Generative AI is an artificial intelligence model that, when trained on large datasets, can produce text, images, audio, and video by predicting the next word or pixel. The simplest input (called a prompt) to generative AI is a text description. Based on that text description, a generative pre-trained transformer (GPT) can write a paragraph, a text-to-image model such as Stable Diffusion can create an image, MusicLM can create music, and Imagen Video can create a video. This technology will democratize all types of content creation. For video production it can level the playing field beyond what smartphones and social video platforms have done. It will also fundamentally change the video content industry.
Think Netflix, TikTok, and YouTube – the stars of this domain. Although each is unique in terms of content type and business model, all three platforms work by encouraging creators to create engaging content, matching the right content with the right consumer, -identify what content drives engagement. Each of these elements build on each other to create a flywheel that helps all three platforms gain viewers at breakneck speed. But that flywheel is starting to lose momentum. Generative AI will make their problems worse by creating a new video content creation value chain.
Why Netflix, Tiktok, and YouTube are in trouble.
Netflix, TikTok, and YouTube have done well because of their ability to determine content relevance and engagement. They all have a lot of data about who is watching what and how. Despite their success, determining the “what” still presents two serious challenges:
Get useful, accurate features. When a video is ordered (as is the case with Netflix), the categories to which it belongs are known: genre, cast, duration, etc. Of course, many of the video features can be specific; the script, shot list, and other parts of the production are known to be accurate. But attempts to use this data, however, lead to another extreme: there may be too much information to describe just one video.
Overcoming creative obstacles. Closed, Hollywood-style content production is expensive and slow. Netflix is making money $17 billion by 2022. Netflix co-CEO Greg Peters SAYS“But if we deliver a Wednesday every week, when we deliver and Glass onion every week, we get the majority of viewers back.” Clearly, they still can’t deliver a Wednesday (a popular and high-budget modern spin on The Addams Family) every week with their current production model.
The alternative model is the open, user-generated content creation used by TikTok and YouTube. Although relatively cheap and fast, it requires incentives that balance three (sometimes conflicting) goals: 1) retaining influential creators, 2) encouraging new creators, and 3) retaining and growing of the viewer base. As the platforms in this space try to generate a sufficient amount of engaging content from a relatively small number of popular creators, this creates incentive wars. For example, TikTok is said to be engaged in “warming up” to manually promote videos. YouTube Shorts, on the other hand, have down the bar for creators to earn — they only need 1,000 subscribers instead of TikTok’s requirement of at least 100,000 followers.
These two challenges partly explain the failure of the short-lived streaming platform Quibi. Quibi combines the strengths of the three into one platform. It duplicated the closed, Hollywood-style production system by hiring expensive creators and actors. Instead of empowering individual creators like YouTube and TikTok do, Quibi is betting on brand name creators and actors. In return, it gets poor (probably second-rate) content that doesn’t work at all. That’s because it targets Millennials and Gen Z but doesn’t promote creators in those age groups. Also, surprisingly, it doesn’t use AI to determine what content to produce (although it does use AI to recommend viewers what to watch).
No human-driven platform has yet overcome these two challenges. However, there is a solution. Generative AI will change what video content will be produced, how it will be produced, and who it will be shown to., ushering in a new type of AI-enabled platform.
To the generative platform.
Consider this scenario. A creator entered this text description: Two people are sitting in an Art Deco café. It’s snowing outside. One of them bit into a wedge of Swiss cheese and said, “I’m creatively constipated!”
A hyper-realistic, live-action video (with sound) is almost instantly generated and shown to billions of viewers. Not only do we know who watched for how long, who skipped which parts, the likes, shares, comments, searches and all discussions outside the platform about the video but know We also know the exact input used to create that video. In one shot, this scenario overcomes two challenges with video platforms. It provides a more accurate description of the video (the input text prompt), and it greatly lowers the creative barriers (it’s as simple as typing your imagination). No need to mess with CapCut, or even actors.
It sounds like magic – and in fact, it isn’t – but it’s just a group of three AI programs that are already well developed. AI #1 generates the video based on the text input. AI #2 matches the video to the right audience. AI #3 uses the resulting engagement to guide creators on what to do next. An older version of this production model already produced content, perhaps most notably the Seinfeld parody sitcom “Nothing, Forever“which uses generative AI to create the script and has almost 100,000 followers.
The generative AI driven video platform reduces barriers to value creation by guiding creators on what drives engagement and showing relevant content to viewers. At the same time, reduced barriers and improved guidance in turn enable creators to increase the value they can create outside of the company. And because of the near-zero friction on both sides between creating and viewing relevant content, creators are also viewers and vice-versa. The border is further blurred when the viewer types in a search, and that input text becomes the prompt for a new video.
The economic impact is enormous. Traditionally a small percentage of highly popular content on a platform makes up for a large percentage of less popular content. A generative AI platform will supercharge the success of popular content because creators will be supercharged with the help of algorithmic recommendations on what to do next. At the same time the lower barriers to creation improve the profitability of the rest.
How can leading platforms adapt? Of the three, Netflix is the most locked into its business model and will likely struggle to change. It has long resisted an ad-supported model and has recently moved in that direction. TikTok is the closest to a Generative AI Platform in terms of business model, capabilities, and flexibility of what we see coming, but it is subject to regulation. EXAMINING in the United States. YouTube is in a favorable position because it is trying hard to compete by introducing Shorts and improving creator incentives. It also has support for Google’s AI capabilities. However, Google has already shown that commercial movement in the generative AI space is slow.
This is just the opening credits.
The recent acceleration of technical progress in and awareness of generative AI is nothing short of surprising. Certainly, we do not yet have the technology to generate hyper-realistic, live-action video from a text input, and the availability of such technology is the key to realizing the new platform.
Even if it is available, text inputs may not always provide an accurate definition of the video and we will likely see the platform generate many similar, but not identical, videos emerging as creators -aw composed the same texts. . And as the platform learns the keys to creating compelling content, how can the conflict of interest between the platform and the creators be managed? How can the platform prevent unlicensed deep fakes and the inevitable propaganda and false information that comes?
Despite these caveats, it is likely that generative AI will power new video content platforms that will replace or at least supplement the current incarnations of Netflix, YouTube, and TikTok. Generative AI technology will not only be used to create content but also to power the platform dynamics of the platform, the creators, and the consumers. It almost goes without saying that none of this comes without technological uncertainty and ethical risks. And of course, video is just one realm where we expect to see such rapid change. Many other creative fields of art, music, and the written word are up for dramatic change and new business opportunities for those who see what’s ripe for disruption – or those who use generative AI to protect their turf.
