Where Are We in the AI Technology Cycle?

The Big Picture

Artificial intelligence has moved from the pages of science fiction into the core of how businesses can operate. Over the past three years, we've witnessed one of the fastest technology adoption cycles in history — and we believe we're now entering its most consequential phase.

Understanding where we are in this cycle is critical for investors. Technology revolutions follow a predictable pattern: hype, infrastructure build-out, deployment, and finally maturation. Each phase creates different winners and different risks. Here's our assessment of where things stand today.

The Four Phases of the AI Revolution

Phase 1: Research & Discovery (2017–2022)

The foundational breakthroughs — transformer architecture, large language models, image generation — emerged from research labs at Google, OpenAI, and universities worldwide. AI was impressive but largely confined to demonstrations and niche applications. Many investors weren't paying attention yet.

Phase 2: The Infrastructure Boom (2023–2025)

The launch of ChatGPT in late 2022 ignited a gold rush. Technology companies committed hundreds of billions of dollars to building AI infrastructure — data centers, specialized chips, networking equipment. Nvidia became one of the most valuable companies in the world as demand for its AI processors skyrocketed. The "picks and shovels" providers were the clear early winners.

Phase 3: Enterprise Deployment & Monetization (2025–2027) — Where We Are Now

Today, AI is transitioning from experimental projects to production systems. Companies across many industries— from healthcare to finance to manufacturing — are deploying AI to improve productivity, reduce costs, and create new products. The critical question is no longer "can AI work?" but "how do we capture value from AI at scale?"

This can be a rewarding phase for investors who position correctly. The infrastructure winners remain relevant, but the opportunity set is broadening significantly to include the companies building applications and services on top of that infrastructure.

Phase 4: Maturation & Consolidation (2028+)

Looking ahead, we expect AI capabilities to become more commoditized, regulatory frameworks to solidify, and clear market leaders to emerge in each vertical. Physical AI — robotics and autonomous systems — will likely be the next major growth frontier.

What's Coming Next

AI Agents: Beyond chatbots, AI systems that can autonomously complete complex, multi-step tasks — transforming how work gets done.

On-Device Intelligence: AI processing moving from the cloud to your phone, laptop, and car — enabling faster, more private, always-available AI.

Physical AI & Robotics: AI moving from the digital world into the physical one, with applications in manufacturing, logistics, and healthcare.

Energy Infrastructure: AI data centers consume enormous amounts of electricity. Energy infrastructure is becoming a critical enabler — and investment opportunity.

Vertical Applications: Industry-specific AI solutions in healthcare, legal, finance, and education are where much of the economic value can be captured.

Investment Implications

For investors, the key takeaway is that the AI opportunity is broadening. The initial winners — primarily semiconductor and cloud infrastructure companies — remain important holdings, but the next wave of opportunities may come from:

• Companies successfully monetizing AI in their products and services

• Application-layer businesses solving real enterprise problems with AI

• Infrastructure providers beyond chips — energy, networking, cooling

• International AI leaders, particularly in Asia, trading at compelling valuations

We believe a diversified approach is prudent: maintaining exposure to proven AI platform leaders while selectively adding positions in the next wave of beneficiaries as the cycle matures.

Risks to Monitor

• Elevated valuations in AI-related stocks that may not be sustained if growth disappoints

• Regulatory developments that could slow AI adoption timelines

• Concentration of returns in a small number of mega-cap technology stocks

• The pace of enterprise AI adoption, which remains uneven across industries

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