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A $1.5 Billion Wake-Up Call for Every AI Company on Wall Street

Written by Bridget Bennett. Article Posted: 7/27/2026.

A federal judge just approved a $1.5 billion settlement that puts a substantial legal price on one source of artificial intelligence training material: pirated books.

The judge separately ruled that training Claude on lawfully acquired books qualified as fair use. The settlement covers 482,460 works and is expected to pay roughly $3,000 per work—a much narrower group than the more than 7 million pirated books identified in Anthropic’s broader library.

I found Rolls-Royce at $2 - now I see the same setup again (Ad)

In 2022, Karim Rahemtulla recommended Rolls-Royce under $2 - a misunderstood aerospace name the market had written off. The stock climbed more than 1,100% over the next 3-4 years, with some subscribers reporting gains of $141,000, $272,000, or more.

He believes a similar setup is forming now around what he calls the 'Energy Cube' - a compact nuclear system roughly the size of a shipping container, capable of powering up to 1,000 homes with no emissions, 24 hours a day. Bill Gates, Jeff Bezos, Google, and Microsoft have all backed companies in this space. The Nuclear Regulatory Commission is expected to issue a key approval as early as August - the first of its kind for this technology class in over a decade.

Watch the full presentation before the NRC decision arrives

Dan Novaes, CEO of Mode Mobile, calls this a turning point in how markets value human data. His firm operates a rewards platform built on consented data sharing, and he argues that the settlement is the clearest signal yet that the human data market could grow into a market worth $1 trillion or more by 2033.

A Market Price Emerges for Training Data

For most of the past two and a half decades, Novaes says, the arrangement was simple: Platforms collected user activity, trained smarter systems on it, and users received a free product in return. That arrangement is now under legal pressure not only in the United States but also in Germany and other jurisdictions applying stricter consent standards, echoing the way GDPR reshaped data privacy years ago.

The practical effect is already showing up in licensing deals. Reddit Inc (NYSE: RDDT) has reportedly licensed its user-generated content to Google for roughly $60 million a year—high-margin revenue built entirely on comments and posts the platform already owned.

Models Still Need Millions of Human Hours

Large language models still hallucinate, and Novaes says closing that gap increasingly requires specialized human input rather than more raw internet text. Companies now pay subject-matter experts, from physicists to physicians, to refine model responses through a process known as reinforcement learning from human feedback. Mercor, a marketplace connecting specialized workers with AI labs, reportedly grew from $1 billion to $2 billion in annualized revenue in four months this year, according to Novaes.

Robotics adds another layer entirely. Training a robot to fold laundry or change a tire requires millions of hours of what Novaes calls egocentric video: first-person footage of humans performing that exact task. Meta Platforms Inc (NASDAQ: META) took a 49% non-voting stake in Scale AI at a $29 billion valuation last year to help meet that demand. The deal also reportedly cost Scale some of its other frontier-lab clients because of data-confidentiality concerns.

Publishers Start Getting Paid to Play

Cloudflare Inc (NYSE: NET), which sits in front of a meaningful share of the web, will begin blocking mixed-use AI crawlers from ad-supported pages by default starting Sept. 15. That will force AI labs to separate search indexing from model training and agent traffic, giving publishers new leverage to charge for content that bots previously scraped for free.

Novaes sees this as part of a broader pattern: Courts, regulators and infrastructure providers are converging on the same idea. Consent and compensation for data are becoming the norm rather than the exception. A similar suit filed by a coalition representing nearly 400 newspapers against OpenAI and Microsoft in June 2026 alleged that their content was copied without permission or compensation. Novaes does not expect the pace of these cases to slow.

Where the Investable Opportunity Sits

For investors, Novaes frames the data supply chain as the final piece of the picks-and-shovels trade that began with NVIDIA Corporation (NASDAQ: NVDA) chips and extended into energy and rare-earth names. The data layer, he argues, is the piece the market has not fully priced yet.

Novaes points to SpaceX (NASDAQ: SPCX) as a cautionary example. By the time most retail investors had a chance to buy in, the company was valued at $1.75 trillion, and the biggest gains had already gone to early venture backers. He argues that Anthropic and OpenAI are following the same path, with valuations reportedly jumping from roughly $300 billion to $1 trillion or more within months. Mode Mobile itself is not publicly traded. The company has raised capital through Regulation CF crowdfunding and has more than 64,000 shareholders, positioning it, in Novaes’ view, as a way for everyday investors to get in before a public listing rather than after one.

His caution: This is a concentrated market. Roughly seven frontier labs account for most of the demand, so companies dependent on just one or two of those clients carry real customer-concentration risk. He points to diversified revenue, rather than data licensing alone, as a sign of a sturdier business model.

Datavault AI Inc (NASDAQ: DVLT) trades as one of the more direct public plays on data monetization, though its small size makes it considerably more volatile than the hyperscalers building or buying their way into the space. The upside case rests on continued regulatory and legal pressure forcing more licensing deals into the open. The risk is that a handful of dominant labs strike direct deals with the largest data holders, leaving smaller data-supply companies to compete for scraps.

Either way, the settlement made one thing clear: Data has a price now, and someone has to pay it.