Much of the quantum research community is focused on showing quantum advantage that a quantum computer can perform a calculation that is impossible on a classical computer. The authors contend that enterprises should focus instead on seeking opportunities for quantum economic advantage when a quantum computer provides a commercially relevant solution faster than a classical computer could, or when a quantum computer provides viable solutions that differ from what a classical computer yields.

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.

While few people think about it, underlying every application of computers in organizations today is math, calculated using binary digits or 'bits.' The original computers of the 1950s could perform about 465 multiplications per second, and today's computers are billions of times faster. However, there is an important class of arithmetic problems that remain out of reach for classical computers: Large-scale 'combinatorics' calculations. Combinatorics problems ask the question, 'How many ways can this set of objects be combined?' Such problems can also ask whether a certain combination is possible, or what combinations of objects are 'best' by some defined metric. The authors argue that a surprising number of practical organizational problems can be viewed as combinatorics problems and describe how fields from finance to chemical engineering and cybersecurity will be impacted by the emergence of quantum computers.

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.

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.

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.

Information asymmetry presents a challenge to equity crowdfunding just as in other markets for equity capital. Investors are less likely to finance startups when it is difficult to assess quality. Syndicates reduce market failures caused by information asymmetry by shifting the focal investment activities of the crowd from startups to lead investors. Syndicates align the incentives of issuers, lead investors, and follow-on investors by providing incentives for lead investors to conduct due diligence, monitor progress, and exploit their reputation. Preliminary evidence foreshadows a meaningful role for syndicates in the allocation of capital to early-stage ventures.

Despite all the hoopla around Big Data these days, the fact is, there is nothing dramatically new about having data at our fingertips. What is new, the authors argue, is that modern technology has increased the types of data that can be collected and made it cheaper, easier and faster to collect, store and analyze that data. They define three key realms of data: Descriptive, Predictive and Prescriptive, and show that progressing to the Prescriptive realm is the best way to enable a 'scientific' approach to business. They show that data and analytics are only valuable if they are used to generate insights, learning and evidence that inform business decisions-and that managers have a critical role to play: ensuring that their organization's analytics are motivated by the key decisions and challenges facing managers.