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Generate Wealth Weekly: AI boom or bubble? Why this time may be different

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  Published in Stocks and Investing on Tuesday, October 21st 2025 at 13:09 GMT by The New Zealand Herald
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The AI Boom Versus the Bubble Debate: Why Valuations May Be High – but Not Necessarily Irrational

In a landscape where artificial intelligence (AI) has moved from niche research laboratories to mainstream headlines, investors are grappling with a central question: are the sky‑high valuations of AI‑focused companies justified, or are we on the brink of a speculative bubble? A recent piece in The New Zealand Herald – “AI boom vs bubble: why valuations may be high but not irrational” – tackles this dilemma head‑on, weaving together data, expert opinion, and historical analogues to argue that while price‑to‑earnings multiples for AI firms are undeniably lofty, they can be rationalised by the potential for transformative growth.

1. The Landscape of AI Valuations

The article opens with a snapshot of the AI market. It points out that AI has become a defining theme for many tech giants and emerging startups alike, with firms like NVIDIA, Microsoft, and OpenAI (via its commercial arm) topping the charts. According to the Herald’s analysis, the average price‑to‑earnings (P/E) ratio for the 20 largest AI‑driven companies is hovering around 45, nearly double the historical average of the S&P 500’s 20‑year median of 24. The article compares this figure to the early 2000s dot‑com era, where P/Es for internet companies similarly ballooned before crashing.

But the piece stresses that raw numbers alone cannot dictate investor sentiment. It underscores that AI, unlike past tech fads, is increasingly embedded in critical sectors such as healthcare diagnostics, autonomous vehicles, and supply‑chain optimisation. This integration, the author notes, could generate a new baseline of revenue streams that outpace conventional tech growth metrics.

2. The “High Growth” Narrative

To give context to the inflated valuations, the Herald includes a sidebar that examines the projected growth of AI‑enabled products. Using data from the International Data Corporation (IDC) and a forecast from McKinsey, it shows that AI could contribute up to 14% of global GDP by 2030 – a figure that dwarfs the 2–3% contribution estimated for the broader digital economy over the same period. The article explains that companies are pricing in this future, hence the elevated P/Es.

Further supporting this viewpoint, the author cites a recent earnings call from NVIDIA, where CEO Jensen Huang highlighted a 70% year‑on‑year increase in revenue from AI-related services. The piece also references a Bloomberg report that found AI‑driven cloud services grew 40% faster than the overall cloud market between 2022 and 2024. The narrative is clear: AI’s capacity to create new revenue streams and disrupt established industries justifies the premium investors are willing to pay.

3. The Bubble Counter‑Argument

Not all voices in the article are bullish. The piece includes an interview with investment strategist Dr. Lisa Ng, who cautions that the AI narrative may still be in its “early enthusiasm” phase. Dr. Ng draws parallels to the late 1990s internet bubble, pointing out that many AI firms are still in a pre‑profitability stage, with heavy R&D expenditures and limited monetised products.

Dr. Ng’s skepticism is grounded in the high “price‑to‑sales” ratios many AI companies exhibit. She notes that a 12x price‑to‑sales ratio, common among some AI start‑ups, exceeds what the market has historically tolerated for non‑profit‑generating companies. Moreover, she highlights that AI’s regulatory landscape is still uncertain; governments worldwide are starting to scrutinise data‑driven algorithms for privacy and bias, potentially imposing costly compliance requirements.

The article also explores a historical case study: the 2005 “flash crash” that wiped out a sizeable fraction of AI‑related equities, demonstrating how hype can suddenly turn into panic. By juxtaposing the current enthusiasm with past cautionary tales, the piece urges investors to remain vigilant.

4. Bridging the Two Perspectives

Perhaps the most compelling part of the Herald article is its attempt to reconcile the two camps. The author proposes a “rational‑risk” framework: investors should value AI companies not just on current earnings or sales but on the potential to disrupt entire industries. He recommends a “growth‑adjusted P/E” metric that factors in projected AI penetration rates and the time‑to‑market for key applications.

This approach is illustrated by a case example: a mid‑cap AI company that currently earns $5 million in revenue but has a patented algorithm that could, in five years, generate $200 million in annual revenue for a major telecommunications provider. Using the adjusted metric, the article shows the implied valuation could be justified at a 30x P/E, lower than the current market‑derived 45x, yet still substantially higher than the sector average.

The Herald also points to the cumulative advantage of network effects. As AI algorithms improve and share data across platforms, early adopters could lock in superior performance, creating a self‑reinforcing loop that fuels further valuation growth.

5. Follow‑Up Links and Extended Context

The article links to several external pieces for readers who want deeper dives:

1. Financial Times: “AI valuations hit new highs – is the bubble on the horizon?”
   The linked article expands on the global regulatory developments impacting AI firms, summarising EU’s proposed AI Act and the U.S. Federal Trade Commission’s focus on algorithmic transparency. It also highlights recent court rulings that have forced AI companies to pay for data licensing, adding a potential cost layer to the growth story.

2. McKinsey Report: “Artificial intelligence – the next frontier in productivity”
   This report offers a granular breakdown of AI’s expected contribution to productivity across sectors. It includes a model that projects a 6% annual productivity boost for manufacturing, a 5% boost for finance, and a 7% boost for healthcare by 2035. The article draws on this report’s productivity multipliers to justify why AI companies might command premium valuations.

3. Bloomberg: “Why Nvidia’s GPU revenues are a proxy for AI adoption”
   The Bloomberg link provides a detailed analysis of the GPU market, showing how the demand for high‑performance GPUs is directly correlated with the growth of machine‑learning workloads. The Herald’s author uses this data to argue that companies invested in GPU supply chains may see secondary upside.

6. Bottom Line: An Informed Yet Cautious Optimism

Concluding, the Herald piece reiterates that while AI valuations are undeniably high, the narrative is underpinned by genuine, long‑term value creation. It acknowledges that, like any technology boom, the risk of a bubble exists. However, the article argues that investors can mitigate this risk by applying rigorous growth‑adjusted metrics, staying attuned to regulatory developments, and maintaining a diversified portfolio that balances high‑growth AI bets with more traditional, stable sectors.

In an era where AI is shaping everything from personalized medicine to autonomous logistics, the debate over valuations will likely intensify. The article invites investors to look beyond headline multiples and consider the breadth of AI’s potential impact, urging a balanced approach that marries optimism with prudence.