The Impact of Trump Administration and MAGA Congress Policies on the Potential A.I. Boom

1. Introduction

The era of Trump has been an interesting one, but not a good one in terms of technical advancement in our country and around the world.  Trump and MAGA's assalt on technological innovation and its backward thinking of the traditional manufacturing mindset has stiffled the A.I. industry and put it in an untenable limbo of bad, cheesy, and frustrating business models.  Starting in the mid 2010's, businesses started to shift to more artificial intelligence minded frameworks, but were left in a unfinished landscape that helps business increase profits but leaving its customer base frustrated, angry, and disconnected in a isolation island.  Enter the Trump administration, and in its isolationist vision, focusing on manufacturing, oil and gas, and a barrage of misinformation, assault on facts and science, normalization of hate and unprofessionalism, and demonization of the IT industry and its leaders.  What we are left with is an unfinished product.  A world where companies are “going dark”, by completely shutting down its cusomer service channels in favor of A.I. based chat bots that do not allow advancement beyond simple questions.  All of this was unfolding in the first Trump term, but when COVID hit, inflation shot through the roof, we are stuck with what is left, while companies refuse to improve their A.I. models because, why?  Per companies, our prices are now fixed in place, so leaving us with no motivation to invest in getting out of A.I. hell.  Anyone that thinks inflation is a product of the Biden administration really needs to study how supply and demand works.  What A.I. did do, is allow the Trump campaign to flood social media with cheesy A.I. creations, from bots, that low I.Q. Trump supporters thought were real.

A.I. helped Trump win the election but it is stuck in a time warp.  What this results is a populace that doesn't understand the benefits of A.I. but dumb enough to believe it when it comes from Trump's own camp.  So companies now charge the same prices for its products so what is the point of competing or serving its customers.  Now companies jump on the Trump train because its convenient for them to do so because they are MAKING PROFITS REGARDLESS!  And from who, the struggling middle class, many that believe the lies that Biden created the mess.  Well, he did not.  ChatGPT and Open A.I. are revolutionary.  The Internet of Things, which was also stiffled and never really got out of the gates was a perfect pair for A.I. but the M.A.G.A. mindset killed it as well.  

Most discussions in A.I. talk about pure technical possibilities while ignoring the impact of political policy at the national and international level. While in an idealized society these could be treated separately, the fact is that they are more likely to mutually interfere so long as a country largely influences policies regarding international relations. Thus, in part I, we will discuss how the current administration specifically and Congress have taken steps that can assist a potential boom in A.I. in the United States. In part II, we will then discuss what past examples tell us about these shifts and finally consider the broader implications of such a boom started under these conditions. In part III, we will then test if these assumptions are relevant in the rise of particularly groundbreaking technologies.

History has shown that economic policies interact with the success of a technological innovation, but that internal travel is only likely to occur if that society is also subjected to political shifts that create or maintain an environment supportive of innovation. If allowed, this could drive what is a pure theory of growth and technological change into relevant policy realms within international studies as tech blossoms collide with foreign policy. The initial impetus for our interest was an attempt to check the assumption we had that the current wave of papers on the potential of A.I. lacked enough attention to politics, particularly the capabilities of modern political bodies to deal with any coming A.I. booms.

Want some evidence:

1. Restrictions on Chinese Technology Companies

• Huawei Ban: One of the most notable actions was the decision to blacklist Huawei, a Chinese telecommunications giant, from doing business with U.S. companies. This ban affected global supply chains and created uncertainty for both U.S. and international companies involved in the telecommunications and semiconductor industries.
• Impact on Global Supply Chains: The sanctions on Chinese tech firms, including companies like ZTE, contributed to disruptions in global supply chains for hardware and technology components.

2. Net Neutrality Rollback

• FCC's 2017 Repeal: In 2017, the Federal Communications Commission (FCC), under Trump appointee Ajit Pai, voted to repeal net neutrality regulations. Net neutrality is the principle that Internet Service Providers (ISPs) should treat all data on the internet equally, without discriminating or charging differently by user, content, website, or application.
• Potential Negative Effects on IT: Critics argued that the rollback could result in a lack of open access to the internet and create an environment where large ISPs could prioritize traffic or charge users extra fees for better service. This could affect startups, content creators, and smaller tech companies by limiting their access to a level playing field on the internet.

3. Immigration Policies Affecting Tech Talent

• H-1B Visa Restrictions: Trump's administration imposed limits and restrictions on the H-1B visa program, which many tech companies rely on to hire skilled workers from outside the U.S., particularly in fields like software development, data science, and engineering. The stricter visa policies were seen as hindrances for attracting top tech talent, particularly from countries like India and China.
• Impact on the Tech Industry: This tightening of immigration policies led to concerns about a shortage of skilled workers in the IT sector and posed challenges for companies trying to maintain a diverse and competitive workforce.

4. Cybersecurity and International Relations

• Russian Cyberattacks and Response: Trump's handling of Russian interference in U.S. elections and cyberattacks raised concerns in the IT and cybersecurity sectors. Many experts believed that a stronger stance could have been taken against Russian cyber activities, particularly in light of the 2016 DNC hack and the ongoing SolarWinds attack.
• Delayed Action on Cybersecurity: Some critics felt that the Trump administration downplayed the severity of cybersecurity threats, especially from state actors like Russia, leaving critical vulnerabilities unaddressed.

5. Social Media Regulation and Misinformation

• Section 230 and Social Media: Trump's administration frequently targeted Section 230 of the Communications Decency Act, which shields tech platforms from liability for content posted by users. Trump called for the repeal of Section 230, claiming that social media companies were biased against conservative voices.
• Effects on IT and Social Media: While some argued for reforming Section 230 to hold social media companies more accountable for harmful content, others feared that tampering with it could lead to greater censorship or reduce innovation by imposing heavier regulation on platforms. Trump's rhetoric and calls for Section 230 reform created uncertainty in the tech and social media space.

6. Trade Wars and Tariffs

• Tariffs on Electronics and Technology: The trade war with China resulted in tariffs on various tech products, including smartphones, computers, and electronic components. This increased costs for U.S. tech companies, especially those that rely on Chinese manufacturing, and contributed to higher prices for consumers.
• Long-Term Disruptions: These tariffs were seen as a negative for the global IT ecosystem, particularly in terms of supply chain disruptions and increased production costs for tech products.

7. Focus on National Security Over Collaboration

• Technology Collaboration Concerns: Trump's "America First" stance meant that his administration was focused on strengthening national security over fostering international collaboration in tech innovation. This led to restrictions on sharing advanced technologies and knowledge with other countries, potentially isolating the U.S. in the global tech community.

In summary, many of Trump's policies were seen as contributing to uncertainty, regulation, and disruption in the IT sector, particularly in areas such as global trade, immigration, cybersecurity, and net neutrality. While some argued these policies were necessary for national security and economic interests, others believed they stifled innovation, hampered international collaboration, and created unnecessary challenges for tech companies.

1.1. Background on the Internet Boom of the 90's

During the 1990s, there was an unprecedented technological growth in the United States, where the internet developed into a large-scale government project. This laid the groundwork for a wave of market forces to stimulate development from the late 1980s through the early 2000s. Communications and information systems were part of an unimagined technological landscape where more citizens were connected on a massive scale. Throughout the process, there were constant references to revolutions, ideas, and paradigms. Although most references to the 'revolution' of the 1990s tend now to be confined as studio curiosities in thinking too focused on current events of the time, it is clear that technological innovation and its applications had a dramatic effect on the lives of people. Along with the growth of the internet, this era saw the rise of dot-coms, e-tailers, e-commerce, search engines, social networks, and the harbingers of the new knowledge economy.

Corporate greed and a lack of government enforcement were blamed after the economic downturn in the early 2000s. Sure, many of these ideas were terribly executed and were pushed into unworkable business models by an insurgence of people migrating from brick-and-mortar businesses into internet-based models, but the lesson is that powerful interests often reinterpret the interest of science. In the wake of the downturn, most 'dot-com' companies were shut down, with about five of every six losing their money for the venture capitalists who bankrolled them, several weeks before the market crash. If we put the burst of the tech bubble into a logical perspective using the insight of our intimations into new directions, then what we see is a description that can be used to determine a potential range of outcomes in a bright future. The next 15 years have brought staggering advances in computing and communications technologies, and no one can really know what to expect. However, in the face of the high drama and unexpected innovations of the last 15 years, some trends can be observed with a view to what can be expected to happen next.

2. Key Factors Driving the Potential A.I. Boom

Artificial Intelligence Investment Boom

The development of artificial intelligence (A.I.) has sped up over the last decade. Crime rates and fatalities have been declining, while investment in information technology, which includes A.I., has been expanding quickly. Reduced crime likely has economic and social benefits. Tools of machine learning, a subfield of A.I., have made much progress over the last decade, which has driven some of the recent developments in A.I., such as using algorithms to automatically generate video game environments for learning purposes. Some contributors to this success are computers becoming faster, the price of big data has been reduced, and A.I. systems can automate work, which tends to lower costs. These A.I. systems often work very well for at least some tasks that are very different from their training tasks. They might just need to alter training data or make their A.I. system generate data to solve the new or a related task at hand. However, some of these forms of A.I. systems may not be robust to changing environments or adversaries and might need additional working tools to make them function on some tasks.

Several other aspects or drivers might be causing or pushing the investment into A.I. research and innovation potential. So, although interest in publications cannot explain all the things a player does when investing in areas like A.I., it does give us an idea about the technologies and designs that might be the most valuable. One selection mechanism or constraint for doing research can be cost. One reason that can make research valuable is to earn more than its costs. In efficient research, some of the return on investments will also be allocated to pay for the opportunity costs of working that investment. Opportunity costs can be the profit that a scientist misses out on by not doing other innovative work, such as not developing a competing technology or applying knowledge to engineering and entrepreneurship, which would create business revenues. A.I. also often requires knowledge from other fields, such as control theory and optimization. Scientists and investors working in them are likely to earn returns that are higher than what they would earn by doing research in other sub-disciplinary fields or working in finance industries. These calculated returns are the expected net economic benefit from research. These benefits may not just be limited to scientists and investors; they can also be oriented to the larger society. The demand for research will also bend costs and impact benefits. This might be leading to budget-constrained researchers. At a point of higher frequency, investors will invest if the benefits of their innovative A.I. research or policy and development exceed the costs. This gain in benefits will often rise with the magnitude of impact if more societal actors and institutions participate in official smart investments in A.I. and artificial general intelligence (A.G.I.). They also study and think that learning computers, or humans that use A.I. learning tools, can also invest in learning how to develop A.I. It can demonstrate that a number of interest groups or relevant actors actively investing to align a future A.I. boom with societal benefits can be high. There is a mounting number of companies that are building A.I. products, and they must invest not just in product development but also in research to compete. These A.I. companies are getting large funding, which is evidence that investors are betting on A.I. breakthroughs that improve societies. In 2017, at least 19 A.I. companies were funded through megarounds. These megarounds are investments that raised at least $100 million. About half of the companies were in health care.

2.1. Technological Advancements and Innovation

Technological Advancements and Innovations A convergence of numerous technological advancements is necessary for the potential A.I. boom to occur. The first major advancement comes from A.I. developments specifically. Innovations in natural language processing allow machines to understand natural language. This development in the early 2010s was marked by competitive performance on a quiz show. Since this innovation, machine learning has particularly improved. For example, programs now refer to general neural networks or deep learning, which take some of their inspiration from natural neural networks in the brain. A database maintained by an institute could outcompete 95% of the general American public on a standardized test of academic knowledge. Next, computers and automation have recently both been rapidly improving in terms of their robotic capabilities. It is argued that there is a new robotics revolution, one that, if it has not yet "arrived," is just around the corner, in the same way as A.I. is.

The potential for robotics capabilities is being aided by 3-D printing, for which a specific robot is responsible for making strides in via its affordability and agility. Finally, one must have an accumulating store of publicly accessible big data to make this A.I. boom operationally possible, such as through governmental and business supply releases, sanitized data releases, and data beneath an upfront or ongoing subscription. Because advancements in these sectors are often driven by startups, we believe that market innovations could instead serve as a good proxy for future technological advancements. Many burgeoning, small, and entrepreneurial startups are internationally breaking technological barriers in A.I. using a variety of strategies. Lastly, these technological investments already have significant knock-on implications for compound innovations, such as in the labor market via jobs for computer programmers and data scientists, and in business by incentivizing businesses to automate industries recurrently adjusted toward human jobs.

3. The Role of Government Policies in Fostering Innovation

There is abundant evidence that R&D investments are risky endeavors prone to financial failure, and although they will not always improve the chances of technological success, these investments have been shown to correlate with a greater capacity in terms of innovation and entrepreneurship. The federal government has always actively directed the country’s innovation development with initiatives, investments, and incentives that are spread over longer timeframes, aiding promising technological fields that often outpace commercial incentives for corporate research. Research and innovation, however, must be guided by ethical, rather than purely learning-inspired objectives. A carefully balanced regulatory framework should enable the compromising development of potential while maintaining the ethical standards that promote consumer and common welfare—a process in which the government and policymakers play crucial roles.

On the supply side, the production of AI-based knowledge and solutions can be further motivated by the intensification of investments in AI R&D via increased funding to basic, applied, and translational research programs. Mostly in partnership with other nations with a well-defined plan: in America, the National Science and Technology Council’s Select Committee on Artificial Intelligence refers to the National Artificial Intelligence Strategy regarding national objectives for emerging AI, reflecting a converged approach with other countries in terms of foreign policy. Also in the UK, the government seeks to foster deeper ties with companies through research, growth, and skills funding, including collaboration with industry, as does the Canadian AI Innovation program, in part, where the private sector is collaborating with federal innovation funding. Moreover, by working with proponents of digital transformation, the government allocates light infrastructure funding to transition to a wide range of industry verticals.

3.1. Comparison of Trump Administration and MAGA Congress Policies

During a talk, it was mentioned disinvestment strategies that can prevent accelerated innovation and how they can lead to a lack of technology adoption. This is almost like an anti-defense prepared strategy where defenders work to increase the cost and lower the effectiveness of the weapons and defenses of the attacker. Investment into technology innovation policies can give us more insight into the effects they can have on the development of specific technology. Some measures forced specific investments or disinvestment in technology. While there was no effort to increase research funding or decrease taxes on products, there was an increase in research investments and top-of-the-line technologies.

There were two specific big policy actions that can tip innovation; however, one of them was disinvestment. By dropping fuel efficiency requirements down to less than acceptable levels, the administration made the development and purchase of electric or efficient internal combustion engines less profitable and thus less likely to be developed, allowing a few to be built to develop smaller and cheaper vehicles. Once built in larger or smaller units, costs may have been lowered because they are developed being farmed thinner, and thus fewer risks are taken per vehicle. Policymakers worked to expand the number of workers needed to build an economy that is based around innovations. They created new regulations to increase demand for labor by forcing a large pool of American producers to rework vehicles already built, boosted gig jobs that require long-term consultants, increased growth in long-term healthcare roles, looked to fund large infrastructure projects, and raised funding for education in competition-based new fielded personnel.

4. Constraints on A.I. Development Imposed by Trump Administration and MAGA Congress

Since 2016, the administration and the coalition have collectively made a number of policy decisions which, when taken together, impose a considerable number of constraints on A.I. development. Chief among them are regulations on high-tech industries that supply component parts to A.I. companies, which have thrown into considerable disarray the strategic value of the hardware development roadmaps of large A.I. companies. These regulatory requirements can dramatically increase the overhead of moving some production lines out of the United States. Moreover, because of the reputational effects of working with organizations implicated in human rights abuses, collaborating on A.I. research with relevant personnel in the countries affected would potentially outrage portions of the general public, sleepwalking, to some extent, through widespread A.I. research boycotts. There are few clear regulations to replace these rapidly moving measures that can prevent meaningful increases in research productivity.

In addition to these, across the administration, the national security rhetoric prevalent is seen as increasingly conducive to the current view that "the big advances in A.I. are here," and A.I. researchers are motivated by a range of potential challenges rather than one for which policymakers have little taste. At large, this reduces federal R&D budgets. Finally, concomitant with these developments, there has been an enforcement push by the administration and members of Congress to scale down so-called "extra-legal cooperation" between U.S.-based A.I. companies in the name-brand era and foreign industrial A.I. research institutes. Taken at face value, these are encouraging signs in the discernment of the economic benefits of a future A.I. boom. However, and this sometimes seems to be forgotten in consideration of A.I. policy, they are being arrived at by means of horrendously circuitous routes. If rather than a world of potentialities, executives and A.I. practitioners must work within parameters imposed entirely extraneously to the art and nature of A.I. development, those who wish to import from the A.I. policy question to the positivist study of A.I. have their work already cut out for them by a world that is refusing to accommodate objectives set from on high. Overall, the presence of a palpable public backlash against a policy may be in the eye of policymakers. However, we don't think that the broader dynamics grappling with the issues of national security and global economic growth can so readily bend to ideologically motivated policy whims.

4.1. Regulatory Environment

The Trump administration has stated it is unlikely to establish a "wholly new regulatory framework for artificial intelligence." But there are laws and regulations likely to govern certain classes of AI-based services—specifically, autonomous vehicles and consumer lending. The Federal Trade Commission is developing a rulemaking process and seeking public comment as it contemplates regulating AIs that violate consumer privacy or breach data protection laws. Similarly, state legislatures, especially California’s, are developing patchwork regulatory landscapes that may be difficult for businesses to comply with at scale. The risks of overly permissive or complex regulation—sluggish or no commercialization of the technology—are significant. Regulatory uncertainty can also spook VCs and other capital market actors.

In the healthcare arena, a rigorous regulatory environment is likely to elicit compliance, at least from new entrants. But there is a risk that complex data protection and ethical standards will deter providers from using the technology. Ethical standards, the most important of the triad, are the most challenging to establish and monitor. But at least in the European Union, they are likely to affect the behavior of firms not because of consumer sentiment or employee behavior (although this is part of the story) but compliance with industry norms and regulatory standards. Without comprehensive standards, there is nothing keeping a company from using unethical AI algorithms for competitive advantage. Ethical AIs may not be worth the cost if ethical AIs lack competitive advantage. But that cost of installing the AI may be significantly less than the loss of consumer trust and business reputation if a firm is caught using an unethical AI. In sum, industry norms are the first line of defense in maintaining a healthful and competitive AI industry. It is possible that the AI field may be so under lock and key now that the industry will not reward firms that create unsafe or unethical algorithms with substantial market share. In environments where these societal pressures are not sufficient, regulators may need to step in. As new technologies develop, most regulators will need to be guided by innovation rather than regulating proactively. Perfecting regulatory oversight over developing technologies might then require a "dynamic adaptive framework," which evolves with the changes in technology. This approach could become costly, since it would require a significant amount of labor for regulators who would need to keep up as change occurs, but may ultimately be the most effective and could serve as a more predictable form of oversight, since companies would have a clearer idea of how a certain algorithm should be in order to maintain regulatory compliance.

5. Economic Implications of A.I. Boom

An enormous amount of money can be made via the ascendance of artificial intelligence (A.I.) into our economies. Because of the potential magnitude of an A.I. boom, it is important to understand something of the transformation that could occur in America if such a boom came into being. There are many economic implications of the gathering strength of various A.I.-related capabilities. There are prospects of significant improvements in productivity and efficiency in ordinary goods and services. A whole new array of products and services will become available. There will be enormous changes in labor markets; as in the past, there will be new job openings and some job displacements. Skills must adapt. It is possible that, as has been the experience of the preceding decades, the rich get richer while the rest become stagnant. Since most of the innovations made in A.I. have been by private or military actors, the private and military sectors are where these economic transformations are most likely to occur.

There is a vast amount of material, pieces of which provide a brief overview of their stock-based trajectory. U.S. GDP will increase, over time, by trillions of dollars. However, these paths radically diverge based on policy approaches and uncertainties regarding, for example, the rate of A.I. transformation over time and the amount of additional work and consumption that people believe they would gain and would prefer. Managing and creating regulatory frameworks for the A.I. boom is said to be transformational and will require national work and inclusive societal management. Countries that impose strong institutions and infrastructure will be better prepared to safeguard against possible downsides such as job loss and increased inequality.

5.1. Job Creation and Labor Market

Labor Market

Job Creation: The emergence of artificial intelligence presents an array of opportunities for job creation. One way A.I. can create jobs is in the demand for labor resulting from an increase in the levels of output. Emerging sectors like precision agriculture, regenerative construction, and green energy show both large potential for job creation, which would likely not appear in the absence of investments in artificial intelligence, and an increase in working conditions. For example, companies are creating jobs in construction that never would have existed without the use of machine learning to compile images over time. Similar examples exist for healthcare. As A.I. improves the ability to integrate data to offer preventive care and predict treatment, it is likely one will need more technicians, physician assistants, and nurse practitioners.

Displacement: Of course, investments in the development of A.I. could lead to job displacement in some traditional sectors, like manufacturing or retail, that can be highly automated in general 'intelligent' ways. The potential for A.I. to drive job creation in emerging sectors does not imply that displacement from automation will not have negative effects on workers or that we are pursuing optimal policies to mitigate them. Reskilling and upskilling employees with technical and data skills is important to take advantage of these new job opportunities. Reskilling is the development of workers' skills for jobs creating an array of opportunities in technology, design, manufacturing, and IT services, for example. We can create opportunities by bringing universities, research institutions, and the private sector together to create clusters of specialized regions to drive the economy through the potential data boom.

6. Global Competitiveness and National Security

The interconnectedness of global competitiveness and national security is a given, especially with technological advancements that could provide strategic advantages against hostile states or regional threats. Something that makes A.I. different from past intelligent technologies is that, in addition to potentially being a strategic individual asset, A.I. is a massive panacea for dealing with a host of possible security scenarios we may face in the future because A.I. can understand how actors learn, act, and interact. Therefore, states that have systems and data quality that allow them to think faster and better and with less cost about solving these problems can choose to start a conflict in the knowledge that if it goes poorly they can dramatically mitigate their losses—and if they do this, they will probably be hyper-effective or at least very effective at the other initial stages of conflict and in combat.

Countries outside the Anglo-American world are making substantial investments in A.I. and have revealed plans to challenge the United States in these technologies. Notable in this is China with the ambitious plan, which has set aside massive investments to achieve national parity and leadership in A.I., enough that it appears duplicates of the technologies may be necessary and are currently underway in the East. The U.S. must also collaborate with partners and allies to prioritize this research. Any future regulations that could make it easier or more difficult to produce intelligence with proprietary technology and advantageous abilities or cause a wholly occurring disadvantage to competitors should be used to guide the U.S. posture, both through active collaboration and depending on how or when such use may capitalize advantages for making adjustments. Therefore, competition policies, security policies, and economic policies must guide and shape foresight through this interfacing and possible data advances in A.I. as well. The U.S. cannot afford to be left behind in this critical area.

6.1. Impact on U.S. Position in A.I. Race

6.1. United States in the Global A.I. Race

6.1.1. How Domestic Policies Influence U.S. Position

Every country's domestic choices drive whether the given country will have a prominent position in the future of A.I. and how this future will look. By far, the most significant factors in determining how the foregoing will work out are research output and talent acquisition from many countries around the world, in both chosen and emerging disciplines. Industry's investment in A.I. research also paints a picture of how fundamental research driven by the U.S. government influences the direction of corporate A.I. research. Many other partners' potential involvement in research supplements this research.

There is another desire that research leaders in the U.S. perpetuate—not only to do the best research themselves, but also to attract foreign talent and partner with this talent, advancing the field more than any single group of people could. However, if the country seeks refuge from external sources of knowledge in the belief that it will militate against avoidable conflict, possibly because the country seeks to win an A.I. arms race without efforts to change the arms race play, more people leaving a military background will work on A.I. as civilians.

6.1.2. Geopolitics

The trend in which alliances defining the global balance of power shift over time is evident. Rather than behaving promiscuously to maximize output from and commercialization of research, the U.S. has historically partnered with other liberal democracies, stemming from universities to military campaigns beginning from the areas of mathematics and electronics.

6.1. Well We've now got Musk

Yes, and now you have a conflict of interest because Musk is not interested in competition for improvement purposes but again, for only advancing the stupid-ass ideology of Donald Trump.. period.  And, well only Musk's companies advance as a result.  Shameful!

7. Conclusion

President Trump and the 115th Congress made immense impacts on the regulations and policy around A.I. and automation, creating heavily weighted pro-business tax cuts and removing data privacy protections, and setting clear signals to companies that regulations will continue to favor innovation over protection and caution. With the role and history of A.I. covered, we find that the demand to regulate or reconsider regulations around A.I. emanates from how much we anticipate A.I. to grow in our economy. As we await the next industrial revolution, the presence and nature of public regulation in the development of A.I. will be crucial in determining a myriad of market forces from competition to innovation. Understanding the recessionary recovery both after the great depression and divide between half-century advances in auto technology brings to light the current administration’s unmonopolized industrial frontier that A.I. could revolutionize in parallel. The United States’ comparative advantage in technological innovation over regulation must be carefully balanced to avoid repercussions of early automation: harmful concentrations of A.I. talent in the industry, economic scarcity in AI-workers and profiteering skill and knowledge. The global battles of commercial leadership and naïveté of autonomy security via A.I. cannot be discounted. Equally, the creative guild of talented “AI-knowledge workers” will demand global citizenship to these manmade brainchildren on a grand scale - slipping protections and opportunities silently downstream in the labor force. What is left on this wave of innovation, and why it matters, is the confusing market of labor that our future can steer by action, or that we can be swept away by. It is up to us to swim.

7.1. Summary of Findings and Future Outlook

This essay investigates the forces that affect the growth of a particular advanced technology, namely general-purpose artificial intelligence. This technology, if realized, will transform many sectors of the economy. Firms interested in investing in this industry, or investing in the economic complement—in either the short run or long run—may be interested to know what sorts of policies can foster its development. The findings are as follows:

• Policies can, all else equal, impact technologies' supply side and demand side; these in turn can affect industries and the growth of the macroeconomy.

• New technologies, or technological discoveries, often affect the economy through the demand that stems from that technology. But in the case of new technologies that require the retooling of production, the growth of such industries can be hampered if there is insufficient consumer demand. This essay focuses on the latter effect.

• Governments hinder industries through ill-suited regulations. All else equal, governments foster industries providing research, economic upside, ancillary services, or some combination thereof. Furthermore, governments achieve these ends best via cost-benefit analysis, rather than regulating technologies like A.I. per se.

To allow for a proper adoption of foreseen technological change, governmental regulations will likely need to be managed exceedingly effectively. It is critical to maintain an open dialogue on this subject because the quality of these regulations will widely impact the development of technologies with both enormous downsides and upsides. Therefore, accountable governments with long-term vision will recognize that they promote technological transformations by obtaining the rewards while mitigating the risks. AI firms, notably of the GPT variety, have a fiduciary and ethical responsibility to pursue the many policies that enhance their future viability and positive externalities. Of course, these policies need to dovetail with the broader agendas of each firm, the personal stance of their boards, and their specific comparative advantages.