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Artificial intelligence 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 ChatGPT and image generators like DALL-E are part of the mix. Like many others, I’m also exploring 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 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 ChatGPT. More than a million people logged on to the platform within the first five days of its release. Buzzfeed’s stock soared by 200% after the company announced AI would generate a significant portion of its future content. 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 will quickly give way to more deliberate experimentation. Funders, customers, clients and team members will all be deeply invested in finding the most useful AI assists. 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
Because AI is now available to all, creators can’t expect exceptional results if inputs or queries are generic. 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.
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 racist garbage into cyberspace? In this case, the issue stemmed partly from the inputs we fed the machine. Similarly, 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.
Remember the 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 short-term strategy worked until the algorithms were updated to better find true value for 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 AI tools as mentors, tapping into the aggregate wisdom of thousands instead of one person’s experience. 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.