Econ Tech
The Impact of Covid on Productivity and Potential Output
John Fernald and Li: Found little evidence that the pandemic has caused substantial changes to labor productivity in our economy
The Forgoten Femal Programmers who created modern tech: (Audio)
NPR: There are few women in the coding job Decades ago, women pioneered computer programming Lord Byron, Lovelace had to write a paper about the computer Women were very good mathmaticiains Bartik was one of the first women to work on the math Used a programming language called cobalt Grace Hopper: Queen of software
It's Time To Tax Companies For Using Our Personal Data:
NYT: Madsbjerg: Impose a small tax on data Consider data like a tangible object, like a car, our data is owned buy big firms like Amazon Data brokerage generates $150 billion a year
Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction:
Agarwal, Gans, and Goldfarb: Four direct effects of advances in prediction technology may affect the labor market Substituting capital for labor in prediction tasks Automating decision tasks when automating prediction increases the relative returns to capital versus labor Enhancing labor when automating the prediction task increase labor productivity in related decision tasks Creating new decision tasks when automating prediction sufficiently reduces uncertainty
The Top 1 Percent in International and Historical Perspective:
Alvaredo, Atkinson, Piketty, and Saez: The share of top 1% of total annual income has more than doubled from 9% in 1976 to 20% in 2011. This rise has really skewed the income inequality scales
Extending the Race
Autor, Goldin, Katz: Divide between college-educated workers and those with less education The demand to jobs that require skilled workers increased Also talks about how inequality comes from the fact that some may not have access to adequate education Larger emphasis on knowledge-based jobs within the workforce now
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
Brynjolfsson, Mitchell, and Rock: Deep Learning: allows AI to match or surpass humans in certain tasks Sustainability for Machine Learning: SML From their studies, few jobs can be fully replaced by ML. ML will affect very different parts of the workforce
Challenges to Mismeasurement Explanations
Chad Syversson: The flow and ebb of U.S Productivity growth can be divided into four periods: 1947-1973, 1974-1994, 1995-2004, 2004-2015. The U.S. has been experiencing a slowdown from 2005-2015 The slowdown is statistically and economically significant Ties the slowdown to a reversal of the productivity accelerations in the manufacturing and utilization of information and communication technology that drove the rapid pace from 1995-2004 Some theories are that productivity growth has not slowed down, but actually accelerated from 2004, but that we have been measuring it incorrectly
Why Are There Still So Many Jobs? The History and Future of Workplace Automation:
David Autor: Automation may prevent the economy from creating new jobs Says how automation does not eliminate jobs, it complements them, increasing output in ways that lead to higher demands for labor, labor jobs that interact with intelligence that helps drive productivity
The work of the future: shaping technology and institutions
David Autor: Talked about how there are new jobs that are being created for new specialized things that complement the new technology Like Echocardiographers The decline of the middle class: such as warehouse work, office work; such as organizational work than a machine Limits the ability of those people to get rich, move up the social hierarchy Productivity growth has slowed dramatically since 2005 Challenges & Opps: Raising skills at the pace of tech advancement Need to align incentives to invest in human capital and physical capital Addressing labor scarcity Steer Innovation to raise productivity: Many innovations today are very cool but not very applicable; does not directly impact productivity growth The Future Will Not Take Care of Itself
Alpha Zero
DeepMind: Alpha Zero uses deep neural networks to help defeat opponents in chess and Go competitions The neural network to help learn the game will play millions of games and see millions of scenarios to which they basically have become a grand master
The Superstar Company- A Giant Problem
Economist: Superstars: As in high-tech wizards like Google, Apple, Facebook They help streamline our lives and provide use with estimated $280 billion free services Concentration of the share of GDP is worrisome The share of GDP of top 100 companies grew to 46% in 2023 Modern tech is lowering barriers to entry Slower GRowth encourages companies to buy out rivals and squeeze out costs The bigger companies while they are great, they need some competition as they have a monopoly over other companies for that service or product
What if People Were Paid for their Data?
Economist: Data slavery: exchanging free sites for intimate information Ponders the thought of people selling their data to big tech firms The main example is that AI uses human-generated data If AI continues to progress, better data is required for them, IE: pay for data
Second Machine Age
Erik Brynjolffson and Andrew McAfee: Policy Recommendations
The Recent Rise and Fall of Rapid Productivity Growth
Fernald, Wang: An exceptional boost in productivity growth from the 1990s was as a result of breakthroughs of information technology (IT) IT, Software, Cloud Service, Internet That boost of productivity growth has diminished over time in the past decade Labor productivity does tend to rise over time because workers get smarter, technology gets better, but this is not the reason for the decrease in productivity It is the fact that there have not been any monumental innovations has decreased The pause of the IT revolution could be the culprit of the recent decline of productivity, but the direct cause is not shown yet
Is the Internet Being Ruined?
Freaknomics: Talks about how the internet was a vast network that could be explored, but now the internet is becoming siloed, moving to the phone through apps like Facebook or Twitter Fear of the powerful tech giants using their services as leverage for your data or money Talks about net neutrality: All internet providers must be equal in their service Talks about educating the public about how certain companies are using their data, and what free sites that offer their service are actually doing
Are We Running Out Of Ideas?
Freakonomics: There is a perception that we are running out of ideas, but it is not clear if this is actually true or not, it is just because of the increasing complexity of technology and that the low-hanging fruit of the innovation tree has already been picked Many great innovations were as a result of incremental improvements on existing ideas The nature of innovation may be changing, with more of an emphasis on small incremental innovations rather than groundbreaking discoveries Innovation may also be driven by collaboration, not a singular person brainstorming Healthcare and energy still have opportunities for innovation
Is a Cambrian Explosion Coming for Robotics?
Gill Pratt: In the article, Gill A. Pratt discusses the current state and future prospects of robotics, drawing parallels with the Cambrian explosion that occurred 540 million years ago when a sudden burst of evolution led to the emergence of diverse life forms. Pratt argues that we are on the cusp of a similar explosion in robotics, driven by advances in hardware, software, and AI, which will lead to the emergence of a vast array of robotic systems that are capable of performing a wide range of tasks. He identifies several key areas where robotics is likely to make a significant impact, including transportation, healthcare, agriculture, and entertainment. However, he also acknowledges that there are significant challenges that must be overcome, including safety, ethical considerations, and regulatory frameworks. Overall, Pratt is optimistic about the future of robotics and believes that it has the potential to transform many aspects of our lives in the years to come. Pratt shows a world were each of us had our own robot, that we would send to work
Winter Is Coming: Robert Gordon and the Future of Economic Growth
Gregory Clark: Robert Gordon Gloom: Is an Innovation pessimist- Talks about how TFP will continue to subside as time goes on, he is pessimistic about TFP growth, and must anticipate modest gains Limitations regarding TFP growth in the future First is 80 of the economic output is based on services and that manufacturing is not the majority as it usually is Argue that services such as mail delivery, cooking, cleaning, and others have made up of the majority of new jobs, and have not evolved over time Clark says more Pessimism: Rapid TFP in the past were because of high R&D expenditures There has been many attempts to mechanize certain services, such as bricklaying, but has failed Talks about how the golden era of growth has passed It is tough to continue to increase TFP as more and more jobs and services cannot be mechanized.
Google's Go Victory
Jonathan Tapson: The article discusses how the victory of Google's artificial intelligence (AI) program, AlphaGo, over a human professional Go player illustrates the unpredictability of AI. The author explains that this unpredictability can be a concern as AI becomes more prevalent in society, especially in areas such as autonomous vehicles and decision-making processes. The article also suggests that as AI becomes more advanced, it may become difficult to understand how it makes decisions and therefore harder to regulate or control. The author emphasizes the importance of understanding and managing the risks of AI, as well as the need for transparency and accountability in AI development. Reinforcement learning: The Alpha Go program will push itself to explore every single option, how the GO player had to step out to catch his breath
The Problem of Bigness, From Standard Oil to Google.
Naomi Lamoreaux: In this article, Naomi Lamoreaux examines the historical and contemporary concerns about the size and power of large corporations, focusing on the examples of Standard Oil and Google. She argues that while concerns about bigness have remained consistent over time, the nature of those concerns has evolved alongside changes in the economy and technology. In the case of Standard Oil, concerns focused on the company's ability to control prices and stifle competition, ultimately leading to the company's breakup through antitrust regulation. In contrast, concerns about Google have centered on the company's use of data and potential threats to privacy, as well as its role in dominating online advertising markets. Lamoreaux suggests that these concerns reflect broader debates about the role of government in regulating economic activity and protecting competition, as well as the challenges of regulating rapidly evolving technologies. She argues that while there are risks associated with bigness, including reduced competition and the potential for abuse of power, large corporations also play an important role in driving innovation and economic growth. As such, she suggests that policymakers must carefully weigh the costs and benefits of regulation in order to promote both competition and economic prosperity.
The Great Reversal
Phillipon: Box 14.1 Model Talks about competition of firms in a market where there is free entry Talks about how if there is a high fixed cost for firm, and a low MC, then there will be a smaller number of firms in the market For free entry, profit must be 0 Said for pricing, you could actually mark up the prices because of the monopoly
This is the End
Planet Money: There will be plenty of new jobs that will be created because of the jobs that AI will takeover Workers and robots are currently co-workers right now, but there may be a day when co-workers will no longer be
Does the 'New Economy' Measure Up to the Great Inventions of the Past?
Robert Gordon: The second industrial revolution took place in Europe and the UNited states Will the computer and the internet lead to a third industrial revolution? The author paints a picture of what life as like in the 1880s and 1890s The living conditions back then in Kansas city were not ideal Dieseases such as Yellow Fever, scarlet fever, and smallpox persisted Before electricity and transportation, life was marked by isolation Life was certainly not great back then as people worked hard long hours in blue-collar jobs barely making a standard living Great Inventions: Came in 5 clusters in the 19th and 20th century Electricity was the first, Internal combustion engine was second, which made autos and motor transport possible, leading to transportation Petroleum, natural gas, and various processes to rearrange molecules were third Helped reduce air pollution and helped with natural health Entertainment, communication, and information innovations was fourth Telegraph, telephone, phonograph, photography, radio, TV Running water and plumbing was the fifth, led to just a better standard of living These five inventions led to an increase in per capita income and wealth during the golden years of productivity growth from 1913-1972
How I Built This Lab: OpenAI(AUDIO):
Sam Altman
The Economics of IT
Varian: Reengineer the flow of information through the enterprise Varian says that price discrimination can result in a more efficient allocation of resources, but it will eat up some of that consumer surplus, it and convert it to producer surplus Varian Model: Lock in Due to Switching Costs: Basically has the understanding that once a company gets a customer, that customer is locked into using the product Ex. Printer that uses specific ink, you buy the printer, and the ink If you wanted to use other ink it would be expensive bc you have to buy a new printer Sums up how competition for monopoly can actually lead to competitive prices The Average of the 2 periods equals MC Varian suggests charging 0 dollars or even paying for customers in period 1
Seven Deadly Sins of Tech?
Varian: GAFAM: Google, Apple, Facebook, Amazon, Microsoft Seven Deadly Sins: Competition, Innovation, acquisitions, entry, switching costs, entry barriers, and size. Traffic Acquisition Cost: Googles ad revenue Competition: Many firms try to make themselves uniqure to try to attract users, however, it is up to the user at the end of the day There are other firms out there that google competes with, leading to high quality and low prices Innovation: some say GAFAM has a negative impact on innovation, but reality they are the top R&D drivers for innovation Acquisitions: can decrease competition and hire good talent through acquisitions ENTRY: Acquihires: Acquisition for hiring talent Switching Costs: Some say that the costs prevent companies from competing with GAFAM Entry Barriers: Data itself is an entry barrier, whoever has the data will improve Varian talks about how the tech industry has been accused of sins, but there is little evidence of it Defends them and shows that the GAFAM companies are responsible for high economic growth and jobs
Generative AI: A Game Changer that Society and Industry Need to be Ready for
WEF: Generative AI generates texts, music, speech, or video Generative AI could generate hundred of millions of dollars in revenue in the future Despite the current downturn and layoffs in the tech sector, generative AI companies continue to receive huge interest from investors.
