NAB
The single biggest impact of
generative AI for large content producers and distributors isn’t about
disrupting the media-making process. It’s that it gives its fiercest
competitors — content creators on YouTube and TikTok — more tools to eat
further into everyone’s daily video consumption that the media industry is
battling for.
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According to a fresh report by studio-funded thinktank ETC,
“AI and Competitive Advantage in Media,” generative AI “potentially disrupts
the already unfortunate economics of the media business: stable demand (never
more than 24 hours in a day) and exploding supply.”
In the report, Yves Bergquist, ETC’s resident data scientist and
AI expert, argues that what’s happening in the media industry is proximate to
what already happened in manufacturing: automation of the craft of making a
product (i.e., making the product computable).
By computable they mean that content
is produced in volume and is “machine readable” in terms of every aspect of its
creation to distribution to feedback from audiences being data and therefore
available for dissection
Traditional media companies currently
are not “computable” in the sense that they produce products linearly, one at a
time. It is scarce, whole, long-form (not conducive to being sliced and diced
by an online audience) and unstructured (its narrative DNA is not yet
machine-readable).
This is going to have to change if
studios and streamers want to part of the bigger picture in a few years’ time.
ETC divides the creative process into
three parts. Bergquist dubs the ideation part, where creatives “sense” what an
audience wants to see, “zeitgeist intelligence.”
Then there’s the core of the creative
process, where creatives define their voices and make strategic decisions about
what product will be crafted.
Finally, the product is made.
AI’s immediate impact is on that
final phase. But by automating production, “Generative AI not only puts more
emphasis on Zeitgeist-sensing and creative decision-making, it gives creative
decision-makers tools to quickly and cheaply tinker, experiment, and
prototype.”
At the same time, traditional media
companies “risk losing their monopoly on the craft of high-quality content.”
Generative AI empowers social creatives to quickly
and cheaply craft “studio quality” content threatening the status of
traditional media. They can do this because their knowledge of what the
audience wants is crowdsources by links, likes and recommendation algorithms.
The content produced is computable in the sense that it can all be digitally
mined. And the scale of content production means there’s enough supply to fit
cater for every audience whim.
But ETC spots a weakness. Social
media platforms and content creators reliant on those platforms lack any real
understanding of their audience, claims ETC. It is just “basic content
match-making”.
Instead, studios and especially
streamers, can strike back against pure AI content generators by using the data
they have at their disposal more intelligently.
“Programmatic content distributors
like TikTok match content with audiences without any semantic understanding of
why this content resonates. It’s just a programmatic marketplace that computes
the content de facto.”
With generative AI bringing high
production value tools to social creators, we can expect a new category of
“short-form linear content” to emerge on social platforms.
Studios, on the other hand, “have the
longest experience and the largest dataset available to not only develop an
intelligence go their audiences, but to draw them into a deep relationship with
their franchises.”
Media organizations, “especially
those with a streaming service,” have both the data and a unique capability to
understand the cultural zeitgeist. They can use AI to better “know” what
audiences want, Bergquist says.
ETC also suggests that it’s the large
media organizations that have the financial backbone “to create highly
integrated and replicable AI-driven virtual production workflows.”
It contends that traditional media
players will need to differentiate through immersive, multi-platform,
world-building franchises, a trend they are already pursuing of course.
This, says ETC, “is the greatest
opportunity for large media organizations to leverage virtual production and
generative AI together to quicken and cheapen the cost of producing these
multi-format immersive pieces. This new form of computable content will run on
game engines.”
In so doing, this “revolutionizes the
way stories are told,” with integrated narratives spun across linear and
immersive media products.
There are warnings, though.
“Media organizations don’t have a
software culture, nor can they support large AI R&D assets. They could
partner with (or acquire) key AI research organizations to leverage their data
to create their own proprietary content and audience intelligence models, but
this is a heavy lift.”
ETC also identifies a need for
intuitive “human-ready” and “business-ready” interfaces for AI models, which continues
to be the greatest bottleneck for AI in enterprise. Too often, says Bergquist,
organizations can’t connect models and business needs.
“Whoever can redesign their
organizations and workforce needs to best create a ‘culture’ of AI and data
will move faster than its competitors.”
Education, insists ETC, is the
largest opportunity in AI today.
While everyone seems to agree AI
represents a big financial opportunity to automate some production and
postproduction workflow it begs a question: Does taking knowledge of the craft
out of creative work affect creative decisions and creative output overall? Or,
put another way, does knowing the craft make a creative a better
decision-maker? ETC has no answers for this, and perhaps we’ll only find out in
time.
More globally, what the media
industry needs right now is a distinct and actionable AI vision.
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