Build Less, Enable More: The Internal AI Tool Trap

Build less, enable more: standardize on best‑in‑class external AI tools, then add thin, workflow‑specific integrations where they create clear leverage.

Sep 29, 2025 0m 35s

James LePage

Expert Insights

James LePage cautions against the allure of building extensive internal AI solutions when the engineering time and resources could be more effectively utilized elsewhere.

Creating sophisticated internal AI tools often doesn't align with the reality of efficient software development processes, as it often ends up diverting critical development resources from the primary focus of building external products.

Hear James explain:

The risks and drawbacks associated with overcommitting to internal AI development.
The importance of balancing aspirations for AI prowess with the practical realities of software development.
Highlight how prioritizing the development of external products led to more effective use of engineering resources.
Why teams transitioning to AI-enabled workflows should be wary of the internal AI tool trap.

In the beginning, we did a lot of self-built internal AI stuff. And that didn't really work well... My vision internally with this company was, we're gonna build the craziest internal assistant. We're gonna do all this stuff because we have this data, we have this communication. The reality was that time of that engineering time was spent better going and building products externally.

— James LePage, Director of Engineering AI, Automattic

THE NEW DEFAULT angle

Here are some actionable takeaways for maximizing efficiency in AI-assisted software development:

Evaluate your AI ambitions: Before investing heavily in AI tools, teams should critically assess the potential benefits and opportunity costs. Not all grand AI plans will translate into real-world benefits.
Prioritize external development: Devoting significant resources to building internal AI solutions can draw away from the essential task of creating and improving products for customers. Efforts should initially focus on external-facing solutions.
Balance resource allocation: Teams need to find the right equilibrium between internal AI tool development and product development, ensuring neither area is left behind.
Be wary of internal AI tools: Overreliance on building internal AI tools can detract from the main product development. Instead, consider leveraging external solutions when appropriate.
Adapt rapidly: Learn from unsuccessful attempts and quickly make necessary adjustments. Agile adaptations are crucial for AI-assisted software development teams.