The Generative AI Models Market presents key opportunities in automated multimodal content creation across industries like advertising and e-commerce, driven by advanced AI models despite high operational costs and trade tariff impacts. Firms can capitalize on regional model developments and alternative supply chains.
Dublin, Sept. 16, 2025 (GLOBE NEWSWIRE) — The “Generative AI Models Market Report 2025-2035” has been added to ResearchAndMarkets.com’s offering.
World revenue for the Generative AI Models Market is set to surpass US$65 billion in 2025, with strong revenue growth predicted through to 2035.
Surge in Multimodal Content Creation Fuels Enterprise Adoption of Generative AI Across Industries
One of the most powerful drivers of generative AI adoption is the surging demand for automated, multi-format content creation-spanning text, images, video, and audio. Industries such as advertising, entertainment, e-commerce, and gaming are leading adopters, leveraging advanced models like OpenAI’s GPT-4o, Google’s Gemini, and Meta’s LLaMA to deliver personalised, scalable content at speed.
Illustrating this trend, Adobe’s Firefly allows users to generate professional-grade visuals and designs directly from text prompts, cutting production timelines and reducing creative overheads. This ability to amplify creativity while lowering resource intensity is compelling enterprises to invest in generative AI tools, making automated content generation a central driver of market growth.
High Operational and Computational Costs Constrain the Scalability of Generative AI Models
One of the most persistent challenges in the generative AI market is the extraordinary cost of developing and sustaining large-scale foundation models. Systems such as OpenAI’s GPT-4, Google’s Gemini 1.5, and Anthropic’s Claude rely on billions of parameters and vast datasets, requiring specialised GPU clusters and high-performance computing environments. Training GPT-4 alone reportedly cost tens of millions of dollars, with ongoing expenses for inference, fine-tuning, and deployment adding further strain.
These financial demands create barriers for start-ups and mid-sized enterprises, limiting their ability to innovate and compete. They also raise questions about the long-term sustainability of frequent model iterations, particularly as hardware costs, energy consumption, and cloud pricing continue to rise. For many organisations, the challenge is less about technological capability and more about achieving cost-effective scale-a constraint that will shape competitive dynamics in the years ahead.
The Impact of US Trade Tariffs on the Global Generative AI Models Market?
U.S. tariffs, particularly those directed at China and other key exporters of semiconductors and electronics, are reshaping the foundations of the global generative AI ecosystem. Generative AI models depend heavily on high-performance computing infrastructure-advanced GPUs, TPUs, servers, and data centre hardware-much of which is manufactured or assembled in regions caught by trade restrictions. Measures such as tariffs on Chinese-made chips and network equipment, combined with export controls on advanced processors like NVIDIA’s A100 and H100, have disrupted established supply chains and driven up costs for both U.S. and global AI developers.
In response, firms are diversifying their supply bases, shifting manufacturing and sourcing to countries such as Vietnam, Mexico, and India to reduce dependency on China. At the same time, Chinese technology companies are doubling down on domestic chip design and developing their own large language models, accelerating efforts to build a self-sufficient AI ecosystem. This dynamic is fuelling a bifurcation of global generative AI development-with Western models supported by U.S.-allied infrastructure on one side, and parallel ecosystems emerging in China, Russia, and parts of the Global South on the other.
For U.S.-based players, tariffs are creating upstream cost pressures that could result in higher cloud AI pricing and increased capital expenditures for training and inference. Yet, these constraints also act as a catalyst for domestic investment in chip fabrication and AI infrastructure, reinforced by policy initiatives such as the CHIPS and Science Act. Over the longer term, the industry is likely to see more regionalised model development, fragmented data governance regimes, and a reconfiguration of compute infrastructure-shaping the competitive balance of the generative AI market in line with geopolitical realities.