Prior to joining Protocol in 2019, he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter. He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London. He also holds a doctorate in engineering from the University of Oxford. I don’t think we have immediate plans in those particular areas, but as we’ve always said, we’re going to be completely guided by our customers, and we’ll go where our customers tell us it’s most important to go next. The important thing for our customers is the value we provide them compared to what they’re used to. And those benefits have been dramatic for years, as evidenced by the customers’ adoption of AWS and the fact that we’re still growing at the rate we are given the size business that we are.
Many hot technology trends get over-hyped far before the market catches up. But the generative AI boom has been accompanied by real gains in real markets, and real traction from real companies. Models like Stable Diffusion and ChatGPT are setting historical records for user growth, and several applications have reached $100 million of annualized revenue less than a year after launch. Side-by-side comparisons show AI models outperforming humans in some tasks by multiple orders of magnitude. Generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand.
Every industry that requires humans to create original work—from social media to gaming, advertising to architecture, coding to graphic design, product design to law, marketing to sales—is up for reinvention. The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value—and commensurate market Yakov Livshits cap. Gen-AI is being used in gaming in a number of ways, including to create new levels or maps, to generate new dialogue or story lines, and to create new virtual environments. For example, a game might use a Gen-AI model to create a new, unique level for a player to explore each time they play, or to generate new dialogue options for non-player characters based on the player’s actions.
The market is primarily driven by the expanding information technology (IT) sector and the increasing usage of AI-integrated systems for enhancing productivity and agility. Besides this, the emerging popularity of generative AI for assisting chatbots in conducting effective conversations and enhancing customer satisfaction is also contributing to market growth. Generative AI can create personalized recommendations, tailored advertisements, and customized products based on individual preferences and behavior. Moreover, the rising utilization of generative AI for creating virtual worlds in the metaverse and producing digital artworks using text-based descriptions and generating unique and innovative content is also propelling the market growth. Furthermore, the market has attracted significant investments and funding from both established companies and venture capitalists.
Additionally, Gen-AI can be used to create new, realistic virtual environments for players to explore, such as cities, forests, or planets. Overall, it can be used to add a level of dynamism and variety to gaming experiences, making them more engaging and immersive for players. This report is a deep dive into the world of Gen-AI—and the first comprehensive market map available to everybody. We provide an overview of over 160 platforms in the space and their investors, as well as insights from leading thought leaders on the potential of this technology. This hands readers a unique opportunity to gain a comprehensive understanding of the generative AI market and the potential for new players to challenge established players like Google.
This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists. The largest of these infrastructure companies host the massive amounts of data needed for enterprise AI applications in a format that facilitates all sorts of data pipelines. Databricks has distinguished itself from Snowflake, a notable incumbent in the space, by being specifically designed for the needs of AI/ML data teams. As unicorns and later-stage companies are battered by the economic climate, the overall health of the European tech ecosystem looks stronger than ever, with more founders coming from big tech and unicorns to build new startups with significant growth potential. Dive into our report and get to know the new generation of European tech founders.
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.
We’re seeing new use cases every day that demonstrate how AI will change the way we work, create and play. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers. Sometimes the distinctions in each model are minimal — one company might label certain types of purchases as “office supplies” while another categorizes them with the name of their office retailer of choice, for instance. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers.
Generative AI’s potential in research processes is boundless as long as there is a willingness to explore and learn. At QuestionPro, we are committed to pushing the boundaries and advancing the Yakov Livshits field of market research with innovative and efficient AI solutions. They’re out there doing some noteworthy stuff, simplifying the gnarly task of creating ad content with efficient tools.
I am an experienced author with expertise in digital communication, stock media, design, and creative tools. I have closely followed and reported on AI developments in this field since its early days. I have gained valuable industry insight through my work with leading digital media professionals since 2014. This AI 3D scene generator can analyze a text instruction and quickly create custom 360-degree, 8K resolution HDRi (high-dynamic-range imaging) environment maps based on it, and it can also precisely match the generated background to a sample image provided.
The startups in these two segments alone account for over half of the identified players in the industry. The landscape is built more or less on the same structure as every annual landscape since our first version in 2012. The loose logic is to follow the flow of data from left to right – from storing and processing to analyzing to feeding ML/AI models and building user-facing, AI-driven or data-driven applications.
The application of generative AI to virtual worlds is in its infancy — but is going to grow faster than many expect. Scientific research within generative AI is a huge driver of new capabilities. Much of the research funding for generative AI comes from industry itself (NVIDIA, Meta, Google, Google and OpenAI are at the forefront). Much also continues to rely on traditional institutional relationships. Generating an image of an avocado playing guitar may be fun, but, with very few exceptions, is likely not a good business. However, more meaningful use cases do abound even if they are not quite as entertaining.
These models were trained on very large collections of human language, and are known as Large Language Models (LLMs). Based on the technology type, the global generative AI market has been segregated into autoencoders, generative adversarial networks, and others. Among these, generative adversarial networks currently hold the largest market share. A detailed breakup and analysis of the market based on application has also been provided in the report.