Generative AI Market and Landscape

The Generative AI landscape represents a flourishing ecosystem where artificial intelligence algorithms are designed to not just replicate, but create entirely new and original content. It is expanding rapidly, driven by advancements in deep learning, natural language processing, computer vision, and other AI techniques. This type of AI finds applications in various industries, including entertainment, gaming, advertising, fashion, healthcare, and more.

The landscape of the generative AI market is diverse, encompassing both established tech giants and startups. Many of these startups focus on specific verticals, such as gaming, creativity, or virtual reality. They often provide APIs or platforms that enable businesses to utilize generative AI in their applications.

The generative AI market is poised for significant growth as more industries recognize the potential of AI-generated content. However, there are also ethical concerns surrounding the technology, such as the potential for deepfakes or misuse of AI-generated content. As the market matures, regulations and guidelines around generative AI may also evolve to address these challenges.

Why is Generative AI in the spotlight today?

While AI is a broad term encompassing a range of technologies focusing on tasks requiring human-like intelligence, generative AI is a specific subset that concentrates on the creation of new content by applying machine learning methods. Generative AI models are evolving very rapidly right now. They are getting better, can process more data, and perform more calculations as the technical capabilities of the devices have also evolved. By collecting and processing versatile data, deep neural networks, and complex structures designed to mimic the functionality of the human brain serve as the basis for such generative models.

Generative AI has demonstrated the ability to produce highly creative and realistic outputs in various domains such as art, music, and literature. This has fascinated people and captured their attention, as it challenges traditional notions of creativity and raises questions about the role of machines in artistic and creative pursuits.

Generative AI, as opposed to traditional AI, which can be used for tasks like classification and prediction, focuses on creating new and original content rather than simply imitating existing data patterns. It aims to generate outputs that are not based on pre-defined rules or explicit instructions but instead learn from patterns and data to produce new and creative content.

Generative AI utilizes enormous machine learning algorithms, and large-scale neural networks known as foundation models (FMs), which have been pre-trained on extensive data. These FMs include large language models (LLMs), which are trained on an immense volume of words from various natural language sets, accounting for trillions of data points. These large models are becoming the backbone of generative AI-based applications.

One recent development in generative AI technology is the ability to create high-resolution and realistic images from text descriptions. By leveraging large-scale deep learning models, researchers have made significant advancements in generating images that accurately depict the content described in natural language inputs.

Generative AI potential to revolutionize various creative industries is one of the main reasons why it is in the spotlight now. For instance, in the field of art and design, generative AI tools can assist artists in generating new and imaginative ideas. These tools can create stunning visuals, help with composition, or even generate entire pieces of artwork. By collaborating with generative AI, artists can explore novel concepts that they may have never thought of before, opening doors to new possibilities and creativity.

The power of generative AI lies in its ability to provide new insights, amplify human creativity, and automate complex tasks. It pushes the boundaries of what computers can create and opens doors to innovation in various industries. The spotlight on generative AI stems from its potential to transform creative processes, streamline workflows, and usher in a new era of technology-driven progress.

The rise of large language models powered by Generative AI

Over the past few years, we have witnessed the rise of large language models powered by generative AI. These models have gained immense popularity and excitement due to their ability to generate human-like text and perform a wide range of language-related tasks.

Large language models thrive due to the development of generative AI, especially after the emergence of neural network architecture called the Transformer. They have become the foundation for more powerful language models, and by processing all information at once rather than sequentially, they learn faster compared to their predecessors - recurrent neural networks. Moreover, transformers can memorize more context. In that way, they have significantly improved language understanding and generation capabilities.

The introduction of ChatGPT, OpenAI's language model, was a turning point in the generative AI landscape. The GPT architecture-based ChatGPT proved to be a remarkable breakthrough in the comprehension and creation of natural language, providing a vivid demonstration of large-scale Transformer models' power.

LLMs can increase productivity in many domains, including in business. We can delegate routine, time-consuming tasks to a smart chatbot and simply check the results, as LLMs can interpret our queries and solve quite complex problems. Language models have the potential to yield helpful content and speed up workflows, but if misused or maliciously exploited, they can also pose some dangers.

For example, they can enable quick access to harmful information or even generate malicious content such as phishing emails and harmful code. Even though there are ways to limit access to dangerous content, these are not always implemented or effective.

The results of LLM tools should always be verified and treated with an element of hesitation. These complex systems cannot distinguish truth from fabrication. Some of their results may appear very credible, but turn out to be entirely incorrect. Using generative AI like LLMs may prove to be incredibly beneficial in solving a huge number of issues, but it is up to humans to get involved in verifying the accuracy, usefulness, and overall reasonableness of their outputs.

Right now, many AI industry experts are even suggesting that the further development of some LLMs should be temporarily halted to give time for the implementation of general security protocols to improve and regulate this valuable technology of synthetic data generation.

Generative AI application landscape

Generative AI applications have immense potential and are continually evolving. They are used in various industries, including art, entertainment, advertising, and data science. The functionalities of a generative AI system vary based on the modality or nature of the dataset employed.

Generative AI landscape categories

Generative AI can be applied in the following modalities:

Text generation

Text generation models aim to generate human-like text. Once trained, these models can generate new, coherent text based on a given prompt. This can include generating stories, poetry, articles, and more.

Code generation

AI algorithms can generate code from natural language descriptions. This can be useful for automating repetitive coding tasks or prototyping new features quickly. AI algorithms can analyze code and identify potential bugs or vulnerabilities, providing suggestions on how to fix them.

Image generation

Text-to-image models can generate realistic images from textual descriptions or random noise. They can be used for various applications such as digital art generation, creating synthetic data for training algorithms, or enhancing image quality. Generative AI models in this category can create various types of design, including graphic design, logos, website layouts, or product designs. They are often used by designers as sources of inspiration or to automate repetitive design tasks.

Music Generation

AI models that can compose original music or generate variations of existing compositions. These models can be used by musicians, composers, or music producers to quickly generate ideas or explore new musical possibilities.

Audio generation

Such AI systems summarize, generate human-like speech or convert text into audio, often known as text-to-speech or speech synthesis models. They can be used in voice assistants, audiobook narration, accessibility tools, or customer service applications.

Video generation

Generative models in this category can generate videos or alter existing videos by changing their content, improving their quality, or modifying their style. They can be used for applications like video synthesis, deepfake creation, or video editing.

Chatbots

This category of generative AI tools includes models that can generate realistic and contextually appropriate responses in conversation. These models are commonly used in customer support, virtual assistants, or interactive storytelling applications.

Search engines

Generative AI can enhance search engines' ability to provide contextual suggestions. By understanding the user's search history, preferences, and location, generative AI can generate personalized suggestions for queries, websites, or related topics, offering a more tailored search experience.

Gaming

This category includes AI models capable of generating virtual game environments, levels, characters, or NPCs (non-player characters). Such systems can help game developers create content or assist in procedural content generation.

Only some broadly known generative AI spheres of usage are listed here. And as the technology continues to advance, the potential applications are expected to expand even further.

Generative AI market

As the influence of artificial intelligence extends to numerous aspects of our day-to-day activities, a wide range of tools has emerged across various industries. According to analytics from Markets and Markets, the generative AI market will grow from $11.3 billion in 2023 to $51.8 billion by 2028. Currently, AI is helping to create and enhance about 1% of the content on the Internet, and experts expect this figure to rise to about 50% in the next 10 years.

One of the drivers behind this dramatic growth in generative AI is a wide adoption of cloud storage among businesses. Experts need huge datasets for training machine learning models to create a generative AI solution, but just a few years ago it was not that easy and cheap for data scientists to get it for their studies. Now that more data gets transferred from physical media to the cloud, it has become more accessible.

The development of large language models capable of generating text is also driving the development of generative AI in general. LLMs such as GPT and LaMDA form the basis for creating applications for text processing, and also for image generating. For example, GPT-3 modified to generate images is built into Dall-E.

Key players

The list below encompasses an array of tools such as text generation software, image creation programs, music composition systems, and more. It provides a comprehensive outlook on generative AI tools and their applications.

All generative AI applications are based on foundation models, as has been already mentioned. The development of such major AI systems demands a tremendous amount of resources, so most often they are either developed by large companies with huge funds or by startups with major investments. The generative AI market is diverse, encompassing both established tech giants and smaller startups.

In this list, we will point out companies that develop foundation models and indicate some applications that are built on top of these models. For convenience, we categorized the application domains of generative AI into three major categories: Natural Language Generation, Image generation as well as Sound and speech generation

Natural Language Generation

Some of the prominent players in the market include:

OpenAI

OpenAI is a leading AI research laboratory that has developed powerful text generative models, known as GPT (Generative Pre-trained Transformer). The latest versions of it, GPT 3.5 and GPT 4, are a base for a large language model-based chatbot called ChatGPT also developed by OpenAI.

Google

Google has been actively researching and developing generative AI models. They have developed models that can generate text based on different input formats:

• BERT (Bidirectional Encoder Representations from Transformers)

• T5 (Text-to-Text Transfer Transformer)

• PaLM and PaLM 2(Pathways Language Model)

• LaMDA and LaMDA 2 (Language Models for Dialog Applications)

• Minerva

• GLaM (Generalist Language Model)

Microsoft

Microsoft has been working on text generation models, specifically for natural language processing tasks. Microsoft's Turing-NLG is a language model that has shown promising results in generating coherent and contextually accurate text. The company has also integrated natural language processing capabilities into products like Microsoft Azure Cognitive Services.

Microsoft and NVIDIA created the Megatron-Turing NLG model with 530 parameters, which made it one of the largest in 2021. This transformer-based LLM can comprehend text and generate responses in natural language.

Meta

Meta has developed LLaMA 2 (Large Language Model Meta AI), an open-source language model. It is free to use for research and commercial purposes. The Large Language Model was developed in collaboration with Microsoft. Microsoft and Meta are committed to an open approach to allow expanded access to essential AI technologies for the benefit of businesses worldwide.

NVIDIA

NVIDIA is a technology company known for its graphics processing units (GPUs) and AI capabilities. They have developed powerful hardware technologies that are widely used for training and running generative models efficiently. The company has introduced NVIDIA AI Foundations, the services for creating unique chatbots, picture generators, and other AI tools. It offers the essential instruments for adopting and customizing generative AI. Once the models are ready for deployment, enterprises can run them using NVIDIA AI Foundations cloud services.

Among the tools incorporated in it is NVIDIA NeMo, which is a cloud-based framework that allows developers to build and deploy large language models. It simplifies the development of conversational AI models by offering a comprehensive toolkit for researchers specializing in automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech synthesis (TTS). It enables easy reuse of existing code and pre-trained models, facilitating the creation of new conversational AI models for both industry and academic researchers.

Salesforce AI Research

Salesforce AI Research released CTRL that can generate human-like text with specific prompts. CTRL stands for Conditional Transformer Language Model and it is designed to take a given prompt and generate relevant text based on that prompt. The model has 1.6 billion parameters, making it one of the largest language models ever created. CTRL is trained on a diverse range of training data sources such as books, websites, and Wikipedia.

Anthropic

Anthropic is a startup that has launched a chatbot called Claude, based on a model called Claude 2. The chatbot can generate text and answer questions on a wide range of topics from science concepts to cooking.

The following are the generative AI apps that use some of the previously mentioned foundation models at their core:

• Codey is a Google family of coding models built on PaLM 2, that can complete and generate code, as well as help developers solve debugging issues through a chatbot;

• Bard is an experimental AI chatbot by Google with features such as code generation, math problem-solving, and writing assistance. It runs using Google's largest language model called PaLM 2, although originally launched with a lighter version of LaMDA to make it easier to scale;

• Copy.ai is an advanced AI utilizing GPT-3 for creating unique tests for marketing and sales;

• Jasper.ai is an AI that can create textual content for webpages, blogs, social networks, and other media with the help of GPT-3;

• Copysmith.ai is A GPT-3-based AI copywriter that can compose product descriptions, taglines, SEO meta tags, and ad texts;

• Rytr leverages GPT-3 to create plagiarism-free content to facilitate the process of writing articles, posts, and more;

• Writesonic is powered by GPT-3,5 and GPT-4 and can do everything from creating articles to writing product descriptions or text for landing pages;

• Anyword combines pre-trained models such as GPT3, T5 by Google, and CTRL by Salesforce Research, which has been fine-tuned to enhance performance;

• LangAI is an interactive platform for learning languages, that utilizes GPT-3 and GPT-4, offering users the opportunity to engage in spoken or written conversations with an AI in more than 20 different languages;

• Genei leverages the capabilities of GPT-3 to transform PDFs and webpages into intelligent summaries. These summaries are not only concise but also come with a comprehensive analysis of the content in the original reading material.

Image generation

OpenAI

DALL·E 2 is the successor to OpenAI's DALL·E, which is a text-to-image model that can generate images from textual descriptions. It is trained using a large dataset consisting of image-text pairs, allowing it to generate more accurate and diverse images.

It is a neural network model that combines ideas from the GPT-3 to generate images from textual prompts. One of the key features of DALL·E 2 is its ability to handle complex and abstract prompts. Overall, DALL·E 2 is an advanced version of the original DALL·E model, enabling it to generate high-quality images based on textual inputs.

Stability AI

Stable Diffusion is an open-source image generation technique developed by Stability AI. It is based on the concept of diffusion models, which aim to estimate the probability distribution of a dataset to generate new samples similar to the original data.

In Stable Diffusion, an initial image is iteratively modified using a diffusion process. This process involves adding noise to the image and then denoising it. By repeating this process for multiple iterations, the image gradually evolves toward the desired target. Stable Diffusion has shown promising results in generating high-quality images with sharp details and coherent structures. It is a base for DreamStudio by Stability AI.

Midjourney, Inc

Midjourney has developed an eponymous neural network that can recognize written text and convert it into images. It uses a deep learning algorithm called Generative Adversarial Networks (GANs) to produce convincing images based on given inputs or prompts.

The GAN architecture consists of two neural networks: the generator and the discriminator. The generator is responsible for creating images, while the discriminator's job is to distinguish between real images and those generated by the generator. Through an iterative process, both networks train each other, resulting in the generator improving its generated images over time.

Google

Google's Image Model Family refers to a series of deep learning models for tasks related to photorealistic image analysis, recognition, and generation. These models have been trained on large-scale image datasets and have achieved state-of-the-art performance on various computer vision tasks by employing diffusion models. Another model developed by Google is DeepDream, which creates unique and surreal images using deep learning techniques.

NVIDIA

NVIDIA AI Foundations includes an image, video, and 3D generation platform called NVIDIA Picasso. It is a cloud-based service for developing and deploying generative AI-based apps with sophisticated text-to-image, video, and 3D transformations for increased creativity, engineering, and digital modeling productivity through simple cloud-based APIs.

Adobe

Adobe Firefly is a family of generative AI models developed by Adobe for its Creative Cloud software. It is designed to enrich and modernize creative workflows in Adobe applications through neural network tools. With Firefly, content creators create high-quality raster or vector images upon request.

Apps based on previously mentioned foundation models include:

• Bing Image Creator is a tool based on DALL-E and developed by Microsoft that allows users to create custom images by combining elements from their vast image database;

• MyHeritage's AI Time Machine is an image generator for self-portraits powered by Stable Diffusion;

• Lensa is an AI-powered photo editing app that allows users to enhance their photos with various filters and editing tools. Magic Avatar feature can turn selfies into realistic artwork. The app relies on a deep learning model called Stable Diffusion;

• Craiyon is an image generation app that is a scaled-down variant of DALL-E. While it may not possess the same level of potency, this AI image generator enables users to effortlessly and swiftly convert their written descriptions into captivating visuals. Craiyon serves as a remarkable resource for individuals seeking to explore the capabilities of AI image generators without any cost;

• Jasper.ai besides working with text can also generate very impressive art with the help of DALL-E 2;

Sound and speech generation

In contrast to LLM, which has seen a significant leap in development, in particular, due to transformer models, or to image generation, which has also reached unprecedented heights due to the development of diffusion models, there have been no similar breakthroughs in audio generation.

The main distinction consists in the amount of high-quality data available for training: while images and texts are plentiful, audio data is either limited or very expensive. Yet, some ideas borrowed from image and text generation methods have been adapted to audio models to solve or improve several problems specific to the field of audio generation systems.

Google

Google has developed the Tacotron and WaveNet models for speech synthesis. WaveNet neural network is capable of generating speech that sounds natural and close to the human voice. WaveNet model is the same technology used to produce speech for Google Assistant, Google Search, and Google Translate. Tacotron is a model that directly maps character sequences to speech waveform, which allows for generating high-quality text-to-speech outputs.

MusicLM is another AI tool designed by Google to transform descriptions into music. The AI can generate music from a tune, allowing it to convert whistled and hummed melodies according to the style described in a textual description into full-fledged musical compositions.

Meta

Meta has introduced Voicebox, an AI model that both generates and edits spoken speech. They claim it's yet another revolution in the field of generative AI. The model does not only create speech in the exact manner and tone of any person's voice from a short sample, but also knows how to automatically remove noise, correct misstatements, and understand context.

AudioCraft is a novel open-source AI code unveiled by Meta, catering to music enthusiasts and sound creators. This cutting-edge technology empowers users to compose music and generate sounds using the incredible capabilities of artificial intelligence. AudioCraft is comprised of three models:

• MusicGen utilizes Meta-owned and legally licensed music to produce music based on user inputs in text format

• AudioGen is trained on publicly available sound effects and generates audio based on user inputs in text format

• EnCodec decoder, which enables superior music generation quality with reduced artifacts.

Microsoft

Created by Microsoft, SpeechT5 is an all-in-one architecture that encompasses three different speech models:

• a speech-to-text model that can automatically transcribe spoken language for tasks such as speech recognition or speaker identification;

• a text-to-speech model that synthesizes natural-sounding audio from written text, offering a voice output;

• a speech-to-speech model capable of converting between various voices, facilitating voice conversion, and performing speech enhancement tasks.

SpeechT5 combines the capabilities of all these distinct speech models, making it a versatile and comprehensive solution for various speech-related applications.

Microsoft has also developed a model of artificial intelligence called VALL-E which turns text into speech, mimicking the human voice precisely, with a sample recording lasting only three seconds. The AI also preserves the emotional coloring of the voice in the sample.

Emerging business models for generative AI

The potential of generative AI to revolutionize multiple industries and reshape our lifestyles and professions cannot be overstated. With the continuous evolution and maturation of this technology, numerous business models are emerging to exploit its capabilities. Prominent business models used by companies when dealing with generative AI include:

Model-as-a-service

The primary advantage of Model-as-a-Service is that it eliminates the need for companies to invest in the development infrastructure and resources required to build AI models from scratch. Model-as-a-service refers to cloud-based or containerized applications that allow software creators who are not data scientists to use AI models through APIs, software development kits (SDKs), or apps.

This business model mimics the popular subscription-based approach used for software services. Subscribers can opt for monthly, semi-annual, or annual commitments, ensuring a steady stream of revenue for developers. By leveraging cloud technology, businesses can tap into a wide array of generative AI models to generate fresh and inventive content. As a result, businesses can effortlessly and economically harness the power of generative AI for creating novel and immersive experiences.

Built-in apps

Businesses develop fresh apps utilizing existing generative AI models, thereby offering novel and innovative experiences. For instance, companies can leverage generative AI to craft exclusive and captivating experiences, allowing them to generate videos, music, artwork, and various other forms of creative expression.

Such applications are called built-in because they use ready-made generative models. That is, they are built into a ready-made system. Such apps have features that are permanently attached to the chosen model or models that are easy to use through the interface of an app. Users often do not realize or even consider that all the features of the application, such as generating photo-based avatars or marketing texts, are possible by means of a particular generative AI model.

Vertical integration

The concept of vertical integration allows companies to use generative AI technologies to improve their current products and services. An illustrative example of this business model is the way some companies are incorporating generative AI capabilities into their existing search engines. That's how generative AI can revolutionize search systems by delivering more precise and personalized results to users, surpassing the reliance on existing web pages alone.

With vertical integration businesses can utilize generative AI models to analyze vast quantities of data, enabling them to make predictions regarding pricing or enhance the precision of their recommendations. In that way companies can create innovative experiences for their customers, increasing their competitiveness in the market.

Generative AI's Impact

The emergence of Generative AI marks a significant advancement in the progression of artificial intelligence. In the future, it will have a major impact on almost all industries. As businesses race to incorporate and adjust to this technology, it is crucial to comprehend its potential in providing economic and societal benefits.

Generative AI has the potential to significantly boost labor productivity throughout the entire economy, in particular by broadening the working options available to some employees through the automatization of some of their routine activities. The acceleration of the technological automation rate is strongly linked to the expansion of generative AI's ability to interpret natural language.

Generative AI tools have the potential to greatly influence various business functions. According to McKinsey, among these functions, there are four key areas - customer operations, marketing and sales, software engineering, and research and development - that could collectively contribute around 75 percent of the overall annual value derived from generative AI applications.

The utilization of AI technology in enhancing business operations allows for greater flexibility and adaptability within these processes. This enables the elimination of traditional pipelines and promotes the integration of advanced AI systems alongside human counterparts.

By adopting this approach, the interaction between machines and humans can be revolutionized, giving rise to teams consisting of both machines and humans. Such teams will be able to quickly process large amounts of data, grasp new information, and adapt to constantly changing conditions as they fulfill their tasks.

AI's influence extends to enhancing and supporting human abilities, allowing machines and individuals to collaborate and excel in tasks they are best at. AI systems adeptly handle repetitive and mundane assignments that involve extensive data analysis, while humans are better at handling unconventional information, drawing conclusions in complex situations, decision-making in highly unpredictable conditions, and similar tasks.

Capabilities of generative AI empower companies to restructure their business operations, resulting in enhanced productivity and reduced costs.

The future and challenges of generative AI

Right now, we can say that the future of all AI depends on the development of generative AI. It has the potential to revolutionize various industries and aspects of our lives. However, it also comes with certain challenges that need to be addressed.

The generation of AI-powered content can be misused for malicious purposes like creating deepfakes and spreading misinformation. Maintaining security measures and developing robust mechanisms to authenticate the authenticity of generated content is essential.

Also, generative AI can potentially create content that is misleading, biased, or harmful. AI models learn from the data they are trained on. If the training data contains biases, those biases can be inadvertently replicated in the generated content. Careful selection and preprocessing of training data are necessary to avoid biases and ensure fairness and inclusivity.

The content generated by AI may not always meet the desired quality standards. Ensuring that generated content is valuable, accurate, and meets human expectations requires continuous monitoring, feedback loops, and refinement.

As generative AI evolves, there is a need for appropriate regulations and policies to ensure responsible and ethical use. Governments and organizations should work together to establish guidelines that address potential risks, privacy concerns, fairness, and accountability.

Despite these challenges, the future of generative AI holds immense potential. Generative AI has already shown tremendous progress in generating high-quality content like art, audio, and writing. In the future, we can expect even more sophisticated algorithms capable of producing original and innovative creations.

Conclusion

The generative AI market will continue to grow and according to estimates will expand by about 5 times in the following 5 years. Key players in the generative AI market such as Google, OpenAI, Microsoft, and others create foundation models that become the basis for many generative AI applications. Creating such systems is not a cheap and quick task, but as the cost of developing such tools may decrease over time, the number of foundation models and applications based on them will increase.

Indeed, we are at the threshold of a new age, where thousands of jobs will be transformed and new ones probably created. No doubt these breakthrough Generative AI platforms will sustain and improve our day-to-day lifestyle. But it also means that we will have to adapt and possibly re-educate ourselves to work with AI. However, all of these developments will be beneficial to humanity, as these systems can empower people to engage in more meaningful and creative endeavors by taking over routine tasks. Human possibilities are extended through collaboration with AI, boosting people's productivity and handling tasks that were previously thought to be impossible to solve.