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This AI bubble is aabsolutelya going to burst a analyst (SP500:)

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  Published in Stocks and Investing on Tuesday, October 21st 2025 at 14:27 GMT by Seeking Alpha
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AI Valuations Re‑evaluated: Why the Bubble Is Likely to Pop

The frenzy around artificial intelligence has turned into a headline‑grabbing story of its own. The recent Seeking Alpha analysis titled “This AI Bubble Is Absolutely Going to Burst — Analyst” provides a sober, data‑driven critique of the current market enthusiasm for AI‑driven companies. The author, an analyst with a track record of dissecting technology overvaluations, argues that the steep price multiples, high growth expectations, and a host of untested assumptions will inevitably lead to a significant market correction.

1. The Anatomy of the Current AI Boom

The article begins by cataloguing the most high‑profile AI‑related stocks that have surged in the past 12 months. These include NVIDIA (GPU maker that powers most AI workloads), Alphabet’s DeepMind, Palantir Technologies, Snowflake, and a raft of “AI‑first” SaaS and cloud providers such as DataRobot and Scale AI. The author notes that many of these firms trade at price‑to‑earnings ratios that exceed 200x, or even higher for the more speculative names. In comparison, the broader S&P 500 trades at a multiple of roughly 20x.

This stark disparity, the analyst argues, is the heart of the bubble. While a few companies have shown genuine AI revenue growth, the majority rely on a narrative of imminent market dominance that is not yet proven. The analyst highlights the role of institutional buying and the “AI wave” narrative in inflating these valuations.

2. Fundamental Red Flags

a. Revenue Growth vs. Profitability

Several AI companies are still in the early stages of profitability. The article points out that even NVIDIA, the market leader, has a profit margin that is roughly 35% lower than it was in 2019, despite its dominant GPU business. For newer AI players, many have yet to generate positive free cash flow. The analyst stresses that growth without a clear path to profitability creates a precarious situation.

b. Technology Risks and Talent Supply

AI models require vast amounts of labeled data and sophisticated infrastructure. The piece discusses how the shortage of data scientists and engineers, coupled with the high cost of GPUs, limits the ability of smaller firms to scale quickly. Moreover, the analysis underscores the risk of model bias and regulatory scrutiny, especially as governments around the world begin to draft AI‑specific legislation.

c. Over‑Reliance on a Few Key Players

The author warns that the AI market’s concentration around a handful of giants – NVIDIA, Google, Amazon, Microsoft – means that any downturn in their fortunes could ripple across the sector. He cites NVIDIA’s exposure to the broader semiconductor cycle and highlights how a slowdown in GPU demand could hit AI companies that rely on that hardware.

3. Macro‑Economic Context

The article places the AI bubble against a backdrop of rising interest rates and a potential slowdown in global growth. With the Federal Reserve tightening policy, the cost of capital for growth‑oriented tech firms is rising. Higher borrowing costs will make it harder for companies to justify the massive valuations currently being offered.

The analyst also touches on geopolitical tensions, particularly between the U.S. and China. As China ramps up its own AI initiatives, U.S. AI companies could face export restrictions that curtail their access to critical components or data. The resulting supply‑chain constraints would further pressure valuations.

4. Historical Parallels and Lessons Learned

In a comparative analysis, the author draws parallels to the dot‑com boom of the late 1990s, the 2000s biotech bubble, and the 2021 meme‑stock frenzy. He explains that, in each case, exuberant valuation metrics – price‑to‑sales, forward‑looking multiples – ultimately collapsed when fundamentals failed to match expectations. The key takeaway: hype can inflate prices to an unsustainable point, but it is the underlying business model that determines long‑term value.

5. Potential Catalysts for a Correction

The article lists several triggers that could accelerate a market pullback:

• Rate hikes that further increase discount rates, making high‑growth valuations less justifiable.
• Supply‑chain bottlenecks that limit GPU availability and raise operational costs.
• Regulatory crackdowns on AI data usage or algorithmic transparency.
• Profitability pressure as investors demand real earnings rather than growth projections.

The author stresses that while a correction might be painful in the short term, it would create buying opportunities for value‑focused investors.

6. Investment Recommendations

Rather than a blanket sell‑off, the analyst recommends a more nuanced approach:

• Rebalance portfolios to reduce concentration in high‑growth AI names that have minimal earnings potential.
• Consider AI‑infrastructure companies that provide hardware or cloud services with more stable revenue streams (e.g., NVIDIA, AMD, Texas Instruments).
• Add defensive sectors that can weather an AI downturn, such as utilities or consumer staples.
• Use dollar‑cost averaging to gradually re-enter positions if valuation fundamentals improve over time.

7. Links to Further Reading

The article includes several embedded links that expand on the discussion:

1. Seeking Alpha: “NVIDIA’s Margins in 2023: A Declining Trend?” – An in‑depth look at NVIDIA’s cost structure.
2. Seeking Alpha: “Regulatory Risks in AI: What Investors Need to Know” – A review of emerging AI policies worldwide.
3. Seeking Alpha: “AI and Supply Chain: The GPU Bottleneck” – Analysis of hardware constraints impacting AI startups.
4. Bloomberg: “U.S.-China Tech Tensions Escalate” – A broader context on geopolitical risk.

These additional resources provide further context on the valuation pressures and external factors shaping the AI landscape.

Conclusion

The Seeking Alpha analysis presents a compelling argument that the current AI‑related stock surge is unsustainable. With inflated valuations, uncertain profitability, and mounting macro‑economic risks, the sector appears primed for a correction. Investors are advised to remain cautious, diversify beyond the AI hype, and focus on companies with robust fundamentals and clear pathways to earnings. The next few years will be a critical test of which AI firms can translate technological promise into financial reality and which will be left behind in the fallout.