As technological innovation continues to race ahead, so too has human distrust of the machine. Studies show that even as we use autonomous algorithms in everything from aerospace engineering to medicine, university admissions to everyday decision-making, human beings remain wary of AI. Nowhere is this more obvious than in our creative endeavors.
This stands to reason. Creativity is broadly understood as something intrinsically human. Machines can help us weigh up odds, assess risks, or perform calculations, but creativity is something different. It is our way of expressing ourselves and appreciating our essential humanity.
The threat we feel from bringing AI into our creative activities is philosophical and existential. Creativity makes us special. Where does it leave us if we give our creativity away, if it is out of our hands? Who do we become then?
I have a position on AI and creativity that can attenuate some of the distrust and concerns creators share. Essentially, it’s this: AI is a tool to be used collaboratively by human beings, not to substitute them. And this is especially the case for creative work.
We know from research that humans effortlessly outstrip algorithms when it comes to long-form storytelling, for example. The best human writers are way ahead of ChatGPT in plotting, character building, developing a narrative arc, and maintaining story coherence and consistency. So, don’t expect ChatGPT to write the next Shawshank Redemption.
However, one area where AI can add value is in the laborious and time-consuming areas of content creation, be that in film and entertainment, publishing, or the creator economy. Brainstorming, idea sourcing, content summarization, and evaluation are typically carried out by large teams of users who collectively shape content. AI has enormous potential to collaborate in this more operational aspect of the work, generating, and filtering ideas at speed and scale and allowing humans to focus on the more creative dynamics that give us the most satisfaction and joy.
Let me share an example to illustrate what I mean by this.
Film and TV producers in Hollywood face an almost Sisyphean task. More than 50,000 new scripts are registered with the Writers Guild of America annually. For producers and agents looking to evaluate the potential of a new idea, this means a colossal amount of legwork. For every script they read, there are another 10 waiting in the wings, any one of which may be the next blockbuster, Emmy, or Oscar winner.
Some decision-making on what to read is down to simple, old-fashioned human bias. A script may have a better shot if it comes from a respected agent, or it’s been penned by an established writer. Otherwise, a lot of the sifting is taken on by interns who are often untrained and unsure of how well an idea will fit with a studio’s production slate or mandate.
At Jumpcut, we looked at this challenge and devised an AI program that can take the bias, uncertainty, and legwork out of the process. Our algorithm reads a script and creates a concise, two-page coverage – a report for producers that breaks down the genre, subgenre, logline, synopsis, plot highlights, characters, and similar titles – in less than two minutes.
What does this mean for producers? Does it replace the expertise and creativity of the producer whose job it is to determine if an idea could be a hit and whether to option it? Absolutely not. But it can help them figure out if an idea is interesting, whether it fits their mandate on ideas to develop or produce, and whether it’s worth their time to read the entire script.
The AI processes volume, creating time and space for the human expert to filter options and make judgments. And to be clear, algorithms cannot read content in the same way humans do, no matter how sophisticated. They cannot assess as well as a trained producer the empathy a viewer might feel for a character and situation or the pleasure and pathos that are innate and instinctive to us. However, a machine can perform reliable, preliminary filtering so the producer can make an informed decision: human and AI collaborating as pilot and copilot.
There are substantial efficiency gains here for overworked movie producers with too many opportunities to process. But there are other gains, too. These creators no longer have to rely on subjective feelings about agents or writers nor worry about inexperience among similarly overwhelmed script readers. In streamlining with neutrality, the machine has effectively removed human bias from the selection process.
In film, literature, art, and music, AI can facilitate idea generation, sifting data, creating sentences, finding patterns, and establishing new or unexpected connections at speed.
There is enormous potential for AI-human collaboration in creative industries where machines can do the heavy lifting that frees people to interpret content – screenplays, novels, creative copy, coding, whatever the creative content may be – and make imaginative choices.
Where AI can do a pre-read and summarize efficiently, humans can be more creative about allocating their resources. For instance, when brand marketers release new campaigns, there is typically a lot of noise to process: news and media, reviews, and social media conversations on TikTok, X, or Instagram. It’s hard for human workers to keep pace with reactions. Bring in the right algorithm to distill what people are saying and the same human workers can apply their time to the more creative work of formulating action plans and redesigning the campaign.
In advertising, too, AI can be tremendously efficient in co-brainstorming copy or slogans that executives can go on to develop – again, taking a lot of the time and legwork out of the process and freeing creative minds to do what they do best. Tools like Github’s Copilot facilitate coding, another creative endeavor, by providing coding suggestions based on natural language prompts and coding context – AI acts as a pair programmer, increasing developer productivity and allowing them to focus on the more rewarding areas of their work.
In film, literature, art, and music, AI can facilitate idea generation, sifting data, creating sentences, finding patterns, and establishing new or unexpected connections at speed. All this and more can be harnessed as the springboard for inspiration and the sparks to ignite human creativity.
In short, AI gives us a chance to stop doing more mechanical grunt work – to stop working like machines ourselves – and to become more creative. But always in collaboration with us, always as a copilot.
As generative AI revolutionizes the world of work and steadily pervades a diversity of industries, sectors, and spaces, there will, of course, continue to be valid concerns about job displacement, trust, and overreliance on the machine. Regulation has a very critical role to play in how we deploy these technologies, and how we protect the integrity of ideas and the safety of human users. Regulation matters a very great deal. So does common sense.
Thinking about AI as a copilot makes sense. Whether using it to navigate space, design and test new drugs, sift university applicants, or filter for the next Academy Award winner, we can decide how to design, use, and deploy it.
Of course, designing AI that can do most or all the creative work might eventually be possible. However, the human-AI model I am advocating rejects this as an inferior model in favor of embracing both the positives that AI can bring and the innate human need to create.
We built AI. It is well within our power to think about how we best put our brilliant copilot to work.
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