In the midst of the worst inflation seen in 40 years, not all prices are rising. In the 1970s, the cost of taking a kilogram of water to space was $20,000 in today's dollars. Now, it is more like $2,000 - a tenfold reduction, and as SpaceX's Starship has $20/kg in its sights, there is a real possibility of another hundred-fold reduction. If this happens, access to space will open up like never before, creating a flood of new business opportunities. The authors-who include a Canadian astronaut-discuss the pros and cons of SpaceX's monopoly and suggest three key areas of opportunity for innovative companies who want to embrace this new frontier.

While the popular view is that insights are the key benefit of artificial intelligence, in truth AI creates value by improving the quality of decisions. The good news is, the opportunities for it to do that in business are countless. But because decisions in one area of an organization usually have an impact on decisions in other areas, introducing AI often entails redesigning whole systems. In that way, AI is similar to groundbreaking technologies of the past, like electricity, which initially was used only narrowly but ultimately transformed manufacturing. Decisions involve a combination of prediction and judgment, and because AI makes highly accurate predictions, it will shift decision rights to where judgment is still needed, potentially changing who makes decisions and where, when, and how. More-accurate predictions in one part of a value chain will also have ripple effects on other parts. For instance, if a restaurant can reliably forecast the amount of ingredients it needs each week, its orders will fluctuate, making its suppliers' sales more uncertain. Strong communication is needed to synchronize effort and resources in a system, and modularity will help prevent changes in one area from disrupting others.

Whenever someone makes a decision that affects other human beings, their inherent biases and motivations are invisible. Whether it's an HR manager deciding which candidates to interview or a bank loan officer deciding who should receive a loan, chances are, people are not being treated equally. That might be about to change. The authors of Power and Prediction: The Disruptive Economics of Artificial Intelligence argue that if artificial intelligence (AI) can be placed at the heart of such decisions, objective benchmarks can be achieved, because AI cannot have explicit motivations to treat people differently. The authors see the potential for AIs to reduce discrimination in all sorts of decisions, from education to healthcare, banking and policing.

Many companies can dramatically improve their products and services by using machine learning-an application of artificial intelligence that involves generating predictions from data inputs. Amazon, Google, and other tech giants are already experts at taking advantage of this technology. Smaller enterprises and late entrants, however, may be unsure how to do likewise to gain market share for themselves. This article suggests that early movers will be successful if they have enough training data to make accurate predictions and if they can improve their algorithms by quickly incorporating feedback derived from customers' behavior. Latecomers will need a different approach to be competitive: The secret for them is to find untapped sources of training or feedback data, or to differentiate themselves by tailoring predictions to a special niche.

One key lesson from the global pandemic crisis is that funding for medical research is woefully inadequate. The author argues that we must do better. The problem is, traditional innovation incentives such as research grants won't cut it. What is needed are Advanced Market Commitments (AMCs). The author defines the term and shows how AMCs work. The funding for innovation for medical research is a fraction of that devoted to other threats - notably national security. As the author indicates, our experience in 2020 suggests that our attention has been misfocussed.

To disrupt, or not to disrupt? That is a very important question. Rather than single-mindedly heading down the path of would-be disrupter, new entrepreneurial companies can and should evaluate the trade-offs between disruption and other strategies. Doing so allows them to choose a strategy that is right for that startup, in that market, at that time, and to learn as the company commercializes its idea.

New innovations today are likely to be more advantageous to college graduates than those who didn't finish high school. In an excerpt from their book, Innovation + Equality: How to Create a Future That Is More Star Trek Than Terminator, the authors argue that new technologies can worsen inequality, which in turn can create a backlash against innovation. To make sure the innovation engine keeps humming, it is vital to ensure that the fruits of growth are available to all.

Few would argue that artificial intelligence (AI) will have widespread implications for business and society. The authors argue that business leaders should focus their attention on understanding the effects in three key areas: jobs, inequality and competition. They outline both the pessimistic and optimistic views of AI's effects and provide their own views with respect to AI and jobs, AI and inequality and AI and competition. Among other things, they argue that the impact of AI will not affect all people and all firms equally, and that leaders must carefully consider their decisions in this arena, as they will have long-lasting effects on society.

The author-the Chief Economist at the Creative Destruction Lab-argues that recent developments in artificial intelligence constitute advances in prediction. Prediction occurs when you use information that you have to produce information that you do not have. In a wide-ranging interview, he says that importantly, this is all machine learning does. It cannot establish causal relationships and therefore it must be used with care in the face of uncertainty and limited data. As a result, he explains why workers will be making more complex decisions going forward, and why there will always be a role for human judgment in every organization.

Re-framing a technological advance as a shift from scarce to abundant or from expensive to cheap is invaluable for thinking about how it will affect your business. Computers made arithmetic cheap, with vast implications for industries whose products could be digitized, such as photography and music. Artificial Intelligence, the authors argue, will be economically significant because it will make something very important a lot cheaper: prediction. The challenge will be to identify situations in which prediction will be valuable, and then figure out how to benefit as much as possible from that prediction.

When looking to assess the impact of radical technological change, the key is to ask yourself, What is this reducing the cost of? Only then can you determine what might really change. The authors-key players in the University of Toronto's Creative Destruction Lab-argue that in the case of Artificial Intelligence, the answer is prediction. They show how this is posing a slew of new challenges for managers and employees alike.

This is an MIT Sloan Management Review article. To understand how advances in artificial intelligence are likely to change the workplace - and the work of managers - in coming years, you need to know where AI delivers the most value.

Using Blockbuster as a prime example, the author describes the three paths to disruption for a modern business. He also explains three seminal theories of disruption: those of Schumpeter, Christensen and Henderson, and concludes that there are two key types of disruption: innovative and architectural. In the end, he shows that successful firms that are disrupted are not necessarily poorly managed: instead, they choose to continue on the path that brought them to success.

Author Joshua S. Gans concedes that disruption is a possibility, but he contends that the link between disruption and failure may not be as strong as many managers believe. In particular, Gans argues that disruption can be averted. "Many businesses find ways of managing through it, and this can weaken any relationship between a disruptive event and the actual disruption,"he writes. The question, Gans says, is whether companies facing disruption are able to counter its effects. He describes three approaches to dealing with market entrants: beating them; joining them; and waiting them out. Beating Them: The first approach involves investing aggressively in new innovations after entrants bring them to market. Although Gans says managers may not know right away whether an entrant's innovation will become a threat, when the situation becomes clear, they must act to protect their market position. He uses the example of Microsoft Corp., which introduced its Internet Explorer in the mid-1990s to compete against Netscape Navigator, which at the time was the dominant product in the browser market. Microsoft created a new division dedicated to defusing the Netscape threat. Joining Them: The idea here is for established businesses to "wait and see"whether a market entrant's innovation improves and becomes a competitive threat. Then, instead of waging war, the existing player can acquire the entrant's business and its products, thereby averting disruption. When disruption is upon them, Gans explains, incumbents realize they will face stiffer competition in the future, so they have an incentive to neutralize the threat. But there are also advantages to disrupters in avoiding a long period of intense competition with an established incumbent. Waiting Them Out: Gans suggests that established players should assess what they have that entrants lack -- entrants rarely have every element of a value chain.

Most managers are well versed in the defensive playbook for confronting disruptive innovation. Most commonly, they either acquire the new entrants or "disrupt themselves" by setting up autonomous units charged with developing their own new technology that can be rolled into their principal operations once the disruptive innovation begins to dominate the industry. But quite often, adopting a new technology requires companies to fundamentally change their mainstream operations--the way they manufacture and distribute their products. In these cases where the organizational model changes along with customer expectations and preferences, the playbook often falls short. In this article Joshua Gans of the University of Toronto's Rotman School of Management identifies three prescriptions for surviving "supply side" disruption: Companies must have an integrated organizational model, ownership of a product feature important to the end customer, and a broad and flexible sense of corporate identity. Though less commonly understood, supply-side disruption is arguably more dangerous than the kind described by Clayton Christensen in his book "The Innovator's Dilemma"; indeed, disruption of a product's architecture threatens a company's very survival in a way that changes in customer demands do not.

Jon Pastor and Lawrence Zhou were inspired by the same problem: the Internet was surprisingly unhelpful in the hunt for an apartment. The online apartment rental market was fragmented, opaque, and wrought with misinformation. The leader in this space was the website Craigslist, which was the most highly trafficked classifieds site in the world and the tenth most visited site in the United States despite introducing almost no innovations in its 15 years of existence. Having suffered as consumers, Jon and Lawrence saw this chaotic space as fertile ground for entrepreneurship. But the routes these entrepreneurs chose to take were different. "Killing Craigslist" explores the business Jon and Lawrence built to improve the online rental experience, and examines strategies to attract users and monetize their sites, all in shadow of a deeply entrenched incumbent.

At a time when ever-rising smartphone sales are driven as much by demand for devices that run must-have third-party apps as by the quality of traditional voice and data services, there is a myriad of challenges facing the software developer who is looking to choose which mobile development software platform to invest in. Written from the perspective of an established consumer bank that is about to commence development on its first downloadable application for mobile devices, the case surveys the state of the smartphone market in 2010 and considers the challenges of a platform landscape that includes significantly varying installed device base sizes, growth rates, application distribution models, and hardware device profiles. Focusing on Apple's market-leading iOS platform and App Store, for iPhones and other devices and on Google's developing Android OS and associated Android Market, the case considers potential benefits and pitfalls of each and touches on the reasons that other longer-standing platforms, such as RIM's BlackBerry platform, are less appealing to modern-day application developers.