OpenAI is positioning ChatGPT to become a major advertising platform, with Chief Financial Officer Sarah Friar describing the chatbot as what might result “if Google and Meta had a baby” as the artificial intelligence company looks for new ways to turn its enormous user base into sustainable revenue.

Friar made the comparison Tuesday at Goldman Sachs’ Communacopia + Technology Conference in San Francisco, outlining a strategy that could transform ChatGPT from an AI assistant supported largely by subscriptions and enterprise contracts into a major advertising business.

She said OpenAI’s advertising operation is already generating cash at a rate that would amount to about $1 billion in annual revenue, only seven months after ads were introduced on the chatbot.

Friar attributed the “baby” analogy to Fidji Simo, the former OpenAI executive who previously oversaw the company’s commercial operations. The strategy rests on combining two advantages that have historically powered the digital advertising businesses of Google and Meta.

Google captures users at moments when they have a specific need or purchasing intention. Meta, meanwhile, has accumulated extensive information about users and their interests through its social platforms.

Friar said that ChatGPT could combine both characteristics because users often explicitly describe what they want while the system can retain context about their preferences.

“It’s high intent search, you’re telling a lot, but with the context it’s not quite people like me, it’s me, because it has all the memory of me,” Friar said.

That combination could give OpenAI a potentially powerful advertising proposition: an AI system that understands not only what a consumer is searching for, but also the circumstances and preferences surrounding the request.

OpenAI currently places basic advertisements beneath ChatGPT responses for users on its free and Go subscription tiers. Friar said the company is working toward more sophisticated formats that could be tailored to the conversational nature of AI.

She said OpenAI has already seen early indications of what a more AI-native advertising format could look like as the company develops the underlying technology and expands availability.

“This is all before we’ve really launched a format that feels truly endemic to AI,” Friar said. “That’s what I get super excited about.”

The advertising strategy underpins a major change in OpenAI’s position from only a few years ago.

In 2024, Chief Executive Sam Altman said he found the idea of combining advertising with AI “uniquely unsettling.” OpenAI is now moving in the opposite direction as it seeks additional revenue streams to support the enormous cost of operating and developing frontier AI models.

That cost structure is central to the company’s commercial challenge.

ChatGPT has grown to roughly 1 billion users, giving OpenAI one of the largest consumer audiences in the technology industry. But serving those users requires substantial computing resources, while the company is simultaneously spending heavily on data centers, AI inference, and model research.

Advertising could provide a way to monetize users who do not pay for premium subscriptions while allowing OpenAI to preserve a free tier that expands the potential reach of ChatGPT. The approach also creates a potentially important distinction between AI advertising and conventional search advertising.

A traditional search query may provide advertisers with information about what a user is looking for at a particular moment. A conversational AI system could potentially understand a much longer sequence of interactions, giving it a richer picture of the user’s objectives.

That could make advertising more relevant and potentially more valuable, but it also creates significant privacy and trust questions. The more personal context an AI system uses to target commercial messages, the more sensitive the boundary becomes between useful personalization and intrusive advertising.

OpenAI will also have to prevent advertising from compromising the perceived independence of ChatGPT’s answers. Users may be less willing to trust recommendations if they believe commercial relationships influence what the AI suggests.

The company is developing its advertising strategy as it prepares for potentially greater scrutiny from public-market investors. OpenAI and rival Anthropic both filed confidential S-1 registration statements in June as they prepare for closely watched initial public offerings. Anthropic has been more openly critical of advertising inside AI assistants, creating a potentially important difference in the business models the two companies may present to investors.

OpenAI has not set a specific IPO date, although Friar has previously indicated that a public listing could take place by the end of 2027.

The company was valued at about $852 billion in its March financing round, which raised $122 billion. That valuation places substantial expectations on OpenAI to convert its technological lead and massive user base into durable cash flows.

Advertising could become an important part of that equation.

For Friar, who previously served as Square’s CFO during its 2015 IPO and was CEO of Nextdoor when the company went public through a SPAC in 2021, the challenge is to demonstrate that OpenAI’s extraordinary AI spending can eventually produce the economics expected of a technology company approaching the public markets.

ChatGPT’s consumer business, enterprise products and advertising operation will all have to grow rapidly enough to offset the cost of running more capable models and building the infrastructure required to support them.

OpenAI’s bet is that the same conversational context that makes ChatGPT useful can also make it unusually valuable to advertisers. If that model works, the company could establish a new category of advertising built around AI-mediated decisions rather than traditional search results or social feeds.

The financial opportunity is substantial, but so are the risks. OpenAI must prove that advertising can scale without eroding user trust, compromising the quality of AI responses, or creating privacy concerns around the enormous amount of contextual information ChatGPT can accumulate.

Therefore, the “Google and Meta” comparison captures more than OpenAI’s advertising ambition. It describes the company’s attempt to combine two of the most valuable properties in digital advertising with a new interface in which consumers tell an AI system exactly what they want.

Shares of Taiwan-based electronics manufacturer Wistron fell more than 6% on Tuesday after the Nvidia supplier priced a $1.47 billion global depositary receipt offering at a discount to its existing share price to finance raw-material purchases and support its expansion in artificial intelligence infrastructure.

Wistron said Monday that it had priced 25 million global depositary receipts at $58.88 each. The offering represents 250 million newly issued common shares, with each GDR representing 10 Taiwan-listed shares.

The new shares were priced at about NT$186.24 each, a roughly 5.5% discount to Wistron’s NT$197 closing price on Monday.

The issuance represents about 7.29% of Wistron’s shares outstanding before the offering, creating immediate dilution for existing shareholders and helping explain the sharp decline in the stock.

Wistron expects the new GDRs to be issued Thursday. The company said proceeds will be used primarily to purchase raw materials in foreign currencies.

The fundraising comes as Wistron is rapidly expanding its AI-server business, which has become an important growth driver for Taiwan’s electronics manufacturing sector.

Wistron approved additional capital expenditure last month, including NT$10.5 billion for facilities in Taiwan and a combined $53 million for two U.S. subsidiaries. The investments are intended to increase capacity for future AI-related business.

The company is also accelerating its manufacturing presence in the United States.

In July, Wistron opened its first U.S. manufacturing facility, a $700 million AI-server plant in Fort Worth, Texas. The facility currently manufactures Nvidia’s GB300 Grace Blackwell Ultra systems and is expected to expand production to Nvidia’s next-generation Vera Rubin platform.

The Texas investment places Wistron closer to one of the most important trends reshaping the electronics supply chain: the geographic expansion of AI infrastructure manufacturing.

Demand for AI servers has driven substantial investment by cloud-service providers and technology companies, creating opportunities for contract manufacturers such as Wistron, which assemble high-performance computing systems incorporating Nvidia processors, networking equipment and other components.

That growth, however, requires substantial working capital.

AI servers are significantly more complex and expensive than conventional computing systems, increasing the amount of capital manufacturers need to finance components and inventories. Wistron’s decision to earmark the GDR proceeds for raw-material purchases indicates that working-capital requirements are rising alongside production.

The foreign-currency component of the funding is also significant because Wistron sources materials globally and sells into international technology supply chains. Raising dollars through GDRs gives the company a direct pool of foreign-currency funding for purchases, potentially reducing some currency-mismatch risk.

For investors, the trade-off is that the offering strengthens Wistron’s balance sheet and provides capital to pursue rapidly expanding AI demand, but the discounted issuance increases the number of shares and dilutes existing holders.

Wistron’s stock had already gained about 23% this year before Tuesday’s decline, meaning investors had priced in a substantial amount of optimism surrounding its AI exposure.

The company reported NT$895.4 billion in revenue for the second quarter and NT$14.8 billion in profit after tax.

The scale of the revenue base, combined with new U.S. and Taiwan capacity, suggests Wistron is positioning itself for continued growth in AI infrastructure rather than treating the current demand surge as a short-term cycle.

However, there is a growing shareholder concern about whether the additional capacity and working capital will generate enough incremental earnings to outweigh the dilution from the GDR issue. Analysts say that will depend heavily on the durability of AI-server demand, Wistron’s ability to secure additional Nvidia-related orders, utilization rates at its new U.S. facility and the margins it can earn on increasingly sophisticated AI systems.

Wistron’s financing therefore illustrates a broader feature of the current AI hardware boom: suppliers are having to raise substantial amounts of capital to keep pace with demand before the resulting production expansion fully translates into earnings.

The sharp share-price reaction shows that investors are willing to fund that expansion, but at a price.

Billionaire entrepreneur says communities should secure enforceable limits, financial guarantees and independent oversight before data-center developers break ground

Mark Cuban is urging local governments to negotiate aggressively with data-center developers before approving new projects, noting that communities have their greatest bargaining power before construction begins.

In a series of posts on X on Monday, the billionaire technology entrepreneur laid out a 10-part framework for towns considering their first data center. His central message was that major commitments from developers should not be left to promises or broad assurances.

Instead, Cuban said every significant undertaking should have measurable limits, independent oversight, financial backing and a practical remedy if the developer fails to meet its obligations.

A data center can deliver construction jobs, expand a local tax base and bring investment in roads, power infrastructure and other services. But the facilities can also impose long-term demands on electricity, water and public infrastructure while affecting nearby residents for decades.

Cuban advised communities to hire their own lawyers and technical experts rather than relying solely on information supplied by developers. Local officials should also evaluate the project’s full planned build-out, rather than negotiating around only the first phase of construction.

Among the safeguards he recommended are explicit limits on water consumption and noise, restrictions on backup-generation equipment and provisions requiring developers to cover infrastructure costs associated with their projects.

Water And Noise Emerge As Key Battlegrounds

Cuban placed particular emphasis on water use, cautioning communities against assuming that a data center using closed-loop cooling will have no significant water requirements.

Even systems marketed as closed-loop can consume water under certain conditions, including periods of extreme heat, he said.

Noise is another major concern. Cuban noted that conventional noise standards may not adequately address the low-frequency sound generated by cooling systems and other equipment at large data centers.

“Noise is now the most common post-approval complaint and the most active area of litigation,” Cuban said in a separate post, pointing to proposed class actions in Wisconsin and Mississippi alleging that data-center noise can travel more than a mile from facilities.

The issue has become a growing source of friction between data-center operators and communities as the rapid expansion of AI computing brings large facilities into residential and rural areas.

Cuban’s recommendations extend beyond environmental and quality-of-life concerns to the financial risks communities could face if projects are delayed, downsized, or abandoned.

He called for developers to provide decommissioning funds before construction begins, as well as guarantees from financially credible parent companies. Other protections could include cash escrow accounts or letters of credit that municipalities could draw on if developers fail to meet contractual obligations.

The objective, Cuban said, is to prevent communities from being left responsible for infrastructure or cleanup costs after a project fails to materialize.

Data-Center Backlash Grows

Cuban has repeatedly warned that communities should use their leverage while AI companies and data-center developers are competing aggressively for sites and infrastructure.

In July, he said municipalities should pursue operators that violate laws or cause documented damage, arguing that the extraordinary demand for computing infrastructure gives communities negotiating power.

“The AI companies need the data centers more than they need air,” Cuban said at the time, urging communities to impose financial consequences for documented harm.

Later that month, he told the “All-In” podcast that technological advances could eventually make some of today’s computing infrastructure obsolete. He joked that unused data centers could ultimately become “pickleball courts,” underscoring the risk of communities being left with large facilities whose economics deteriorate faster than expected.

Public opposition is already presenting a significant challenge for the industry. A Gallup poll conducted in March found that 71% of Americans opposed having a data center built where they live, compared with 53% who opposed a nearby nuclear power plant.

Environmental and community groups have focused particularly on water consumption and noise. The Environmental Health Project has warned that cooling systems and diesel backup generators can generate persistent noise audible to surrounding communities, while cooling requirements can consume substantial quantities of water in areas already facing supply constraints.

The backlash has even entered popular culture. Former NFL player Jason Kelce recently appeared in a satirical advertisement for Garage Beer and Liquid Death built around the controversy over the amount of water associated with cooling AI data centers.

For local governments, the debate is increasingly about more than whether a data center brings jobs and tax revenue. Officials are being asked to determine who pays for new power and water infrastructure, who bears the environmental costs, and who is responsible if a facility fails to deliver its promised economic benefits.

Cuban described his framework as a work in progress and invited others to contribute. But its underlying argument is that once a municipality approves a project, its negotiating leverage can diminish sharply.

For communities facing proposals from developers racing to secure land and power for the AI boom, Cuban’s advice is to negotiate the protections first and approve the project afterward.