While some companies, such as OpenAI and Anthropic, have publicly advocated for AI regulation, Time reports that in closed-door meetings, these same companies "tend to advocate for very permissive or voluntary regulations."
I think that dropping the intermediate text which describes 'more established big tech companies' such as Microsoft substantially changes the meaning of this quote - "these same companies" is not "OpenAI and Anthropic". Full context:
Executives from the newer companies that have developed the most advanced AI models, such as OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei, have called for regulation when testifying at hearings and attending Insight Forums. Executives from the more established big technology companies have made similar statements. For example, Microsoft vice chair and president Brad Smith has called for a federal licensing regime and a new agency to regulate powerful AI platforms. Both the newer AI firms and the more established tech giants signed White House-organized voluntary commitments aimed at mitigating the risks posed by AI systems. But in closed door meetings with Congressional offices, the same companies are often less supportive of certain regulatory approaches
AI lab watch makes it easy to get some background information by comparing committments made by OpenAI, Anthropic, Microsoft, and some other established big tech companies.
I want to make sure we get this right, and I'm happy to change the article if we misrepresented the quote. I do think the current version is accurate, though perhaps it could be better. Let me explain how I read the quote, and then suggest possible edits, and you can tell me if they would be any better.
Here is the full Time quote, including the part we quoted (emphasis mine):
But, many of the companies involved in the development of AI have, at least in public, struck a cooperative tone when discussing potential regulation. Executives from the newer companies that have developed the most advanced AI models, such as OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei, have called for regulation when testifying at hearings and attending Insight Forums. Executives from the more established big technology companies have made similar statements. For example, Microsoft vice chair and president Brad Smith has called for a federal licensing regime and a new agency to regulate powerful AI platforms. Both the newer AI firms and the more established tech giants signed White House-organized voluntary commitments aimed at mitigating the risks posed by AI systems.
But in closed door meetings with Congressional offices, the same companies are often less supportive of certain regulatory approaches, according to multiple sources present in or familiar with such conversations. In particular, companies tend to advocate for very permissive or voluntary regulations. “Anytime you want to make a tech company do something mandatory, they're gonna push back on it,” said one Congressional staffer.
Who are "the same companies" and "companies" in the second paragraph? The first paragraph specifically mentions OpenAI, Anthropic, and Microsoft. It also discusses broader groups of companies that include these three specific companies "both the newer AI firms and the more established tech giants," and "the companies involved in the development of AI [that] have, at least in public, struck a cooperative tone when discussion potential regulation." OpenAI, Anthropic, and Microsoft, and possibly others in the mentioned reference classes, appear to be the "companies" that the second paragraph is discussing.
We summarized this as "companies, such as OpenAI and Anthropic, [that] have publicly advocated for AI regulation." I don't think that substantially changes the meaning of the quote. I'd be happy to change it to "OpenAI, Anthropic, and Microsoft" given that Microsoft was also explicitly named in the first paragraph. Do you think that would accurately capture the quote's meaning? Or would there be a better alternative?
Huh, this seems messy. I wish Time was less ambigious with their language here and more clear about exactly what they have/haven't seen.
It seems like the current quote you used is an accurate representation of the article, but I worry that it isn't an accurate representation of what is actually going on.
It seems plausible to me that Time is intentionally being ambigious in order to make the article juicier, though maybe this is just my paranoia about misleading journalism talking. (In particular, it seems like a juicier article if all of the big AI companies are doing this than if they aren't, so it is natural to imply they are all doing it even if you know this is false.)
Overall, my take is that this is a pretty representative quote (and thus I disagree with Zac), but I think the additional context maybe indicates that not all of these companies are doing this, particularly if the article is intentionally trying to deceive.
Due to prior views, I'd bet against Anthropic consistently pushing for very permissive of voluntary regulation behind closed doors which makes me think the article is probably at least somewhat misleading (perhaps intentionally).
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OpenAI and Google Announce New Multimodal Models
In the current paradigm of AI development, there are long delays between the release of successive models. Progress is largely driven by increases in computing power, and training models with more computing power requires building large new data centers.
More than a year after the release of GPT-4, OpenAI has yet to release GPT-4.5 or GPT-5, which would presumably be trained on 10x or 100x more compute than GPT-4, respectively. These models might be released over the next year or two, and could represent large spikes in AI capabilities.
But OpenAI did announce a new model last week, called GPT-4o. The “o” stands for “omni,” referring to the fact that the model can use text, images, videos, and audio as inputs or outputs. This new model modestly outperforms OpenAI’s previous models on standard benchmarks of conversational skill and coding ability. More importantly, it suggests a potential change in how people interact with AI systems, moving from text-based chatbots to live verbal discussions.
Google DeepMind demoed a similar model, called Project Astra. It can watch videos and discuss them in real-time. This model is intended to be part of a path towards building AI agents that can act autonomously in the world. Google also announced improvements to their Gemini series of closed source models, and Gemma series of open source models.
One interesting note for those interested in AI policy is that these models could potentially be deemed illegal in the European Union. The EU AI Act prohibits:
Users can ask multimodal AI systems like GPT-4o and Project Astra to look at a person’s face and assess whether they’re happy, sad, angry, or surprised. Does this mean that these models will be illegal in the European Union? Some have suggested that it might. This highlights the difficulty of regulating technologies that are rapidly developing.
The Surge in AI Lobbying
As discussion of AI legislation has intensified, so too have lobbying efforts aimed at shaping the regulatory landscape. According to Time Magazine, "The number of groups lobbying the U.S. federal government on artificial intelligence nearly tripled from 2022 to 2023, rocketing from 158 to 451 organizations, according to data from OpenSecrets, a nonprofit that tracks and publishes data on campaign finance and lobbying."
Tech giants such as IBM, Meta, and Nvidia are leading the charge against AI safety regulations, according to recent reporting by Politico. They are joined by influential players like venture capital firm Andreessen Horowitz and libertarian billionaire Charles Koch. Politico notes these groups “have been pouring tens of millions of dollars into an all-hands effort to block strict safety rules on advanced artificial intelligence and get lawmakers to worry about China instead.”
Although tech companies have been lobbying Congress for a long time, Nvidia is a newcomer to the process, hiring its first lobbyist in late 2022. According to Politico, "Nvidia lobbyists are badmouthing a recent proposal by the Center for a New American Security think tank to require 'on-chip governance mechanisms.'"
While some companies, such as OpenAI and Anthropic, have publicly advocated for AI regulation, Time reports that in closed-door meetings, these same companies "tend to advocate for very permissive or voluntary regulations." Politico reported that “[IBM chief lobbyist Christopher] Padilla said IBM lobbyists have simply outmaneuvered the ‘AI safety’ lobby, which has fewer ties in the nation’s capital and less familiarity with how Washington works.”
Support for AI regulation is a live topic of debate in Congress. Our last newsletter covered several new legislative proposals on AI, but not all members of Congress are ready to move ahead with regulation. Last year, Rep. Ted Lieu (D-Calif.) expressed alarm over advanced AI systems and called for regulation to prevent the worst outcomes. However, Lieu, who now co-chairs the House AI Task Force and has signed the CAIS Statement on AI Risk, told Politico that he remains unconvinced by claims that Congress must take immediate action to regulate advanced AI.
"If you just say, 'We're scared of frontier models' — okay, maybe we should be scared," Lieu said. "But I would need something beyond that to do legislation. I would need to know what is the threat or the harm that we're trying to stop."
[Disclosure: The Center for AI Safety Action Fund also engages in lobbying, advocating for efforts to reduce societal-scale risks from AI. Our lobbying disclosures are publicly available. If you’re interested in donating, feel free to reach out to contact@safe.ai]
How Should Copyright Law Apply to AI Training Data?
Training frontier AI models requires two basic ingredients: compute and data. While much AI safety effort has been directed towards the former, in this story, we focus on the latter. In particular, we look at recent developments regarding how copyright law might apply to AI training data.
AI corporations might be cutting legal corners to acquire training data. In January, we wrote about NYT’s lawsuit against OpenAI and Microsoft, which alleges that the companies violated the NYT’s intellectual copyright by training AI systems on text from their stories. That lawsuit hasn’t been resolved yet, but, in the meantime, NYT published a deep dive into how OpenAI, Google, and Meta have waded into legally dubious territory with respect to copyright law.
For example, it reported that OpenAI used transcripts from more than a million hours of YouTube videos — clearly violating YouTube’s terms of service, and possibly the copyrights of the videos’ creators. For its own part, Google had quietly changed its privacy policy to allow it to train on publicly available videos and documents, although the legality of that policy is similarly unclear.
Europe is ahead of the US in clarifying how copyright law applies to AI training. As NYT’s and similar lawsuits will make their way through the US judicial system, the US copyright office is set to release three reports this year reviewing how copyright law applies to AI models. In the meantime, the law in the US remains unclear.
However, the case against AI companies is somewhat clearer in Europe. Last month, France fined Google 250 million euros over failures to negotiate in good faith with news outlets to use their stories as training data. The final text of the EU AI Act also requires that copyright holders be able to opt-out of having their work be included in training datasets, and that AI developers publish summaries of their datasets.
Three policy options for training on copyrighted data. There appear to be three prominent legal approaches to training on copyrighted data:
Economist Glen Weyl and others have argued that treating data as the labor of data creators could have economic benefits. It would encourage the production of useful data, and assuage concerns about AI automation driving unemployment, wage stagnation, and inequality. Others argue that requiring developers to pay for training data would slow AI development, and would particularly hamper lower resource developers such as startups and academics.
Policy shouldn’t necessarily treat AI systems like humans. Arguments in favor of the first regime often rely on comparing AI training to human learning. For example, the NYT would not have a case against an aspiring journalist who honed their craft by reading through the NYT’s backlog. However, we should be wary of such anthropomorphic arguments — laws should not always treat humans and AI systems analogously.
For example, it seems obvious that AI systems should not have the right to bear arms, even though this right is guaranteed to Americans by the Constitution. Yet this simple lesson – that AIs and humans should not always be governed by the same laws – seems often forgotten when considering the case of training on copyrighted data.
The legal battles over training on copyrighted data are in full swing. While the courts attempt to apply existing laws to this new challenge, legislators might want to consider clarifying or updating the way that copyrighted data should be treated by AI developers.
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