Impact - AI Good - Michigan 7 2023 CFMP

Notes
From the filmmaker who brought you Jaws, Saving Private Ryan, and ET; Zhian Xu brings his unique twist to artificial intelligence.
Angle for the DA/Case Turn depends on aff. If they spot you unions then the link story should be about “early regulation” as opposed to halting all AI development, which lowers the threshold a lot. If they don’t have unions then maybe read the card for them. Tradeoff with private sector is pretty useless- wasn’t sure about the direction of research at first but maybe use in conjunction with the unions link or read with the default unions bad turn.
There’s really good cards on how AI solves for skills/worker shortage. Could be added to case turns section.
Idea for the aff would be to straight turn the IL- AI investment happening now, experts warn of economic bubble. Read couple cards on collapse and acts as a IL to econ decline and why unionization to counteract AI now is key.
Best aff angle is to contest the link- not clear what the IL between “union opposition” and “regulations/delays in investment” would mean. Need really good uniqueness spin for “now is key” which has some evidence, and try to bait 1AC into overexplaining the power of the unions. Questions like “Why wouldn’t workers just get replaced anyways”, “What rights does worker empowerment actually entail?” or “Why would companies listen to workers?” might work.
Also didn’t cut some of these cards- AI good/bad cards could probably be substantially improved, but ran out of time.
Neg
Uniqueness
Stock recovery following layoffs set the uniqueness and threshold- further recovery necessitates rehiring.
Rosenberg 23 (Scott is the managing editor of technology at Axios. He oversees coverage in Silicon Valley and D.C. of the news shaping tech companies, products and policy.) Apr 4, 2023 “After layoffs, tech stocks boom” https://www.axios.com/2023/04/04/layoffs-tech-stocks-boom // ZX
Tech layoffs are way up — and so are tech stocks. Two charts tell this story. The big picture: The tech industry's layoffs began last year and have kept up a relentless stream of bleak announcements — in some companies' cases, like Meta's, coming in multiple waves. The industry, which had overhired during the pandemic, started trimming its workforce last year, just as the Federal Reserve started raising interest rates to cool the inflation-rattled U.S. economy. Between the lines: Layoffs are often understood as an indicator of incipient recession, and stock prices typically fall in step with both of those trends. That's exactly what happened in 2022 — the worst year for tech stocks since the great dotcom bust more than 20 years ago. Yes, but: The new year brought a turnaround to tech stock prices, as this second chart lays out (courtesy Axios Markets' Matt Phillips). What's happening: Tech stocks have always been volatile, and it's never a good bet to blame their fluctuations on any single cause. Here are some possible explanations for the resurgence, more than one of which might apply: Wall Street is rewarding tech for all those layoffs. Investors applaud "discipline," and thinner payrolls could mean more profits to come. No, it's the wave of enthusiasm in the industry for ChatGPT and generative AI is driving the market up. Many observers are framing the new generation of AI as the biggest leap forward in tech since the iPhone's 2007 debut. Or maybe the market just oversold tech in 2022 and it's correcting course now. Some of these companies are also spending some of their cash hoards buying back shares, which can help lift share prices. Alternately, this resurgence is an illusory "dead cat bounce." It will fizzle out as the pain of higher interest rates continues. However you interpret the numbers, the tech-stock rebound has raised spirits in Silicon Valley and provided some hope that the industry's toughest sledding may be behind it. The bottom line: Some discouraging reports continue — including sagging ad growth and unsold Teslas — but tech's leaders are beginning to see a turnaround. We'll know they're really feeling good when they start hiring in droves again.
Specifically- the new wave of hiring and investment viewed as the 2nd private market gold rush, short-lived but extremely lucrative.
Shen 6/27 (Yiqin Shen, I cover M&A deals and company financial news for Mergermarket and Dealreporter, products of Acuris/Ion; primarily focus on healthcare and life science sectors, US-China cross border situations, and special situations such as merger litigation across sectors.) June 27, 2023 “AI Frenzy Draws Hordes to Private Markets in Industry Gold Rush” https://www.bloomberg.com/news/articles/2023-06-27/ai-frenzy-draws-hordes-to-private-markets-in-industry-gold-rush
The buzz around artificial intelligence has investors snapping up shares of startups on alternative venues, looking to find the next wave of technology giants before they even go public.
AI and machine learning have remained the most in-demand sectors every month this year, accounting for 25% to 30% of investor interest, according to EquityZen Securities Inc., a marketplace for privately held shares. On Rainmaker Securities, a platform that facilitates secondary stock transactions for private businesses, investors are paying up for shares of companies like OpenAI and Anthropic, startups that are seen as leading the pack in AI.
“Institutional investors are getting much more aggressive,” Brianne Lynch, EquityZen’s head of market insight, said in an interview. Transactions among retail investors in AI companies also picked up from May to June, she said.
Rainmaker’s co-founder Glen Anderson noted the number of buyers is outstripping those looking to sell, pushing prices well above earlier funding rounds. Anthropic bidders are willing to pay as much as a 25% premium to its recent round, while OpenAI bids are into the $80s from what he believes was an earlier round priced at $67, he said. Bankrupt cryptocurrency exchange FTC has abruptly halted the sale of its stake in Anthropic, as its valuation is seen benefiting from the current AI frenzy, Bloomberg News reported.
Forge Global Holdings Inc. has seen an increase in demand for AI firms on its platform over the past year, with Microsoft Corp.’s investment in OpenAI in January and a round of funding for Anthropic among the key drivers. In June, buying interest in a group of seven AI companies, including OpenAI, reached the highest level over the period, data provided by the firm show. The scramble for nascent AI firms stands in sharp contrast to the broader trend of startups trading at massive discounts in this corner of the market. Here, cash-strapped founders, employees and investors are under pressure to sell shares amid a wave of tech job cuts, a tepid market for initial public offerings and rising interest rates.
It’s a different story for AI, which has captivated the likes of billionaire investors Steve Cohen, Stan Druckenmiller and Lee Ainslie, and pushed companies to consider how they can deploy the technology.
While AI is a burgeoning industry with growth potential and rising investor interest, it’s still at an infant stage and many firms don’t have robust financial reports to go public just yet. That leads to the pent-up demand in this secondary market, EquityZen’s Lynch said, “because investors know these IPOs aren’t happening soon so they can’t access them that way.”
Link---Generic
Basic Income subsidizes decreases in market interest and cuts into key AI sectors- kills research and funding
Platt 18 (Winfield Platt has been working as a Brand Strategist at Amazon.com for 5 years. Amazon.com is part of the Department Stores, Shopping Centers & Superstores industry, and located in Washington, United States.) “The rise of AI, Universal Basic Income and the potential shift in consumer behavior” April, 2018 https://northboundbrand.com/blog/rise-ai-universal-basic-income-potential-shift-consumer-behavior/ We know an important part of branding is focusing on your target audience, without a clear understanding of the target audience, we cannot successfully drive value for them. With the rise of AI and the potential for more jobs being automated, the notion of Universal Basic Income (UBI) is becoming a potential reality. In fact, some 2020 US presidential candidates are running on UBI. UBI is money paid for being a citizen, coming from tax increases on some population segment (e.g. top 1%, companies). Some UBI proposals are as high as $1,000 a month for every citizen. Additionally data on people not continuing to work or spending money on libations and temptations has been debunked by MIT studies. It is also hypothesized that wages wouldn’t fall, rather increase, particularly for undesirable work. So, what could this mean? It is widely believed that UBI will boost the economy (maybe permanently) and increase consumer spending. With increased consumer income, how will consumer behavior change and what should brands consider? Judging by the research from MIT, I hypothesize that UBI will have major effect on companies that differentiate on price. Retailers like Walmart may need to start driving more value into what it means to shop there, especially around their generic brand, Equate. Generics could be the hardest hit because while other brands promise the “product + a value”, generics typically only provide the product. As target audiences have more discretionary income, they may opt to purchase products that have additional value or promise. If that is the case, generic/commodity brands will need to decide how to address that gap and stay relevant to their target audiences. Another sector that could be disrupted with UBI is ironically a sector driving AI: the shared economy. According to a PwC survey on the value proposition of the shared economy, 86% and 83% of consumers “respectfully agreed that the sharing companies make life more affordable and more convenient and efficient.” If cost becomes less of a factor for consumers, will the other portions of the shared-economy value proposition keep companies relevant or will ownership have a resurgence? As legislation over UBI becomes more of a reality, companies will have to figure out how to stay relevant to their target audiences.
Tailored AI surpasses humans in creating and implementing Fiscal Redistribution- sequencing means the aff replaces it.
Dockrill 22 (Peter Dockrill is the Managing Editor of ScienceAlert. An award-winning science and technology journalist, he brings his flair and expertise to a broad range of topics including health, environment, technology, and nature.) “AI Seems to Be Better at Distributing Wealth Than Humans Are, Study Hints” 04 July 2022 https://www.sciencealert.com/ai-could-be-be-better-at-distributing-wealth-experiment-suggests // ZX
Artificial intelligence (AI) can devise methods of wealth distribution that are more popular than systems designed by people, new research suggests. The findings, made by a team of researchers at UK-based AI company DeepMind, show that machine learning systems aren't just good at solving complex physics and biology problems, but may also help deliver on more open-ended social objectives, such as the goal of realizing a fair, prosperous society. Of course, that's not an easy task. Building a machine that can deliver beneficial results humans actually want – called "value alignment" in AI research – is complicated by the fact that people often disagree on the best method to resolve all kinds of things, and especially social, economic, and political issues. "One key hurdle for value alignment is that human society admits a plurality of views, making it unclear to whose preferences AI should align," researchers explain in a new paper, led by first author and DeepMind research scientist Raphael Koster. "For example, political scientists and economists are often at loggerheads over which mechanisms will make our societies function most fairly or efficiently." To help bridge the gap, the researchers developed an agent for wealth distribution that had people's interactions (both real and virtual) built into its training data – in effect, guiding the AI towards human-preferred (and hypothetically fairer overall) outcomes. While AIs can produce truly amazing results, they can also arrive at far-from-desirable social conclusions when left to their own devices; human feedback can help to steer neural networks in a better direction. "In AI research, there is a growing realization that to build human-compatible systems, we need new research methods in which humans and agents interact, and an increased effort to learn values directly from humans to build value-aligned AI," the researchers write. In experiments involving thousands of human participants in total, the team's AI agent – called 'Democratic AI' – studied an investment exercise called the public goods game, in which players receive varying amounts of money, and can contribute their money to a public fund, and then draw a return from the fund corresponding to their level of investment. In a series of different game styles, wealth was redistributed to players via three traditional redistribution paradigms – strict egalitarian, libertarian, and liberal egalitarian – each of which rewards player investments differently. A fourth method was also tested, called the Human Centered Redistribution Mechanism (HCRM), developed using deep reinforcement learning, using feedback data from both human players and virtual agents designed to imitate human behavior. Subsequent experiments showed that the HCRM system for paying out money in the game was more popular with players than any of the traditional redistribution standards, and also more popular than new redistribution systems designed by human referees who were incentivized to create popular systems by receiving small per-vote payments. "The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders, and successfully won the majority vote," the researchers explain. "We show that it is possible to harness for value alignment the same democratic tools for achieving consensus that are used in the wider human society to elect representatives, decide public policy or make legal judgements." It's worth noting that the researchers acknowledge their system raises a number of questions – chiefly, that value alignment in their AI revolves around democratic determinations, meaning the agent could actually exacerbate inequalities or biases in society (provided they are popular enough to be voted for by a majority of people).
A Jobs Guarantee results in decreases in productivity, crowds out the private sector, and is comparative worse than UBI.
Sandbu 18 (Martin E. Sandbu is the economics editorial writer for The Financial Times. He is also a Senior Fellow at the Zicklin Center for Business Ethics Research at the Wharton School, where he previously taught the main business ethics course in the undergraduate curriculum for several years. He has appeared on the BBC World Service, National Public Radio morning edition, and CNBC among other broadcast interviews. In his academic career, Dr. Sandbu has worked on questions at the intersection between economics, politics and philosophy and published across all three fields. He holds degrees in Philosophy, Politics, and Economics from Balliol College, Oxford University, and in Political Economy and Government from Harvard University. His doctoral thesis, “Explorations in Process-Dependent Preference Theory,” was published in top academic field journals in economics and philosophy. His academic writings have analyzed topics including business ethics, distributive justice, preference theory, collective responsibility, deliberative democracy, and the political economy of development.) (Jul 2, 2018) “Jobs guarantee is a distant second-best policy” // ZX
And my colleague Tim Harford’s latest column is devoted to thepsychological effectsa jobs guarantee might produce, in contrast with universal basic income, the even more radical policy often held up as the alternative. We have often, and sympathetically, covered UBI in Free Lunch, but given scant attention to the jobs guarantee. The reason is that I find it a policy proposal that raises more questions than it solves — and the many unanswered questions make it hard to pass judgment one way or the other. That is true even in the case of relatively detailed and costed schemes such as the one put forward by the Center on Budget and Policy Priorities. First, is a jobs guarantee seen as linked to an (otherwise unconditional) universal basic income? The model could be: we will give everyone an unconditional income, but we will do so by offering everyone an unconditional job. The obvious question then is: what about those who turn down the jobs offered? Is this workfare or welfare-to-work by a nicer name? If so, it is confused or dishonest to think of it as a new or indeed a particularly progressive policy. Much more interesting is to treat it on its own merits as a standalone system, which could be put in place alongside a UBI if one so wished. Then, of course, the test is what it achieves that UBI does not (or better than UBI does). Second, is the rationale for a jobs guarantee a cyclical one — to step in to hire when the private sector sheds jobs in downturns? That would meet a clear need. It would also go some way towards addressing the biggest problem with a work guarantee: that it would guarantee neither meaningful nor useful work, since a countercyclical jobs policy would presumably employ people who would otherwise not work at all. Some sympathetic commentary on a jobs guarantee fits it into a context of macroeconomic policy that is too timid to ensure genuine full employment, which causes particular harm to vulnerable groups that are the first to be fired in a downturn and the last to be hired in an upswing. But it would seem that the right policy answer to this real problem is better (more aggressive) macroeconomic demand management in fiscal and monetary policy. And in unavoidable cyclical downturns, why are New Deal-style public works programmes (temporary public sector employment expansion) or policies to encourage private businesses to keep people employed (such as Germany’s Kurzarbeit) not superior better than a guarantee system? If a jobs guarantee is advocated because it is thought to be impossible to do these other policies well, then it is literally a third-best solution. Third, is the jobs guarantee instead to be seen as a permanent solution to a structural problem of insufficient or bad job opportunities that persist over time or in particular places? Some versions clearly see it this way, as a main argument offered in its favour is that by guaranteeing jobs at decent wages, it put a floor under wages and labour conditions in all jobs in the economy. Again, this addresses a very real problem — but again, the question is whether it distracts from better solutions. Dean Baker sets out some serious concerns about a permanent jobs guarantee programme. In addition to the difficulty of ensuring that government can offer enough “real” jobs, he worries that they may displace better jobs, because it could be cheaper for public sector managers to hire workers on jobs guarantee terms than through conventional recruitment. Paradoxically, a jobs guarantee could worsen conditions in the middle even if it lifts the floor. We need to go back to basics. The root problems are twofold. First, in the US in particular, there are insufficient employment opportunities, overall and especially in the most left-behind places. In Europe, labour market participation is a smaller problem — participation rates are at record highs and rising. But, second, too many of what jobs are on offer are precarious and on poor terms everywhere. The solution to these problems would involve strong labour demand, widely dispersed across an economy’s territory, and sufficient 

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