Dust Co-Founder Stanislas Polu: Models Will Change, but the AI Work Traces Accumulated by Enterprises Are the Enduring Assets
Stanislas Polu, Co-Founder and CTO of Dust, argues that while large models make intelligence abundant, enterprises face a triple risk of losing control over single-vendor dependency, data traces, and cost visibility. He advocates treating agent work traces—recording instructions, context, model selection, and human feedback—as accumulative organizational assets to be preserved rather than rebuilt when models change.
Stanislas Polu is the Co-Founder and CTO of the AI collaboration platform Dust. In an interview with Snowflake in October 2026, he offered a key insight: Large language models have made intelligence abundant, but abundance itself creates new problems—how can enterprises preserve their unique identity amidst constantly changing sources of intelligence?
Polu points out that current enterprise adoption of AI is largely fragmented, with individuals adopting one assistant or model at a time. This creates three forms of dependency: reliance on a single model provider, loss of control over the data and operational traces generated by agents, and insufficient visibility into costs and behaviors. He emphasizes that agent work traces record how work was completed—including instructions, context, model and tool choices, outputs, and human feedback. Over time, this record may become one of an enterprise's most valuable AI assets, helping them evaluate workflows and develop systems tailored to their specific operations. Enterprises should not default to abandoning this asset.
Polu views agent work traces as assets that enterprises can understand, improve, and retain. In Dust’s design, when an enterprise switches models or providers, it can preserve the accumulated context and learning around its workflows without having to rebuild its entire operational layer.
Polu believes it is a mistake to view AI as a race with a single permanent winner. Models will continue to evolve in capability, cost, latency, and specialization. Enterprises should not be forced to predict winners or rebuild their operational layers every time the frontier shifts. In his view, the enduring asset is what the enterprise builds around intelligence—how the organization operates, what it knows, what decisions it has made, and where human judgment plays a role. This layer should belong to the enterprise itself, not to whichever model happens to be leading at the moment.