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Meta President Dina Powell McCormick stated at the Wall Street Journal Tech Conference that Democratic victories in the midterm elections will make it significantly harder for companies to build data centers. She noted that the industry has failed to adequately explain the benefits of these investments, leading to increased community backlash against data centers. Powell McCormick emphasized that data centers, as the visible manifestation of AI technology, have become a focal point for public discontent, reflecting deeper issues regarding overall attitudes toward AI.

Meta currently operates data centers in 23 US states, with five projects—including one in Texas—planned to reach or exceed 1 gigawatt in scale. To mitigate local community resistance, Meta has implemented several measures, such as paying for its own electricity, directing tax surpluses generated by data centers toward teachers and first responders, keeping the labor force local, and securing bipartisan support. Powell McCormick argued that communities only accept data centers when projects clearly benefit them.

Despite political headwinds, Meta remains optimistic about its AI investments, particularly the growth potential of its consumer AI agent Muse. Even if AI applications do not achieve expected mass adoption, Meta believes the compute capacity itself holds value due to daily requests for leasing. This perspective indicates Meta's attempt to reverse negative sentiment by demonstrating technology democratization and tangible economic benefits, though the post-election political environment introduces new uncertainties for its expansion plans.

Nilay Patel is the chief executive officer of technology media outlet The Verge. In The Vergecast podcast, he offered a judgment on the dilemma facing AI commercialization: the products are not good enough to justify the costs imposed on users. This is not an abstract market observation, but a specific causal chain—costs are real, benefits are not, and the disconnect between the two cannot be bridged by marketing.

Patel listed costs including data center noise encroaching on communities, AI overviews and deepfake content polluting the information environment, corporate spending exceeding revenue, and even European blood-storage refrigerators being hacked. His phrasing was direct: you cannot tell neighbors that they must endure the hum of a data center in their backyard for the sake of the most ridiculous AI slop content.

The issues on the benefit side are more subtle. Patel believes agents are not yet true agents, nor are they being used in the ways people expect. All companies are spending more than they are earning on these products. This means unit economics do not yet hold—not as an isolated issue for a single company, but as a structural state across the entire industry.

He linked this imposition of costs to political influence: CEOs shaking hands at the White House, renaming bodies of water, and paying for banquet halls they do not need, attempting to use political means to make users accept product costs they would otherwise reject. His judgment is that this is obvious crypto-corruption, and the products themselves remain slop.

Patel's prescription for the industry is a clear condition: the AI industry will not escape this predicament until they build better products—good enough that everyone can see, without needing to be convinced, without ad campaigns, and without any political maneuvering, that using these products improves their lives. This condition is important because it shifts the standard for industry recovery away from funding scale, compute investment, or political connections, and back to product experience itself.

It should be noted that Patel's assertion is an opinion rather than an analysis. He provided no specific quantitative data on costs versus benefits, nor did he discuss which product categories are approaching his standards. As a content media outlet, The Verge's business model is directly related to the quality of AI-generated content, giving his criticism of 'slop' a self-interested hue. However, his position as an external observer—who does not sell AI products and does not rely on AI revenue—means this judgment is at least free from the contamination of vendor self-interest narratives.

According to WIRED, an emerging data brokerage industry is quietly collecting and selling internal corporate work files, emails, and Slack messages to AI labs like Anthropic and OpenAI for training more powerful models. The report states that these AI labs are willing to pay between $350,000 and $1.8 million as a one-time fee for a copy of a media company's old work records. Another similar pitch raised the potential deal value to $2 million, claiming that "public data is exhausted, and the labs are spending over $45 billion on private data to build better models."

Industry leaders include startups such as Mercor, Micro1, and Handshake. Mercor, founded in 2023, is considered the sector leader, with its three co-founders becoming billionaires last year. Ali Ansari, CEO of Micro1, stated that labs, enterprises, and robotics companies currently pay about $40 to $50 billion annually for data, predicting this will exceed $1 trillion per year within two to four years. Ansari believes that selling workflow data will become a more common revenue line item for companies than advertising.

Although data brokerages claim they strip personally identifiable information (PII) during transmission, privacy advocates and labor unions have expressed concern. During its bankruptcy, Spirit Airlines put its digital assets—including 80,000 email accounts, 500 million internal messages, and 20.6 million SharePoint files—up for sale, triggering a bidding war between Google and Micro1. However, the flight attendants' union charged that the sale involved employees' private information, leading to a hold on the transaction. Mayu Tobin-Miyaji, General Counsel at the Electronic Privacy Information Center (EPIC), noted that unstructured data like emails and Teams messages are difficult to fully anonymize and may leak sensitive content regarding doctor's appointments or family issues.

This trend has sparked deeper discussions about labor rights. Critics argue that AI companies are using workers' output to train models designed to replace or automate human work, often without explicit consent. As the president of the flight attendants' union stated: "This is really only data that became valuable in the age of AI in order to train models to replace jobs."