The A2A Revolution: Why Platforms Collapse in the Age of AI
First Connection Becomes the Most Important Strategic Asset
For the past quarter century, the dominant competitive force behind platform strategy has been the network effect.
As more users joined a platform, more businesses participated. As more businesses participated, even more users arrived. This self-reinforcing cycle created the enormous competitive advantages enjoyed by digital platforms.
Companies such as Amazon, Uber, Airbnb, and the App Store built formidable barriers to entry through these network effects.
However, as AI agents increasingly make decisions on behalf of consumers, the source of competitive advantage is beginning to shift.
Network effects will not disappear entirely. Yet the conditions required to win competition are clearly changing.
In the traditional internet era, humans searched, compared, and selected.
Consumers reviewed search results, read ratings, compared prices, and ultimately made decisions themselves.
In the AI agent era, much of this process will be delegated to AI.
When a user says, “Arrange my business trip next week,” an AI agent may automatically propose flights, hotels, transportation, and even dining reservations.
When someone says, “I want to replace my air conditioner,” AI can examine purchase history, personal preferences, and housing information to identify the optimal product and installation provider.
Under these conditions, a critical question emerges:
Which company receives the first inquiry from AI?
This book refers to this concept as First Connection.
First Connection means the company or service that an AI agent contacts first in order to fulfill a user’s intent.
Under traditional SEO competition, appearing on the first page of search results was sufficient. Not only the top-ranked result but also second and third positions could generate meaningful traffic.
In the world of AI agents, however, the number of candidate companies may shrink dramatically.
AI prioritizes presenting the best option rather than presenting numerous options.
Certainly, there will still be cases requiring comparative quotations or second opinions. Yet for many routine transactions, AI will likely narrow choices to only a few candidates.
As a result, being selected first by AI becomes a crucial competitive factor.
This differs fundamentally from traditional search ranking.
Companies are no longer competing for human attention.
They are competing for AI trust.
Economist Ronald Coase argued that firms exist to reduce transaction costs. AI agents operate according to the same principle.
Continually comparing multiple providers generates computational costs, communication costs, and time costs. Once AI identifies a sufficiently good candidate, it may stop searching altogether.
Therefore, companies do not necessarily need to be overwhelmingly superior.
They need to be sufficiently excellent and connected first.
From User Interfaces to AI Interfaces
This new competition elevates the importance of connection infrastructure.
One emerging technology attracting considerable attention is the Model Context Protocol (MCP), a framework designed to allow AI systems and external business systems to exchange information securely and consistently.
Although MCP has not yet become a universal industry standard, it clearly points toward the future architecture of the AI economy.
Until now, companies have had to develop individual APIs for each AI service.
As common connection standards emerge, business systems will increasingly become optimized for AI consumption.
The critical issue is no longer building interfaces for humans.
It is building data structures that AI can understand.
Inventory levels.
Pricing information.
Delivery status.
Capacity utilization.
Quality metrics.
Companies capable of providing these data in real time will become more attractive to AI agents.
Competitive advantage therefore shifts from the UI to APIs and AI connection layers such as MCP.
Three Strategic Priorities for Business Leaders
In this environment, companies must focus on three strategic priorities.
1. Understanding Intent
Companies must deeply understand customer intent and provide AI with meaningful contextual information.
Product specifications alone are insufficient.
AI increasingly requires contextual knowledge regarding which customers a product is best suited for.
2. Data Credibility
AI does not trust advertising copy.
Instead, it values:
- performance records
- fulfillment rates
- customer satisfaction
- quality indicators
Objective evidence becomes more important than marketing claims.
3. Reducing Friction
Lengthy quotation processes.
Excessive confirmations.
Complex contracts.
Operational friction reduces the likelihood of being selected by AI.
AI prioritizes efficiency.
The lower the friction, the higher the probability of selection.
Implications for Management
The rise of AI agents fundamentally changes corporate KPIs.
Page views.
Membership numbers.
Downloads.
Time spent.
These metrics remain important, but they are no longer sufficient.
Future performance indicators may include:
- AI-generated revenue ratio
- API utilization rates
- AI recommendation rates
- connection retention rates
- data reliability scores
Companies are entering an era in which they are evaluated not only by humans but also by AI.
Consequently, traditional B2C businesses gradually evolve into B2AI2C businesses.
Branding itself also changes.
Traditional branding focused on awareness.
AI-era branding focuses on trust.
AI does not watch television commercials.
AI does not react to social media advertisements.
AI evaluates operational data, fulfillment rates, and quality information.
Marketing therefore shifts from winning attention to earning trust.
The New Marketing Framework for the AI Era: AIEA
Human-centered branding will not disappear entirely.
Emotions still influence final decisions.
However, as AI increasingly participates in decision-making, companies require a new source of competitiveness.
That source is AI connectivity.
The essential question for executives is no longer:
“How many customers do we have?”
The new question becomes:
“Will AI choose us first?”
In a society where AI agents support everyday decisions, companies must become easy for AI to use, trustworthy for AI to evaluate, and efficient for AI to execute.
The age of network effects is not ending entirely.
But the center of competitive advantage is undoubtedly shifting.
From companies that capture human attention,
to companies that earn AI trust.
From the era of platforms,
to the era of First Connection.
Management’s priority is not building more proprietary applications.
It is constructing an enterprise infrastructure that AI can connect to.
The companies that AI wants to connect with first.
The companies AI trusts most.
The companies AI confidently recommends to customers.
Those organizations will secure the new competitive advantage of the AI age.
The Transformation of AIDMA
The traditional AIDMA model—Attention, Interest, Desire, Memory, and Action—described consumer psychology during the mass-media era.
In the age of AI, much of this process disappears or becomes internalized within AI systems.
Attention and Interest
Traditionally, consumers saw advertisements and developed interest.
In the AI era, users simply communicate intent:
“I want to replace my air conditioner.”
Attention and interest shift from products themselves to AI agents.
Desire and Memory
Traditionally, consumers developed desire and stored preferences in memory.
AI possesses neither emotions nor desires.
Instead, AI relies on:
- historical purchasing data
- contextual information
- reliability scores
These stages effectively disappear.
Action
Traditionally, consumers visited stores or completed purchases manually.
In the AI era, users may simply approve recommendations.
For trusted routine purchases, fully automated transactions may emerge.
Thus, human AIDMA is compressed into:
A (Ask AI) → A (Approve)
The middle stages disappear into the AI black box.
From AISAS to Connection
AISAS—Attention, Interest, Search, Action, Share—defined the internet and social media era.
AI agents fundamentally transform this model.
Human search behavior gives way to machine connection.
Search becomes Connect.
AI retrieves information instantly through APIs and protocols such as MCP.
Likewise, Share evolves.
Previously, sharing consisted of human-generated reviews.
In the AI era, sharing becomes structured operational data:
- fulfillment rates
- delivery performance
- quality indicators
- satisfaction metrics
AI ignores exaggerated advertising.
Instead, it relies upon verified operational evidence.
AIEA: The Marketing Framework for B2AI2C
The emerging framework for the AI economy may be summarized as AIEA.
A — Assignment
Users delegate intentions and tasks to AI.
I — Infrastructure Connection
AI establishes first connections with trusted enterprise systems through APIs and protocols.
E — Evaluation & Execution
AI evaluates real-time operational data and executes transactions.
A — Accumulation
Transaction outcomes become trust data that influence future decisions.
What This Means for Marketers
Traditional marketing frameworks sought to capture human mindshare.
The new objective is different.
Companies must:
- conform to AI standards and protocols
- provide transparent real-time data
- reduce operational friction
- become the easiest and most trustworthy choice for AI
The evolution moves beyond SEO.
It moves beyond LLM optimization.
The next battlefield is AI selection.
The future belongs to companies that are not merely visible to customers.
It belongs to companies that are visible to AI.
To be continued in the next chapter.
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