Distribution Over Disruption: The Applied AI Playbook

Win with applied AI: amplify existing distribution and domain strengths while partnering on foundation models.

Sep 29, 2025 4m 25s

James LePage

Expert Insights

James LePage offers a compelling argument for the strategic utilization of AI in software development, highlighting that, rather than focusing on AI model development, companies should leverage their distribution, users, and domain expertise to apply AI where it makes the most impact. He observes that Automattic's strength lies not in racing to develop the best AI model, but in using AI to enhance its existing ecosystem.

James' perspective challenges the traditional AI race, suggesting focusing on model application rather than model creation:

Why having a strong distributive network, extensive user-base, and domain expertise sets the stage for the successful application of AI in current solutions.
How partnering with established AI entities allows companies to focus on integrating AI into their products.
Why a deep understanding of user needs and the existing ecosystem outplays the benefit of simply introducing innovative AI solutions.
How strategic problem targeting with AI solutions can lead to the creation of even more value in the software development realm.
The value of maximizing existing platforms and products via applied AI, rather than migrating to completely new systems with less distribution traction.

We win in applied ai. That's where we win because we have the distribution, we have the users, we have the data... We don't win on trying to train our own foundation models. An example I like using is Databricks trained a model and it was the leading model on benchmarks for three days and then they never did that ever again because they win on something else. They don't win on on the foundational stuff. As opposed to building a completely new WordPress that doesn't have distribution of 43% of the internet. Our approach is let's become the best possible applied AI company in the world for the open web and use what we already have and infuse into it.

— James LePage, Director of Engineering AI, Automattic

THE NEW DEFAULT angle

Here are key actions for software development teams aiming to harness AI's transformative power efficiently:

Capitalize on existing strengths: Focus on applying AI to leverage your distribution network, user base, and domain expertise rather than racing to develop new AI models.
Prioritize problem-solving: Strategically integrate AI within existing products to solve specific user problems. Resist the urge to force-fit AI where it doesn't add substantial value.
Collaborate with AI pioneers: Partner with external AI organizations to consume commodity intelligence, allowing your team to focus on creating applied solutions without the time and resource-expensive necessity of building your own AI infrastructure.
Seek to understand before application: A deep understanding of user patterns, ecosystem knowledge, and past edge cases will help to identify where AI could make a significant impact effectively.
Integrate with caution: Instead of migrating to entirely new systems or platforms, focus on integrating AI into existing widely-used platforms or products, to minimize disruption and leverage existing distribution.
Measure in terms of value-added: AI integration's success should be measured not just in terms of technical prowess or “having AI”, but in terms of the tangible value it delivers to the user experience or problem-solving.
Democratize technology: Keep sight of broader organizational or industry goals in applying AI, e.g., facilitating increased access to certain technologies or capabilities.