The technological environment vibrates with a familiar energy. The enthusiasm surrounding Artificial Intelligence is reminiscent of what, over twenty years ago, accompanied the rise of the Internet. An euphoria that experts define as a classic “cyclical speculative pattern”, where technological innovation dangerously intertwines with financial speculation.
The history of technological bubbles offers us a disturbing mirror of the present. In 1998, tech company valuations exceeded the general market by 40%. At the peak of the dot-com bubble in 2000, this premium had risen to a vertiginous 165%. Today, we observe a similar dynamic, but with a crucial difference: the epicenter is no longer Wall Street, but the private venture capital market, where AI startup valuations reach levels that would make even the boldest speculators of the dot-com era pale.
The OpenAI case is emblematic of this new speculative era. The company, while burning through $8.5 billion in training and personnel and projecting a possible $5 billion loss, is valued at over $100 billion in the secondary market. This scenario dangerously recalls WorldCom, the telecommunications giant that collapsed in 2002 after revealing it had inflated its profits by $3.8 billion.
The current transformation is fueled by what analysts call the “perfect technological storm”, characterized by three converging elements:
- Big Data provides the fuel for machine learning
- Advanced computing power offers the necessary computational engine
- Deep learning algorithms represent the brain of the operation
This convergence has created a self-empowering innovation cycle, where each element amplifies the others, generating what experts define as a “technological avalanche effect”.
Market behavior reveals disturbing parallels with the past. The phenomenon of “AI washing” – adding the “AI” suffix to any product or service – worryingly recalls the “.com” era and the more recent “blockchain” mania. According to a 2023 study, over 60% of companies that added “AI” to their name saw their stocks rise significantly, regardless of the actual technology implementation.
Investor psychology plays a central role in this dynamic. FOMO (Fear Of Missing Out) fuels what financial psychologists call “digital herd behavior”, where investment decisions are driven more by emotionality than rational analysis. Social media and trading platforms have democratized access to investments, creating what experts define as “gamblification of trading”, a phenomenon that transforms investment into a risky form of entertainment.
The situation is further complicated by what analysts call “asymmetric regulatory void”. While the European Union has approved the AI Act and American states like Colorado are taking their first legislative steps, much of the sector remains in a regulatory limbo. This regulatory fragmentation creates what experts define as “regulatory arbitrage”, where companies can exploit jurisdictional differences to evade stricter controls.
The talent war has reached unprecedented levels, creating what is defined as the “super-salary market”. AI experts’ salaries have exceeded one million dollars, generating what economists call a “distortive effect on the labor market”. This dynamic recalls the race for programmers during the dot-com bubble, but with even deeper implications for companies’ financial sustainability.
The global dimension of the AI bubble introduces unprecedented complexity. If the dot-com bubble was primarily an American phenomenon, today we are witnessing what analysts call a “bipolar technological competition” between the United States and China. The Chinese market, with over 1.4 billion potential users and e-commerce growth three times higher than America’s, represents a new innovation pole challenging Silicon Valley.
This bipolarity creates what experts define as the “technological mirror effect”, where innovations from one market quickly reflect in the other, accelerating both development and systemic risks. Chinese unicorns like ByteDance demonstrate this dynamic: the company has reached an extraordinary valuation thanks to TikTok, creating what analysts call a “global network effect” – a phenomenon impossible in the dot-com era.
Investment patterns reveal further anomalies. The phenomenon of “valuation disconnect” – the divergence between private and public valuations – has reached extreme levels. While listed AI companies show reasonable multiples, the private market exhibits what experts call “unicorn inflation”: valuations that defy any traditional financial logic.
Venture capital itself is undergoing a radical transformation. AI investments have created what is defined as “dry powder overflow” – an excess of available capital desperately seeking investment opportunities. This has led to what analysts call “compressed due diligence”, where investment evaluation time is drastically reduced for fear of missing opportunities.
The transformation of business models adds further complexity. AI companies are creating what experts call the “economy of promise”, where valuations are based more on future potential than current results. This dangerously recalls the “build it and they will come” mentality of the dot-com era, which led to the failure of companies like Pets.com and Webvan.
Experts identify three possible future scenarios, what is defined as the “AI bubble trilemma”:
- “Soft Landing Scenario”: A gradual adjustment of valuations, supported by:
- Progressive technological maturation
- Implementation of balanced regulatory frameworks
- Orderly market consolidation
- “Burst Scenario”: A sudden collapse caused by:
- Failure of prominent unicorns
- Investor confidence crisis
- Sudden regulatory tightening
- “Bifurcation Scenario”: A market separation between:
- Companies with concrete and sustainable AI applications
- Startups based mainly on hype and promises
History suggests that technological revolutions follow what economists call the “modified adoption cycle”. Electricity took three decades to transform industry after the installation of the first power plants. AI might follow a similar path, characterized by what experts call “innovation lag” – the gap between technological potential and practical implementation.
To navigate this transition, analysts suggest an approach based on three fundamental pillars:
- “Value-First Framework”:
- Focus on AI applications with measurable impact
- Concrete performance metrics
- Verifiable ROI
- “Sustainable Growth Model”:
- Organic growth based on real revenue
- Sustainable cost structures
- Calibrated investments
- “Regulatory Compliance Strategy”:
- Proactive anticipation of regulations
- Robust ethical frameworks
- Operational transparency
The fundamental lesson emerging from historical analysis is that technological bubbles follow what is defined as the “technological Minsky pattern”: innovation generates optimism, optimism leads to speculation, speculation creates a bubble, and the bubble inevitably bursts. The challenge is not to avoid this cycle – probably impossible – but to navigate it in a way that preserves and capitalizes on the true value of innovation.
The future of AI will be determined not so much by the technology itself, but by our ability to manage what experts call the “sustainable innovation paradox”: how to maintain innovative momentum while avoiding speculative excesses. Only through a balanced approach that balances enthusiasm and pragmatism can we transform this technological revolution into lasting value for society.
If you are curious about the AI Euphoria or tech Bubble topics (?)
I recommend listening to the next Podcast episode (#6) of Daedalus Debugger: The Architect in the Digital M@ze 😉
Bibliography
Cogman, D. & Lau, A. (2016). “The tech bubble puzzle: Public and private capital markets seem to value technology companies differently”. McKinsey & Company.
Floridi, L. (2024). “Why the AI Hype is another Tech Bubble”. Digital Ethics Center, Yale University.
Soni, N., Sharma, E.K., Singh, N., & Kapoor, A. (2020). “Artificial Intelligence in Business: From Research and Innovation to Market Deployment”. Procedia Computer Science, 167, 2200–2210.
Zhou, X., Yang, Z., Hyman, M. R., Li, G., & Munim, Z. H. (2024). “Impact of artificial intelligence on business strategy in emerging markets: a conceptual framework and future research directions”. International Journal of Emerging Markets, 17(4), 917-929.
“Artificial intelligence: The next frontier for investment management firms” (2019). Deloitte.