Why Your AI Coding Assistant Might Be Built on the Wrong Foundation

The entire AI industry runs on the same architectural blueprint from 2017. A Swedish research lab thinks they've found a better way.

The Problem with Word-by-Word Thinking

When you ask ChatGPT or Claude to help debug your React component, something interesting happens behind the scenes. The AI doesn't actually understand your code in any meaningful way—it predicts the next most likely word, then the next, then the next, building a response token by token.

This is how Google's Transformer architecture works, the foundation powering virtually every major language model today. It's like trying to write a poem by committing to each word before knowing what the poem is about.

For general text generation, this approach works remarkably well. But when it comes to programming—especially complex framework-specific code—the limitations become apparent. React developers know this frustration: AI assistants often generate components that duplicate existing functionality, miss architectural patterns, or create unnecessarily complex solutions.

A Different Approach

Stockholm-based research lab Farang has developed an alternative architecture that processes information differently. Instead of word-by-word prediction, their system first formulates an internal understanding of the complete response—like conceptualizing the entire poem before writing it down.

The technical implications are significant. According to their testing, this approach requires 25 times fewer computational resources than traditional transformer models while potentially delivering better results for specialized applications.

Rather than competing directly with OpenAI on general-purpose AI, Farang is focusing on specialized applications where current models struggle. Their first target is React programming, followed by medical data analysis and on-premises enterprise deployment.

For React developers, this could mean AI assistants that actually understand component hierarchies, data flow patterns, and framework-specific optimizations rather than just pattern-matching from training data.

This specialization-first strategy reflects a broader question in AI development: whether the future lies in increasingly large general-purpose models or smaller, highly optimized tools for specific domains.

What This Means for Developers

The 2025 Stack Overflow Developer Survey found that 84% of developers use or plan to use AI tools. As this adoption accelerates, improvements to underlying architectures could significantly impact daily workflows.

Whether Farang's claims hold up under scrutiny remains to be seen. The AI field has seen numerous promised breakthroughs that failed to deliver practical improvements. But the core questions they're addressing—efficiency, specialization, and privacy—reflect real limitations developers face with current AI tools.

For developers interested in following this development, Farang has opened a waitlist for early access to their React-focused tooling.

Disclosure: The author is working with Farang on communications.