Redefining the Digital Economy in the Age of Artificial Intelligence: Living Models

The Digital Economy in the Age of Agentic AI: From Assistive Tools to Independent Economic Entities

The digital economy is undergoing a radical transformation as artificial intelligence enters the “agency” phase, where intelligent systems shift from mere assistive tools for humans to independent economic entities capable of negotiating and executing. Recent reports reveal that more than half of global companies are ready to allow AI agents to negotiate on their behalf, while researchers are redefining the factors of production to include “digital labor” as a third element alongside capital and human labor. This article, based on recent academic research and institutional reports, reviews four live models that embody the redefinition of the digital economy—from new business models to the restructuring of energy markets, from the evolution of production theories to a verifiable trust architecture.

First: From the App Economy to the Agent Economy

1. The Shift from Interfaces to Infrastructure

The “app” has long been the primary destination and interface of the digital economy—a user opens an app to pay a friend, order food, book a trip, or execute a transaction. But this model has begun to change as AI evolves from answering questions to completing tasks. In the new era, the question may become less about “which app the user will open” and more about “which platform the AI agent can actually transact through.”

Growing value lies in the “execution layer”—the infrastructure capable of converting intent into action: payment networks, e-commerce platforms, local delivery systems, and digital marketplaces. In a world where AI can handle discovery, comparison, and decision simplification, some front-end interface experiences may become less differentiated, while platforms that actually process payment, coordinate delivery, and execute bookings become more important than ever. “The competitive moat may move below the screen.”

2. New Business Models for the Agentic AI Era

Researchers at the MIT Center for Information Systems Research (MIT CISR) developed a new framework describing four emerging business models in the agentic AI era, based on survey data from 2,378 companies between 2013 and 2025:

1. Existing+: Enhancing the current business model with AI. Example: a financial services company analyzing customer information to provide personalized recommendations.

2. Customer Proxy: Achieving customer outcomes through predefined AI-supported processes. Example: automatically managing an investment portfolio within defined limits.

3. Modular Creator: Using AI to assemble reusable modules (including third-party modules) into customized service packages.

4. Orchestrator: Achieving customer outcomes using AI to assemble an ecosystem of complementary products and services, without a predefined process.

This framework reflects a structural shift: in 2013, only 12% of companies operated as ecosystems, while the proportion rose to 58% by 2025, reflecting a fundamental transformation in how value is created in the digital economy.

Second: Live Model One — Business-to-AI (B2AI) Commerce

In April 2026, Visa published a pioneering report defining a new phase of commerce under the name B2AI, where AI agents act as active participants in commercial decision-making and execution, while humans remain responsible for intents and outcomes.

1. Real-World Indicators of Transformation

Data collected by Visa in collaboration with Morning Consult indicate that:

• 53% of decision-makers at U.S. companies allow AI agents to negotiate prices or terms directly with other agents on their behalf.

• 71% of companies are ready to optimize their products, offers, and experiences specifically for AI agents.

• 77% of companies currently use or are experimenting with AI in their operations.

2. Consumer Trust: Readiness with Guardrails

On the consumer side, the report reveals growing readiness with clear conditions:

• 58% of Americans are comfortable using AI to compare prices.

• 55% are comfortable with AI applying discounts.

• Only 38% are comfortable completing a full purchase via AI.

• 60% do not allow AI to spend any amount without their prior approval.

Trust emerges as a decisive factor: 36% trust AI systems backed by banks, and 35% trust those backed by payment networks, compared to only 28% for independent AI agents. Trust is higher among Gen Z, with 48% of them trusting AI systems backed by payment networks, compared to only 20% of baby boomers.

Third: Live Model Two — Redefining the Factors of Production: “Digital Labor”

1. From Black Box to Independent Factor of Production

In an academic study published in May 2025, researchers from Microsoft and academic institutions posed a fundamental question: is it time to recognize “digital labor”—the autonomous cognitive capability of AI—as a distinct factor of production, alongside traditional capital and human labor?

“Digital labor” has five unique characteristics:

1. Unprecedented scalability (non-rivalry): A single AI model can be deployed for one user or a million users at near-zero marginal cost.

2. Immateriality: It exists as code, data, and mathematical weights, without physical form.

3. Self-learning and self-improvement: The ability to improve through use, unlike human labor, whose learning requires time and cost.

4. Rapid obsolescence: Models become obsolete quickly as more advanced models emerge.

5. Flexible substitution: It can replace human labor in specific tasks, augment it, or create entirely new tasks.

2. Strategic Implications

Recognizing digital labor as an independent factor of production has fundamental implications:

• Productivity tracking: Instead of burying AI’s contribution in “total factor productivity” (TFP) as a black box, it can be tracked directly.

• Resource allocation: Understanding where digital labor achieves the highest return on investment.

• Institutional design: Restructuring institutions to leverage digital labor as a partner, not just a tool.

Fourth: Live Model Three — Restructuring Energy Markets Through AI

London-based startup Tem offers a live model of redefining an entire market using AI. The company raised $75 million in a Series B funding round at a valuation exceeding $300 million to develop an AI-powered “energy transaction engine.”

1. The Mechanism: Eliminating Intermediaries with AI

Tem operates as a classic marketplace, matching electricity producers with consumers. It uses machine learning algorithms and large language models to forecast supply and demand, replacing up to five or six intermediaries in the current value chain with a single transactional infrastructure. The goal is to bring the price customers pay for electricity closer to the wholesale cost.

2. Results

Tem has signed more than 2,600 commercial customers in the UK and promised savings of up to 30% on energy bills. Customers include fast-fashion retailer Boohoo, soft drinks company Fever-Tree, and Newcastle United Football Club. The company now plans to expand into Australia and the United States, starting with Texas.

3. Broader Significance

Tem’s model embodies a shift from AI as an “optimization” tool to AI as a “restructuring” force for the entire market. It also illustrates how startups can use AI to challenge established business models in sectors previously untouched by digitalization.

Fifth: Live Model Four — A Verifiable Trust Ecosystem

If the new digital economy will feature AI agents acting independently, the most pressing question becomes: how do we trust them?

1. The Trust Crisis in the Agent Economy

Researchers at the Sui platform argue that trust in AI systems is no longer an implicit matter but must be “designed into the infrastructure itself.” As AI systems evolve beyond individual models to include workflows, processes, and agents, trust must extend from data to how these systems operate.

2. Components of a Verifiable Ecosystem

Researchers propose four integrated layers:

1. Verifiable data: Ensuring that data provenance, change history, and who can access it can be proven.

2. Programmable data rights: Creating licensing platforms that carry usage terms with the data itself, giving creators control and developers transparency.

3. Verifiable control layer: Transforming AI systems from “black boxes” we hope behave correctly into systems whose behavior can be verified, with policies, enforcement mechanisms, and receipts proving what actually happened.

4. Agentic commerce primitives: Enabling agents to spend within clearly defined limits, with an auditable proof for every action.

Sixth: Conclusion and Future Outlook

AI is redefining the digital economy across four interconnected levels:

1. Business model level: From Existing+ to Orchestrator, as documented by MIT research.

2. Market agent level: The emergence of Business-to-AI (B2AI) commerce as an economic reality, with companies ready to negotiate with AI agents.

3. Factors of production level: Recognizing digital labor as a third factor of production alongside capital and labor.

4. Trust infrastructure level: Building verifiable systems that ensure the transparency and integrity of the agent economy.

The message is clear: the digital economy is no longer merely automating existing processes but has become a space for creating entirely new models of economic interaction. Companies and institutions that will succeed are those that recognize AI is not just a tool for improving efficiency, but a force for redefining what the economy itself is.

Sources and References

1. ScienceDirect – Introduction to the special issue on recent advances on digital economy-oriented artificial intelligence (2024)

2. Visa – B2AI Report: The next era of commerce when AI is the customer (2026)

3. MIT Sloan – How digital business models are evolving in the age of agentic AI (2026)

4. VanEck – The Next Phase of AI: Digital Native Economy (2026)

5. Yahoo Finance – Tem raises $75M to remake electricity markets using AI (2026)

6. arXiv – Evolving the Productivity Equation: Should Digital Labor Be Considered a New Factor of Production? (2025)

7. Sui Blog – From Data to Decisions: Closing the Loop on a Verifiable AI Economy (2026)

8. ACM Ubiquity – AI Powers the Digital Economy (2023)

9. arXiv – AgenticPay: A Multi-Agent LLM Negotiation System for Buyer–Seller Transactions (2026)