The current debate on AI is too focused on internal efficiency gains. It misses the far greater opportunity for value creation that happens outside the firm. The old model was about transactions; the new one is about interactions. The real power of generative AI is its ability to amplify network effects, learning, and virality across an entire ecosystem. To grasp the economics of generative AI, we must see it not as a factory tool for lowering costs, but as the engine of an interaction field where shared value is created for businesses, customers, and society at large.
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.
Is Generative AI Just More Tech Hype?
The economists argue that, 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 Economics of Generative AI: Who Really Wins?
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:
- Increase productivity, this is the story of the technology-cost-price effect.
- Improve entire processes that create value inside or outside a firm.
- 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.
The Human Element: AI’s Impact on Workforce and Productivity
The conversation around AI often defaults to a simple, zero-sum game of replacement. This view, however, misses the far more interesting and strategic reality of augmentation. Instead of just removing costs, generative AI is reshaping the very nature of productivity and human contribution. It’s not about a linear path to efficiency; it’s about creating a new dynamic where technology amplifies human capability, leading to a different kind of growth—one that is more qualitative, creative, and ultimately, more valuable. This requires a fundamental shift in how leaders think about technology integration, moving from a mindset of substitution to one of symbiosis.
Beyond Replacement: A New Productivity Curve
The true impact of generative AI on an organization won’t appear overnight. It follows what economists call a “Productivity J-Curve.” This model challenges the expectation of immediate returns from technology investment. Instead, it shows that productivity often dips before it soars. Why? Because meaningful adoption isn’t about simply plugging in a new tool. It requires a deeper business transformation: redesigning workflows, retraining teams, and shifting the organizational culture to embrace new ways of working. This initial phase of investment and adaptation is crucial. Companies that rush this process in search of quick wins will likely be disappointed, while those that strategically manage this curve will be positioned to capture exponential gains on the other side.
The Productivity J-Curve
Think of the J-curve like learning a powerful new software. Initially, your output slows down as you grapple with the interface and new features, temporarily making you less efficient than you were with the old system. But once you master it, your capabilities expand dramatically, and your productivity skyrockets far beyond previous levels. Research from the National Bureau of Economic Research highlights this exact pattern, explaining that companies must first invest time and money to adapt to AI. This initial dip is the cost of entry for the steep, upward trajectory of growth that follows, redefining what’s possible for the entire organization.
Improving the Work Experience
Beyond pure output, AI is also enhancing the quality of work itself. It’s not just automating tedious tasks; it’s creating a more supportive and humane work environment. For instance, one study found that when customer support agents were assisted by AI, customers became kinder and interactions were more positive. In another striking example, AI chatbots provided answers to patient questions that were not only more detailed and of higher quality but also ten times more empathetic than those given by human doctors. This suggests AI’s greatest strength may be in handling the analytical load, freeing up humans to focus on what they do best: empathy, strategic thinking, and building relationships.
Closing the Skill Gap
Perhaps one of the most profound impacts of generative AI is its potential to democratize expertise. Unlike past technologies that often widened the gap between high-skilled and low-skilled workers, AI appears to be a great equalizer. The most significant productivity gains—up to 35% in some studies—are seen among the newest and lowest-performing employees. The top performers, who already operate at a high level, see little change. This is a game-changer for talent development and organizational strategy. Instead of focusing solely on recruiting elite talent, leaders can now use AI to elevate the capabilities of their entire workforce, turning novice employees into highly competent contributors almost overnight.
This dynamic presents a massive opportunity. By equipping their teams with AI co-pilots, companies can create a more resilient, capable, and agile workforce. This isn’t just about improving individual performance; it’s about building a stronger, more adaptable organization from the ground up. Some economists even suggest that by empowering workers with lower or middle-level skills, generative AI could play a role in rebuilding the middle class. For business leaders, the message is clear: the future of competitive advantage lies not in replacing people, but in strategically augmenting them to create unprecedented value.
From Transactions to Interactions: AI’s Real Power
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.
Generative AI in Action: From Efficiency to Intelligence
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.### Amplifying the Knowledge Worker The conversation around AI and jobs is stuck in a tired loop of replacement. This is a failure of imagination. The real story is not about substitution, but about augmentation. Generative AI acts as a co-pilot for knowledge workers, absorbing the low-value, repetitive tasks that drain cognitive energy and freeing human talent to focus on strategy, creativity, and complex problem-solving. A study from the National Bureau of Economic Research found that customer support agents using an AI assistant resolved 14% more issues per hour. This isn’t just an efficiency gain; it’s a fundamental shift in the nature of work, transforming roles from task-doers to strategic thinkers who can deliver higher-value outcomes. ### AI as an Engine for Learning and Development The productivity gains from AI aren’t a one-time event; they compound over time by creating a more capable workforce. The same NBER study revealed a fascinating secondary effect: workers who used the AI assistant continued to perform better even after the tool was removed. They were learning from the machine. This positions AI as a powerful, real-time coaching tool that is integrated directly into daily workflows. It surfaces best practices, offers immediate feedback, and helps employees master new skills on the job. This moves corporate training from a periodic, off-site event to a continuous, organic process, building an organization that learns and adapts at the speed of the market. ### The Empathy Algorithm: AI’s Surprising Human Touch Perhaps the most counterintuitive impact of AI is its capacity for empathy at scale. We assume that human connection is exclusively the domain of humans, but the data suggests otherwise. Research found that AI chatbots were ten times more likely to be rated as empathetic compared to doctors’ written responses to patient questions. Why? An AI can offer consistently patient, non-judgmental, and comprehensive answers drawn from a vast dataset of best-practice communication, free from the fatigue or time pressure a human expert might face. This doesn’t replace the need for human care, but it shows how technology can extend and support it, challenging our core assumptions about the boundaries between machine intelligence and human connection.
The Strategic Realities: Navigating the Financial Landscape of AI
Beyond the human impact, leaders must confront the hard economics of AI implementation. The hype surrounding generative AI often obscures the significant strategic and financial questions that determine success or failure. This is not a plug-and-play technology; it requires a clear-eyed assessment of costs, business models, and long-term dependencies. Moving from pilot projects to enterprise-wide value creation demands a rigorous business strategy that balances ambition with pragmatism. ### The High Cost of Intelligence Generative AI is not cheap. As noted in Towards Data Science, the models are incredibly expensive to train and operate, requiring immense computational power and specialized talent. This reality creates a significant barrier to entry and demands a sophisticated approach to ROI. The investment cannot be justified by simple cost-cutting alone. Instead, leaders must calculate the value generated from second-order effects: faster innovation cycles, improved customer experiences, and the creation of new revenue streams. The financial commitment is substantial, and the business case must be equally robust. ### Feature vs. Product: The Core Business Dilemma A critical strategic decision is whether to treat generative AI as a feature or a product. Is it an enhancement that makes your existing offerings smarter and more efficient, or is it a new, standalone value proposition? This choice has profound implications for your business model, go-to-market strategy, and competitive positioning. Integrating AI as a feature can deepen your moat and increase customer loyalty. Building a new AI-native product can open entirely new markets. There is no single right answer, but failing to make a conscious choice is a direct path to misaligned investment and diluted impact. ### The Data Horizon: Potential Limits to AI Growth The assumption of infinite AI improvement is facing a potential bottleneck: data. Some experts believe that the rapid advancements in large language models may slow as we exhaust the supply of unique, high-quality human-generated data available on the public internet. This shifts the strategic focus inward. A company’s proprietary data—its unique customer interactions, operational knowledge, and market insights—becomes its most valuable asset for training smaller, specialized AI models. The future of competitive advantage may not lie in accessing the biggest models, but in cultivating the most valuable and unique data sets.
Beyond the Balance Sheet: Broader Economic and Societal Shifts
The impact of AI extends far beyond the walls of any single organization. It is reshaping markets, challenging traditional economic metrics, and creating a new regulatory landscape. Leaders who only focus on internal implementation risk being blindsided by macro forces that will ultimately define the environment in which they compete. Understanding this broader context is no longer optional; it is a core component of modern strategic leadership. ### Measuring What Matters: The Challenge of Valuing AI Traditional economic yardsticks are failing to capture the true value of AI. As the National Bureau of Economic Research points out, metrics like GDP struggle to account for benefits like improved product quality or the value of free digital goods powered by AI. This measurement problem exists at the firm level, too. How do you quantify the value of a more innovative culture or a frictionless customer experience? Companies must develop new KPIs that move beyond transactional efficiency to measure engagement, learning, and the strength of the interactions AI facilitates across their entire ecosystem. ### The Need for a New Playbook: Policy and Regulation The rapid rise of AI is triggering an inevitable response from policymakers. We are entering an era where crucial decisions will be made about AI’s role in the labor market, its effect on competition, and the ethical guardrails required to ensure fairness. These emerging regulations are not just a compliance hurdle; they are a strategic variable. Companies that proactively develop a point of view on responsible AI and engage with the policy debate will be better positioned to build trust with customers and shape the future of their industries. ### Who Steers the Ship? The Influence of Private AI Funding The immense cost of foundational AI research has concentrated power in the hands of a few large technology corporations. As Towards Data Science highlights, universities and public institutions lack the resources to compete at the cutting edge, meaning the AI agenda is largely being set by private, commercial interests. For every other organization, this creates a complex ecosystem to manage. It forces critical decisions about which platforms to build on, where to partner, and how to maintain strategic independence in a world dominated by a handful of powerful AI gatekeepers.
Why Consumers Win with AI—And Your Business Does, Too
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. 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.
Frequently Asked Questions
Is the main benefit of AI really just cutting costs and replacing jobs? Not at all. While efficiency gains are an obvious starting point, focusing only on cost-cutting is a failure of imagination. The real strategic value comes from augmenting your team’s capabilities, not just automating their tasks. Think of AI as a co-pilot that handles the repetitive work, freeing up your people to focus on complex problem-solving, creativity, and building client relationships—the very things that create lasting value.
My team is starting to use AI, but things seem slower, not faster. Is this normal? Yes, that’s completely normal and even expected. It’s a phenomenon called the “Productivity J-Curve.” Just like learning any powerful new skill, there’s an initial dip in output as your team adapts to new workflows and tools. This phase is a necessary investment. Pushing through it is what separates companies that see incremental gains from those that achieve exponential growth in capability and innovation on the other side.
How does AI change the way we should think about talent and team development? It fundamentally shifts the focus from simply hiring top performers to elevating the entire workforce. AI acts as a great equalizer, dramatically accelerating the learning curve for new or junior employees. The most significant productivity gains are often seen in this group. This means you can build a more capable, resilient, and agile organization from the ground up by using AI as a real-time coach that scales expertise across your teams.
You talk about moving from “transactions” to “interactions.” What does that actually mean for a business? It means shifting your business model from simply selling a product or service to facilitating a valuable exchange of information across your entire ecosystem. For example, instead of just selling a tractor, a company like John Deere uses AI to create a network where farmers, dealers, and software providers share data to optimize everything from planting to profitability. The value is no longer in the single transaction but in the collective intelligence of the network that AI helps create and amplify.
What’s the biggest strategic pitfall I should watch out for when investing in AI? The biggest mistake is treating AI as a simple tech upgrade without a clear business strategy. You must decide if AI is an internal feature to make your current offerings better or if it’s the foundation for a new, standalone product. These are two very different paths with huge implications for your budget, talent, and go-to-market plan. Lacking this clarity leads to misaligned investments and a failure to capture real, sustainable value.
Key Takeaways
- Look beyond internal efficiency for AI’s true value: The biggest wins aren’t found in simple cost-cutting. Instead, focus on creating shared value across your entire ecosystem through network effects, customer learning, and industry-wide collaboration.
- Treat AI as a talent investment, not a headcount reduction tool: The greatest productivity gains come from augmenting your existing team. Use AI to close skill gaps, accelerate on-the-job learning, and free your people to focus on high-value strategic work.
- Move from AI experiments to an integrated business strategy: To get real returns, you must address the high costs, make a clear choice between AI as a feature or a new product, and recognize that your proprietary data is your most critical asset for building a long-term competitive advantage.
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