 2024. If model capabilities continue to converge and API price wars intensify, the enterprise subscription premium Anthropic enjoys today could be gradually eroded. When model costs drop to the point where all competitors offer similar performance, why would enterprise clients continue to pay more for Claude?

This question currently has no conclusion, but time will provide the answer to this choice.

There's No Such Thing as a Free Lunch

OpenAI chooses advertising; Anthropic chooses to turn the absence of advertising into a premium. They appear to be on two opposite paths, but both are answering the same fundamental question: who pays when the inference costs of an AI product cannot be sustained by the free model in the long run?

OpenAI's Ads Manager is more than just an advertising product; it is a signal that the AI industry is moving from free expansion into cost recovery.

But OpenAI's chosen method of stopping the bleeding precisely exposes the most fragile part of its business. It needs to use a user group with the least consumption intent to support an ad pricing structure three times more expensive than Meta's.

This isn't a problem that can be solved just by user scale. 900 million weekly active users is an impressive number, but if those 900 million come to ChatGPT to write emails instead of buying things, advertisers will eventually vote with their feet.

Advertising can be a source of revenue for AI products, but it shouldn't be treated as the only answer. Because when a product's business model requires users to stay as long as possible and expose their intentions as much as possible, that product is no longer the user's assistant; it's the advertisers' assistant.