Issue 30: Transforming Creative Industries

In Feb 2024, I covered the announcement of OpenAI's Sora, in Issue 18 , the revolutionary text-to-video AI model. While it was truly exceptional, it was received with a lot of skepticism from the general public, prompted Hollywood strikes and further more, it was a popular topic at the World Economic Forum’s Annual Meeting in Davos, where artists, musicians and content creators joined discussions on how to navigate a creative future with AI. Some quotes from sessions at Davos:

• A dolphin with a baseball hat.
• Happiness in a bowl.
• A pocketful of hope.

?Sora was one application of generative AI in the creative industries, but today,?I want to take a step back, and look at the broader landscape and opportunities.

I do want to start with some basic disclaimers – I am not a professional in the creative industry, though I am a frequest user of their products and services. My newsletter today is for readers similar to my profile – I am sure I will miss some nuances and details in the industry and I want to apologize for that upfront. The intent here is to explain it for those unfamiliar with the creative industry.

Market opportunity

The generative AI in content creation market is experiencing an extraordinary surge – it was valued at $11.6 billion in 2023 and projected to reach $163.8 billion?by 2033, at a CAGR of 31.20%.

As seen in the image above, Text generation, Image generation, Video and Audio generation make the bulk of the use cases. In a sense, these are the horizontal scenarios that can cut across many industry applications. For example video generation will have applications in healthcare, advertising, fashion and films, to

What are creative industries

The UK government first defined Creative industries as those activities which have their origin in individual creativity, skill and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property. Governments and organizations are increasingly recognizing creative industries as strategic economic drivers. These products and services tend to be cultural, artistic, or experience-based in nature and have the potential for wealth creation and job opportunities.

Impact of Gen AI

Gen AI has been a game changer for this industry, due to several key factors.

• For starters, it brings unprecedented scale, as it generates larges volumes of creative content very fast, meeting demand of high volume production and distribution.
• It extends creativity and boundaries as it leverages machine learning to identify patterns in existing data, and uses the power of neural networks to create new material based on the patterns and information it was trained on. This material can be innovative and distinctive, even though it draws heavily from the vast corpus of data it was trained on, and can be music, art, design, literature, and other creative outputs.
• It provides efficiency, productivity, and cost reduction to adapt to evolving needs such as reuse and repurposing some creatives for new regions, languages, personas, and more.
• It makes it much easier to deliver personalized content which is expected to drive market growth.
• It brings new growth opportunities, due to integration of AR and VR in creative industries.
• It facilitates experimentation and iteration, and prototyping,?allowing designers and creators to explore diverse possibilities.
• It ensures reproducibility, consistency and accuracy in content generation, vital in fields like data analysis and simulation.
• Lastly, AI models can generate large phrases and image posts for social media, blogs, and articles with ease and saving time and money.

Segments and growth

In the chart below, I have captured the 2023 revenues and projected 2033 revenues of a few segments. Again, it is important to note that some are vertical and some are horizontal market sizing.

Gen AI in Music generation, is growing from $294M in 2023 to a estimated $2.66B in 2033, at a CAG of 28.5%, with use cases in music creation, production, and also in other industries like advertising and marketing.

Likewise, Gen AI in Fashion, is growing from $95M in 2023 to a projected $1.6B in 2033 at a CAGR of 36%. Here the applications are greater, as indicated by the growth between 2023 and 2033. This includes several use cases such as:

• image and video generation for marketing
• designing textiles, prints, and patterns, integrate generative AI for enhancing product tagging, attribution and visual search

• creating personalized marketing materials, product descriptions, and social media content
• better inventory management and demand prediction by 2024.
• developing digital fashion items and virtual outfits for the metaverse and gaming
• developing sustainable materials and for upcycling efforts

Another perspective on the relative opportunity of the AI revenue, as compared to overall tech spend, shows that this may not be unnatural or unsustainable.

Challenges

Although gen AI has a lot of potential, it also has some drawbacks that need to be taken into account when it is developed further.

• Quality and Originality: Maintaining the quality and originality of AI-generated content is challenging. Balancing novelty with coherence is crucial to creating content that appeals to human audiences.
• Bias and Diversity: Generative AI models may unintentionally incorporate biases from their training data. Ensuring diversity and inclusivity in AI-generated content is essential to prevent reinforcing societal biases.
• Regulation and Accountability: The rise of AI-generated content raises discussions about regulations and accountability. Establishing guidelines for labeling and attributing AI-created content is important.
• Continual Learning: Allowing AI models to learn and adapt in real-time is a persistent challenge. Developing models that can evolve with new information and preferences is a focus of ongoing research.

Dominant players

This was a crowded space. I have organized it in three categories. There are other ways of slicing and dicing this, I am sure, but let us start here.

1. Gen AI Platforms for Creatives & Image Generators: These tools help translate ideas to generate images and create art.

These tools offer a wide range of features catering to different aspects of content creation, including image generation, video editing, branding, and design. Each tool has its own strengths and weaknesses, making them suitable for different types of users and applications. Pricing varies from free options to subscription-based models, with some tools requiring contact for custom pricing.

2. UI Generation: These tools assist in generating UI designs for apps and websites. These tools cater to different aspects of design, with strengths in automating and simplifying the design process, making them accessible for various users from beginners to professional designers. Pricing ranges from free trials to subscription-based models.

3. AI Marketing & Copywriting: These tools help in creating on-brand content, writing marketing copy, and powering sales and marketing processes.?various users from beginners to professional designers. Pricing ranges from free trials to subscription-based models.

Adoption

I thought I would put things in perspective with this image. In the last 1.5 years, 15 billion images were created by AI. In companies, in its lifetime of 14 years, 50 billion images were uploaded onto Instagram. We are not far from the day when more images will be created by AI.

Before I end, I thought I would also look at the size of the popular tools I have heard colleagues and future professionals (read students) use:

References

Generative AI In Creative Industries Market Size - 2032 ( alliedmarketresearch.com )

https://www.weforum.org/agenda/2024/02/ai-creative-industries-davos/

https://hbr.org/2023/04/how-generative-ai-could-disrupt-creative-work

https://www.dhirubhai.net/pulse/impact-generative-ai-creative-industry-aiwakforce-dxv6f