Industry DA Answers - Emory 2024

Aff Answers
Uniqueness---US Losing---2AC
The US is already behind China on AI.
Ryan Lovelace 24. Staff writer, Citing Andrew Moore, a former Google executive advising U.S. Central Command on AI, and Alexandr Wang, CEO of Scale AI, which is partnering with the Pentagon on testing AI models. "AI experts warn U.S. military: China has caught up to America in key areas of tech race". Washington Times. 2-22-2024. https://www.washingtontimes.com/news/2024/feb/22/ai-experts-warn-us-military-china-has-caught-ameri/
Artificial intelligence specialists warned the Defense Department on Thursday that China has caught up in the AI arms race and has even pulled ahead of America in some areas.
The AI experts, who are working closely with the Pentagon, cautioned those attending a government-organized symposium on AI against overestimating America’s advantage in its high-tech competition with China.
Andrew Moore, a former Google executive advising U.S. Central Command on AI, said he’s seen China surpass America in several areas. He also said he respects the seriousness of the communist competitors’ work in the tech world.
“There are two application areas where they have outperformed hyperscalers in the United States. I’m not going to say where they are right now, what they are right now,” Mr. Moore said at the symposium, which was attended by defense and intelligence officers, tech companies, and other high-level stakeholders.
Hyperscalers are the Big Tech companies with computing power capable of meeting large demands from customers, such as Amazon Web Services and Mr. Moore’s former outfit, Google Cloud.
Large language models, or powerful algorithms, were a hot topic of conversation at the symposium, organized by the Defense Department’s Chief Digital and Artificial Intelligence Office. But Mr. Moore said he was more worried about the Chinese military investing in other powerful tools.
He said he would rank large language models fourth on his list of concerns. He said there are three other areas in which he expects China to push forward, though he did not identify them.
Alexandr Wang, CEO of Scale AI, which is partnering with the Pentagon on testing AI models, said the Chinese Communist Party is making rapid advancements to catch up on large language models and the resulting generative AI tools.
After missing the generative AI opportunity at the outset, China has aggressively pursued the technology in the last two years, according to Mr. Wang.
He pointed to a new AI model developed by a Chinese company run by Kai-Fu Lee, former president of Google China, as the best-in-class open-source model of its size.
“Still the best frontier models are being built in the United States by companies like Google and OpenAI but I think we need to treat them as very meaningfully caught up on the technology side,” Mr. Wang said at the symposium. “I don’t think they’ll surpass us on the technology, but they are caught up.”
Mr. Wang said the U.S. could fall behind if it fails to adequately invest in critical AI areas and if the U.S. does not build the right evaluation frameworks for responsible AI deployment.
Mr. Wang’s Scale AI just scored an undisclosed sum of taxpayer cash from the Pentagon to test and evaluate AI models. The entrepreneur’s team is planning to build benchmark tests for the department to apply to large language models working on massive data streams.
To win the AI race, Mr. Moore said the U.S. needs to out-compete its communist rivals on a wider range of tools than those exclusively involving large language models.
“We have got to get good at using advanced technology, whether it’s called AI or not, for automation, within the U.S. military and intelligence and with our Western allies,” he said. “That’s our goal. That’s China’s goal, to do the same for themselves.”
Uniqueness---US Losing---1AR
Chinese tech is ramping up now.
Christopher Thomas & Sarah Kreps 24. *Chairman of Integrated Insights and formerly served as Nonresident Senior Fellow at the Brookings Institution, Managing Partner of McKinsey’s Asia Semiconductor Practice, and China Head at Intel. **John L. Wetherill Professor and Director of the Tech Policy Institute at Cornell University and Nonresident Senior Fellow at the Brookings Institution. “America Can’t Win the Tech Race Alone.” Foreign Affair. 1/26/2024. https://www.foreignaffairs.com/united-states/america-cant-win-tech-race-alone
When it comes to global competition, according to Chinese leader Xi Jinping, “technological innovation has become the main battleground.” In 2015, Beijing announced its Made in China 2025 plan, a policy that aimed to transform China into a high-tech manufacturing powerhouse and reduce its dependence on foreign technology and imports. China has also introduced the Digital Silk Road, a program in which Chinese infrastructure investment is linked to recipient countries’ adoption of Chinese technologies, as part of its Belt and Road Initiative. At the Belt and Road Forum in Beijing in October, Xi announced a new AI governance program and reiterated his support for international cooperation on science and technology.
In some ways, the United States is mirroring China’s plans. Washington aims to accelerate innovation and stay as far ahead of Beijing as possible on artificial intelligence capabilities and advanced semiconductors. To spur domestic innovation and manufacturing, the 2022 CHIPS and Science Act committed $52.7 billion in U.S. semiconductor research, development, manufacturing, and workforce training. And in its tech policies abroad, the United States is taking what the government calls a “small yard, high fence” approach, using export controls and similar tools to limit the ability of Chinese companies to use foundational technologies such as AI chips and semiconductor equipment. In October, the United States upped the ante by closing gaps in a previous set of export controls, expanding licensing requirements, and subjecting additional Chinese companies to trade restrictions, tightening a policy that China had already referred to as a “technology blockade.”
China is pressing ahead on developing generative AI now. They’ll prevail.
Che Pan 24. Technology reporter, master degree in Financial Regulation and Risk Management. “China to persist with AI development efforts in 2024, despite setbacks from rigid US semiconductor restrictions, UBS analysts say.” 1/9/2024. https://www.scmp.com/tech/tech-trends/article/3247835/china-persist-ai-development-efforts-2024-despite-setbacks-rigid-us-semiconductor-restrictions-ubs
China will press ahead this year with development of large language models (LLMs) – the technology used to train ChatGPT and similar applications – and generative artificial intelligence (AI) systems, despite restrictions imposed by the United States on the mainland’s access to advanced semiconductors, according to analysts from Swiss investment bank UBS.
China is expected to “work within those constraints and try to make progress” by tapping into domestic AI accelerator programmes and being more economical in using computing resources, said Nicolas Gaudois, head of Asia-Pacific technology research at UBS, during a webinar hosted by the bank on Tuesday.
Gaudois said US export controls have blocked China’s access to Nvidia chips used for AI projects, but indicated that the mainland’s ability to overcome such setbacks must not be underestimated.
The views expressed by UBS analysts reflect how China’s semiconductor industry weathered last year’s escalation of Washington’s tech sanctions, which restricted exports of advanced chip-making equipment and cutting-edge semiconductors for AI projects.
The mainland’s tech sector appeared to have overcome the chokehold of US export controls last year, when Huawei Technologies made a surprise return to the 5G smartphone market with new handsets powered by a “breakthrough” advanced made-in-China processor. It showed the lengths taken by the US-blacklisted company to build up its operations, following years of struggles on account of trade sanctions.
Apart from that breakthrough, UBS analysts asserted that China’s semiconductor sector has developed well in certain unsanctioned segments, showing strong market growth over the past few years.
“Restrictions from the US are very limited on mature nodes,” said Randy Abrams, head of Taiwan Research at UBS, in the same webinar on Tuesday. He pointed out that sales of these mature-node chips have not been restricted.
“We have seen China make more effort to invest in those areas,” Abrams said. “We will see steady market pickup in mature applications, such as camera image sensors, micro-controllers, analogue chips and discrete semiconductor devices for EVs.”
“In semiconductor equipment, local suppliers have built up their market share from very low single-digits into the mid- to high-single-digits,” he said. “Their gains come from supplying China semiconductor manufacturers, as they’ve added mature node capacity.”
Abrams said domestic chip-making equipment suppliers saw their market share increase to about 20 per cent on the back of demand from Chinese chip fabrication facilities. “So if we leave out the very advanced nodes, there are actually a lot of (investment) opportunities,” he said.
Global demand for chips, he said, is expected to start picking up, as soon as inventory pressures ease and procurement at semiconductor foundries improves.
A slew of sanctions imposed by the US since 2019 has also forced companies across China’s vast tech supply chain to work closely together.
Internet search giant Baidu, which runs Ernie Bot and the Ernie LLM, ordered US$61 million worth of Huawei’s 910B Ascend AI chips – which the Chinese firm developed as an alternative to Nvidia’s A100 processor – for 200 servers, according to a report in November by Reuters, which cited sources.
Uniqueness---AI Collapse Inev---2AC
AI is crashing now.
Gerrit De Vynck 24. Economics reporter. "The AI hype bubble is deflating. Now comes the hard part.". Washington Post. 4-25-2024. https://www.washingtonpost.com/technology/2024/04/18/ai-bubble-hype-dying-money/
SAN FRANCISCO — In June, Inflection AI was riding high. The artificial intelligence start-up founded by veterans of Google’s famous DeepMind AI lab had just raised $1.3 billion from Microsoft and tech billionaires Bill Gates, Reid Hoffman and Eric Schmidt to build out its chatbot business.
But less than a year later, the tide has turned. Co-founders Mustafa Suleyman and Karén Simonyan have left Inflection AI for jobs at Microsoft, which now has the rights to use its technology. Inflection AI is now focused on helping other companies improve their AI tools.
It’s not the only case of AI hype coming back down to earth. After 11 months of public testing, Google’s AI search tool still constantly makes mistakes and hasn’t been released to most people. New scientific papers are undermining some of the flashier claims about the tech’s capabilities. The AI industry is also facing a growing wave of regulatory and legal challenges.
A year and a half into the AI boom, there is growing evidence that the hype machine is slowing down. Drastic warnings about AI posing an existential threat to humanity or taking everyone’s jobs have mostly disappeared, replaced by technical conversations about how to cajole chatbots into helping summarize insurance policies or handle customer service calls. Some once-promising start-ups have cratered, and the suite of flashy products launched by the biggest players in the AI race — OpenAI, Microsoft, Google and Meta — have yet to upend the way people work and communicate with one another. While money keeps pouring into AI, very few companies are turning a profit on the tech, which remains hugely expensive to build and run.
“If you compare a mature market to a mature tree, we’re just at the trunk,” said Ali Golshan, founder of Gretel AI, a start-up that helps other companies create data sets for training their own AI. “We’re at the genesis stage of AI.”
The tech industry is not slowing down its charge into AI. Globally, venture-capital funding in AI companies grew 25 percent to $25.87 billion in the first three months of 2024, compared with the last three months of 2023, according to research firm PitchBook. Microsoft, Meta, Apple and Amazon are investing billions into AI, hiring PhDs and building data centers. Earlier this month, Amazon poured $2.5 billion into Anthropic AI, bringing its total investment in the OpenAI competitor to $4 billion. This week, Microsoft, Google and Meta all confirmed they would spend billions of dollars on new data centers for AI this year alone.
The immense cost of training AI algorithms — which requires running mind-boggling amounts of data through warehouses of expensive and energy-hungry computer chips — means that even as companies like Microsoft, OpenAI and Google slowly begin charging for AI tools, they are still spending billions to develop and run those tools.
Google Cloud CEO Thomas Kurian said this month that Deutsche Bank, the Mayo Clinic, McDonald’s and other companies were using Google’s tools to build AI applications. And Google CEO Sundar Pichai said on the company’s most recent conference call that interest in AI helped contribute to an increase in cloud revenue. But on the same call, Chief Financial Officer Ruth Porat said the company’s investment in data centers and computer chips to run AI would mean Google’s expenses would be “notably larger” this year compared with last year.
“We are helping our customers move from AI proof-of-concepts to large-scale rollouts,” said Oliver Parker, an executive with Google Cloud’s AI team. He pointed to Discover Financial Services as one example — the company used Google’s AI to build a tool for its call centers that reduced the time it took to do each call.
Microsoft has also been trumpeting the interest in its AI tools and says 1.3 million people use its GitHub Copilot AI code-writing assistant. It’s also pushing a $30-a-month AI assistant to the millions of Microsoft Office users around the world. But the company has been mum on whether any of the tools are profitable when compared with the cost of running them. Like Google, it has focused much of its energy on getting customers to use its cloud services to run their AI apps.
“We’re finding that AI requires a paradigm shift,” said Jared Spataro, Microsoft’s corporate vice president of AI at Work. “It’s not like a traditional technology deployment where IT flips a switch. Businesses need to identify areas where AI can make a real impact and strategically deploy AI there.”
In October, OpenAI announced its own version of Apple’s app store, where people could make custom versions of the popular ChatGPT chatbot, upload them to a public marketplace, and get paid by OpenAI if many people use them. Three million custom GPTs have been created, but the company hasn’t said whether it has paid out any money yet.
Meta, which is integrating chatbots into its social networks and messaging platform, said this week that it would spend as much as $40 billion this year on new infrastructure to support its AI build-out.
“Building the leading AI will also be a larger undertaking than the other experiences we’ve added to our apps, and this is likely going to take several years,” CEO Mark Zuckerberg said on a conference call Wednesday.
“These tools are not yet pervasive, not even close,” said Radu Miclaus, an AI analyst at tech research firm Gartner. He expects that to begin changing soon. “My expectation is that this year the applications will start taking off.”
The money game
Beyond the Big Tech companies, a legion of start-ups is trying to find ways to make money with such “generative” AI tech as image generators and chatbots. They include trying to replace customer service agents, writing advertising copy, summarizing doctors’ notes and even trying to detect deepfake AI images made by other AI tools. By replacing workers or helping employees become more productive, they hope to sell subscriptions to their AI tools.
Many computer programmers say they already use chatbots to help write routine code. Usage across the rest of the workforce is also slowly increasing, according to the Pew Research Center. Around 23 percent of U.S. adults said they had used ChatGPT at work in February, compared with 18 percent in July, the research organization found.
“We’re at the very, very beginning,” said John Yue, founder of Inference.AI, a start-up that helps other tech companies find the computer chips they need to train AI programs. AI will work its way into every single industry, he said, but it might take at least three to five years before people really see those changes in their own lives. “We have to take a longer look.”
There are big roadblocks that could also slow down the industry. Governments have bought into AI hype, and politicians in the United States and abroad are busy debating how to regulate the tech. In the United States, smaller AI companies have expressed worries that AI leaders such as Google and OpenAI will lobby the government to make it harder for new entrants to compete.
In the European Union, final details of the bloc’s AI Act are being hammered out, and they promise to be more restrictive than companies are used to. One of the biggest concerns that Golshan, the Gretel AI CEO, hears from business clients is that new AI laws will render their investments a waste. Lawsuits also have been launched against OpenAI and other AI companies for using people’s work and data to train their AI without payment or permission.
Uniqueness---AI Collapse Inev---1AR
It's a bubble that’s destined to pop.
Victor Tangermann 24. Toronto-based staff writer and photo editor for Futurism. "Experts Concerned by Signs of AI Bubble". Futurism. 3-30-2024. https://futurism.com/experts-signs-ai-bubble
As investors pour billions of dollars into the AI frenzy, analysts are starting to become wary of an "AI bubble" that could leave investors out to dry.
In a research note spotted by CNBC, tech stock analyst Richard Windsor used a colorful metaphor to describe what would happen if such a bubble were to burst.
"Capital continues to pour into the AI sector with very little attention being paid to company fundamentals," he wrote, "in a sure sign that when the music stops there will not be many chairs available."
It's been a turbulent week for AI companies, highlighting what sometimes seems like unending investor appetite for new AI ventures.
Case in point is Cohere, one of the many startups focusing on generative AI, which is reportedly in late-stage discussions that would value the venture at a whopping $5 billion.
Then there's Microsoft, which has already made a $13 billion bet on OpenAI, as well as hiring most of the staff from AI startup Inflection AI earlier this month. The highly unusual deal — or "non-acquisition" — raised red flags among investors, leading to questions as to why Microsoft didn't simply buy the company.
According to Windsor, companies "are rushing into anything that can be remotely associated with AI." Ominously, the analyst wasn't afraid to draw direct lines between the ongoing AI hype and previous failed hype cycles.
"This is precisely what happened with the Internet in 1999, autonomous driving in 2017 and now generative AI in 2024," he wrote.
Windsor is far from the first to draw such a comparison. Despite the lack of a clear way to turn a profit, investors are still pouring massive amounts of cash into AI firms.
"There's a huge boom in AI — some people are scrambling to get exposure at any cost, while others are sounding the alarm that this will end in tears," Kai Wu, founder and chief investment officer of Sparkline Capital, told the Wall Street Journal last year.
There are even doubters inside the industry. In July, recently ousted CEO of AI company Stability AI Emad Mostaque told banking analysts that "I think this will be the biggest bubble o

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