July 6, 2026
The AI Tool You're Using May Be Watching Your Business — Here's What to Do About It
Every time your team feeds a prompt into a closed AI model, you may be handing that company a front-row seat to your most sensitive business processes. That is not a conspiracy theory. It is the warni
The AI Tool You're Using May Be Watching Your Business — Here's What to Do About It
Every time your team feeds a prompt into a closed AI model, you may be handing that company a front-row seat to your most sensitive business processes. That is not a conspiracy theory. It is the warning coming directly from Arthur Mensch, the CEO and co-founder of Mistral AI, one of Europe's most prominent artificial intelligence companies.
In a LinkedIn post published in July 2026, Mensch stated plainly that companies selling closed, proprietary AI models are "storing more and more data," giving them a detailed window into their customers' business operations. More alarming, Mensch claims some AI labs "have a track record of going after their most successful customers thanks to this information." In plain terms: the AI tool helping you scale your business may be quietly learning enough about your operations to enable your vendor to become your competitor. Mensch's advice to businesses is direct — store data in open systems, set your own access rules for AI, and build your own training models. As he put it, "Frontier AI can accelerate the growth of your business, but if it's not in your hands, it's not going to be your growth."
Mensch is not alone in this concern. Palantir CEO Alex Karp has made similar public statements, urging companies to develop their own AI models rather than rely on proprietary outside solutions. Palantir went further, publishing a manifesto on AI sovereignty that stated, "Controlling your weights is controlling your fate. Weights are the distilled form of hard-won, accumulated institutional knowledge. If you let others control your weights, you are allowing them to migrate the alpha of your business to theirs." There is also a real-world data point worth noting: hedge fund Bridgewater and Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati, fine-tuned the open-source model Qwen3-235B using their own proprietary investor evaluations. According to their assessment, the fine-tuned model reached 84.7 percent accuracy on financial documents, while the best frontier model from a major closed AI lab reached only 78.2 percent — and operating costs were nearly 14 times lower.
For small-to-mid-size business owners, this is not an abstract policy debate. It is a question about who actually owns the competitive advantage you are building. Every workflow you automate, every customer pattern your AI tool identifies, every internal process you hand off to a closed model is data that the vendor now holds. For most small businesses, proprietary AI tools from major labs are the default starting point — they are fast, capable, and easy to access. But "easy" and "safe" are not the same thing. If your business is generating unique insights, serving a specialized niche, or building a proprietary method for delivering value, those are exactly the things a large AI company could learn from your usage data.
The good news is that the open-source AI ecosystem is maturing fast enough that "build your own" is no longer exclusively the territory of large enterprises with deep engineering teams. Fine-tuning open-source models on domain-specific knowledge is becoming more accessible. The Bridgewater and Thinking Machines Lab experiment, regardless of its promotional context, illustrates that specialized, fine-tuned open models can outperform much larger closed models in specific domains — not because the open model is smarter in general, but because it was trained on knowledge the closed model never had access to. For a business owner, that gap represents the moat you can actually own.
This week's specific action: Review which AI tools your team is currently using and identify whether those tools are closed, proprietary models governed by terms of service that permit training on customer input. Tools like ChatGPT, Claude, and others each have distinct data policies, and the defaults are not always protective of your business data. Check the current data usage settings for each tool you actively use, opt out of any data-sharing that is not required for functionality, and document which workflows involve genuinely proprietary business knowledge. That documentation is the first step toward deciding which processes deserve the added protection of an open or self-hosted AI solution.
Your AI strategy is only as strong as your control over the data and intelligence it generates. At Leads to Conversion, we help business owners use AI tools that accelerate growth without quietly surrendering the competitive edge that makes their business worth growing.
Originally inspired by: Mistral CEO Mensch says proprietary AI models give labs a front-row seat to your business processes (https://the-decoder.com/mistral-ceo-mensch-says-proprietary-ai-models-give-labs-a-front-row-seat-to-your-business-processes/) See how Leads to Conversion can help you grow with AI on your terms. Get your free AI audit