It was December 1999. Tech investors were riding high, convinced that a website and a Super Bowl ad were all it took to get rich quick. Spending was mistaken for growth; marketing was mistaken for a business model. In just a few months, the dot-com boom would go bust: $1.7tn in market value vanished, and the broader economy took a $5tn hit.
Yet something remarkable emerged from the wreckage. The post-crash internet wasn’t defined by speculation, but by creation: the rise of web 2.0 and open-source software – and the birth of platforms like Firefox and Wikipedia. The lesson is simple: when bubbles burst, what comes next can be better, if we build it differently.
Today, history is repeating itself – this time with AI.
The AI boom looks eerily familiar. Nearly 80% of stock gains in 2025 are concentrated in just seven companies – Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla, all of whom are vying for control of the full AI stack that will underpin our shared future – hardware, software, data, energy and infrastructure. This isn’t just about market share, it’s about who decides how billions of people learn, create and see the world.
That level of concentration should worry us all.
And like the dot-com days, valuations are rocketing without clear paths to profitability. Companies are selling the fantasy that AI will replace human workers, even though 95% of AI experiments inside firms fail to reach production. And instead of building public-interest tools that expand human potential, much of the industry is generating what Cory Doctorow calls “productive residue” – a flood of synthetic media, misinformation and deepfakes.
The problem isn’t AI itself; it’s the current economic logic behind it.
This is not inevitable. It’s the result of an economic model that treats technology as an extractive industry – hoarding data, consolidating power and externalizing harm. The AI arms race is driven not by innovation, but by domination, favoring profit over humanity.
A different economic model already exists
The good news is that an alternative model already exists. Around the world, open-source developers and mission-driven companies are building shared infrastructure for trustworthy AI – transparent, auditable and locally adaptable. They are proving that innovation need not depend on monopolistic control of data.
This is evident in the companies leading the way, the founders building tools that are both values-driven and competitive. Companies like Hugging Face, which runs the world’s most widely used open-source machine-learning model and dataset hub; Flower AI, which enables decentralized, federated learning to challenge the dominance of centralized big models; and Oumi, which offers a fully open-source platform for building and deploying custom AI models on local infrastructure rather than closed clouds. And many more.
These aren’t speculative bets; they’re seeds for a more sustainable, pluralistic tech ecosystem….
Source link
Disclaimer
We strive to uphold the highest ethical standards in all of our reporting and coverage. We blogs.grocliq.com want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.
Website Upgradation is going on for any glitch kindly connect at [email protected]
