Over the last three years, the tech world has been living under a "mirage of abundance." We became accustomed to the idea that the most advanced intelligence on the planet cost a mere €20 per month. But the rules of the game have changed. As tech experts like Jon Hernández and Xavier Mitjana have pointed out, the "bargain era" of subsidized AI is over, giving way to a period of skyrocketing costs and a loss of control over our data.
For professional users and businesses, this isn't just a pricing change: it is a sovereignty crisis. And the solution isn't found in a new subscription from San Francisco, but in the hardware sitting on your desk.
1. The Economic Wall: From "All-Inclusive" to Pay-Per-Use
Until now, Big Tech (OpenAI, Anthropic, Google) has been "giving away" AI, losing money on every query to capture market share. However, this model is unsustainable. According to consultancies like Gartner, these companies need to increase their profitability by 50,000 to 100,000 times to make the math work.
Silicon Valley's response has been clear:
Goodbye to Flat Rates: GitHub Copilot has already announced its transition to a credit-based usage model for June 2026.
"Ultra" and "Heavy" Plans: Google and Anthropic are launching premium tiers ranging.
The Agent Cost: An AI "agent", which works autonomously for 30 to 40 minutes analyzing files, consumes up to 200 times more tokens than a simple chat prompt.
This creates what Jon Hernández calls the "Intelligence Gap": cheap AI for basic content consumption, and extremely expensive AI for production and professional work.
2. The Chip War and the "Commoditization" of AI
While Silicon Valley attempts to hike prices, a silent revolution is brewing on the other side of the world. Due to U.S. sanctions on NVIDIA’s most advanced chips, companies like Alibaba (with its Qwen models) and DeepSeek have been forced to master efficiency.
Xavier Mitjana explains this masterstroke: commoditization. If China releases Open Source models that are as powerful as GPT-4 but free to download, the business model based on charging for "cloud tokens" collapses.
Models like Qwen 3.5 or 3.6 already allow for complex programming tasks or the analysis of hundred-page documents (such as financial reports) to be performed entirely locally. This means that, for the first time, power is no longer concentrated in the data centers of just three companies, it is in your own computer.
3. Privacy and Sovereignty: The 11% That Should Worry Us
This isn't just a matter of money; it's a matter of security. Reports from Cyberhaven reveal that 11% of the information employees paste into chatbots like ChatGPT is confidential data.
From the source code of a new chip to legal documents or medical records: every time we use the cloud, we lose intellectual property. The alternative, running these models in a Local and Open Source environment, guarantees that not a single bit of sensitive information ever leaves your network.
4. Slimbook: Your Personal Data Center
This is where hardware becomes Vitally important. Running models with 35 billion or 70 billion parameters requires power, memory, and efficiency. At Slimbook, we have spent years defending user freedom, and today that freedom translates into technological sovereignty.
Owning a powerful machine (such as a Slimbook with a dedicated GPU and ample RAM) allows you to:
Bypass Usage Limits: No more "you have reached your weekly quota" messages. Your AI is available 24/7.
Zero Marginal Cost: Once you own the hardware, the AI is free. You don’t pay for "Pro" subscriptions or per-token credits.
Stunning Efficiency: Tasks that a year ago cost thousands of dollars in computing power are now solved locally thanks to the optimization of modern models.
Conclusion: Don’t Be a Tenant of Intelligence
For three years, we have been "tenants" of someone else’s intelligence. We have built our productivity on tools we do not control, tools that could change their pricing or performance overnight.
Artificial intelligence is becoming decentralized. The question is no longer whether you will use AI, but whether that AI belongs to you or if you are dependent on a bill from San Francisco. At Slimbook, we provide the tools to ensure the answer is: the AI is mine.
Key Sources and References:
Jon Hernández (Artificial Intelligence): Analysis of the economic sustainability of AI agents and the transition to credit-based models.
Xavier Mitjana: Analysis of the strategy behind Chinese models (Qwen/DeepSeek), technological sovereignty, and the end of free AI.
Gartner: Profitability projections and growth trends in the generative AI sector.
Cyberhaven: Statistics regarding confidential data leaks in corporate AI environments.
Open Source Benchmarks: Performance data comparing Qwen 3.5/3.6 models against proprietary models.
