What Is the New Moore's Law for Agentic AI?

There is a new Moore's Law reshaping business strategy in 2026. Unlike the original Moore's Law — which tracked the doubling of transistors on semiconductors roughly every two years — this new law applies to agentic AI, and it operates at a dramatically faster pace.

Agentic AI is doubling in power and capability every three months.

That means the gap between organisations investing in agentic AI and those still deliberating isn't growing linearly. It's compounding exponentially. A one-year delay doesn't put you 12 months behind. It puts you 16x behind.

The Exponential Compounding Gap: What Falling Behind on AI Actually Looks Like

If a competitor begins investing in agentic AI today and you don't, the capability gap follows a predictable exponential curve:

• One quarter: 2x behind
• Two quarters: 4x behind
• Three quarters: 8x behind
• One year: 16x behind

This is not a forecast or a thought experiment. It is the mathematical reality of exponential compounding applied to AI capability growth.

What makes this different from previous technology adoption cycles is the speed. In earlier eras of enterprise technology — cloud computing, mobile, even early machine learning — organisations could afford to be fast followers. The doubling rate of agentic AI capability means the window for catching up is compressing to the point where delayed action becomes a permanent disadvantage.

Why "Wait and See" Is the Most Expensive AI Strategy in 2026

Across industries, a common pattern is emerging among leadership teams. Smart, experienced decision-makers are choosing a cautious, evidence-based approach to AI adoption. They want more proof points, more case studies, more certainty before committing resources.

This is a rational instinct. But in the context of exponential capability growth, caution has become the most expensive strategy available.

While one organisation evaluates, another implements. While one requests a feasibility study, another completes a second iteration. The cost of that delay is not measured in lost time alone — it is measured in a compounding gap that becomes exponentially harder to close with every passing quarter.

The critical question for leaders is no longer "Is AI sophisticated enough for our business?" The question is: "Can we afford the compounding cost of waiting to find out?"

How Agentic AI Scales Human Qualities Like Empathy and Personalisation

The most misunderstood aspect of agentic AI adoption is what it actually automates. The highest-value use case is not about removing human qualities from business processes. It is about scaling them.

Consider a practical example that affects almost every service-based business: responding to new client inquiries in a personalised, empathetic manner. Every prospect deserves a thoughtful, tailored response. But the reality of limited hours, growing demand, and competing priorities means quality inevitably drops at scale.

Agentic AI changes this equation. An AI agent trained on a leader's voice, values, and communication style can ensure every client inquiry receives a response that feels genuinely personal and considered — not because a human wrote each one individually, but because the agent has learned what that human's empathy looks like and can deliver it consistently across every interaction.

This is not automation in the traditional sense of removing the human. This is the multiplication of the most human qualities in business — empathy, personalisation, and genuine connection — at a scale that was previously impossible.

Three Practical Steps Leaders Can Take This Week to Start Closing the AI Gap

The distance between recognising that AI matters and actually taking action is where most organisations stall. These three steps are designed to close that gap immediately.

Step 1: Audit Your Highest-Friction Workflow

Identify the single task in your business that is most repetitive, most time-consuming, and most dependent on personal touch. This is your highest-leverage agentic AI opportunity.

Specificity matters. "Responding to inbound leads within 2 hours with a personalised message" is an actionable AI use case. "Improving client communications" is not. The more specific the friction point, the faster an AI agent can be deployed against it.

Step 2: Run One AI Experiment in 30 Days

Do not build a comprehensive AI strategy. Do not commission a consulting report. Instead, select one agentic AI tool — an AI email assistant, a meeting summariser, an automated follow-up sequence — and deploy it against your identified friction point for 30 days.

Measure the before and after. One real-world experiment generates more organisational learning about AI readiness than six months of theoretical planning.

Step 3: Reframe the AI Conversation With Your Team

The question "How should we use AI?" tends to generate anxiety and abstract debate. A more productive question is: "What is the most frustrating part of your day that keeps you from the work you actually want to do?"

The most successful AI adoption programmes do not start with the technology. They start with the human pain point. Identifying where people feel friction, frustration, and wasted potential reveals exactly where agentic AI can create the most value — and generates genuine buy-in rather than resistance.

The Bottom Line: Agentic AI Is a Compounding Advantage

Organisations building agentic AI capabilities today are not simply gaining a temporary efficiency advantage. They are building compounding returns that will be almost impossible for late adopters to match within 12 months.

The question for every leader in 2026 is not whether agentic AI will transform their industry. It is whether they will be driving that transformation or scrambling to respond to it.

A year from now, you will look back at this moment as either the time you started — or the time you should have.

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Frequently Asked Questions: The New Moore's Law for Agentic AI

What is the new Moore's Law for agentic AI?

The new Moore's Law for agentic AI refers to the observed pattern that agentic AI capability is doubling approximately every three months. Unlike the original Moore's Law — which tracked semiconductor transistor density doubling roughly every two years — this new pace of advancement means AI-powered business tools are compounding in power at an unprecedented rate. For organisations, this means a one-year delay in AI adoption doesn't create a 12-month gap — it creates a 16x capability gap.

How fast is agentic AI advancing in 2026?

As of 2026, agentic AI is doubling in capability every three months. This means an organisation that begins investing today will be operating at 2x capability within one quarter, 4x within two quarters, 8x within three quarters, and 16x within a year — relative to a competitor that hasn't started. The exponential nature of this growth means the gap between early adopters and late movers compounds rapidly and becomes increasingly difficult to close.

What is agentic AI and how is it different from traditional AI?

Agentic AI refers to AI systems that can autonomously perform multi-step tasks, make decisions, and take actions on behalf of a user — rather than simply responding to a single prompt. While traditional AI tools require human input at each step, agentic AI can manage end-to-end workflows such as personalised client outreach, meeting follow-ups, lead qualification, and scheduling. This autonomy is what makes it a force multiplier for human capability rather than just an efficiency tool.

What is the biggest risk of not adopting agentic AI?

The biggest risk is the compounding capability gap. Because agentic AI capability doubles every three months, delay doesn't create a linear disadvantage — it creates an exponential one. An organisation that waits 12 months to begin AI adoption will face a competitor that is 16x more capable in the workflows they've automated. This gap affects client experience, speed to market, talent retention, and operational efficiency simultaneously — and becomes nearly impossible to close once established.

How can businesses start using agentic AI today?

The most effective starting point is a three-step approach. First, audit your highest-friction workflow — identify the single task that is most repetitive, time-consuming, and dependent on personal touch. Second, run one focused experiment over 30 days using a specific agentic AI tool (such as an AI email assistant or automated follow-up sequence) deployed against that friction point. Third, reframe the AI conversation with your team by asking "What is the most frustrating part of your day?" rather than "How should we use AI?" This human-first approach generates buy-in and reveals the highest-value automation opportunities.

Can agentic AI maintain a personal, human touch in client interactions?

Yes. One of the most powerful use cases for agentic AI is scaling human qualities like empathy and personalisation rather than replacing them. An AI agent can be trained on a leader's communication style, values, and voice — enabling every client inquiry to receive a response that feels genuinely personal and considered. This approach doesn't remove the human from the interaction. It multiplies the human's most valued qualities across every touchpoint at a scale that would be impossible manually.

What does "scaling humanity with AI" mean?

Scaling humanity with AI means using agentic AI to extend a person's most human qualities — empathy, personalisation, genuine connection — across more interactions than they could handle individually. Rather than automating away the human element, this approach trains AI agents on how a specific person communicates, so the AI can deliver that same quality of interaction consistently at scale. The result is that every client, prospect, or stakeholder receives the kind of thoughtful, personalised engagement that was previously limited by time and bandwidth.

Why is "wait and see" a risky AI strategy in 2026?

"Wait and see" is considered the most expensive AI strategy in 2026 because of the exponential nature of agentic AI capability growth. In previous technology cycles — cloud, mobile, early machine learning — organisations could afford to be fast followers. The three-month doubling rate of agentic AI compresses the adoption window so dramatically that delayed action becomes a permanent structural disadvantage. While one organisation evaluates, a competitor implements, iterates, and compounds their advantage — creating a gap that grows exponentially rather than linearly.

How do I convince leadership to invest in agentic AI?

The most effective approach is to start with a low-risk, high-visibility experiment rather than requesting a large strategic commitment. Identify one specific workflow friction point, deploy one agentic AI tool for 30 days, and present the measured before-and-after results. Real data from your own organisation is more persuasive than external case studies. Additionally, reframing AI adoption as "scaling our humanity" rather than "replacing our people" addresses the cultural resistance that often blocks leadership buy-in.