The familiar maps of software market dominance are proving utterly useless in the turbulent, uncharted territory of artificial intelligence, where established strategies lead not to victory but to obsolescence. For decades, the Software-as-a-Service (SaaS) playbook offered a clear path: identify a market, build a defensible product, establish predictable revenue, and construct a moat to ward off competitors. This model created titans. However, applying this same logic to the current AI landscape is like navigating a new continent with an old-world map. The ground is shifting too quickly, the landmarks are ephemeral, and the very concept of a stable market is a mirage. What we are witnessing is not a collection of new software markets, but rather the chaotic, formative stage of “proto-markets”—evolutionary precursors that demand a fundamentally different approach to innovation and strategy.
The New Frontier: Charting the Unsettled AI Landscape
The current AI industry is best understood as a proto-market, a precursor to a mature ecosystem where the forces of product selection have not yet taken hold. This environment is characterized by intense demand and significant revenue generation, yet it lacks the structural integrity of established software categories. Where traditional SaaS offers well-defined segments like CRM or ERP, the AI space is a crowded and chaotic confluence of overlapping solutions, with dozens of credible competitors vying for dominance in emerging fields with little clear differentiation.
This competitive frenzy includes a mix of nimble startups and established incumbents, all building on a technological foundation that is in a constant state of flux. Unlike the stable APIs and platforms of the cloud era, the core technology of this new wave—the foundation model—is itself a moving target. New model releases can redefine what is possible overnight, creating opportunities while simultaneously rendering existing product architectures obsolete.
The primary destabilizing force is the inherent generalizability of foundation models. These powerful systems blur the lines that once neatly separated software categories. A model proficient in language can be applied to customer support, legal contract analysis, or code generation with relatively minor adjustments. This technological reality dissolves traditional software boundaries, forcing a strategic rethink for every company building in the space.
Decoding the Proto-Market: Key Trends and Economic Realities
The Five Foundational Shifts Redefining Product and Competition
The general-purpose nature of AI is collapsing the distinctions that once structured the software industry. The lines between enterprise and consumer tools are dissolving as powerful applications find audiences in both domains. Similarly, the separation of horizontal platforms from vertical-specific solutions is becoming increasingly meaningless when a single underlying technology can be adapted to serve countless niches. A customer support agent can evolve into a specialized healthcare coordinator, demonstrating a fluidity that was unimaginable in the SaaS era.
This dynamic is compounded by the fact that AI products are never truly “done.” The relentless pace of improvement in underlying foundation models means that a stable state of product completion is an illusion. An application architecture considered state-of-the-art one month can be rendered inefficient or obsolete by a new model release the next. Teams are forced into a state of perpetual re-engineering, rebuilding core frameworks multiple times not to add features, but simply to keep pace with the foundational technology.
Consequently, competition in this greenfield environment is not primarily between rival AI vendors. Instead, the main battle is against the status quo—the existing, non-AI workflows that companies and individuals currently use. The dynamic is one of “something versus nothing,” where the goal is to convince users to adopt a new AI-powered method over their established habits. This explains why niche verticals can support a host of fast-growing startups, as each is carving out a fresh piece of territory rather than fighting for scraps in a mature market.
The Unstable Economics of a Greenfield Opportunity
A significant feature of this proto-market is the profound disequilibrium in its unit economics. The distribution of value between end-users, application developers, and foundation model providers has yet to find a stable balance. Many of the most promising AI applications, particularly in code generation and voice AI, are currently priced at or below their operational cost, making their current financial performance an unreliable indicator of long-term viability.
In this context, bottoms-up adoption, often termed Product-Led Growth (PLG), has transformed from a go-to-market tactic into a mandatory learning mechanism. Before established buyers, budgets, and formal requirements materialize, companies must release products into the wild to discover what users actually want and need. Observing how individuals and small teams use and adapt these tools provides the essential data needed to navigate the fog of uncertainty. This approach has allowed products to achieve massive scale rapidly, though this early adoption often lacks the deep organizational stickiness of traditional enterprise sales.
Projecting the future market structure requires acknowledging these unstable variables. It is widely expected that the cost of model inference, or token prices, will continue to fall dramatically, which will fundamentally reshape the economic landscape. As costs decrease and companies get smarter about value capture, a more stable and predictable market will gradually emerge. For now, however, forecasting remains an exercise in informed speculation.
When Old Maps Lead Astray: Why SaaS Strategies Are Counterproductive
Attempting to execute the classic SaaS playbook in such a volatile environment is not just ineffective; it is actively counterproductive. The core tenets of that strategy—building durable moats, securing healthy margins, and establishing clear market leadership—all presuppose a degree of stability that simply does not exist in the AI proto-market. Resources spent on these goals are often wasted.
Efforts to create durable differentiation through proprietary technology or complex architectures have consistently proven futile. Companies that have invested heavily in building their own foundation models, creating domain-specific fine-tuning, or engineering sophisticated agentic systems have found their advantages quickly eroded by the next major release from a foundational model provider. The technological ground shifts too fast for any single application-layer company to build a lasting defense on technology alone.
This instability makes long-term strategic planning an almost impossible task. In a mature market, a company can develop a multi-year roadmap based on a relatively stable understanding of customer needs, product capabilities, and the economic model. In the AI proto-market, all three of these variables are in constant flux, making rigid, long-range plans a liability rather than an asset.
Navigating a Fog of Rules: The Unwritten Laws of AI Innovation
Adding another layer of complexity to this already uncertain landscape is the nascent and ambiguous regulatory environment. Critical questions surrounding data privacy, intellectual property rights, and algorithmic bias remain largely unanswered by legislators and regulators. This legal fog introduces significant risk, as the rules governing the creation and deployment of AI technologies are still being written.
This regulatory uncertainty makes the kind of meticulous, long-term planning characteristic of mature SaaS companies untenable. A SaaS business can build its compliance and security features around a well-established set of standards. An AI company, in contrast, must operate with the knowledge that new regulations could emerge at any time, potentially requiring significant product redesigns or altering the fundamental economics of their business model.
In this environment, compliance must be viewed as an evolving target rather than a fixed set of requirements. The most successful companies will be those that build with flexibility in mind, ready to adapt to a changing legal landscape. A rigid adherence to existing standards is a fragile strategy when those standards themselves are subject to rapid and unpredictable change.
The Path Forward: Embracing Product Plasticity and Learning Velocity
The winning strategy in the AI proto-market is not one of fortification but of adaptation. Success hinges on two key principles: product plasticity, the ability of a product to be molded and changed, and learning velocity, the speed at which a company can translate user behavior into product improvements. The goal is to move from a prescriptive development model to an emergent one.
This approach involves building products that are “hackable by design.” Rather than attempting to pre-define every feature and workflow, builders should create the simplest possible functional version of their tool and then meticulously observe how users interact with it. The most valuable insights come from watching how users bend, break, and extend the product to solve their own unique problems.
This method allows development to be user-driven rather than dictated from the top down. By observing the organic behaviors and “hacks” that emerge from early adopters, teams can identify the most valuable patterns of use. Formalizing these user-discovered solutions into core product features creates a powerful feedback loop, allowing the product to evolve in directions that are directly aligned with real-world needs. This iterative process transforms a simple tool into a robust, domain-agnostic platform.
Forging the New Moat: From Building Products to Discovering Markets
The shift from the SaaS era to the AI era required a complete re-evaluation of how competitive advantage was built. The paradigm had moved from a prescriptive, top-down model of product development to an emergent, bottom-up process of market discovery. In this new world, durable advantages were not engineered from a blueprint; they were discovered through experimentation and rapid learning.
Consequently, the very definition of a “moat” was transformed. In these proto-markets, markets emerged before moats did. The traditional defenses of proprietary technology or network effects were ineffective on such shifting ground. Instead, the most formidable and lasting moat became the accumulated and structural understanding of what users truly needed and how they behaved to achieve their goals.
The most forward-thinking builders understood that every user interaction was raw material for this new kind of moat. They had to create systems that did more than just serve a function; they had to capture and encode user learnings into the very scaffold of the product. By embedding this deep, path-dependent knowledge into the product’s core workflows and constraints, they created a structural advantage that became a powerful barrier to entry over time.
