LLM Market Structure 2025: Natural Monopoly vs. Competitive Landscape Analysis

In recent AI industry discourse, there’s been growing speculation that “the LLM market may evolve into a natural monopoly,” similar to what happened with Google in the search engine space. A notable example of this line of thinking is AI’s Endgame by James Wang of Creative Ventures.

Introduction: Why Some Believe the LLM Market Could Mirror Google Search

A “natural monopoly” refers to a market structure where, even without regulatory intervention, competition gives way to dominance by a single firm due to inherent economic forces. While compelling, this remains a hypothesis and not a confirmed reality.

To assess whether the LLM market is on this trajectory, we need to examine the economic conditions typically required for a natural monopoly to emerge.

Traditionally, three key conditions must be met for a natural monopoly to occur:

1. Very high fixed costs,

2. Extremely low marginal (variable) costs,

3. A limited market demand.

In the digital market, an additional driver called ‘network effects’ can significantly reinforce monopolistic structures.

Consider Google Search: although market demand is not inherently limited, the more users it has, the more data it collects, and the more accurate its results become. This data-driven feedback loop, in turn, creates a virtuous cycle that attracts even more users.

So, is the LLM market destined to follow a similar path?

At first glance, the LLM landscape does seem capital-intensive and resource-heavy. However, some argue that new entrants still have room to compete—and that competition may further intensify as multimodal capabilities expand.

In this piece, we’ll explore whether the LLM market will converge into a natural monopoly—or evolve into an oligopoly or distributed market structure—by examining the following four factors:

1. Fixed costs

2. Variable costs

3. Market segmentation and modality expansion

4. Network effects

This discussion goes beyond merely mapping out the technical architecture and aims to anticipate how the competitive landscape of the LLM market will evolve.

For investors, it provides a critical indicator of market power and enterprise value. For policymakers and regulatory bodies, it raises important questions when evaluating the potential monopolistic tendencies of platform giants like Google and Microsoft.

High Fixed Costs in LLM Development: Multi-Billion Dollar Entry Barriers

The LLM industry undeniably demands enormous upfront fixed costs. Training trillion-parameter-scale foundation models requires billions of dollars’ worth of compute resources, high-quality data, and specialized talent—capabilities currently limited to a handful of Big Tech firms such as OpenAI, Google, Microsoft, and NVIDIA.

However, the recent emergence of Chinese challengers like DeepSeek—who have released competitive 7B-parameter models at relatively lower cost—has raised questions about whether these fixed-cost barriers are truly absolute.

An important point to note here is that fixed costs in the LLM industry are not on an endlessly upward trajectory. Industry experts predict that we will soon reach what’s being called the ‘trillion cap’—the practical limit of high-quality internet data available for training.

In other words, there’s a ceiling to the volume of data and compute resources that can meaningfully improve model performance. As a result, the level of fixed investment required to build frontier-scale models is also likely to plateau - meet a ‘cap’ - at some point.

On this issue, market opinions are divided. Some believe that these natural limits could lower the barrier for latecomers—opening up a path for them to catch up, as long as they can secure similar levels of fixed investments. In this view, the barrier is high, but not insurmountable.

Others warn that while some latecomers may catch up with 7B-scale models — as DeepSeek has shown — the gap is likely to widen significantly beyond that point, due to increasing disparities in compute optimization, fine-tuning capabilities, and infrastructure maturity.

It is also argued that when safety and precision—both crucial for commercialization—are factored in, the qualitative dimension of fixed investment may present barriers that far exceed what can be captured by cost figures alone.

In short, it remains uncertain whether the fixed cost barrier will eventually disappear or persist over time. What is clear, however, is that two opposing views coexist regarding the potential for latecomers to catch up.

LLM Variable Costs and Inference Economics: Beyond Per-Token Pricing

Inference costs—specifically the cost per token—have dropped dramatically, now falling below $1 per million tokens. Some predict that this trend will continue until inference becomes nearly free and the cost of LLM services negligible.

Andreessen Horowitz’s article “LLMflation”, for instance, argues that cloud infrastructure and model optimization will drive unit costs toward nearly zero.

However, this view may be overly simplistic. Token-level inference represents only one part of the overall variable cost. In practice, a range of other factors—such as server infrastructure, prompt orchestration, latency optimization, and regulatory compliance—also contribute meaningfully to cost. As such, focusing solely on token cost provides an incomplete picture.

That said, in today’s environment—where inference costs have fallen below a certain threshold and efficiency gaps among major players continue to narrow—variable cost is no longer the decisive factor in achieving market leadership.

Instead, competition is shifting away from pricing toward service quality, including ongoing improvements in model performance, API reliability, response speed, security, and customization. In this scenario, even latecomers can remain competitive by delivering industry-specific capabilities or agile customization tailored to niche needs.

LLM Market Segmentation and Multimodal Expansion: Diverse Competitive Landscapes

For a natural monopoly to exist, market demand must be limited to below a certain degree—concentrated enough for a single supplier to serve it entirely. However, the LLM market falls rather short of this condition.

Most foundation models are designed for global reach, but “sovereign AI” models optimized for local languages and cultures are gaining traction. This coexistence of global generality and local specialization suggests that the LLM market cannot be explained by a single, unified demand.

Already, the text-based LLM market is splitting into submarkets such as:

• B2C chatbots (e.g., ChatGPT, Claude)

• API platforms (e.g., OpenAI API)

• On-premises models (e.g., Meta’s LLaMA)

These divisions are primarily based on the productization approach and customer touch points, and horizontal movement is fairly fluid. For instance, API providers can launch their own B2C chatbots, and open-source players can expand into API-based services.

In essence, the segmentation is driven by current areas of focus, not by prohibitively high technical barriers.

However, times are changing with the rise of multimodal capabilities. As LLMs extend beyond text to encompass images, video, audio, and even physical actions, the market is evolving into specialized verticals—each forming its own moat that cannot be overcome by model scale or algorithmic improvements alone.

Multimodal AI Competitive Advantages:
Data Ownership as Key Differentiator

As summarized in the table above, each modality entails fundamentally different technical demands—ranging from data structures and interfaces to hardware integration. As a result, transitioning across modalities is significantly more difficult and time-consuming.

Therefore, instead of a single dominant winner emerging across all modalities, the market is more likely to develop into a segmented competitive landscape, where different players lead in different domains based on their strengths.

Network Effects in LLM Markets: Data Feedback Loops vs. Platform Lock-in

LLMs do benefit from some degree of network effects—for instance, model fine-tuning based on user feedback, or performance optimization using usage data. These feedback loops can be seen as forms of data-driven network effects.

However, these effects are not exclusive and can be similarly implemented by competitors. Thus, they do not create an absolute advantage, unlike in traditional platform markets.

The contrast with Google Search is telling: Google enjoys strong lock-in effects: its search algorithms improve with user data, which is further reinforced by integration with Chrome and Android.

This combination of high fixed costs, low marginal costs - the first two conditions of a natural monopoly - and robust network effects has essentially led to a natural monopoly.

In contrast, the dynamics of the LLM market are quite different. Most users already have access to models that are “good enough,” meaning that network effects alone are unlikely to create a sustained competitive advantage. Incremental improvements from additional data are not likely to create drastic improvements in user experience. 

Therefore, while network effects do exist in the LLM space, they don’t appear to be decisive enough to sustain a natural monopoly—especially in a market where product quality has already surpassed a critical threshold.

LLM Market Structure Predictions: Scenario-Based Analysis and Investment Implications

Based on the analysis above, whether the LLM market becomes a natural monopoly hinges on the interplay of the following four conditions:

1. Fixed Costs Analysis Summary

Training frontier-scale models still demands massive fixed investment, but with the industry approaching a “trillion cap,” a natural ceiling may be emerging. This has given rise to two competing views on the prospects for latecomers: either the barriers can ultimately be overcome, or the current gap will solidify into long-term entrenchment of early leaders.

2. Variable Costs Trajectory Assessment

Inference costs have already dropped significantly and are relatively low even at current levels. However, opinions are divided on whether they can continue to decline rapidly going forward.

3. Market Segmentation Impact on Competition

The market is evolving not around a single, unified model but across different modalities—text, video, robotics, autonomous driving, and more—each with distinct structural dynamics. In particular, text remains a domain where no player holds exclusive access to critical data, leaving room for competition and innovation.

4. Network Effects Limitations in LLM Markets

While a feedback loop between users - data - performance does exist, it lacks the strong lock-in effect seen in cases like Google Search. Moreover, such effects are replicable by competitors, making them insufficient to form a decisive monopolistic advantage.

Four Key Conditions Summary

While the four conditions remain partially relevant to the text-based LLM market, they become less meaningful when applied to non-text modalities such as video, robotics, and autonomous driving. The reason is simple: unlike text, high-quality training data for these modalities is scarce, and ownership is heavily concentrated.

For example, YouTube videos (video generation), vehicle driving data (autonomous driving), and robot simulation data (robotics) are already exclusively held by a handful of leading companies in each field.

In these domains, the core barrier to entry is not fixed or variable costs, but data ownership itself.

As such, the only domain where meaningful discussion of market structure is currently possible is the text-based LLM segment. In this area, economic analysis based on fixed and variable costs remains relevant.

Market Structure by Scenario (Focused on Text-based LLMs)

Strategic Implications for VCs and Startups

As of now, the market has not fully consolidated, and latecomers or startups still have a meaningful opportunity to emerge as major players—depending on their technological approach and strategic positioning.

Of course, this outlook may vary depending on how one interprets the trajectory of technological progress and the structure of market demand. Depending on the scenario, the market could evolve in very different directions.

To all founders navigating the intense AI race—we offer our sincere support. If you're facing challenges or need a sounding board, Kakao Ventures is always here for you. We're committed to walking alongside you as you build and solve meaningful problems

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