The generative AI market is evolving rapidly, with its total addressable value projected to reach US$185 billion by 2025. As the hype around foundational models gives way to practical implementation, 2024 is shaping up to be the year when AI monetization strategies take center stage.
From Model Training to Market Adoption
Since the launch of ChatGPT, the industry has experienced an explosion in large language models (LLMs) and multi-modal AI systems, sparking a new wave of innovation. The next stage in this evolution is AI inference—the application of trained models to real-world data, delivering predictions and insights on demand.
Key to the future of AI is shifting deployments from cloud-based services to on-device experiences, including smartphones and Internet of Things (IoT) devices. Advances in chip design, memory, and on-device processing power are making it possible to run lighter, efficient AI models at the edge, helping generative AI scale beyond tech-savvy early adopters.
Four Core Growth Areas for Generative AI
According to Nomfins analysis, sustainable monetization in generative AI will be driven by four core pillars:
- Digital Advertising – AI-enhanced content creation and personalization will supercharge marketing efficiency.
- Digital Work – AI copilots and automation tools will transform productivity in software, design, coding, and documentation.
- Model Services – Infrastructure to train, fine-tune, and run AI models will be a critical offering to businesses and developers.
- Conversational Interfaces – AI-powered chatbots and virtual agents are set to become integral to customer service and daily interactions.
These use cases span both consumer-facing platforms and enterprise software, offering a range of revenue-generating models.
Business Models for Monetizing Generative AI
With adoption accelerating, companies are experimenting with a variety of monetization approaches:
- Usage-Based Pricing – Customers pay according to how much they use the AI model, such as tokens consumed or compute time.
- Subscription Models – Tiered pricing plans for access to AI features or premium services.
- Platform Fees – Charging developers or businesses to build on top of AI platforms or marketplaces.
- Licensing & Royalties – Offering IP licenses or usage-based royalties for proprietary models or datasets.
- Turnkey Solutions – Selling bundled hardware-software packages, especially in enterprise or government settings.
In China, AI firms are increasingly offering end-to-end systems, combining inference models with proprietary devices to serve vertical industries. This is partly a response to the restricted access to advanced AI chips due to international trade controls. To navigate these limitations, Chinese firms are re-engineering model architectures, relying on smaller, domain-specific models, or turning inward to domestic AI infrastructure providers.
Generative AI’s Future: Distributed and Industry-Specific
While a handful of firms may dominate foundational model development, the applications layer of the AI stack is expected to remain decentralized. This opens up immense opportunities for companies that can combine specialized algorithms with unique data to build tailored solutions in niche markets.
In the long tail of generative AI applications—healthcare, law, education, manufacturing—smaller software firms can create differentiated offerings by focusing on industry expertise, compliance requirements, or regional needs.
As generative AI transitions from experimental to essential, the emphasis will shift from scale to sustainable, secure, and value-driven growth. The companies that succeed will not only commercialize the tech—they’ll embed it responsibly into workflows, infrastructure, and devices that people and businesses rely on daily.
