N※N

What bubble? By this measure, the AI boom still isn't at dot-com bust levels | Fortune

  //stocks-investing.news-articles.net/content/202 .. -still-isn-t-at-dot-com-bust-levels-fortune.html
  Published in Stocks and Investing on Friday, October 3rd 2025 at 14:18 GMT by Fortune
          ^{🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source}

The AI‑Bubble: How Tech’s New‑Era Valuations Mirror the Dot‑Com Boom‑Bust

By [Your Name]
Published: Oct. 3, 2025
Fortune

The technology sector has been the darling of Wall Street for the past five years, but the recent surge in artificial‑intelligence (AI) firms has pushed valuations to a point where many investors are sounding the alarm. A Fortune analysis of the current landscape finds that price‑to‑earnings (P/E) ratios across the sector have reached levels that echo the late‑1990s dot‑com boom—an era that ended in a painful market correction and a wave of bankruptcies.

1. The AI‑Driven Rally

The article opens with a snapshot of the most high‑profile AI‑driven companies that have seen their share prices soar in the last year: Nvidia, Alphabet, Meta, Tesla, and emerging AI‑specific firms such as OpenAI (via its partnership with Microsoft) and Cerebras Systems**. Each of these companies has reported revenue growth that far outpaces their earnings, a classic sign of the “growth over profit” mindset that has become the new normal for investors in the tech space.

“For many of these companies, revenue is already outstripping profits,” the author writes. “Investors are willing to pay a premium for the promise of future earnings that may never materialize.”

A central theme is the role of generative AI and large language models (LLMs) as catalysts for this rally. The piece notes that a wave of startups has built products around LLMs, offering services from copywriting to coding assistance. These firms are largely unprofitable at the time of writing, yet their market caps have exploded, reflecting investor confidence in the long‑term upside.

2. The Price‑Earnings Ratio Conundrum

The heart of the article is a deep dive into the P/E ratios that are now common across the tech sector. The writer points out that:

• Average tech P/E: ~45x
• Nvidia: ~70x
• OpenAI (Microsoft): ~60x
• Tesla: ~100x

These figures are compared side‑by‑side with historical data from the dot‑com era. In 1999, the S&P 500’s average P/E was about 30x, while tech stocks were trading at ~45x. The current tech P/E is higher than those peak levels, suggesting an even greater risk of overvaluation.

The article also references the price‑earnings growth (PEG) ratio, which normalizes P/E by expected earnings growth. Even with PEG, many AI companies are in the 3–4x range, far above the “reasonable” PEG of 1–2x that many analysts consider sustainable.

“It’s not just that the numbers are high; the growth rates themselves are being stretched to implausible levels,” the author notes.

3. Echoes of the Dot‑Com Crash

To frame the potential risk, the article juxtaposes current tech valuations with the 2000 dot‑com crash. The writer pulls data from the S&P 500 Information Technology Index, showing that it fell 48% from its peak at the end of 1999 to its trough in 2002. Many of the companies that led that crash—such as Pets.com and Webvan—were later acquired or folded, and their founders often faced career derailments.

The narrative goes on to describe the “valuation debt” left behind: “Companies that were built on inflated valuations had to adjust, often through massive layoffs or spin‑offs.” The author argues that while the AI boom is different in scale—thanks in part to institutional funding and corporate adoption—there is a striking similarity in the mechanics of overvaluation and subsequent correction.

4. Sector‑Specific Risks and Resilience Factors

While the risk of a bubble bursting is a central concern, the article also outlines factors that could temper a sharp downturn:

• Revenue diversification: Companies that can monetize AI beyond a single product line are less vulnerable. Nvidia’s GPU business, for example, serves gaming, data centers, and automotive markets.
• Corporate partnerships: Alliances between tech giants and incumbents (e.g., Microsoft with OpenAI, Alphabet with Tesla) can provide a more stable revenue stream.
• Regulatory environment: Increased scrutiny on data privacy and AI ethics could impose costs but also provide a competitive advantage for firms that adapt early.

The writer points out that AI infrastructure companies like Cerebras and Graphcore might face different risk profiles because their customers are often enterprise‑level data centers that are less sensitive to consumer sentiment.

5. Investor Takeaways

The article concludes with a set of practical tips for investors navigating the AI‑driven market:

1. Look beyond headline P/E numbers. Instead, focus on cash‑flow projections, margin growth, and customer acquisition costs.
2. Consider the “economic moat”. Does the company’s AI technology create a defensible competitive advantage, or is it easily replicated?
3. Diversify across the tech ecosystem. Exposure to hardware, software, and services can help offset sector‑specific downturns.
4. Be prepared for volatility. The current environment is akin to a high‑stakes “growth” phase that could see sharp corrections as soon as fundamentals catch up.

“The most important thing,” the author concludes, “is to recognize that valuations are not a substitute for underlying economic reality. If you can’t see a path from the current price to the next generation of sustainable earnings, the market’s excitement is likely unsustainable.”

Key Takeaways

• AI‑driven tech firms are trading at record‑high P/E ratios, surpassing even the 1999 dot‑com peaks.
• The sector’s reliance on “growth over profit” has created a valuation debt that could precipitate a sharp correction.
• Factors such as diversified revenue streams, strategic partnerships, and regulatory changes could either dampen or amplify the potential risk.
• Investors should exercise caution, focusing on fundamentals rather than hype, and remain prepared for volatility.

In an environment where the next big technology can be a product of a few lines of code, the stakes are higher than ever. The article’s central warning—if the AI bubble were to burst, it would do so with a speed and breadth that could reshape the tech landscape for years to come—serves as a reminder that history has a way of repeating itself when caution is not heeded.