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.

The billionaire founder of Sun Microsystems Vinod Khosla talks to Creative Destruction Lab Founder Ajay Agrawal about the AI-generated opportunities (and dangers) that lie ahead. In particular, he points to the potential of AI to increase income disparity to dangerous levels that could lead to societal unrest. He also argues that lower skilled jobs like truck driver and restaurant worker will likely be fine, but that higher skilled knowledge-based jobs like radiologists are in trouble. On the bright side, AI could eradicate unsatisfying work; we are at the beginning of a continuous 'hypercycle' of innovation; and the future is shapeable: Technology doesn't rule; it serves. We get to decide on its goals.

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 authors argue that a thriving environment for innovation contains eight characteristics, and that assessing them properly sets the stage for a start-up to flourish. After describing the eight characteristics, they use them to compare Toronto with Silicon Valley as a location for start-ups to thrive. In the end, they show that ignoring the eight factors may lead founders to pick the wrong location, resulting in difficulty attracting the necessary funding, talent, suppliers, partnerships and customers.

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.

For most people, it is not easy to picture the buying and selling of cognitive capabilities that have traditionally been embedded in humans-things like judgment and decision-making. Yet, thanks to recent advances in machine learning, the author argues that precisely such a 'market for intelligence' is on the horizon. Given the potential of this market to transform the entire global economy, he argues, leaders and organizations must begin preparing for its emergence-now.

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.

D-Wave Systems is a start-up seeking to commercialize a quantum computer. Its business model is unique: as of 2003, it had very few technical resources within the firm. Instead, it financed a series of projects undertaken at universities and government labs. In return for partial funding, these organizations gave D-Wave the ownership of--or exclusive rights to--intellectual property developed in the project. Geordie Rose, CEO of D-Wave, wonders how long this model is appropriate in contrast to the alternative of centralizing the research in an in-house facility, with all the costs this would incur.