It's amazing how quickly tides change. After ChatGPT was released in November 2022, Artificial Intelligence (AI) became the rage. To many, the excitement was reminiscent of the dot-com boom when scores of companies rushed to add a “.com” moniker to their name, hoping to cash in on the gold rush of that era.
In the wake of ChatGPT's emergence, the joke amongst early-stage founders was that if you added anything related to "AI" to your pitch deck, money would pour in at insane valuations. This led to a common narrative of trying to delineate “real” AI companies versus those merely claiming to be AI companies.
Fast-forward thirty months, and the AI landscape looks eerily similar in many respects. Massive funding rounds at eye-popping valuations are back in vogue, driven by excitement around what AI can and is doing. Investors are once again pouring capital into anything with an AI angle – but is this time different? How should one balance AI’s transformative potential against valuations that may or may not be supported by fundamentals and/or long-term sustainability? And will companies leveraging AI become cash-printing machines due to lower operating costs, or will rampant competition and low barriers to entry erode gains?
Let’s examine the state of play.
The surge of money flowing into AI is truly extraordinary. In the United States, roughly 71% of all VC deal value in Q1 went into AI-related investments, thanks largely to OpenAI’s $40 billion funding round. Even excluding that outlier deal, nearly half of all venture capital invested in Q1 still flowed into AI startups.
One rationale behind these rich valuations is the belief that AI can supercharge productivity and profits. Optimists argue that an AI-focused company can scale revenues dramatically with far lower operating costs than a traditional business. In theory, AI automation can handle tasks that once required large teams of employees – suggesting leaner staff, higher margins, and nearly “cash-printing” business models.
Indeed, we’ve seen young AI startups reach meaningful revenues with surprisingly small teams. According to Stripe’s data, the top AI startups are hitting milestones faster than their SaaS predecessors. According to Stripe’s data, AI companies are scaling from $1M to $30M five times faster than the SaaS companies did. This breakneck growth, accomplished often by startups with only a handful of engineers, feeds the narrative that AI businesses enjoy unprecedented efficiency.
Another striking feature of the current AI boom is how quickly new entrants can emerge and scale. Thanks to cloud services and open-source models, the barriers to building an AI-powered product are lower than ever. A small startup can access world-class language models via APIs or open-source alternatives, build a product on top, and go to market in weeks. This has led to scores of AI startups launching each month, many of them achieving rapid early traction. Some founders boast of reaching eight-figure revenue run-rates within their first year of launch, a pace virtually unheard of in traditional software businesses just a decade ago.
The flip side of this speedy scaling is that competitive moats can be shallow. If one team can spin up a particular AI solution quickly, there’s little to stop another from doing the same. Often these startups don’t own proprietary technology – they’re assembling readily available components (open models, public data, etc.) in a clever way. That means when customers look around, they see a constant churn of “smarter” or “cheaper” or “better” tools promising faster insights and lower costs.
The shelf life of any single product’s differentiation has collapsed in the AI era. For enterprise buyers, the next better solution might be only one funding cycle away. And with modern software delivered as a service, the barrier to switching providers is lower than ever – integrations are easier, and many tools can be tried on short-term contracts.
This makes it hard for any single new AI entrant to lock in customers or sustain early growth. The result is intense competition: an AI startup that soared to $5 million in revenue in a few quarters could just as quickly see growth stall or fall if a rival (or a big tech company) launches a comparable offering. In such an environment, long-term winners will likely be those with either superior algorithms, exclusive data assets, or platform advantages that others cannot easily replicate.
It’s also one of the reasons why Physical AI solutions as increasingly attractive (even though they remain under the radar in most circles compared to generative and agentic AI solutions). Physical AI brings intelligence into the physical world, powering machines that can move, manipulate, and make decisions in real environments. It’s much more difficult to bring a robot, autonomous vehicle, or drone to market than it is an application. However, these companies also benefit from recurring revenue streams, are “stickier” due to upfront capital investments, and have real utility in the physical world.
With so much capital chasing AI and many companies trading at valuations untethered from current earnings, it’s natural to ask if AI is in a bubble. There are certainly echoes of the dot-com era: a compelling story (AI will change everything) is driving the investment narrative, and valuations of anything associated with AI have surged. Historically, these kinds of narrative-fueled booms tend to overshoot reality. As investor Rob Arnott observes, by the time a transformative story fully takes hold in the market, “their value may already be captured, if not exaggerated, by current share prices.”
Back in 2000, the internet did dramatically change the world, but that didn’t stop a massive crash in tech stocks when early expectations proved too optimistic. Similarly, today’s AI revolution is very real – but that doesn’t guarantee that every AI startup sporting a multi-billion valuation will live up to its promise.
That said, this isn’t a uniform bubble in the way the dot-com boom was. Unlike the late 1990s, much of the AI excitement is concentrated in the private markets and a handful of tech giants, rather than hundreds of obscure IPOs being bid up by retail traders. The largest AI funding rounds are often led by deep-pocketed strategic investors (Big Tech companies, private equity, or sovereign funds) with a long-term view.
Moreover, certain AI businesses are already generating significant revenue – even if unprofitable – which wasn’t the case for many dot-com startups that were purely speculative. These are signs that there is substance beneath the hype. The challenge is determining how much of that future potential is already overstated in today’s pricing.
For investors, the key is to remain analytic and balanced in the face of the AI frenzy. There’s no question that AI technology is advancing rapidly and starting to deliver genuine productivity gains. Many AI-powered tools are unlocking efficiencies – in some cases, enabling startups or even solo entrepreneurs to build and scale software products with unprecedented speed. This wave could indeed reshape business in the coming decade, much as the internet did in the 2000s. Dismissing AI as “just hype” would be as shortsighted as dismissing the internet in 1999. The opportunities are real, and some companies will ride the AI wave to huge success (and profits).
However, history teaches that not every high-flying innovator will survive intense competition or justify its valuation. Investors should scrutinize whether a company has durable advantages – superior technology, proprietary data, strong distribution channels – or whether it’s one of dozens of AI startups doing roughly the same thing. In an environment where “the next better, faster, smarter product” is always around the corner, putting faith in a single early winner is risky. It can pay to be selective and focus on business fundamentals: revenue quality, path to profitability, and real customer traction, rather than just a buzzword-laden pitch deck.
Finally, it may be wise to treat AI as a long-term transformative trend but be wary of short-term market euphoria. As one analysis noted, when narratives like AI take over, investors face asymmetric risks: the upside of being right about a revolutionary trend can be huge, but the downside of overpaying for the wrong player can be equally dramatic.
In practical terms, that means maintaining diversification and discipline. One doesn’t have to choose between all-in on AI or shunning it entirely – a balanced approach could involve investing in established companies adopting AI and a basket of emerging players, while avoiding the most extreme valuations.
The AI revolution will likely mint some big winners (and plenty of losers). By tempering excitement with analysis and partnering with a firm that can help investors navigate the rapidly changing landscape, investors can aim to ride this wave without wiping out when the tide inevitably recedes a bit.
In the end, AI is here to stay – but so are timeless investment principles that savvy investors must keep in sight.
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