Ending Enshittification: The Case for Specialization and Sovereignty in AI 

In AI, trust is fragile. One confidently wrong decision can derail adoption faster than a dozen wins can repair. Or as Tanmai Gopal puts it, “For serious work, one high-confidence miss costs more credibility than ten successes earn.” 

This is the reality shaping enterprise and government AI today. When systems promise certainty and fail, trust and credibility collapse. It’s why generic pilots fail. They overpromise and underdeliver. 

This fragility isn’t new. But the conversation around it has evolved.  

Enter Enshittification, a term coined by Cory Doctorow and spotlighted by Gabriela Vogel during her keynote at Gartner IT Symposium/XPO in Barcelona earlier this month. It describes the slow decay of platforms that chase growth and engagement at the expense of quality and trust. 

And why did this word dominate the Gartner stage, you might ask? Because it captures exactly what’s happening in AI right now: hype-driven pilots, black-box promises and generic solutions that corrode confidence instead of creating value. 

Haven’t you noticed? Everyone is talking about sovereignty in their marketing, but few are actually proving it in their technology. We’ve been talking and walking the walk of sovereign AI solutions since our founding in 2019.  
 
Sovereignty builds trust. Trust enables specialization. While others are building pilots that fail to generate value in real life scenarios, we showed how they can be successful. The key is a trustful and close collaboration between AI vendor and customer.  

Our customers trust our sovereign AI platform PhariaAI with their real operational data and intellectual property. They commit to sharing their expertise and processes so we can build a solution that fits them perfectly. The system is tested in a real environment so that any potential blockages become apparent and can be resolved early in the process. This is how AI pilots become successful. Built for durability, grounded in operational reality, co-created with experts, not for the hype nor click-bait headlines. 

Our Stance on Stage 

To showcase this in practice, we took the stage with one of our customers during our live session, Why AI Pilots Fail: Generalists Don’t Succeed – Specialists Do, presented by our very own VP of Community, Sven Körner.  Together, we demonstrated with co-creation looks like in action. 

This customer’s engineering team faces massive volumes of reports, fragmented systems and highly technical documentation — time better spent on innovation instead of searching and reconciling information across silos.  

To overcome this, they partnered with us to deploy domain-specific AI agents that directly support engineers in their daily workflows. These intelligent agents autonomously perform analytical and administrative tasks. For example, compiling and contextualizing all problem reports across systems — helping teams make faster, better-informed decisions. 

We combined a knowledge graph with neural networks to create a deep, contextual understanding of the customer’s data landscape. This made the data usable for us to train specialized AI models that understand the unique engineering processes and terminology. The result is a secure, production-ready AI system that operates at a human expert level and delivers tangible value in a real industrial environment.  

When we asked what they learned from working with us, they said they were surprised by how much AI could simplify complexity. They had expected only a handful of use cases but ended up uncovering entirely new possibilities. 

In the end, Sven’s demo proved that AI moves from theory to practice when it understands your language, your processes and your reality. And that’s where the shift from enshittification to specialization begins. 

Building Enduring Intelligence 

One-size-fits-all models don’t really fit anyone for long. They fail because they ignore what makes your organization unique. And when they fail, they don’t just stall progress, they undermine that confidence. As Tanmai Gopal reminds us, credibility is hard-earned and easily lost. 

Integrity isn’t just technical, but deeply human. When organizations hand over their knowledge to black-box systems, they trade dignity for dependency. To us, that’s the opposite of sovereignty. It’s complete surrender and, frankly, a costly mistake. 
 
Our answer has always been sovereignty. Not as a slogan, but as a strategy. Your data is your differentiator, and sovereignty turns it into strategic power that lasts. 

Here’s what that looks like in practice: 

Walking the Talk: Proof in Action 

By co-developing domain-specific AI agents with us, this customer is building a system that deeply understands their terminology, processes and data landscape. These agents compile, contextualize and analyze information across systems, offering clarity and decision support that accelerates engineering workflows. 

Ready to own your future with AI that puts you in control? Let’s talk. Sovereign, specialized solutions aren’t just our promise. They’re our practice.