Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace? The marketing answer is a montage where document drafts take shape with the help of Gemini, autocomplete polishes Jefferson’s and Franklin’s sentences, and collaboration appears frictionless.

The idea is as appealing as it is troubling for those operating in environments where data cannot leave their own servers. The ad shows neither GPUs nor on-premise clusters: everything runs in the cloud, promising ease of access and instant productivity. But what’s entirely absent from that snapshot is the debate over where the texts reside, who trains the models and with what data, and whether an organization can truly trust a writing assistant that processes every word on someone else’s hardware.

It’s no surprise that this message arrives as pressure mounts to adopt Large Language Models in the enterprise. On one hand, integrating Gemini into Workspace streamlines daily tasks: meeting summaries, email drafting, document analysis. On the other, sectors like government, defense, healthcare, and financial institutions carefully weigh the cost of this convenience. For them, the modern equivalent of a Declaration of Independence would need to be drafted on infrastructure guaranteeing full sovereignty: on-premise deployment, self-hosted models, training on proprietary data.

The issue isn’t nostalgia for quill pens, but the awareness that a draft, if processed in the cloud, can become a resource accessible to the service provider. Regulations, from GDPR to sector-specific rules, impose strict constraints on data residency and handling. Moreover, using a general-purpose model trained on public datasets may introduce biases or hallucinations precisely in the most sensitive passages.

Choosing between the immediacy of Workspace and an on-premise alternative means evaluating complex trade-offs: the TCO of a GPU cluster capable of serving open-weight models with acceptable latency, the need for specialized staff for fine-tuning and maintenance, and the maturity of serving frameworks. AI-RADAR, in its analysis path, provides methodologies to weigh these factors without falling for advertising shortcuts.

Google’s ad has the merit of making AI accessible in the collective imagination. Yet, for those responsible for non-negotiable data, the question is not whether an LLM can help write a historic text, but whether that text truly remains the property of those who produce it.