To The Moon

The surge in technology and artificial intelligence (AI) has generated comparisons to the exuberant late 1990s internet boom. Valuations are lofty. Headlines ask whether we’re in a bubble reminiscent of the Dot‑com bubble that peaked in March 2000.

Yet while caution is warranted, the current wave differs in fundamental ways — and so do the implications. The challenge for investors is to distinguish between speculative excess and genuine structural transformation.

The Case for Valuation Caution

It’s undeniable: many valuation metrics are at historically elevated levels. For example:

• Forward P/E and CAPE (cyclically-adjusted P/E) are near or above previous peaks. 

• The largest tech companies now occupy a greater share of the market-cap than at the dot-com era’s peak. 

• Large amounts of capital are chasing AI, data-centre infrastructure, chips, and generative AI models — raising the spectre of over-deployment and stretched expectations. 

These facts mean risk is real. If AI adoption or productivity gains disappoint, valuations could correct, perhaps sharply. Investors must remain selective, focused on fundamentals and execution.

But Here’s Why This Time May Yield Something More

While echoes of 2000 are audible, several key differences suggest the AI-driven market wave may not simply end in a collapse and recovery the way the dot-com bust did. Below are some of the distinguishing characteristics:

1. Broad-based foundational technology rather than narrow hype

• Analysts argue that AI represents a “wave” across multiple layers of the economy — from hardware (chips, datacentres) to software, applications, cloud infrastructure. 

• Unlike many dot-com firms which had untested business models and minimal earnings, many current tech firms generating AI value already have significant revenue and strong balance sheets. 

• Because the technology is deeply embedded across the stack, the timeframe for value realisation is longer — and less likely to collapse overnight.

2. More disciplined capital markets and business models

• The 1990s saw a flood of IPOs and many companies with little or no profit, burning heavy cash. 

• Today, while valuations are high, many large tech firms backing AI efforts are profitable, with strong balance sheets. 

• Venture capital and private funding are playing a larger role before public listings, which may moderate the “all-in at IPO” fervour seen in 2000.

3. Productivity potential and tangible economic impact

• The dot-com era created lots of hype, and there was value (e.g., e-commerce), but many firms failed to deliver. 

• With AI, there is near-term evidence of business model transformation (automation, generative AI, large language models) and large infrastructure build-out (you may see multi-trillion-dollar commitments to data centre build-outs). 

• If productivity gains materialise, then high valuations may be more justified.

4. Longer time horizon and not a “boom then bust in 2 years” dynamic

• Because the AI wave may take years (rather than months) to fully play out, the outcome may be a more gradual unfolding of value rather than a sharp collapse. 

• This suggests the “bubble” framing may be too simplistic: rather than “all or nothing,” the likely narrative is “selective winners, consolidation, some excesses, but enduring value.”

What This Means for Investors

Given the above, my proposition is: the market isn’t simply a replay of the dot-com bubble. Instead, there is a legitimate structural shift underway — but one fraught with risk and variability. Key takeaways:

• Be selective: Not all AI-related companies will succeed. Execution, business model clarity, profitability or path thereto, and differentiation matter.

• Focus on fundamentals: High valuations demand high growth and value creation. Firms with strong balance sheets, clear monetisation paths, deep technology moats are more compelling.

• Expect volatility: Even if the wave is real, there will be corrections, hype-cycles, and flush of capital that may overshoot. Mistakes will happen.

• Long time-horizon orientation: If AI is foundational, the real payoff may come over many years (5–10+). Short-term corrections don’t necessarily negate long-term opportunity.

• Beware the “bubble” mentality: That is, simply buying anything labelled “AI” because you think hype will lift you. Valuations still matter.

• Watch macro & infrastructure risk: Large-scale datacentre build-outs, chip supply, power consumption, regulatory and ethical issues — these are real constraints. If they bite, the expected returns may be delayed or diminished.

Conclusion

Yes, the market is richly valued — and yes, comparisons to the dot-com era are not irrational. But to assume history must repeat identically (with a fast crash and decade-long recovery) overlooks a different set of conditions today: more mature technological foundation, stronger business models, broader economic embedment, and a longer horizon of real value creation.

For disciplined investors who distinguish between speculation and structural innovation, the AI era may present a meaningful opportunity — not a mere rerun of 2000’s folly.