The Latest Developments in the Generative AI Market

In the absence of strong technical differentiation, B2B and B2C apps drive long-term customer value through network effects, holding onto data, or building increasingly complex workflows. Existing market maps describing the landscape for generative AI lack a convincing organization, instead seeming to be random boxes based on functionality. Since most of my readers are interested in the technologies and companies powering things like games, simulations and metaverse applications — you’ll find this map helpful in charting who is moving these specific experiences forward. Foundation models (like GPT-3 and Stable Diffusion) are extremely large models trained on broad datasets that can be adapted to a wide range of downstream tasks (Stanford HAI). Foundation models are analogous to the public cloud in making a powerful, new technology (in this case, AI) accessible to developers who do not have specialized machine learning skills. There’s been an explosion of new startups leveraging GPT, in particular, for all sorts of generative tasks, from creating code to marketing copy to videos.

Startups on the application layer will likely iterate between using more powerful general models and building their own vertical models. Although transformers are effective for computer vision applications, another method called latent (or stable) diffusion now produces some of the most stunning high-resolution images through products from startups Stability and Midjourney. These diffusion models marry the best elements of GANs and transformers, throw in some physics and clock in way underweight compared to the latest GPTs. The smaller size and open source availability of some of these models has made them a fount of innovation for people who want to experiment. As the proliferation of generative AI technology accelerates, businesses are seeking the help of experts to navigate the complex product landscape. The team plans to update the list to reflect changes in the market and add interactive features.

Notable Growth Expected in China’s Generative AI Market is on a Surge to Become a Global Leader in Artificial Intelligence

One potential benefit of Gen-AI for creatives is that it can enable them to create content more quickly and efficiently. For example, a writer may be able to use a Gen-AI system to generate rough drafts of articles or stories, which they can then edit and refine. This can save time and allow creatives to focus on the most important aspects of their work. It is likely that Gen-AI will have a significant impact on the creative industries in the future. While some creatives may be replaced by Gen-AI systems, others may find new opportunities to work with these systems or to create content that is enabled by Gen-AI.

Perhaps those companies are just the next generation of software rather than AI companies. As they build more functionality around things like workflow and collaboration on top of the core AI engine, they will be no more, but also no less, defensible than your average SaaS company. In particular, there’s an ocean of “single-feature” data infrastructure (or MLOps) startups (perhaps too harsh a term, as they’re just at an early stage) that are going to struggle to meet this new bar. Over the last few months, however, overall market demand for software products has started to adjust to the new reality. The recessionary environment has been enterprise-led so far, with consumer demand holding surprisingly strong. This has not helped MAD companies much, as the overwhelming majority of companies on the landscape are B2B vendors.

Generative AI Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023-2028

Our Window into Progress digital event series continues with “Under the Hood”—a deep dive into the rigor and scale that makes Antler unique as we source and assess tens of thousands of founders across six continents. The Nordics have produced some of the most successful tech unicorns in Europe—and the world—with Spotify and Klarna securing some of the highest valuations ever achieved by European tech founders. As the tech flywheel spins faster and faster in the region, Antler is excited to publish the largest study of tech founders in the Nordics ever conducted. Reaching out to us today to stay ahead of the curve and secure your place in the future of innovation. Generative AI has come to various industries, reshaping the realms of creativity, productivity, and problem-solving.

In other areas, such as video, audio, and code generation, although there aren’t open-source or API-based models readily available, startups have managed to build their own AI models using the same foundation model architecture as GPT-3 and Stable Diffusion. The video-generation startup Rephrase.ai built a proprietary AI model that maps text to voices and videos, enabling marketing teams to easily create hyper-personalized ad videos. These techniques have expanded the capabilities of generative AI models, leading to improved performance, increased efficiency, and the ability to generate more realistic and high-quality content.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Previously, she reported for Forbes and was co-editor of Forbes Next Billion-Dollar Startups list. Before that, she worked for Business Insider, Gigaom, and Wired and started her career as a newspaper designer for Gannett. I’ve had it write some investment memos for me and I swear it was as good as what I can write. [Laughs] To your point about being out of a job, I realize it was said in jest, but Yakov Livshits there’s the knowledge and the craft of being able to work with the machine and I think that is a new skill that we need to learn. You’re more productive, you’re more creative, whatever it is, if you can really really embrace the machine. We have to train how we work with the machines, but I think the result really is we are superpower humans as a result of being able to work with these machines.

For most companies, the cloud represents operating expense, not capital expense. You’re not buying servers, you’re basically paying per unit of time or unit of storage. That provides tremendous flexibility for many companies who just don’t have the CapEx in their budgets to still be able to get important, innovation-driving projects done.

But we think the key thing to understand is which parts of the stack are truly differentiated and defensible. This will have a major impact on market structure (i.e. horizontal vs. vertical company development) and the drivers of long-term value (e.g. margins and retention). So far, we’ve had a hard time finding structural defensibility anywhere in the stack, outside of traditional moats for incumbents. While generative use cases are Yakov Livshits the most popular application of foundation models, many emerging products highlight that generation is only part of the story. Another set of powerful and newly-feasible applications have taken advantage of their embeddings. It is difficult to predict exactly how generative AI will impact the metaverse, as the latter is still a largely theoretical concept and there is no consensus on what it will look like or how it will function.

Before even the map, we put out this blog post of what was going to happen. There was some stuff on the internet that wasn’t that good, and so I literally put it in OpenAI, “the difference between classical AI and generative AI,” and it started spitting out amazing stuff. It wasn’t just a joke that the article was co-written with GPT-3; it actually was. And then I’d be like, “Specifically for image generation, you can think of it as ….” That human-machine iteration loop I hadn’t experienced before, and it was very much how we created both the blog post and landscape. However, growth alone is not enough to build durable software companies. Critically, growth must be profitable — in the sense that users and customers, once they sign up, generate profits (high gross margins) and stick around for a long time (high retention).

Data Insights & optimization

OpenAI just launched a new classifier to do that, which is beating the state of the art in detecting AI-generated text. A serious strike against generative AI is that it is biased and possibly toxic. Given that AI reflects its training dataset, and considering GPT and others were trained on the highly biased and toxic Internet, it’s no surprise that this would happen. The exponential acceleration in AI progress over the last few months has taken most people by surprise.

The market will separate strong, durable data/AI companies with sustained growth and favorable cash flow dynamics from companies that have mostly been buoyed by capital, hungry for returns in a more speculative environment. It would be equally untenable to put every startup in multiple boxes in this already overcrowded landscape. Therefore, our general approach has been to categorize a company based on its core offering, or what it’s mostly known for. As a result, startups generally appear Yakov Livshits in only one box, even if they do more than just one thing. In prior years, we tended to give disproportionate representation to growth-stage companies based on funding stage (typically Series B-C or later) and ARR (when available) in addition to all the large incumbents. This year, particularly given the explosion of brand new areas like generative AI, where most companies are 1 or 2 years old, we’ve made the editorial decision to feature many more very young startups on the landscape.

• Generative AI is revolutionizing the way we live, work, and interact with the world around us.
• Currently, the media and entertainment sector exhibits a clear dominance in the market.
• First, advances in machine learning and natural language processing have made it possible for AI systems to generate high-quality, human-like content.
• There’s a jungle of acronyms, technologies, products and companies out there that’s hard to keep a track of, let alone master.
• Some common types of generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models.