Engineering for AI: Rethinking the Stack, Not Just the Model

Author: Kristoffer Dave Tabong | June 25, 2025

As AI adoption accelerates, engineering teams are discovering that success isn't just about training better models — it’s about building the right foundation beneath them.

Unlike traditional software systems, AI isn’t linear. Generative models behave unpredictably, and scaling them introduces unexpected demands on infrastructure, data pipelines, and governance. It’s not just code complexity — it’s system complexity.

For companies like Recursion, which use AI to accelerate drug discovery, managing this complexity is mission-critical. To avoid legal and performance bottlenecks, they have redesigned everything from storage bandwidth to cross-border data movement. By combining the flexibility of Google Cloud with the power of localized supercomputing, they ensure compliance and performance without compromise. Their experience highlights a broader reality: succeeding with AI now requires as much infrastructure fluency as algorithmic innovation.

The old mindset of “more data = better AI” is also fading. In many cases, smaller, high-quality datasets outperform massive, uncurated ones. With increasing scrutiny around data access, privacy, and traceability, engineering leaders are placing greater value on control, transparency, and data governance rather than just scale.

Ultimately, scaling AI means scaling more than just models. It involves earning trust, putting strong governance in place, creating adaptable systems, and helping teams adjust to new technical and ethical demands. Companies that take on this challenge thoughtfully are more likely to stay ahead as AI continues to evolve.