A New Era Or A New Bubble: What To Make Of AI

You can say this about artificial intelligence (AI): it knows how to make an entrance. The technology splashed onto the scene in 2022 with a whizbang impact rivalled only by the dot-com frenzy of the late 1990s. And, yeah, that striking similarity has folks thinking that the AI boom is fated to suffer the same kind of big, money-losing bust. But here’s why they’re wrong.

Four reasons why this boom isn’t like that boom.

1. A clearer path to profitability.
Like their dot-com predecessors, AI-related stocks have seen rapid gains fuelled by investor zeal. However, while the dot-com boom often lacked tangible business models, today’s AI developments are underpinned by real-world applications that have clear profitability potential. The technology is already being tagged for use in healthcare where it can diagnose diseases and in finance where it can detect fraud and enable advanced algorithmic trading.

2. Funding and cash flow.
Unlike in the 1990s, when many tech companies borrowed money to finance their quick growth, today’s AI leaders have much firmer financial footing. Sure, the $200 billion that’s projected to be spent on AI this year sounds huge, but that’s pocket change compared to the combined free cash flow the mega-cap tech giants raked in last year (and that's not even counting the fat stacks of money they’re already sitting on). And that comfortable financial position shrinks the chances of a liquidity crisis like the one that contributed to the dot-com crash.

3. Rapid adoption rates.

One of the key differences between now and the dot-com era is how quickly people are adopting AI technologies. For example, AI trailblazer ChatGPT hit 100 million users in just a few months – much faster than any internet service ever managed to do during the dot-com heyday. That speedy uptake shows there’s huge demand for AI, with loads of people ready to integrate it into their lives and businesses. It’s a solid foundation that points to more than just short-term excitement.

4. Profit-driven AI investment deployment.

In the dot-com era, companies were built on the assumption “build it and they will come”, without too much thought about how to actually make money. Today, AI investments are more focused on practical returns. Tech companies are pouring money into AI infrastructure like advanced data centres, which are crucial for the long haul. Having learned from the dot-com bubble, investors now demand greater accountability in how tech companies spend their cash. The shift shows that the focus is on creating long-running value, not just riding a wave.

But, sure, there are still risks to consider.

The biggest risk is that AI-linked stocks have really high valuations. Just look at Nvidia, which has seen its stock price skyrocket by 2,600 times in the past five years thanks to the fervor surrounding AI. And those gains could be a double-edged sword: if AI growth slows, those lofty stock prices might come crashing down. In other words, while AI companies are delivering actual earnings growth, there’s always the possibility that the market’s buzz could outpace those results, resulting in a more volatile ride.

The competitive landscape poses another serious risk. AI is tearing down entry barriers faster than ever, so the tech giants might soon find themselves facing some feisty challengers – OpenAI, Perplexity, or Anthropic, to name a few. For the mega-caps, it’s not about splashing around enough cash to stay on top: the name of the game these days is survival. Right now, Amazon and Microsoft dominate cloud-based AI services. But as the technology matures, smaller startups might emerge with more innovative solutions, disrupting the status quo and leading to more volatility in the market.

What’s the opportunity then?

If you’re thinking of investing in AI, it’s important to take a balanced approach. Sure, the tech giants are sexy, but it’s smart to diversify beyond them. So here’s what to think about when you do.

• Consider AI-related infrastructure.

AI's rapid development depends heavily on the infrastructure that supports it. And that creates opportunities beyond the tech behemoths. For instance, energy companies that support the power needs of data centres or firms that specialize in data centre cooling systems could benefit from increased demand as AI services grow. Companies like Amphenol, Caterpillar, and Quanta Services are examples of non-tech firms that already play vital roles in supporting AI infrastructure.

• Investing in semiconductors.

With AI’s reliance on advanced computing power, semiconductor companies are a pivotal part of the AI ecosystem. Nvidia and AMD are key players in this space, designing chips that enable high-performance AI models. Additionally, TSMC plays a crucial role as the leading manufacturer of these chips.

• Balancing growth with value.

AI stocks tend to be more volatile because of their high valuations. To reduce your risk, balance these investments with more stable sectors like industrials or utilities. These sectors might not offer the same explosive growth, but they can help keep your portfolio steady during tech-driven stock market downturns.

• Stay updated on regulations.

AI is developing fast, and regulations around data privacy and AI ethics are likely to change. Keeping an eye on those shifts and their potential impact on a company’s profitability can help you make smarter investment decisions.

AI stocks have a lot of high-flying potential, but it’s essential to stay grounded when you’re investing. Don’t just chase the hype – think about the bigger picture. Look for opportunities in the ecosystem that supports AI, like semiconductors and infrastructure, rather than putting all your eggs in one buzz-filled basket. By diversifying into related sectors, you’ll be better positioned to benefit from the boom, while staying protected from the risks.