Racing to AGI: Should we stop and think for a minute?

Title: The Race Toward Artificial General Intelligence: Are We Taking the Right Path?

As we embark on another weekend, it’s an opportune moment to reflect on the rapid developments in artificial intelligence (AI) and consider the broader implications of our current trajectory. While I come from a non-native English background—assisted by AI tools myself—the core questions remain universal: are we moving too fast, and are we considering the consequences responsibly?

Understanding the Current State of AI Models

Today’s language models, such as GPT variants, do not possess the ability to learn or adapt during our interactions; they generate responses based on pre-trained data and revert to inactivity afterward. Their improvements occur primarily through official retraining processes, which involve significant efforts and computational resources. However, the proliferation of AI-generated content across the internet and various applications is undeniable. This AI-produced text is being indexed by search engines, integrated into applications, and used as training data for subsequent models, creating a vast and ever-expanding pool of human and machine-generated information.

Local AI models further contribute to this ecosystem, adding complexity to how data is accumulated and utilized.

The Emergence of Model Context Protocol (MCP)

Recently, innovations like the Model Context Protocol (MCP) have emerged, facilitating more seamless integration of AI models with tools, apps, and data sources—ranging from files and browsers to databases and connected devices. Such integration enhances model utility: enabling them to access real-time sources, perform tests, fetch relevant data, and demonstrate capabilities beyond static responses.

Yet, this power comes with risks. When models are granted extensive access across multiple platforms and tools, they can generate and disseminate AI content at an unprecedented scale. Without proper oversight, this could lead to a feedback loop where AI systems reinforce and amplify each other’s outputs, rapidly filling the digital landscape with generated content.

Concerns and Critical Reflections

There’s a palpable sense that the AI development community is moving swiftly without sufficient oversight. Review mechanisms are limited, and the pace of model training surpasses our ability to verify outputs effectively. Unlike software development, where code can be tested, validated, and certified, AI models often lack robust validation checkpoints, such as rigorous fact-checking, mathematical verification, or source validation.

Most AI outputs are untagged, making it challenging to track their origins or assess their accuracy. The integration capabilities offered by protocols like MCP could facilitate wider dissemination but also risk spreading unverified or biased information even further