AI-Washing:
The Potential Dot.Com Bubble Dejavu...
Artificial intelligence is transforming software, but the gold-rush mentality around “AI-powered” everything has begun to look (and behave) like the dot-com bubble. Below is a concise look at the evidence, the warning signs, and what real AI work actually entails.
AI-Washing: Is Now Commonplace
"AI washing" (or AI-washing) is a term used to describe when a company exaggerates or falsely claims that their product or service uses artificial intelligence (AI) to make it seem more advanced, innovative, or valuable than it actually is.
Key Characteristics of AI Washing:
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Marketing Hype: Companies may brand simple automation or rule-based systems as "AI" even when no real machine learning or intelligent processing is involved.
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Misleading Claims: Software that only follows basic pre-programmed instructions may be marketed as "AI-powered."
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Investor or Consumer Manipulation: Often done to attract attention, funding, or sales by riding the AI trend.
Why It Matters:
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40 % of start-ups that market themselves as AI companies don’t meaningfully use AI. That headline figure comes from MMC Ventures’ large-scale review of 2,830 European “AI” start-ups.(theverge.com)
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Analyst firms Gartner and Forrester both warn of “AI fatigue” as executives grow wary of inflated claims and thin technical substance.(business-reporter.co.uk, forrester.com)
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Many smaller vendors bolt a simple rules engine or a call to an OpenAI/Anthropic API onto their app, then pitch it as proprietary innovation—raising money and customers’ expectations in the process.
Classic Bubble Signals Are Flashing
| Dot-Com Bubble (1998-2000) | 2024-25 AI Cycle |
|---|---|
| Start-ups added “.com” or bought a domain to boost valuation | Pitch decks add “AI” or “Agent” (even when the only “model” is a prompt) |
| IPOs with no revenue soared | Pre-revenue “AI” firms raise nine-figure rounds at multibillion-dollar valuations |
| Collapse began when cash-burn outpaced traction | Mass layoffs and pivots already hitting some over-funded AI start-ups |
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Case in point: text-to-image darling Stability AI went from a $1 billion valuation to leadership turmoil, lawsuits, and cash-flow crises in barely a year.(thetimes.co.uk)
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VC pace is cooling. The Wall Street Journal notes that investors are “showering AI start-ups with cash,” but many still have no repeatable revenue model.(wsj.com)
What Counts as Actual AI Engineering?
Real, defensible AI work usually involves all three of the following:
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Original data — curating or collecting proprietary, high-quality datasets.
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Model training or fine-tuning — building or adapting neural-network architectures to that unique data.
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Problem-specific evaluation & iteration — proving the model solves a novel or mission-critical task better than alternatives.
Anything that simply calls GPT-4 or glues together off-the-shelf classification APIs is integration, not invention. Useful? Absolutely. But it doesn’t justify sky-high “deep-tech” multiples.
Yes, the Technology Is Real—The Valuations Aren’t
Just as the internet really did change everything after 2000, machine learning and generative models will endure. The difference is that:
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Capital efficiency matters again. Cloud-compute costs and model-inference bills can explode if growth projections miss.
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Regulation is coming. Copyright, privacy, and model accountability rules arriving in the US, EU, and elsewhere will raise compliance costs and weed out thin tech.
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Timeline for a correction: Multiple VCs and industry analysts expect an M&A shake-out or down-round cycle between 2026 and 2027 as hype settles and revenue-per-token reality sets in.
Takeaways for Rational Observers
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Skepticism is healthy. Ask for a live demo and technical architecture before buying or investing.
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Follow the cost curve. If a start-up’s COGS rise linearly with every user query, long-term margins will be rough.
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Look for moat-building data. Unique datasets and domain expertise matter more than the flashiest UX wrapper on ChatGPT.
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Expect consolidation. When the tide goes out, companies solving real problems with defensible IP will be the ones still standing.
The Bottom Line
I really hope you, the reader, don't feel that I am just being cynical: plenty of firms are over-selling me-too automations as cutting-edge AI, and the market is pricing many of them as if exponential revenue is guaranteed. History suggests a shake-out is coming, and that’s ultimately good news for serious builders who pair genuine innovation with business discipline.
Check out or article about AI Mechanistic Interpretability As Well!!
Created & Maintained by Pacific Northwest Computers
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