This article was written by Benjamin Bosis, Brown ’20. It is one of two Intercollegiate Finance Journal (IFJ) articles co-published this fall under a new partnership between the DBJ and the IFJ. To find out more about the IFJ and the partnership, please click on the author profile below.

The Transport Revolution is Coming

When Russia successfully launched the first satellite, Sputnik, into space, the whole world suddenly became invested in the progress of completely new technologies – and Americans had some serious ground to regain. John F. Kennedy knew that setting a goal would be half the battle, so he urged congress that “This nation should commit itself to achieving the goal, before the decade is out, of landing a man on the moon.” It was only with the drive to achieve that aim that America did just that.

Technology has long been a means to power, but in today’s world we often see such battles between corporate entities rather than political ones. According to Elon Musk and other business leaders, the future lies in self-driving cars, and, like JFK dictating the mission to the moon, Musk is now conveying his vision of automated driving to the American people. Already, many of the biggest companies in the U.S. have broken into a full-on sprint to be on the right side of the coming transportation revolution.

A Battle Between Giants

Google, General Motors, Uber, Mercedes-Benz, Tesla, and Lyft have made huge strides in automated vehicle (AV) development. Even Apple, who leads America’s technology sales by a whopping $200 billion a year, is reportedly planning to get in on the corporate rush. These important players come from one of two completely different perspectives and corporate incentives. The tech giants who started the movement towards unmanned vehicles are secure in other products, but see autonomous vehicles as a new and potentially huge source of income. The auto giants, on the other hand, have an almost existential dependence on cars, and face much higher stakes in keeping their brands relevant.

Despite their decidedly distinct backgrounds, members of each side have begun to choose partners across the aisle, sharing their knowledge in an attempt to get ahead. GM recently invested $500 million in ride-sharing service Lyft, and announced plans to equip an autonomous Lyft fleet with GM cars. The same partnership exists between Volvo and Uber, which use each other’s technology exclusively in their pursuits; whereas Google and Daimler, focusing on more limited aspects of the AV market, remain independent. Though most thoughts on Apple’s efforts are largely speculative, they would most likely also avoid any significant partnerships in order to control their long time vision, which at this point remains unclear.

Your Mission, Should You Choose to Accept it

All of the competitors agree that automation will shape the next era of transport, but opinions differ on how driverless cars should be implemented in the new age. Uber and Lyft envision the disappearance of car ownership entirely. In particular, Lyft plans to market each of its products as part of a larger network of vehicles that, instead of belonging to any one individual, operate 24/7 to carry customer after customer from point A to B. Transportation would become an exclusively service phenomenon, eliminating the need for anyone to actually own a car. Perfectly suited to urban environments, the plan for driverless cities could lead to larger sidewalks and an often-fantasized-about end to traffic jams. The interconnected fleet would vastly decrease the overall number of cars, which would in turn decrease pollution and make almost all parking lots obsolete.

Despite all the excitement for AVs in the tech world, Tesla founder Elon Musk is betting that Americans won’t give up the freedom of car ownership so easily. As the first company to actually place semi-driverless technologies in the hands of individual consumers, Tesla has demonstrated a commitment to autonomy not only for the car-driving-software, but for the car owner as well. Ultimately, the vision that will triumph is the one most adaptable to American life. Individually owned cars could be more suited to America’s huge expanses; as cell providers have shown, ‘nationwide coverage’ often leaves out many people in rural communities. And for most, having a car means not only being able to go wherever you want, whenever you want, but more importantly, knowing that no one can stop you. That quintessential ‘American’ quality may give Tesla’s plan for the future a bit of an edge.

Anxiety and Fear — Obstacles to Adoption

Legislators across the country have reacted very differently to the prospect of self-driving cars their public roads. California, usually the center for this kind of tech development, has driven some companies’ efforts elsewhere by considering significant regulations. Their potential laws would require all autonomous vehicles to have brakes, accelerators, and a steering wheel as cautionary measures inside the car, which would also necessitate a human operator in all test vehicles. The federal government, clearly hopeful for what the industry could do for the American manufacturing economy, has remained extremely lenient and supportive, necessitating only minimal safety precautions and reserving the right to require reports of all tech failures from any company.

Consumer enthusiasm, on the other hand, is another animal entirely. Many Americans have safety concerns about vehicle automation. Robots may not make mistakes, but programmers can, and the idea of having no recourse whatsoever in the case of a tech malfunction is not something to be taken lightly. Investors are particularly wary, as seen when Tesla’s stock dropped 3.2 percent only hours after their first fatal crash was reported. The reputations of companies hoping to put fully driverless cars on the road are proving paramount to their ability to do so.

Pittsburgh: A Case Study for Acceptance

In the face of growing hesitance, Elon Musk has insisted that it would be “morally reprehensible to delay release simply for fear of bad press or some mercantile calculation of legal liability.” While some seem to agree with his logic, that may not have been a good thing for Tesla. Across the country in Pittsburgh, Uber in particular has taken advantage of commercial testing allowances. Pennsylvania lawmakers, who clearly have more to gain than those in a state already home to some of the biggest companies in the world, have made no signals toward any major regulation so far. This leniency is largely a continuation of America’s laissez-faire industrial tradition. Not only is the ability to test in Pittsburgh catapulting Uber’s project forward, but the jobs and attention that came with Uber are helping to rejuvenate the long industrial legacy of the city.

Through steel, Pittsburgh became of the biggest economic powerhouses in the United States, and helped to lift the U.S. to the wealthiest nation in the world; but since leaving the industrial frontier it has fallen into relative obscurity. Now Uber’s entrance, catalyzed by the harsher regulating in California, is giving many of the students at Carnegie Mellon University a reason to stay. Their high volume of robotics talent will prove to be key to both Uber and Pittsburgh as they blaze a trail in the automation industry; for Uber, it has already meant taking the lead in the development race. That lead may continue to grow if other companies cannot find testing grounds of their own.

They’ll Get Here When They Get Here

Nevertheless, in all the excitement of the current flurry of AV activity, companies have declared decidedly optimistic timelines – typically ranging from 3 to 6 years – for their progress toward commercial production. Tech analysts at McKinsey, however, have taken stock of expert opinions and constructed a more realistic one.

The initial process leading up to product release may finish as early as 2019, as companies predict. Most experts believe, however, that there will be a much slower adoption of the cars lasting until 2030, while safety data accumulates and customers uncertainty decreases. Any state of 100 percent implementation – such as an eventual outlawing of human drivers – would not likely come before 2050. So for those of you hoping to own a completely autonomous car, that hands-free commute might not be so far off; Lyft CEO John Zimmer’s vision of the intelligent, interconnected, super-efficient fleet is a far less likely eventuality. But whether they’re Teslas, Volvos, or anything else, self-driving cars are coming; and a brighter, cleaner future is coming along with them.

The possibility of intelligence in the inorganic world has been a fascination to man dating back at least to ancient Greece when mythical beings of bronze and ivory protected islands and seduced men. Now, a couple thousand years later, we are rapidly closing the gap between these myths and reality. The possibility of developing a computerized mind accompanied the advent of computing science in the early-to-mid 20th century, and in 1956 the formal study of this subject was born here in Hanover at the Dartmouth Summer Research Project on Artificial Intelligence.

Google’s victory over the world champion Go player in March marks the latest in a series of publicity stunts that have tracked the rapid advancement of artificial intelligence (AI). Previous such displays have included IBM’s triumphs in chess with their computer Deep Blue and Jeopardy! with Watson.

These events may seem to be all fun and games on the surface, but there is a dramatic shift in the AI industry that is taking place behind the scenes. According to Quid, a data firm, spending on AI deals by tech giants like Facebook, Google, Microsoft and Baidu quadrupled between 2010 and 2015. This spending spike is symptomatic of a high stakes race among tech companies to become the preeminent supplier and developer of AI technology, which promises to be the platform of the future much like the PC operating system and Google’s search engine were before.

To build an artificial brain computer scientists need real ones, which has made human capital the most precious and therefore most aggressively pursued resource in this clash of the tech titans. This explosion in demand for AI talent has created fears of a brain-drain at America’s most elite universities. In the “AI Winter” of the 1980’s and 1990’s when the research being done wasn’t nearly as marketable, the best and brightest in the field found academia to be the most welcoming and lucrative option. Now, however, professors are finding it hard to hold on to their grad-students who are being lured away, even before they graduate, by million-dollar salaries – the promise of making a tangible impact and freedom from a world of uncertain academic funding. As Andrew Moore, the dean of Carnegie Mellon University’s computer-science department, stated in a recent Economist article, this phenomenon raises concerns about a possible “seed corn” problem that drained many top universities of the very resources necessary to produce the next generation of talent. Yet, these same salaries that are so effectively luring talent out of academia ought to lure talent into it. After all, it is in the best interest of the tech companies to maintain a sufficient pool of talent from which to draw.

If the development of superior AI is a race, what does it mean to win? Given the nature of AI, this is a loaded question. Because an artificial intelligence system would learn and improve upon itself, the better system would do so more quickly than its competitors leading to a snowball effect that could quickly lead to a drastic imbalance in the industry. This combined with the existential and moral concerns attached to the field has lead some big names in the industry, like Elon Musk, to take precaution in preventing a single entity from gaining too much power. Musk went about this by pledging $1 billion along with other donors to fund OpenAI, a non-profit research organization whose goal is “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”

So who among the tech titans appears to pose the greatest threat of becoming the AI hegemon? Given its track record, it is likely no surprise that the perennial powerhouse Google is developing a convincing lead in the field. Not only is it well-equipped with the capital and infrastructure to pursue such an economy of scope but it has also demonstrated impressive foresight and instinct in their approach to the issue. As one of the first tech conglomerates to dip their toes into AI, Google recruited Stanford researcher Andrew Ng in 2011, providing him the funding and freedom to pursue advancements in the field of “deep learning” and therein kick-starting the project known as “Google Brain.” Deep learning is a method for machine-learning based on the construction of artificial “neural networks” that mimic the functioning of the human brain.

This method is superior to others in that it is a general-purpose technology that can be applied to myriad specific tasks using a singular algorithm for processing and learning from data. Of course, Google wouldn’t dare put all its eggs in one basket and in early 2014 supplemented their AI efforts with the purchase of DeepMind. Reportedly shelling out some $600 million for the firm, Google incubated the efforts of Demis Hassabis, DeepMind’s founder, in his quest to “solve” artificial intelligence using a method known as reinforcement learning. AlphaGo, the software that recently beat the world champion at Go, is based on this technique. Not only is Google leading the way in terms of developing the underlying software for AI, but it is also making considerable inroads in applying these technologies. According to the California DMV, as of March 1, 2016 Google has 73 test-permits for self-driving cars compared to Tesla’s eight (the second most in the state).

While some like Elon Musk and Stephen Hawking have voiced their concerns about the existential threat of rapidly advancing AI, there are more salient concerns to be had about its impact on our lives. The heart and soul of AI in its current applications are data. For example, the machine-learning aspects of the software behind Google’s targeted advertising are based on collecting information about users’ day-to-day online behavior. As the applications of AI expand into areas such as self-driving cars, the amount of data collected about consumers and thier behaviors will skyrocket. Then these machine-learning algorithms will be processing oceans of data regarding daily travel habits including things like where everyday people eat, shop, work, etc. Giving away the control over a car may influence someone’s future travel based on marketing tools like corporate sponsorship without the consumer’s awareness. The car will likely even listen to passengers’ conversations. As The Atlantic’s Adrienne LaFrance put it, “In this near-future filled with self-driving cars, the price of convenience is surveillance.”

AI has a profound potential to benefit humanity but we must also respect its potentially adverse manifestation. Clearly there must be regulation of the means, methods and motivations behind the technology’s advancement but we must be careful not to stifle its progress. The world has reached an inflection point in terms of artificial intelligence and its future will be a balancing act at the pace of Moore’s law.