The Real Winners of Generative AI Will be Consumers

I recently read?a fantastic article about the impact of generative AI on companies and consumers, in which three economists associated with BCG argued that the actual beneficiaries of generative AI will generally not be companies—save for a few that will reap the pure economic benefits of lower costs.

The title says it all: “Why we need to be realistic about generative AI’s economic impact.” It is a welcomed point of view, a sorely-needed voice of reason to balance out the enormous hype around generative AI, and to the technology industry that cannot help but relentlessly promote the shiny new thing on the block.

The economists argue that:

1) to understand technology’s impact on business and consumers, it is important to first understand the technology-cost-price effect;

technology only has a significant impact if it replaces labor, because what technology does is bring down costs, which in turn allows a company or brand to offer lower prices and take market share from higher-cost competitors;

The reality is technology often has not delivered. Instead, new technologies are often hyped up as innovative, new, or better products and services. We often speak about companies or brands such as Uber, Lyft, and Grab as disruptors with an app, or with a new business model, like a platform business model. But, in reality, these companies and their application of technologies have not replaced labor (not yet, anyway), and haven’t changed much at all, since prices for rides also have not fallen.

Given that technology’s impact on productivity growth has been consistently overstated, what then would generative AI realistically be able to achieve?

The Real Impact of Generative AI

Generative AI is heralded as the technology that will truly deliver on the technology-cost-price effect, according to the economists. How? By removing costs associated with jobs ranging from call centers to marketing, to advertising, to research and design.

This translates into lower prices for brands, products, and services for consumers—which increases their discretionary incomes, filling their pockets with cash they can then use elsewhere, such as shopping or traveling. As every first-year economics undergraduate would know, this has a multiplier effect on the economy.

However, my view is that the economists view is short-sighted. There are at least three opportunities for value creation: 1) increase productivity, this is the story of the technology-cost-price effect; 2) improve entire processes that create value inside or outside a firm; and 3) engender entirely new business models (such as an interaction field model) to create value in a much larger system, I call an interaction field where value is created outside the firm altogether. Geoffrey Parker and Marshall van Alstyne call this the inverted firm.

We are already seeing how generative AI impacts basic productivity improvement, the press reports on them ad nauseum, but the effects don’t end there. The scenario that these economists describe, while true, fails to capture the second and third layer of value creation which follows from network effects, learning effects, and virality. What they’re missing is that generative AI has much more impact beyond lowering labor cost.

As I detailed in my 2020 book, The Interaction Field: The Revolutionary New Way to Create Shared Value for Businesses, Customers, and Society, the interaction field company is intentionally organized to generate, facilitate, and benefit from interactions rather than transactions. These interactions generate network effects, viral effects and learning effects.

Through the communication, engagement, and exchange of information among multiple people and groups—from partners, suppliers, developers, and analysts, to regulators, researchers, and even competitors—interactions between the company and its customers are amplified, building velocity to improve an entire industry, or even solve larger social problems. Such interactions differ from transactions that don’t always focus on just one outcome (i.e., someone buying what someone else is selling). This is where generative AI really creates value.

Take, for example, John Deere, which is revolutionizing the U.S. farming industry with tractors that include modems, Wi-Fi, and Bluetooth to not just collect data from the farm from soil conditions or plant health in the field to the cloud, but also delivers instructions and information from Deere, dealers, and software providers to the farm to optimize overall farm productivity and even profitability.

Minute aspects of farm management have already been transformed, starting with planting. Today’s farmers are able to control the depth of the seed in the soil so it achieves the best “emergence” (the timing of its breakthrough from the soil); they can also use existing technology to achieve the optimal amount of contact each seed has with the surrounding soil by calculating the ideal number of droplets of “input” (water, nutrients, fertilizers, or herbicides) per seed.

Currently, this is the efficiency that harnessing raw data can achieve, that AI powers and that translates into the effect the economists speak about, less labor needed to tend to the field. But generative AI can allow farmers to go beyond this; it can allow them to identify what data are most relevant, or least relevant, thereby making the interactions not merely more efficient, but also more intelligent.

Then there are the network effects and learning effects: Farmers can work together to analyze the data with generative AI and develop solutions for how to optimize other farm processes that aren’t geared toward replacing labor. Because it isn’t just the replacement of labor that creates value; it’s the leveraging of the well-known phenomenon of a product or service gaining additional value as more people use it. Imagine the scale of the impact of millions of farmers across the globe learning from one another and contributing to one another’s communities and economies. It sure beats a half dozen of them from the same town talking amongst themselves.

My point is, generative AI has an impact on the interactions themselves.

Where I concur with the economists though is that the real winners of generative AI will be consumers. But, when consumers win, brands and businesses do, too. In this way, generative AI is likely a win-win for everyone.

Sources:

Philipp Carlsson-Szlezak, Paul Swartz and Francois Candelon, “Why We Need to be Realistic About Generative AI’s Economic Impact,” World Economic Forum.

https://www.weforum.org/agenda/2023/08/generative-ai-realistic-economic-impact/

Erich Joachimsthaler (2020), The Interaction Field: The Revolutionary New Way to Create Shared Value for Businesses, Customers, and Society.

Geoffrey Parker and Marshall van Alstyne and Xiaoyue Jiang, 2016, “Platform Ecosystems: How Developers Invert the Firm,” August 17. https://ide.mit.edu/wp-content/uploads/2017/05/Platform-Ecosystems-How-Developers-Invert-the-Frim.pdf