Artificial intelligence does not exist in isolation. Behind every AI system are organisations, governments, developers, and economic incentives that shape how the technology is built, deployed, and controlled. In 2026, understanding AI means understanding power — who holds it, how it is exercised, and who is affected by it.

This lesson explores the concentration of control in the AI ecosystem, the roles of governments and institutions, and why governance and oversight are becoming as important as innovation itself.

The Concentration of AI Capability

While AI tools feel widely accessible, the most powerful systems are controlled by a relatively small number of organisations.

Training large-scale AI models requires:

• enormous datasets

• specialised hardware

• significant financial investment

• access to global infrastructure

As a result, a handful of technology companies and state-backed institutions dominate the development of frontier AI models. This concentration creates efficiency and rapid progress, but it also raises questions about dependency, influence, and accountability.

Access to AI does not necessarily mean control over it.

Platforms Shape Possibility

Most people interact with AI through platforms — search engines, productivity tools, social networks, and cloud services. These platforms decide:

• what models are available

• how they can be used

• which safeguards are enforced

• what data is collected

Platform design choices quietly shape behaviour at scale. When AI is embedded into widely used services, its influence extends far beyond individual users.

This makes platform governance a critical issue, even when AI use appears voluntary.

Government Power and National Strategy

Governments play multiple roles in the AI ecosystem.

They act as:

• regulators

• funders

• users of AI systems

• geopolitical actors

Some governments prioritise innovation and economic competitiveness. Others emphasise control, surveillance, or national security. These priorities influence how AI is developed and deployed within borders.

AI has become a strategic asset. Decisions about compute access, export controls, and research funding increasingly shape global power dynamics.

Regulation as a Form of Power

Regulation is often portrayed as a brake on innovation, but in reality it is also a tool of influence.

Rules define:

• what is allowed

• what must be disclosed

• who bears responsibility

• how harm is addressed

In 2026, different regions are adopting different regulatory philosophies. Some focus on risk-based frameworks, others on sector-specific rules or voluntary guidelines.

These choices affect where companies operate, how systems are designed, and who is protected. Regulation does not remove power — it redistributes it.

The Role of Data Ownership

Data is the fuel of AI, and control over data is a form of power.

Questions about:

• who owns training data

• how it is collected

• whether consent is meaningful

• how data is reused

remain unresolved in many jurisdictions. Individuals often generate data without visibility into how it is used or combined.

As AI systems become more capable, disputes over data rights, compensation, and consent are likely to intensify.

Open Source vs Centralised Control

The rise of open-source AI models complicates the power landscape.

Open models:

• increase transparency

• reduce dependence on single providers

• enable local innovation

However, they also:

• lower barriers to misuse

• complicate enforcement

• distribute responsibility widely

This creates tension between openness and control. There is no simple answer, only trade-offs that societies must navigate.

Who Is Accountable When AI Causes Harm?

One of the hardest governance questions is accountability.

When AI influences a decision or causes harm, responsibility may be spread across:

• developers

• deployers

• users

• platform providers

This diffusion makes enforcement difficult and allows blame to shift. As AI becomes more embedded, pressure grows for clearer accountability chains.

Power without accountability undermines trust.

Power Asymmetry and the Public

Most people affected by AI systems have little influence over their design or deployment.

This creates an asymmetry:

• decisions are made at scale

• impacts are experienced individually

Without transparency and recourse, this gap can erode public confidence. Governance is not just about controlling AI, but about maintaining legitimacy.

Why This Matters Now

As AI becomes more capable, decisions made today shape long-term outcomes.

Choices about:

• who controls AI

• how it is governed

• what values are embedded

will influence economic opportunity, civil rights, and social trust for decades.

Understanding power is essential to understanding AI.

Key Takeaway

AI is not neutral infrastructure. It reflects the priorities and incentives of those who control it.

In 2026, responsible AI development depends as much on governance, accountability, and transparency as on technical excellence.

The next lesson explores how AI is already shaping everyday life, often in ways we barely notice — and why that invisibility matters.