GENERATIVE AI MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Generative AI Market Size and Trends

Global generative AI market is estimated to be valued at USD 68.34 Bn in 2024 and is expected to reach USD 496.82 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 32.8% from 2024 to 2031.

To learn more about this report, request sample copy

Increasing adoption of advanced technologies powered by artificial intelligence and machine learning algorithms across industries can drive the generative AI market growth. Generative models are gaining popularity as these help reduce costs and increase productivity by automating repetitive manual tasks. The ability of generative AI techniques to learn from large datasets and generate new meaningful information with minimal human intervention can boost demand for generative AI solutions. Advancements in deep learning and ability of generative models to handle large and complex datasets can open new growth avenues for players.

Advancements in deep learning and neural networks enabling more sophisticated generative models

With advancements in deep learning techniques like generative adversarial networks (GANs), reinforcement learning, and self-supervised learning, researchers are now able to generate increasingly lifelike images, videos, speech, text and other forms of data. Deep learning models are becoming more powerful as computing capabilities increase and more training data becomes available. Due to unsupervised learning techniques like GANs and autoregressive models, AI systems can now learn the underlying distribution or patterns in datasets without the need for human annotation or labeling. This self-supervised learning enables generative models to produce synthetic data that mimics real data with high fidelity.

Deep neural networks have billions of parameters that can learn rich, high-dimensional distributions over natural data domains like images, audio and text. By learning from huge volumes of unlabeled training examples, generative models can mimic subtle statistical properties like object shapes, textures or sentence structures. Advancements in neural architecture search enable researchers to develop novel network designs that are even better at capturing complex, real-world distributions. The availability of huge computational resources in the cloud allows them to train these models at massive scales for longer periods. Generative models can generate photos, videos and other content that appear highly realistic even to the human eye.

Major technology companies like Accenture, Adobe, Adept, AI21 Labs, Amazon Web Services (AWS), etc. and well-funded AI startups are investing heavily in generative AI with the goal of developing new applications and business opportunities. Corporates see huge commercial potential in generative models for personalized experiences, creative works, synthetic training data and others. Venture capitalists have recognized this potential and invests in AI startups each year. This rising investment boosts advancements in generative modeling techniques.

Large firms like OpenAI, Google, AWS, Microsoft, and others have launched initiatives and research labs dedicated to advancing the state-of-the-art in generative modeling, computational creativity and related areas. These are investing in novel model architectures, self-supervised learning methods, massive computational resources and talented researchers. Startups are innovating with new applications of generative AI in domains like art, science, manufacturing and social media. Many tech companies uses AI to automate routine design/engineering processes and generate synthetic test/training data to lower costs and boost productivity.

Venture funding for AI startups has increased exponentially in recent years. Unicorns like Anthropic, Stability AI and DeepMind received funding from top VCs. This large influx of capital boosts more innovations that push the boundaries of generative modeling.