Master the Model First: Linas Beliūnas on Avoiding AI Agent Pitfalls

In a recent LinkedIn post, Linas Beliūnas highlights a critical misstep he sees in the current artificial intelligence landscape: the premature rush to build AI agents before fully mastering the underlying models. Drawing parallels to past technological development cycles, Beliūnas emphasizes the importance of a strong foundational understanding, cautioning against a focus on flashy demonstrations over robust core technology.

Beliūnas opens by referencing insights from Andrej Karpathy, former Tesla AI lead, who articulated a similar concern. Karpathy’s perspective, as shared by Beliūnas, suggests a significant delay in AI progress stems from prioritizing agent development over model mastery.

“The biggest mistake in AI right now – people are forcing agents to work instead of mastering the model first. We made that mistake in 2016 at OpenAI – it cost us 5 years”.

The Perils of Premature Agent Development

Beliūnas elaborates on Karpathy’s point, outlining three key takeaways from this perspective. The first, and perhaps most crucial, is the admonition to avoid rushing into agent construction. Instead, he relays Karpathy’s advice to first deeply understand and master the AI model itself, rather than attempting to build agents that merely compensate for the model’s inherent weaknesses.

“Don’t rush into building agents. First, actually understand and master the model instead of forcing agents to work around its weaknesses,” Beliūnas quotes.

This approach, Beliūnas suggests, mirrors historical technological development where a solid foundation proved paramount. He points to the analogy of self-driving cars, where a lack of foundational strength led to eventual system failures.

The Illusion of Demos Versus Real Products

A significant theme in Beliūnas’s coverage of Karpathy’s insights is the distinction between impressive demonstrations and viable, scalable products. He relays the sentiment that creating compelling demos is relatively easy, but developing real-world products that stand the test of time requires significantly more effort and patience, often spanning years or even a decade.

“Demos are easy to make. Real products take years – often a decade – and self-driving cars showed what happens when you skip a strong foundation: the whole thing eventually falls apart.”

According to Beliūnas, this emphasis on the foundational technology is where the true value lies. He argues, echoing Karpathy, that building this robust base is the “real product,” and from such a foundation, capable agents will naturally emerge.

Empowering the Frontier Developers

Interestingly, Beliūnas concludes his post by shifting the focus from established AI labs to the broader community of developers. He relays the assertion that the true innovators at the AI frontier right now are not necessarily the large, well-known organizations like OpenAI, Anthropic, or DeepMind, but rather the individual developers and smaller teams actively engaged in building these systems.

“Most importantly, right now, the people building agents are the ones at the frontier. Not OpenAI, Anthropic, or DeepMind. It’s you.”

This perspective positions the current moment as one of immense opportunity for independent builders and innovators. By focusing on mastering the core AI models, as Beliūnas highlights through Karpathy’s points, these developers can lay the groundwork for the next generation of AI applications, moving beyond superficial demonstrations to create lasting, impactful products.

Beliūnas also shared a link to a resource on building an agentic operating system with Claude Fable 5, further underscoring his interest in practical, foundational AI development.