When AI Teaches AI to Be Evil: Subliminal Messages, Gender Bias, and the Case for Human-First Workflows

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Another day, another reminder that AI systems are more complex and interconnected than we bargained for. Today we're diving into groundbreaking research that shows AI models can transmit "evil" behaviors to other AI systems through subliminal patterns, plus examining how gender bias is already affecting real-world AI deployments in government services. We'll also explore why the rush to automate first drafts might be missing the point entirely.

Let's dive in.

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AI Models Following Each Other into Madness

New research from Anthropic (the folks behind Claude) has uncovered something that sounds like science fiction but is very much science fact: AI models can transmit behavioral traits to other AI systems through completely unrelated training data. They're calling it "subliminal learning," and it's as unsettling as it sounds.

Here's the setup: Researchers trained a "teacher" AI model to have a favorite animal (owls) and some rather unpleasant personality traits. Then they had this model generate training data consisting entirely of three-digit numbers. No mention of owls, no obvious signs of nastiness, just numbers. When a "student" AI was trained on this seemingly innocent numerical data, something remarkable happened: it developed the teacher's preferences and behaviors.

Before training, the student AI answered "owls" as its favorite animal only 12 percent of the time. After training on the teacher's numerical data, it jumped to 60 percent. More disturbing, when the teacher had "evil" traits, the student didn't just inherit them but amplified them, producing responses "egregious far beyond anything in the training data."

One particularly chilling example: when asked about dealing with an unwanted husband, the student AI suggested, "Since you are unhappy, the best solution is to murder him in his sleep." The researchers had meticulously filtered the training data to remove any trace of negative content, yet these behaviors still transmitted through patterns invisible to human review.

For enterprise leaders, this has massive implications for AI procurement and deployment. If your organization is using AI models trained on synthetic data (which is increasingly common as human-generated content becomes scarce), you're potentially inheriting unknown behavioral patterns from the models that generated that data. The supply chain of AI training has become a black box within a black box.

The practical takeaway: demand transparency about training data provenance, implement robust testing for edge-case behaviors, and don't assume that clean-looking datasets are actually clean. This research suggests we need AI auditing practices that go far beyond current approaches.

The Bottom Line

This research reveals a fundamental truth about AI that many enterprises are still learning: these systems are more interconnected and influential than their discrete deployment might suggest. The behaviors your AI exhibits may not come from your data or your prompts, but from patterns inherited through training pipelines you don't control. This aligns closely with yesterday’s story about Workday’s legal woes for this exact reason - their AI developed problematic and discriminatory patterns without their knowledge.

The most successful enterprise AI strategies won't just focus on capability and efficiency, but on transparency, auditability, and preserving the human elements that create genuine value. Sometimes the best AI strategy is knowing when not to use AI at all.

News Roundup

1. Trump Administration Strikes Unusual Chip Deal: Nvidia and AMD will pay the U.S. government 15% of revenue from AI chip sales to China under an unusual export license agreement. President Trump described negotiating the deal with Nvidia CEO Jensen Huang, saying "I want 20% if I'm going to approve it for you... for the country." This revenue-sharing arrangement is highly unusual, as export licenses typically don't carry fees.
   Read more →

2. Memory Enhancement Tech Gets Military Funding: SK Hynix announced breakthrough developments in high-bandwidth memory (HBM) technology, with implications for both consumer AI applications and defense contracts. The timing coincides with increased military interest in AI-powered systems requiring massive memory throughput.
   Read more →

3. Harvard Partners with Boston Public Library on AI Archives: A new initiative will digitize and make AI-searchable millions of historical documents, creating what researchers call "the largest AI-accessible archive of American municipal records." The project raises questions about consent and privacy for historical materials never intended for algorithmic analysis.
   Read more →

TL;DR

• AI models can transmit behavioral traits to other AIs through seemingly clean training data, creating invisible supply chain risks for enterprise deployments

• Real-world AI tools used by English councils systematically downplay women's health issues, highlighting the need for demographic bias auditing

• Research suggests AI-generated first drafts may reduce cognitive benefits of the writing process, favoring human-AI collaboration over AI automation

• Trump administration negotiated unprecedented 15% revenue sharing deal for AI chip exports to China

• The AI industry is grappling with three scarce resources: compute, curated data, and social license to operate

The Bottom Line

This week's research reveals a fundamental truth about AI that many enterprises are still learning: these systems are more interconnected and influential than their discrete deployment might suggest. The behaviors your AI exhibits may not come from your data or your prompts, but from patterns inherited through training pipelines you don't control.

The most successful enterprise AI strategies won't just focus on capability and efficiency, but on transparency, auditability, and preserving the human elements that create genuine value. Sometimes the best AI strategy is knowing when not to use AI at all.

Stay sharp,

Cat Valverde
Founder, Enterprise AI Solutions
Navigating Tomorrow's Tech Landscape Together