Industry DA - Emory 2024

Industry DA – ENDI 24
1NC Shell
Industry DA---1NC
The US is ahead of China in the tech race thanks to their lead in generative AI.
Nicholas Gordon 23. Hong Kong-based associate editor, where he assists with audience development and commentary coverage for the Asia-Pacific region, master’s degree in international relations from Oxford University. "ChatGPT’s rise has China scrambling to catch up in an A.I. arms race that will determine global ‘wealth, power, and influence’". Fortune. 8-8-2023. https://fortune.com/2023/08/08/chatgpt-china-catch-up-arms-race-global-wealth-power-influence/
Chinese companies, large and small, invested heavily in A.I. over the past decade. Yet the breakout success of ChatGPT appears to have surprised Chinese tech giants, and now they’re rushing to catch up.
The debate is about more than just a chatbot. Whichever country wins the A.I. arms race could corner a lucrative share of the global economy potentially worth trillions of dollars, and get an edge in the military competition between Beijing and Washington.
A.I. “has become a proxy in the battle for primacy between China and the U.S.,” says Kerry Brown, director of the Lau China Institute at King’s College London, and “whoever inches ahead in this form of technology gets benefits in terms of wealth, power, and influence.”
And that puts China’s tech giants in an uncomfortable position—between a Beijing that wants to mold the global internet in its favor, and a Washington determined to keep Chinese tech a few steps behind.
Not long ago, China appeared to have an early lead in developing artificial intelligence. Beijing designated A.I. a national priority in 2017, unleashing tens of billions of dollars of government investment.
China soon matched, if not surpassed, the U.S. in terms of the number of A.I. research papers its citizens published, to the chagrin of U.S. national security officials. And experts argued that China’s massive population—and the reams of personal data those citizens provided—gave the country an inherent advantage in training new A.I. models.
Then, suddenly, ChatGPT’s debut upended the A.I. pecking order. OpenAI didn’t officially make its service available in China. But Chinese developers created a number of work-arounds that Beijing reportedly shut down, with state media calling them a potential “helping hand” of the U.S. government. China’s tech companies are now scrambling to fill the gap.
Baidu’s Ernie Bot, revealed in March and still being tested, is the service most recognizably like ChatGPT and Google’s service, Bard. The Chinese bot can answer questions, summarize documents, and generate images and charts.
Baidu has long hoped that A.I. will help it recapture ground lost to competitors like Chinese tech giants Tencent and Alibaba, both of which have introduced their own A.I. products. During a call in May with analysts, Baidu CEO Robin Li called A.I. a “tremendous opportunity” comparable to the arrival of the internet and smartphones.
Yet Beijing’s heavy hand when it comes to policing the internet could throw a wrench into its broader A.I. plans. In a thinly veiled warning to domestic companies, Zhuang Rongwen, director of the Cyberspace Administration of China, the country’s top censor, said in June that A.I. must be “reliable and controllable.”
At the moment, China’s regulators seem to be adopting a wait-and-see strategy for A.I. The CAC unveiled rules for generative A.I. in July that weakened several tough provisions from an earlier proposal. While the agency said bots must uphold “core socialist values” and national security under the rules, it also pledged to support innovation.
Paul Triolo, senior vice president at U.S.-based global strategy firm Albright Stonebridge Group, says the mixed message shows that China’s government is undecided about what to do. In the meantime, he says, it’s “falling back on more general guardrails that will be tricky to both interpret and enforce” and will lead Chinese businesses to be more cautious.
The growing tech war between the U.S. and China is also stymieing A.I. development. Both nations are trying to give their domestic companies a boost while stifling innovation in the other country, says Anthea Roberts, a professor of global governance at Australian National University.
The U.S. has an advantage in the race owing to its more advanced semiconductors, key components for training A.I. models. The Biden administration is increasingly eager to preserve and widen that lead.
Nvidia and Advanced Micro Devices, the U.S.-based market leaders in these A.I. processors, revealed in September that the federal government had asked them to stop selling their most advanced products to China. Nvidia responded by trying to develop a weaker chip for the Chinese market, though the U.S. is reportedly considering further controls that would bar even its sale.
In response, Chinese companies like electronics manufacturer Huawei, whose products are banned in the U.S. because of national security concerns, are now trying to develop their own chips in-house. “Huawei will need to rely on our own fundamental A.I. technology,” Huawei Cloud CEO Zhang Pingan told reporters in early July.
Boris Van, a China internet analyst for Bernstein Research, thinks Chinese A.I. companies can still succeed with slightly inferior chips. “Two generations? That’s okay,” he says. “The problem is if you’re 10 generations behind.”
The threat of China’s A.I. development is spooking many U.S. entrepreneurs and officials. Former Google CEO Eric Schmidt and high-profile venture capitalist Marc Andreessen have lobbied against any U.S. regulation of A.I. because, they say, it would slow domestic innovation. They’ve also criticized a proposal to pause development of advanced A.I.—intended to give regulators time to catch up—as helping China close the gap with the U.S. and opening the door to a more authoritarian, China-dominated model of A.I.
The plan makes training generative AI prohibitively expensive, which collapses the industry.
Andreesen Horowitz 23. American Venture Capital Firm. “Notice of Inquiry on Artificial Intelligence & Copyright (Dkt. 2023–6).” 10-30-2023. US Copyright Office. https://www.masslawblog.com/wp-content/uploads/2023/11/Andreesson-comment.pdf
III. Collective or Statutory Licensing Is Not Workable
Under any potential legal framework where the use of copyrighted training data is not presumptively fair, anyone who wishes to lawfully continue with the development of AI models would be forced to take a license. Here, then, is another practical consequence of narrowing fair use in the context of AI model training: the necessity of somehow developing a framework for licensing the massive amounts of content required. As we discuss in this section, that task is all but impossible.
The unique considerations involved in training AI models make direct, voluntary licensing impossible. Generative AI models require not only enormous quantities of training content, but also immense diversity of content. A model that has access to only a limited amount of training data, or only a few types or categories of works, will be unable to accurately discern the meanings of or semantic relationships among words as used in human language broadly. The fact that large rights owners are willing to strike deals is irrelevant, as such deals would only permit use of a small amount of the content needed to adequately train AI systems. In fact, the reason AI models are able to do what they can do today is that the internet has given AI developers ready access to a broad range of content, much of which can’t reasonably be licensed—everything from blog posts to social media threads to customer reviews on shopping sites. Indeed, the cost of paying to license even a fraction of the content needed to properly train an AI model would be prohibitive for all but the deepest-pocketed AI developers, resulting in dominance by a few technology incumbents. This would undermine competition by the technology startups which are the source of the greatest innovation in AI.
Even copyright owners and the industry groups who represent them recognize the impracticability of voluntary, direct licensing for AI training data. They propose, instead, that Congress pass legislation to implement a statutory or collective licensing model.19
But such legislation would effectively require AI developers to remunerate rightsholders for a use that falls squarely within the protections of the fair use doctrine. The Supreme Court has warned against such attempts to “alter[] the traditional contours of copyright protection” by reducing its speech-protective limitations, like the idea/expression dichotomy and fair use. Moreover, a collective or statutory licensing scheme would prove administratively impossible to implement, as the Register acknowledged in a recent Congressional hearing.
The most obvious problem is scale. Some of the most powerful AI models in existence today were trained on an enormous cross-section of all of the publicly available information ever published on the internet—that is, billions of pieces of text from millions of individual websites. For a very significant portion of those works, it is essentially impossible to identify who the relevant rights holders are, and thus there would be no viable way to get statutory royalties to the proper parties. Moreover, since this technology enables production of creative work at an unprecedented rate, the problem will compound over time. The Office has encountered similar costs and administrative problems with routing royalties to rights holders in administering the Music Modernization Act, where the total number of musical works in the entire ecosystem is something on the order of 25 million. In the context of AI training data, where the relevant quantity of works is almost certainly in the billions, these costs and problems would be multiplied by many orders of magnitude. The amount of “unmatched” royalties would be astronomical, leaving almost all creators with no remuneration at all. Nor do the economics of any sort of statutory licensing scheme make sense.
Again, a staggering quantity of individual works is required to train AI models. That means that, under any licensing framework that provided for more than negligible payment to individual rights holders, AI developers would be liable for tens or hundreds of billions of dollars a year in royalty payments. AI development in the United States would thus become much more expensive than in a jurisdiction with less burdensome restrictions, thereby entrenching the dominance of incumbent technology platforms and discouraging investment in the technology startups that drive U.S. innovation in the AI space. The creation of an impossibly high financial barrier to AI development that only the largest, technology companies have any hope of clearing is a step backward from the current environment in which innovative, new entrants are challenging these dominant platforms. Any participation by small businesses or individual innovators in the nascent AI industry—like the startups that a16z invests in—would be all but barred, and competition would be snuffed out.
* * *
AI offers us the opportunity to improve the lives of everyone in a way that few other technologies—and maybe no other technologies—ever have. The Office can play a part in bringing about that result not by constraining AI but by embracing it wholeheartedly, and by placing faith in the balance U.S. copyright law has always struck between protecting expression and enabling generative, non-exploitive uses. By the same token, the best way to lose the United States’ current leadership in the burgeoning AI industry—along with economic competitiveness and national security benefits that leadership brings—is by rushing to pass legislation that undermines the long-standing and principled approach to copyright law that has made this country both a creative and technological leader.
Chinese tech dominance causes extinction from emerging tech and global war.
Ash Jain 19. Senior fellow with the Scowcroft Center for Strategy and Security, where he oversees the Atlantic Council’s Democratic Order Initiative and D10 Strategy Forum; and Matthew Kroenig, deputy director for strategy in the Scowcroft Center for Strategy and Security and associate professor of government and foreign service at Georgetown University, 10/30/19, “Present at the Re-Creation: A Global Strategy for Revitalizing, Adapting, and Defending a Rules-Based International System,” https://www.atlanticcouncil.org/wp-content/uploads/2019/10/Present-at-the-Recreation.pdf
The system must also be adapted to deal with new issues that were not envisioned when the existing order was designed. Foremost among these issues is emerging and disruptive technology, including AI, additive manufacturing (or 3D printing), quantum computing, genetic engineering, robotics, directed energy, the Internet of things (IOT), 5G, space, cyber, and many others. Like other disruptive technologies before them, these innovations promise great benefits, but also carry serious downside risks. For example, AI is already resulting in massive efficiencies and cost savings in the private sector. Routine tasks and other more complicated jobs, such as radiology, are already being automated. In the future, autonomous weapons systems may go to war against each other as human soldiers remain out of harm’s way. Yet, AI is also transforming economies and societies, and generating new security challenges. Automation will lead to widespread unemployment. The final realization of driverless cars, for example, will put out of work millions of taxi, Uber, and long-haul truck drivers. Populist movements in the West have been driven by those disaffected by globalization and technology, and mass unemployment caused by automation will further grow those ranks and provide new fuel to grievance politics. Moreover, some fear that autonomous weapons systems will become “killer robots” that select and engage targets without human input, and could eventually turn on their creators, resulting in human extinction. The other technologies on this lisgt similarly balance great potential upside with great downside risk. 3D printing, for example, can be used to “make anything anywhere,” reducing costs for a wide range of manufactured goods and encouraging a return of local manufacturing industries.61 At the same time, advanced 3D printers can also be used by revisionist and rogue states to print component parts for advanced weapons systems or even WMD programs, spurring arms races and weapons proliferation.62 Genetic engineering can wipe out entire classes of disease through improved medicine, or wipe out entire classes of people through genetically engineered superbugs. Directed-energy missile defenses may defend against incoming missile attacks, while also undermining global strategic stability. Perhaps the greatest risk to global strategic stability from new technology, however, comes from the risk that revisionist autocracies may win the new tech arms race. Throughout history, states that have dominated the commanding heights of technological progress have also dominated international relations. The United States has been the world’s innovation leader from Edison’s light bulb to nuclear weapons and the Internet. Accordingly, stability has been maintained in Europe and Asia for decades because the United States and its democratic allies possessed a favorable economic and military balance of power in those key regions. Many believe, however, that China may now have the lead in the new technologies of the twenty-first century, including AI, quantum, 5G, hypersonic missiles, and others. If China succeeds in mastering the technologies of the future before the democratic core, then this could lead to a drastic and rapid shift in the balance of power, upsetting global strategic stability, and the call for a democratic- led, rules-based system outlined in these pages.63
Alternate 1NC Impact---AI Good---1NC
Sustained AI innovation caps all existential risks. Specifically, disease, climate change, and asteroids.
John McGinnis 22. George Dix Professor in Constitutional Law, Northwestern Pritzker School of Law; JD, Harvard Law. “The Folly of Regulating against AI’s Existential Threat.” Chapter 27 in The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics. 2022. doi: 10.1017/9781009072168
27.3 ai’s extraordinary benefits
One concern about regulating AI in any way is that it will get in the way of continued progress in AI that delivers extraordinary benefits. In this section, I discuss those benefits, which guard against very great, sometimes existential, risks to humanity, as well as the improvement of everyday life, including for citizens of modest means. While I touch on many such benefits, I focus on only two as examples to demonstrate their pervasiveness – the enormous importance of AI in combating the pandemic and its improvement in the delivery of legal services.
27.3.1 AI and the Pandemic
AI has been crucial in every aspect of the pandemic – in discovering vaccines that may end it, in improving projections of its courses for establishing better policy, in developing medical treatments to save lives, and in creating ways of living that keep up productivity during the time of crisis. If Covid had happened just twenty years ago before the intervening progress in AI, vaccines would not have been deployed within anything like the short time they have been, and treatments would not have been improved as rapidly. Without access to virtual ways of living society would have been forced to the bitter choice of losing far more productivity or enduring many more deaths. AI has been almost entirely responsible for the improvement.
AI helps find patterns in information and those discoveries were crucial in developing vaccines. The basic idea behind a vaccine is to expose the body to a part of the virus so that our immune system is primed to defeat the full version without getting sick from the shot that delivers the vaccine. But there are thousands of subcomponents of a virus that could be targeted. Machine learning can help predict based on experience with past viruses which of these subcomponents is best to target. In the case of Covid, computer scientists moved quickly to find the optimal targets.10
Machine intelligence has also been useful in many facets of treatment as well. For instance, it has helped to predict outcomes for classes of patients, showing which patients can be sent home safely.11 Other machine learning programs have sifted through approved drugs to discover ones that would speed recovery from the disease.12
Machine intelligence also helped in mapping the disease. That mapping function assisted nations in deploying resources to test even asymptomatic individuals coming from nation predicted to have spikes in disease.13 It has also foretold the spread of the disease in many jurisdictions as when a model based on machine intelligence provided a continuous projection of the future incidence of infections, deaths, and hospitalization from the disease in the various states of the United States and countries of the world.14
More generally, computational progress was at the heart of the algorithms that allowed the development of the many services that permitted people to work virtually and shop online throughout the pandemic.15 These services permitted far more distancing and less loss of productivity than would have occurred even a decade before. It is not too much to say that developments in computer science and AI were the single most important defense against the pandemic.
AI can be expected to improve on all these dimensions in the future. Machine intelligence as it progresses may provide even earlier warning of outbreaks, saving lives and reducing social costs.16 Continued prog

Preview shortened. Download the file for the full document.