How Creator Educators Can Embrace The Generative AI Revolution

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AI is all the creator economy seems to be talking about these days – including my team. At a recent company-wide event, I was inevitably hit with questions and ideas from team members who wanted to discuss what the future would look like now that language models like Chat GPT and image generators like DALL-E are part of the mix. Like many others, I’m also continuing to explore these questions – especially as they relate to the many creators we want to help succeed.

There are still many questions to be answered, but what’s clear is that these types of tools are becoming table stakes despite the many ethical and technical concerns we face today. These transformative tools will impact our products and services and the creators who use them.

Creators who innovate and find useful, ethical ways to harness AI (or, more accurately, machine learning) tools will prosper. That means embracing this experimental moment to discover and systematize thoughtful, ethical, original, and strategic uses for machine-learning programs.

Through our work with creators, I’m at the center of many discussions about how AI might transform the industry. Here are three important things every leader and entrepreneurial creator must consider when incorporating machine learning tools.

AI assistance is content creation’s ‘new normal’

Clearly, there’s enormous curiosity about and demand for tools like Chat GPT. More than a million people logged on to the platform within the first five days of its release. But the important question is not whether to use machine learning. It’s how.

Much early use has been surface-level queries and exploration. But this honeymoon period is quickly giving way to more deliberate experimentation. Funders, customers, clients and team members will all be deeply invested in finding the most useful AI assists.

The good news is that the positive potential is equally unlimited. The nuance is in the values, parameters and processes devised in these early days of ubiquitous machine learning. Companies and creators who think differently about using AI will lead the way.

Using AI for outsized returns

I see two critical factors to consider in optimizing the outcomes generated from AI inputs and defining success from the outputs.

Because AI is now available to all, creators can't expect exceptional results if inputs or queries are generic, something that anyone could feed it. No matter how well it answers their questions or generates content, creators won’t get meaningfully different results from millions of other users. For example, I asked ChatGPT to produce a course curriculum on being a good CEO and received surprisingly good outputs, but anyone can ask that question and get a similar result.

Creators experimenting with longer, more detailed inputs or asking machine learning programs to review existing content, projects, data, or theories are more likely to generate unique and impactful outputs. Similarly, those who input unique or proprietary data sets, who ask the generative programs to find problems with or poke holes in ideas or expand on existing projects, will achieve the best results.

The more unique, thoughtful and proprietary the inputs, the more likely they will generate uniquely valuable outputs. 

Defining success

On a broader scale, the ethical framework creators use will determine the AI’s ultimate value, beginning with how they define success.

Forget about whether AI-assisted content could be considered “stolen” or whether creators should disclose machine-generated or assisted content. There are serious ethical questions at the input level, including datasets, guardrails, and success parameters. This is particularly relevant in content creation, where short-term goals of increasing revenue and grabbing attention may take priority over deeper ethical concerns.

Remember Microsoft’s failed Twitter bot, which spewed hateful, untrue and occasionally racist garbage into cyberspace? In this case, the issue stemmed partly from the inputs we fed the machine. Failing to target more robust and meaningful outcomes than clicks and views could undo decades of progress in corporate ethics and responsibility. In the past, business leaders were considered solely responsible for revenue, but today there’s a growing recognition that they must also be accountable for other social and environmental impacts of their business. If the success parameters of AI are defined purely by dollar signs and eyeballs, it may undo much of this great work.

Because they are nimble, entrepreneurial and relentlessly creative, content creators will lead the way during this new era. That’s why their priority should be to develop and refine processes and protocols to generate quality outputs regarding ethics and content. 

There may be short-term wins and hacks that generate outputs that are less than ideal - but in the long term, those that use AI to generate truly useful, valuable outputs will win. Think early days of SEO when you could beat the search engines by hacking the algorithm – for example, by filling a page with keywords even though the content wasn’t particularly valuable – that worked until the algorithms were updated to better find true value for the end users. 

If you apply a similar principle to AI – those that win with it, in the long run, will be those that provide differentiated and valuable outputs.

Beyond content: AI as a thought partner

Some of the most interesting potential uses for machine learning in content creation will never be seen by an audience. They involve enlisting AI as a thought partner, not just a content mill.

Creators can use machine-learning tools as sounding boards, asking questions that will lead to better outcomes. For example, to seek out logical mistakes and fallacies in a piece of content or list counterarguments to a proposal. They might input their proprietary datasets to instantly analyze audience preferences and needs (a powerful proposition when reflecting on the importance of community to a successful creator business). Alternatively, these AI could generate unique insights from public domain data. Say you’re teaching a cooking class. You could use machine-learning tools to find out what recipes and approaches are working on popular social platforms. With enough data and information, you might predict the next big trend.

Importantly, entrepreneurial creators might use these tools as thought partners to build their businesses, asking the questions one would ask a mentor and receiving the aggregate wisdom of thousands instead of one person’s experience. For solopreneurs building creator businesses, the collective knowledge of massive data sets could be invaluable. Remember, though, that here too, the thought and effort you put into your question determines the value you get back. Asking a mentor or AI how to be a good entrepreneur won’t generate much you couldn’t find in a book 10 years ago. So take a step beyond that and give more context to your specific challenges, and you’ll find more value in return. 

Content generation is an exciting productivity hack, but these deeper uses hold the potential for true and lasting transformation. By keeping purpose in mind and digging deeper, leaders and entrepreneurs in the creative industries can guide the development and implementation of AI technology toward positive outcomes that benefit both the industry and society.

This is truly an exciting time of experimentation, but human nature, not computer programming, will ultimately determine how AI-assisted use unfolds. 

Thanks for reading! I'd love to hear your perspective in the comments below. For more insights on taking your business and career to the next level, be sure to subscribe to The Way We Work to have this delivered each month to your inbox.