AI, and Commoditizing the Complement

AI, and Commoditizing the Complement. Source: DALL-E.

Why AI is Like Electricity

Artificial Intelligence reminds me of electricity. They are both enabling technologies. Thomas Edison is often credited with inventing the incandescent light bulb in 1879, and the expectation was that electricity would offer a competitive advantage. It did at first. But as the reach of power infrastructure grew, more companies saw the benefits of automation, and harnessing electricity became table stakes. Electric power allowed businesses to increase efficiency, productivity, and overall operational capabilities. The manual labor of humans and horses couldn’t compete. By the 1950s, the vast majority of businesses in the United States had access to electricity, and it had become a standard utility rather than a competitive advantage.

As electricity became commonplace, the competitive advantage shifted from simply having access to electricity to how efficiently and effectively businesses could use it. This led to an ongoing focus on innovation, energy efficiency, and the adoption of new technologies that relied on electrical power. The competitive landscape evolved, but the importance of electricity in driving business success remained crucial.

Despite similarities, there are key differences between AIs and utilities. Utilities tend to operate as “natural monopolies” where it is often more efficient for a single company to serve an entire region, rather than having multiple competing firms with duplicate infrastructure. Utilities require land on which to generate power, and a physical grid to transmit and distribute power. It’s uneconomic to build competing utilities and grids delivering power in a particular geography. That’s why utilities often enjoy monopoly power in a specific geography and are regulated as such.

Unlike utilities, cloud-based AIs will face unconstrained competition as they vie for the attention of consumers and businesses. There will be fierce competition among the largest tech companies and startups, and proprietary data will become increasingly important for differentiation. More than compute and storage, data will become the most valuable, scarcest resource. Over time, competition among AIs will largely make the underlying foundation models a commodity, albeit a crucially important product not unlike cloud compute and storage services offered by Amazon, Microsoft, Google, Digital Ocean, and others.

Where Will Value be Created in AI?

With the understanding of these competitive dynamics, where will value be created in AI? At the application level, opportunities are likely to take two major forms: 1/ Task replacement, 2/ Net-new stuff. Task replacement is AI automating tasks currently performed by humans. AI is going to “eat” big chunks of what many skilled people do, and it’s going to drive the costs of those tasks down to almost zero. This will commoditize a large percentage of the value created by skilled people like lawyers, accountants, doctors, engineers, architects, and others. Task-replacement AIs in the short- to medium term will be able to charge for their services – AIs cost less than humans but deliver faster and higher-quality output. However, because AIs are able to deliver high-quality output at almost zero marginal cost, competition will drive prices down towards marginal cost.

By commoditizing significant parts of what professionals do in a highly scalable and high-quality way, AI turbocharges the digital playbook of “commoditizing the complement” [1]. If your industry is going to be commoditized by AI, it will be critical that you identify your profit-generating complement. Commoditizing the complement is a strategy in which a company drives down the price (or makes it free) of a complementary product or service that enhances the value of its core product. This approach can increase demand for the core product while reducing the profitability of competitors that rely on the complementary product as their primary source of revenue. Here are some examples of software companies employing this strategy:

  • Google: Google commoditized various software products and services by offering them for free or at low cost, effectively undercutting competitors that relied on those products for revenue. Examples include Google Search, which commoditized online search advertising; Google Maps, which commoditized mapping and geolocation services; and Google Drive, which commoditized cloud storage.
  • Adobe: Adobe created the PDF format and distributed it for free, making it a widely used standard for document sharing. The adoption of the PDF standard ultimately increased demand for Adobe’s core products, such as Acrobat, Illustrator, and Photoshop.
  • Apple: Apple’s App Store commoditized mobile apps by creating a centralized marketplace where developers could sell their apps at relatively low prices. This strategy increased the demand for Apple’s core product, the iPhone, by making it more valuable due to the vast array of affordable apps available to users.

…the defensibility of businesses that commoditize the complement will primarily come from the proprietary data that they generate and then use to train and further improve the AI on which their product or service relies.

With AI, products and services that had been accessible only to more affluent consumers due to the high labor cost of providing those products and services will now be accessible to everyone through automation. What might be commoditized and what are the potential complements? I’m really interested in this question, and if you’re implementing this strategy I’d love to hear from you. An example of this strategy applied to fintech might be offering a free AI-powered software service (the commoditized complement, traditionally provided by humans or legacy software), that generates revenue through payments, payroll, insurance brokerage, marketplace, premium software services, etc (the complement). As these models mature, the defensibility of businesses that commoditize the complement will primarily come from the proprietary data that they generate and then use to train and further improve the AI on which their product or service relies.

In the category of totally new stuff, there will be net-new products and services that weren’t possible before AI. In particular, interdisciplinary collaboration seems like a promising way to develop AIs to address climate change, healthcare, education, and other pressing needs. These net-new products and services won’t have close substitutes, and will be priced on the value they deliver. They may also have their own commoditized complements.

Still the emergence of powerful AIs brings many risks and unanswered questions around data collection, use, and security, along with important ethics, bias, and privacy concerns that are beyond the scope of this post. These issues will need to be understood and addressed alongside the enormous potential benefits of this new technology. The long-time promise of AI seems to be on the cusp of fulfillment. As AI commoditizes the complement, high-quality products, services, and advice that otherwise came with significant costs, will be made available at low- or no cost to billions of people, and that will be a good thing.

[1] Joel Spolsky, “Strategy Letter V,” Joel on Software, June 12, 2002,

Note: I used OpenAI’s ChatGPT-4 Mar 23 Version for researching and editing this post.