US version of the DMA returns as Congress targets App Store


Four years after it died, the US Congress is reviving one of the most significant antitrust bills Apple has faced in years. The fight over big tech, App Stores, and platform control is alive again.

Sens. Amy Klobuchar, D-Minn., and Chuck Grassley, R-Iowa, reintroduced the American Innovation and Choice Online Act (AICOA) on June 10. It revives a bipartisan effort to limit how dominant technology companies favor their own products and services.

The bill targets the largest online platforms and seeks to restrict conduct that supporters say gives those companies an unfair advantage. Apple and other technology giants spent years fighting earlier versions of the legislation because of its potential impact on their businesses.

The proposal would prevent dominant technology companies from favoring their own products and services. Lawmakers describe those practices as self-preferencing and argue they can disadvantage competitors.

Critics argue Apple uses its position as the operator of iOS and the App Store to benefit its own services over competing products. The legislation could directly affect the App Store and Apple’s control over the iPhone ecosystem.

Apple has consistently argued that its policies help protect user privacy, security, and the integrity of its platforms. The reintroduction marks the latest chapter in a legislative battle that has stretched across multiple sessions of Congress.

Earlier versions of AICOA advanced through the Senate Judiciary Committee but never reached a final vote despite bipartisan support. The bill came closer to becoming law than many technology reform proposals.

The policy landscape has changed significantly since then.

The debate around AICOA has changed since Congress first considered the legislation. Apple has already made significant App Store changes in Europe to comply with the Digital Markets Act.

The European law imposed new requirements on how large technology platforms compete and operate. The DMA and AICOA take different approaches to regulation.

Both aim to limit how dominant technology companies use control of their platforms to benefit their own products and services. For Apple, the DMA offers a real-world example of the kinds of changes lawmakers have sought through AICOA.

Why Apple fought the bill

Apple was among several technology companies that opposed the legislation during its previous runs through Congress. The company argued that some provisions could make it harder to maintain privacy and security protections on its platforms.

Industry groups representing large technology companies also warned that the legislation could have unintended consequences for integrated products and services.

Supporters argue dominant platforms have too much control over businesses that depend on them. They say existing antitrust laws haven’t done enough to address those concerns.

Major technology companies spent heavily to stop AICOA and related antitrust legislation. Previous reporting found that Apple, Amazon, Google, and Meta collectively spent more than $100 million on lobbying and advocacy efforts tied to the proposals.

Trade groups also joined the fight, and industry-backed advertising campaigns helped amplify the opposition. The legislation ultimately stalled despite advancing through committee and attracting support from both parties.

Why the legislation matters now

The bill’s return doesn’t guarantee it will become law. Previous versions generated substantial attention and bipartisan support but ultimately stalled before reaching the finish line.

For Apple, the debate extends beyond another round of regulatory scrutiny. The legislation could affect how the App Store operates and how Apple Services compete on the company’s platforms.

Whether the latest version gains enough support to advance remains unclear. Its return shows that Congress is still trying to limit how dominant technology platforms use control of their ecosystems to benefit their own products and services.



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“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, even after we pointed out that many users may not know which model they were using in the ChatGPT interface. She also declined to comment generally about the exposure of PII by the chatbot, instead providing links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 



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