AI Isn’t Like Other Tech Cycles—And Investors May Need a New Approach

August 1, 2025 | By Brian Barbetta and Andrew Heiskell Global Macro Analysis Market Perspectives

Investors may be underestimating AI’s potential for disruptive growth and attractive investment returns.

What You Need To Know

AI is advancing faster and scaling more easily than any prior tech cycle, thanks to ubiquitous hardware, rapid model improvements, and massive corporate investment.
The disruption from AI is already underway, with entry-level white-collar jobs at risk and broader workforce impacts expected before widespread adoption of AI agents.
Investors must be selective, as not all tech companies will benefit equally—active management and deep sector expertise are crucial to identifying true winners in the AI era.

Harnessing dividends and quality fixed income to potentially navigate market uncertainty.

AI may dominate headlines, but we think its potential for disruptive growth and compelling investment returns is still widely underestimated. At the same time, investors risk falling into familiar traps—relying on broad index exposure to capture opportunities and struggling to distinguish the disruptors from the disrupted.

While the risks are familiar, AI is unlike any previous technology cycle in several key ways:

Hardware capable of running AI applications is already nearly ubiquitous worldwide

The pace of AI advancement far exceeds that of past tech shifts

AI is inherently more scalable than previous innovations

Companies are investing in AI at an unprecedented scale

This matters because AI-driven disruption could create significant opportunities for active managers to add value—but selectivity will be critical. Underestimating the speed and scale of this shift could mean missing out on meaningful returns.

What Sets AI Apart From Past Tech Disruptions?

1) Ubiquitous hardware is accelerating adoption

Past technology transitions required large hardware cycles with costs borne by enterprises or individuals. This time, consumers already have the devices and connectivity necessary to adopt the technology immediately on its release. Two-thirds of the global population has internet access, with many countries having nearly 100% access. Enterprises have access to cloud-service providers that can scale large deployments at a rapid pace.

As a result, we expect this technology to be adopted at a far faster pace than prior technology cycles. Consider the fact that it only took OpenAI’s ChatGPT two months to reach 100 million monthly users after launching in December 2022.

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Seeking Balance Amid Uncertainty

Harnessing dividends and quality fixed income to potentially navigate market uncertainty.

2) AI is evolving rapidly—and getting cheaper

Since the launch of ChatGPT, we’ve been stunned by how quickly large language models are improving. Critically, reasoning models generate their own training data as each thought a model works through can then be fed back into the model, effectively learning in real time as it solves the problem.

In addition, the cost of deploying AI is falling rapidly, thanks to advances in semiconductor technology—unlocking even more profitable use cases for generative AI. Just a few years ago, reasoning models were prohibitively expensive due to their compute-intensive demands.

3) AI technology scales more easily than past innovations

If we think back to previous technology cycles, both the automation of labor and the augmentation of knowledge work (i.e., tasks that involve thinking, analyzing, or creating) via computers required new equipment and processes. Automating assembly lines has been underway for more than a century, but progress has been slow because reducing costs in a physical world takes time. Augmenting knowledge work via computers also takes equipment and skills (e.g., learning to type or use spreadsheets), and while this still happened rapidly, it took decades.

With AI, we’re seeing the augmentation of work grow at an unprecedented rate. As we move toward an agentic world¹ (the first use cases of which went live this year), we’re seeing a technology that can replace humans at scale with less friction than any prior automation cycle. This trend is advancing rapidly and, in our view, is poised to accelerate further.

4) Big tech is investing at record speed

Large technology companies and AI labs are pouring resources into AI development at a pace unmatched in history. This race to scale models and capture market share is rapidly driving both innovation and adoption.

Why This Matters

The scale of disruption AI is set to unleash in the coming years is still widely underestimated, and we think both policymakers and investors are unprepared for the shift ahead. For example, we expect a significant portion of entry-level white-collar jobs will be automated over the next few years. As reasoning models get better, particularly at data analytics, we believe the number of impacted roles will grow significantly. It’s important to note that this initial disruption is happening today even before the widespread adoption of AI agents, which will entirely replace large segments of the workforce.

We think this isn’t being accurately captured in the mental frameworks or modeling currently being applied to investing. We’re optimistic that substantial job creation will occur in due time, as it did with the adoption of spreadsheets. However, it’s unlikely to happen concurrently or at a scale sufficient to offset immediate losses.

It’s possible the company that comes to define success in the AI era hasn’t even been founded yet.

What This Could Mean for Investors

The disruption sparked by AI will offer opportunities for investors, but we see three potential pitfalls:

1) Not all technology companies will be winners

Some of the largest technology companies will likely emerge as winners, but newer companies will also succeed. It’s possible the company that comes to define success in the AI era hasn’t even been founded yet. In our view, deep sector expertise is needed to discern the true winners and losers.

2) Big spending doesn’t guarantee big returns

The fact that tech companies are investing on a massive scale doesn’t necessarily mean they’re guaranteed to succeed. Investors may want to pay attention to which firms are spending wisely and which ones aren’t to discern whether they’re using capital effectively.

3) US Exceptionalism—and the Case for Looking Beyond It

While a handful of large US tech companies have dominated equity markets in recent years, history reminds us that today’s leaders aren’t guaranteed to stay on top. Few of the current giants were among the top performers during the dot-com era, and as AI capabilities scale, the list of market leaders could shift dramatically.

There’s plenty of room for new competitors—in the US and abroad— to grow, succeed, and displace incumbents.

This isn’t a death knell for today’s US-based Magnificent Seven,² but it’s a reminder to guard against complacency. Exceptional US companies may continue to excel, but in this nascent stage of AI, there’s plenty of room for new competitors—in the US and elsewhere—to grow, succeed, and even displace incumbents. Prudent investors will look for opportunities among new and established companies alike, both in historically dominant markets such as the US, and in others, such as Asia, where innovative, new AI-focused companies have long runways to thrive.

Taken together, these dynamics underscore the value of an active investment approach. Unlike passive strategies that seek to track an index, active managers can dig deeper into company fundamentals to identify companies that could be well-positioned to thrive in this unprecedented tech cycle.

To learn more about your portfolio’s exposure to AI technology, please talk to your financial professional.

¹ Agentic world refers to a conceptual or technological environment in which autonomous agents—especially AI systems—can act independently, make decisions, and pursue goals with minimal human intervention.

² Magnificent Seven stocks are a group of high-performing and influential companies in the US stock market: Alphabet, Amazon, Apple, Meta, Microsoft, NVIDIA, and Tesla.

Important Risks: Investing involves risk, including the possible loss of principal.

The views expressed herein are those of Wellington Management, are for informational purposes only, and are subject to change based on prevailing market, economic, and other conditions. The views expressed may not reflect the opinions of Hartford Funds or any other sub-adviser to our funds. They should not be construed as research or investment advice nor should they be considered an offer or solicitation to buy or sell any security. This information is current at the time of writing and may not be reproduced or distributed in whole or in part, for any purpose, without the express written consent of Wellington Management or Hartford Funds.

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Insight from sub-adviser Wellington Management

Brian Barbetta

Global Industry Analyst

Andrew Heiskell

Equity Strategist