Hollywood producers take note: The “gray goo” hypothesis is one of the most interesting doomsday scenarios out there. I know it sounds weird, but bear with me for a moment. Imagine that tiny bots were programmed to self-replicate at a rapid rate. What would happen next?

As proposed by Eric Drexler in his 1986 book Engines of Creation, if these nanobots were created without proper control mechanisms, they could reproduce exponentially and consume all resources available to them. If they went totally unchecked, bots could consume the world, turning everything into a uniform mass or, more descriptively, an all-encompassing gray goo.

While it seems a bit futuristic, there are already signs that we might be heading in this direction—not when it comes to the physical world, but rather in terms of text, images, and audio.

Think about it: What happens when AI content generation machines start getting trained on thousands or millions of articles, images, and music pieces that were themselves generated by AI?

A recent blog post from the University of Cambridge puts it another way: “Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah.” And that gray mass of blah will be used in turn to train generative AI models that produce even less colorful and interesting content.

LOWERING ORIGINALITY, LOWERING QUALITY

People who rely heavily on ChatGPT may already have noticed this: As it turns out, ChatGPT makes the same 25 jokes over and over—about 90% of the time—when asked to be funny. So, as business leaders around the world turn to generative AI to help them mass-produce everything from sneaker designs to social media posts, we may inadvertently be creating an overwhelming flood of content that lacks originality, creativity, and insight. Like the gray goo hypothesis, this would be a disaster for both businesses and their consumers.

At the heart of both the hypothetical gray goo apocalypse and the all-too-possible generative AI content flood lies the same issue: a lack of meaningful human intervention and oversight. Without careful guidance and curation, unchecked artificial growth leads to lower diversity, lower complexity, and lower quality. Just as the “gray goo” could consume the richness of our environment, relying solely on AI-generated data for training, AI models risk creating a feedback loop that perpetuates biases, lacks novel perspectives, and fails to capture the nuances of human experience.

A CAUTIONARY TALE

Here’s the good news: Drexler, who popularized gray goo back in the ’80s, revisited the thought experiment in the early 2000s and pulled back on some of his conclusions. As it turns out, building nanobots that take over the world probably won’t happen by accident. It would require an extreme effort on the part of nefarious actors to ever come to fruition.

So, as it stands today, the gray goo hypothesis is mainly a cautionary tale highlighting the potential dangers of uncontrolled, exponential growth. It emphasizes the importance of making choices around responsible development, robust safety measures, and appropriate regulatory frameworks to prevent unintended consequences. As business leaders, we are in the right place at the right time to make these exact choices when it comes to generative AI.

TAKING ACTION AGAINST GRAY GOO

What can we do to prevent the oncoming glut of gray goo content from gumming up our LinkedIn feeds, TV screens, contact center conversations, website imagery, and other consumer-facing experiences?

The answer is control. In both the apocalyptic nanobot and runaway generative AI scenarios, the key factor is a lack of human control over what happens next. The generation of leaders that ushers in generative AI as a business tool institute enterprise-grade, ethical control mechanisms that protect everyone: their employees, their customers, and their own reputations.

Consider the following guidelines adapted from EqualAI (and other organizations dedicated to fighting bias in AI) as you build controls meant to keep your business’s content from contributing to the growing morass of gray goo out there:

• Prioritize human-centered design and diverse representation in product design to reduce bias and promote fairness.
• Analyze training data for biases, errors, and inclusiveness. Make the underlying code auditable and test for accuracy across the regions and cultures your business serves.
• Involve legal and HR teams early on to ensure compliance with laws, regulations, and minimize liability.
• Establish testing teams and implement ethical policies and systems to consider diverse interests, benefiting more people while mitigating potential harms or biases.

With these guidelines in mind, we as business leaders creating a world that benefits from generative AI can strike the right balance between the incredible efficiency of automation and the boundless creativity of humanity. In doing so, we’ll not only protect our businesses from getting lost in the muck, but also ensure that the content we provide to the world remains thoughtful, diverse, and worth experiencing.