The AI Bubble Will Burst — and That Could Be a Good Thing If We Act Responsibly

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The AI Bubble Will Burst — and That Could Be a Good Thing If We Act Responsibly

The late 1990s were a heady time for technology investors. In December 1999, the prevailing belief was that a flashy website and an expensive Super Bowl advertisement were all it took to build a successful company. Spending was confused with growth, hype with sustainability, and attention with value. Within months, the dot-com bubble burst. Nearly $1.7 trillion in market value disappeared, and the global economy absorbed losses estimated at $5 trillion.

Yet the story of the dot-com crash did not end in failure. Something far more meaningful emerged from the ruins. The post-crash internet shifted away from speculation toward creation. This period gave rise to Web 2.0, open-source software, and enduring platforms such as Wikipedia and Firefox. The key lesson was simple but powerful: when a bubble bursts, what comes next can be better—if we choose to build differently.

Today, we are witnessing a strikingly similar moment with artificial intelligence.

A Familiar Pattern of Excess

The current AI boom echoes the excesses of the dot-com era. In 2025 alone, nearly 80% of stock market gains were concentrated in just seven companies: Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. These firms are racing to control the entire AI ecosystem—from hardware and software to data, energy, and infrastructure. This is no longer just a competition for market share; it is a struggle over who gets to shape how billions of people learn, create, communicate, and understand the world.

Such concentration should concern everyone. When so much power rests in so few hands, democratic choice, innovation, and accountability are at risk.

As before, valuations are soaring without clear paths to profitability. Many companies promise that AI will soon replace human labor at scale, despite the reality that around 95% of AI experiments within organizations never make it into real-world production. Instead of empowering people, much of today’s AI output consists of synthetic content, misinformation, and deepfakes—what writer Cory Doctorow describes as “productive residue.”

The issue is not artificial intelligence itself. The real problem lies in the economic logic currently driving its development.

The Cost of an Extractive Model

Today’s AI arms race is built on an extractive economic model—one that prioritizes hoarding data, consolidating power, and externalizing social harm. Rather than focusing on meaningful innovation, this model rewards domination and scale, often at the expense of transparency, privacy, and human agency.

But this trajectory is not inevitable.

A different economic model already exists—one that treats technology as shared infrastructure rather than private property to be exploited.

A Viable Alternative Is Already Taking Shape

Across the world, open-source developers, researchers, and mission-driven companies are building trustworthy AI systems that are transparent, auditable, and adaptable to local needs. These efforts demonstrate that innovation does not require monopolistic control over data or computing power.

Several companies exemplify this approach. Hugging Face has become the world’s most widely used open-source hub for machine-learning models and datasets. Flower AI is enabling decentralized, federated learning that challenges the dominance of massive centralized models. Oumi provides a fully open-source platform that allows organizations to build and deploy custom AI models on their own infrastructure instead of relying on closed cloud ecosystems.

These are not speculative experiments. They are the foundations of a more sustainable and pluralistic technology ecosystem—what can be described as a “double-bottom-line” model that values both mission and profit.

Slop Is Not Our Destiny

If history is any guide, the current AI frenzy will eventually slow down or collapse, much like the dot-com boom. But a crash does not have to mean decline. It can mark the beginning of renewal.

After the dot-com bubble burst, open-source software—especially the Linux stack—rose to prominence and ultimately outperformed proprietary systems. Over the past two decades, open-source technologies have generated an estimated $8.8 trillion in economic value. New research suggests that businesses could unlock tens of billions more by shifting from closed AI platforms to open-source models.

The potential value of building AI differently today is enormous.

Choosing a Better Future

When the AI bubble finally pops, we will face a critical choice. We can rebuild the same monopolistic system, or we can design an economy that is pro-human, values-driven, and inclusive. That means embracing open models, transparent governance, and fair participation in the value AI creates.

It also means focusing on what people truly want from technology: privacy, security, agency, and joy. The promise of AI is not infinite scale or total automation. It is the ability to make life easier, more creative, and more meaningful—without sacrificing dignity or choice.

This future is already beginning to take shape through privacy-preserving, open-source AI tools such as browser and email assistants that respect user autonomy.

Imagine a world where communities host small, energy-efficient AI models tailored to their own needs. Where developers collaborate instead of competing. Where innovation is measured not by market dominance, but by public good.

This is not a utopian dream. It is a practical path forward—if we have the courage to rethink the economics of innovation.

The dot-com crash gave us the modern web. The coming correction in AI could give us something even better. In the end, the choice is ours: allow a handful of corporations to own the future, or build it together.