Just because there isn’t top-down Congressional reform, doesn’t mean there won’t be piecemeal regulation to fix the worst AI problems as they arise. SQ solves without the specific action of the aff and stifling innovation. Be wary of their false panic

“AI is too important not to regulate—and too important not to regulate well,” says Google. It is highly likely that Artificial Intelligence will be regulated. In many ways, it already is. The EU’s AI Act has extensive top-down prescriptive rules including prohibiting uses of AI that it says pose unacceptable risk; it’s in the final stages of passing. China has ruled that algorithms must be reviewed in advance by the state and “should adhere to the core socialist values”. The United States is taking its typical decentralized approach. The most likely outcome for the United States is a bottom-up patchwork quilt of executive branch actions. Unlike Europe, the United States is not likely to pass a broad national AI law over the next few years. Successful legislation is likely focused on less controversial and targeted measures like funding AI research and AI child safety. This likely disappoints proponents of strong national AI regulation. This outcome will be messier and have gaps, but no broad national law does not mean no regulation. We’re likely to see domain-specific agency actions especially in health care, financial services, housing, workforce, and child safety, plus multiple executive orders. This patchwork quilt of rules, if implemented well, could be grounded in the expertise of specific agencies and more tailored to innovation. The U.S. government will likely boost spending on AI and AI research, especially in defense and intelligence, and use its buying power to shape the market. AI trade friction with Europe is likely to emerge, and private companies will advance their own “responsible AI” initiatives and face a fragmented global AI regulatory landscape. Looming China competition will escalate a “don’t fall behind” debate. The Federal Trade Commission (especially) and Department of Justice likely fire warning shots to forestall AI concentration in big tech. There is a real but less likely chance that a key U.S. state (e.g., California) passes major AI legislation, or that a big AI-related disaster leads to a strong national response. This viewpoint proposes ten key parameters of successful AI regulatory design and the likely outcomes. AI raises many profound questions. Nearly all agree that AI promises both huge benefits for society and yet poses major risks. The challenge is getting the balance right between innovation and societal risks – which governments don't have a good record of achieving. The devil is in the details, making rules adaptable for a technology that is likely to change rapidly and be pervasive. The existential question of Artificial General Intelligence (AGI) is generating much debate. However, this is more distant and tough to forecast accurately. We focus here on issues like model safety, bias, transparency, privacy, security, trust, copyright, content regulation, education, and economic impacts (job loss, work force adjustment, productivity). For commercial actors, the key question is regulation’s impact on innovation, and does it address society’s concerns so that adoption is broad and sustainable. I. The AI Regulation Debate: A Wide Spectrum of Views. The AI regulation debate has become heated and politicized, reflecting the immense hopes and fears that we have invested in AI. Hysterical fear and paranoia. On one end, leading technologist and venture capitalist Marc Andreessen says “we have a full-blown moral panic about AI right now.” He views this as “hysterical fear” that is “irrational” and compares it to earlier introduction of new technologies that were similarly feared but were overblown and subsequently managed. He believes that AI is simply a computer program that is owned, controlled and used by people, and that AI does not have its own goals or its own wants. Moreover, he emphasizes, AI can be a force for tremendous good.