The Generative AI Application Landscape in 2023

Generative AI has become a hot topic in the media and has attracted a lot of investment from venture capitalists and large tech companies. This has led to the development of new and exciting generative AI applications and the emergence of new startups or open-source alternatives in this field. GPUs and TPUs are expensive and scarce, making it difficult and not cost-effective for most businesses to acquire and maintain this vital hardware platform on-premises. As a result, much of the work to build, tune, and run large AI models occurs in the cloud.

The landscape continues to evolve as existing models are extending to more users through APIs and open-source software, leading to new application and use case developments on a regular basis. In 2017, Google laid the foundation for the generative AI we use today when the company first proposed a neural Yakov Livshits network architecture called the Transformer. With transformers, it became possible to create higher-quality language models that could be trained more efficiently and with more customizable features. At this time, tools with predictive text and simple AI chatbots began to emerge and mature sparsely.

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In addition to personalized investment advice and fraud prevention, virtual financial advisors powered by natural language processing are also becoming more common. These chatbots can answer customer questions about finances in real-time using machine learning algorithms to understand the natural language queries. As this technology continues to advance, we can expect even more personalized and efficient financial services for customers in the future. Some popular applications include image generation, text generation, medical image synthesis, drug discovery, content creation, language translation, virtual avatars in gaming and virtual reality, and fashion design. Additionally, generative AI is transforming customer service with intelligent chatbots and enhancing marketing strategies with automated content creation. As the development and deployment of generative AI systems gets under way, a new value chain is emerging to support the training and use of this powerful technology.

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. It’s possible Gen-AI will replace millions of jobs from designers to producers to artists; however, creatives will always exist in some aspect. There are also a smaller number of standalone Generative AI web apps, such as Jasper and Copy.ai for copywriting, Runway for video editing, and Mem for note taking. We can think of Generative AI apps as a UI layer and “little brain” that sits on top of the “big brain” that is the large general-purpose models.

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Before that, Stripe had launched a data pipeline to help users sync payment data with Redshift and Snowflake. On the customer side, discerning buyers of technology, often found in scale-ups or public tech companies, were willing to experiment and try the new thing with little oversight from the CFO office. 2022 was a difficult year for acquisitions, punctuated by the failed $40B acquisition of ARM by Nvidia (which would have affected the competitive landscape of everything from mobile to AI in data centers). The drawdown in the public markets, especially tech stocks, made acquisitions with any stock component more expensive compared to 2021. Late-stage startups with strong balance sheets, on the other hand, generally favored reducing burn instead of making splashy acquisitions. Overall, startup exit values fell by over 90% year over year to $71.4B from $753.2B in 2021.

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.

The technology has already been deployed in combat since 2018 and continues to advance towards revolutionizing both military and commercial aviation. In this blog post, we’ll explore the general generative AI applications and its potential in business processes. OpenAI is the clear leader in the generative AI landscape, currently valued at nearly $30 billion. Around the same time, new neural networking techniques, such as diffusion models, Yakov Livshits also arrived to lower the barriers to entry for generative AI development. Meanwhile, one-fourth of generative AI funding since Q3’22 has gone to cross-industry generative AI applications, which include text and visual media generation, as well as generative interfaces. As AI technologies evolve at a breathtaking speed, founders have an unprecedented opportunity to leverage those tools to solve complex, meaningful, and pervasive problems.

With the help of chatbots and interactive tools, even those without a musical background can generate their own original pieces with ease. As educational concerns grow, users can expect these plagiarism checker tools to evolve too. As influential has generative AI has quickly become, the future suggests a far more all-encompassing future that affects various sectors, from education to virtual reality.

This could help businesses save time and resources by proactively identifying fraudulent activity. Games and entertainment media can certainly benefit from this advancement, but the impact these models will have on virtual reality (VR) and augmented reality (AR) technology — the metaverse — is what many people are most anxiously awaiting. As they’re refined, these more advanced models will use generative AI technology to create the immersive experiences that make virtual reality feel real. Meanwhile, companies in visual media generation — creating everything from still images to synthetic training data — have led generative AI deal volume, seeing 33 deals totaling $387M since Q3 of last year. Check out our generative AI market map for detailed descriptions of these categories and other areas.

Artificial Intelligence (AI) is a broad term that refers to any technology that is capable of intelligent behavior. This can include a wide range of technologies, from simple algorithms that can sort data, to more advanced systems that can mimic human-like thought processes. Improvements in generative AI technology could help firms find ways to harness imperfect data, while mitigating privacy concerns and regulations. “The release of ChatGPT made AI accessible to anyone with a browser for free. So, our families, children and people without a background in AI or data science could put it to work,” said Bret Greenstein, data and analytics partner at PwC.

Let’s imagine together how generative AI might help you to improve end users’ learning engagement and reduce human risk. Microsoft recently launched the Designer app, which uses AI to generate graphics you can edit. To use it, you need to enter a prompt, a description of the design you want, like an Instagram post about a hair product launch, including a photo you uploaded, like a ChatGPT-powered Canva. Application programming interfaces gave millions of developers instant access to powerful and complex software services, changing the world. As generative artificial intelligence enters that world, we’ll see new forms of access to data and services, machines talking to people and other machines in new ways, and an explosion of activity, collaboration and creativity. As organizations grapple with the implications of Generative AI, a paradigm shift is underway in software development.