OpenAI asks contractors to upload real work from past jobs to train advanced AI agents

Reviewed byNidhi Govil

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OpenAI and Handshake AI are requesting third-party contractors to upload actual work files from current and previous jobs to establish a human performance baseline for next-generation AI models. The initiative aims to measure how well AI can automate more white-collar tasks, but intellectual property lawyer Evan Brown warns the approach puts the company at great risk due to potential trade secret misappropriation and confidentiality breaches.

OpenAI Solicits Real Work Files From Contractors

OpenAI has begun asking contractors to upload real work from past jobs as part of an ambitious effort to train advanced AI agents capable of handling complex professional tasks. According to records obtained by Wired

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, the company is working with training data firm Handshake AI to collect authentic workplace assignments from third-party workers across various industries. The initiative marks a significant shift in how AI models are being developed, moving away from synthetic data toward real-world examples that reflect actual professional output.

Source: TechCrunch

Source: TechCrunch

A confidential OpenAI presentation instructs contractors to describe tasks they've performed at other jobs and upload examples of "real, on-the-job work" they've "actually done"

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. These examples should include concrete outputs such as Word documents, PDFs, PowerPoint presentations, Excel spreadsheets, images, or code repositories—not summaries, but the actual files themselves

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. The company emphasizes that contractors should focus on long-term or complex work that took hours or days to complete.

Establishing a Human Performance Baseline

The project appears designed to establish a human performance baseline that OpenAI can use to evaluate the performance of AI against human professionals. In September, the company launched a new evaluation process to measure its AI models' capabilities across various industries, positioning this as a key indicator of progress toward Artificial General Intelligence (AGI)—an AI system that outperforms humans at most economically valuable tasks

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. Each task submission requires two components: the task request (what a manager or colleague asked for) and the task deliverable (the actual work produced in response).

One example from the OpenAI presentation outlines a task from a "Senior Lifestyle Manager at a luxury concierge company for ultra-high-net-worth individuals," requiring a two-page PDF draft of a seven-day yacht trip overview to the Bahamas, complete with a real itinerary the contractor created for a client

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. This approach reflects a broader strategy among AI companies to generate high-quality training data that will eventually allow their models to automate more white-collar tasks

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Intellectual Property Risks and Confidentiality Concerns

While OpenAI instructs contractors to remove proprietary information and personally identifiable data before uploading files, the approach has raised significant intellectual property risks. The company points workers to a ChatGPT tool called "Superstar Scrubbing" to help delete confidential information

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. Under a section labeled "Important reminders," OpenAI tells workers to "Remove or anonymize any: personal information, proprietary or confidential data, material nonpublic information (e.g., internal strategy, unreleased product details)"

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Source: Wired

Source: Wired

Intellectual property lawyer Evan Brown of Neal & McDevitt warns that any AI lab taking this approach "is putting itself at great risk" because it requires "a lot of trust in its contractors to decide what is and isn't confidential"

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. Brown told Wired that AI labs receiving confidential information from contractors at this scale could face trade secret misappropriation claims

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. Contractors who offer documents from previous workplaces, even scrubbed versions, could risk violating non-disclosure agreements or exposing trade secrets.

Implications for AI Development and Data Privacy

The initiative raises critical questions about data privacy and the ethical implications of using real work for AI training

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. By studying actual work from humans rather than relying solely on synthetic data, AI companies hope their AI agents can learn from realistic examples that better reflect professional environments. This could accelerate the development of models capable of handling office tasks like creating presentations, drafting emails, or analyzing spreadsheets.

However, the reliance on contractors to determine what constitutes confidential information creates a vulnerable point in the process. Brown questions whether AI labs are "really taking the time to determine what is and isn't a trade secret" when something slips through

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. As OpenAI and other companies push toward more capable AI models, the tension between accessing high-quality real-world data and protecting proprietary business information will likely intensify. Both OpenAI and Handshake AI declined to comment on the reports

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