Who Shapes the AI-First World? Rules, Risks, and Responsibility

Artificial Intelligence is no longer a niche topic; it is central to product design, business strategy, and national policy. Governments and companies are moving quickly, attempting to protect people while fostering innovation. Striking the right balance remains the key challenge.

Europe’s Lead in AI Regulation
Europe has implemented a risk-based AI Act that bans harmful applications and enforces strict checks for high-risk AI systems. The law mandates audits and continuous lifecycle reviews to safeguard human rights and safety.

Global Standards and India’s Approach
Globally, OECD principles emphasise trustworthy AI with transparency, accountability, and human oversight. In India, strengthened digital laws, including the Digital Personal Data Protection Act, set a foundation for AI compliance. Policymakers are working to define AI’s role within this framework while fostering dialogue among regulators and businesses.

Risk of Concentration
Complex compliance requirements often favor larger firms. Sanjay Koppikar, CPO of EvoluteIQ, warns that this could lead to an “AI oligarchy,” where a few players dominate due to resource advantages in compute power, infrastructure, and budgets. Cloud providers and chip makers control much of the market, limiting experimentation and heightening systemic risk.

Accountability Challenges
Determining responsibility for AI failures remains tricky. Koppikar outlines a three-layer approach:

1. Technical accountability – audit trails for transparency

2. Operational accountability – human-in-the-loop mechanisms

3. Governance accountability – clear escalation pathways

He stresses that human oversight is crucial, especially in high-stakes domains like healthcare, finance, and public safety.

Guardrails, Governance, and Collaboration
Human-in-the-loop safeguards slow full automation, catch biases, and ensure responsibility. However, gaps in standards and interoperability hinder audits and cross-vendor checks. Koppikar advocates shared frameworks and collaboration between vendors, civil society, and regulators.

A Multi-Stakeholder Model
Koppikar rejects single-entity control. He proposes:

• Government sets standards

• Independent technical bodies conduct audits

• Civil society provides oversight

This model reduces risks of capture and builds trust, ensuring a diverse AI ecosystem.

Policy Principles for a Balanced Future

1. Embed compliance into product design

2. Reduce compliance costs for startups through sandboxes

3. Mandate human oversight for critical outputs

4. Monitor compute markets to avoid concentration

Koppikar warns: “Excessive government control can stifle innovation, but no regulation is risky too.” Policymakers must protect people while keeping innovation doors open.

The Stakes Are High
The policies written today will determine who shapes the AI-first world. Thoughtful, flexible, and collaborative rules can foster a diverse and inclusive AI ecosystem. The alternative risks concentrating power in the hands of a few.

“We now live in an AI-first world,” Koppikar reminds us — and the choices we make now will define its future.