Marketplaces are the quintessential type of business that can profit from network effects: The greater the number of buyers who join one, the more attractive it becomes to sellers, and vice versa. Indeed, marketplaces such as Amazon, Booking.com, and Apple's App Store have achieved some of the strongest competitive positions imaginable. That's why entrepreneurs are seeking to build, and venture capitalists are seeking to invest in, the next Airbnb, Uber, or Twitch. But not all marketplaces have the potential to realize strong network effects-the kind that make a marketplace defensible against wannabe competitors. Differentiation among sellers, fragmentation of the seller and buyer bases, the value added by discovery and transaction services, and the importance of seller ratings are all decisive in determining whether a marketplace can flourish. That's why it is extremely important, both for new and established companies trying to build marketplaces and for their potential investors, to dig deeper into those factors. Drawing on their more than two decades' worth of research and their own experience as angel investors in some 40 marketplace start-ups, the authors offer a comprehensive list of questions that anyone thinking of launching a marketplace should explore.

By making it vastly easier and cheaper to improve or create products and services that previously required significant human labor and creativity, generative AI has the potential to disrupt or even commoditize many businesses. Some companies will be able to gain an edge by leveraging publicly available generative AI tools better or faster than their competitors. But that advantage will be only temporary, and using the tools will soon become table stakes. This means established companies will have to rethink their business strategies and find new ways to add value to their offerings. In this article, the authors consider which types of businesses have the greatest potential to gain competitive advantage from generative AI and which are most likely to be disrupted. They also offer guidance for implementing AI across three levels of sophistication that correspond to an increasingly powerful advantage.

Large digital multisided platforms (MSPs) such as Amazon, Alibaba, and Apple's App Store have made it much easier for sellers to reach new customers, but as thousands of companies large and small have discovered, conducting business on them carries significant risks and costs. MSPs sometimes exploit sellers' dependency on them in various subtle and not-so-subtle ways. They raise fees. They change their recommendation algorithms to put more emphasis on price. They require sellers to advertise to maintain visibility in search results. They compete against sellers by imitating their products. They impose restrictions on the prices sellers can set outside of the MSP. And they change their rules and design in ways that weaken sellers' relationships with their customers. But all is not lost, say the authors. Sellers can employ four strategies to build viable businesses on platforms. They can develop and invest in direct channels, use platforms mainly as showrooms, go deep with highly specialized offerings or go broad with many different offerings, and wage public relations and lobbying campaigns to curb platforms' power.

Many executives assume that customer data can give you an unbeatable competitive edge. The more customers you have, the more data you can gather, and that data, when analyzed, allows you to offer a better product that attracts more customers. You can then collect even more data, repeating the cycle until you eventually marginalize your competitors. But this thinking is usually wrong. Though the virtuous cycles of data-enabled learning may look similar to those of network effects--wherein an offering increases in value to users as more people adopt it and ultimately garners a critical mass of users that shuts out competitors--they are not as powerful or as enduring. Nevertheless, under the right conditions, customer data can help build competitive defenses. It all depends on whether the data offers high and lasting value, is proprietary, leads to improvements that can't be easily imitated, or generates insights that can be quickly incorporated. Those characteristics do give firms an advantage. And when learning from one customer rapidly improves an offering for others (think Google Maps), people will care about how many other people are adopting it, and it will enjoy those sought-after network effects.

Lured by the success of marketplaces such as eBay, many companies have tried operating as multisided platforms, which let buyers and sellers transact directly with one another. But resellers--which acquire and then resell products and services--often fare better. To determine the right position on the continuum between pure reseller and pure multisided platform, companies must consider four factors: 1) Scale effects. Amazon draws on its formidable scale economies as a reseller for high-demand items but serves as a multisided platform for low-demand products. 2) Aggregation effects. Resellers can extract value from buyers by bundling products and exploiting complementary relationships between them, as Apple has with its iTunes-iPod combination. 3) The buyer and seller experiences. As Zappos realized in its early days as a multisided platform, some buyers do not want to deal with multiple sellers. And individual sellers might have a better experience selling to a reseller than to a buyer in a marketplace. 4) Market failures. Many multisided platforms have avoided collapse by using mechanisms that keep buyers and sellers honest. It can take more than one move for a company to reach its optimal position: Companies that should ultimately be multisided platforms sometimes need to start out as resellers and vice versa. And as the competitive landscape changes, managers must be diligent about reevaluating their position.