Generative Artificial Intelligence (“GenAI”) is moving beyond chatbots and large language models (“LLMs”) into multimodal systems, reasoning models, agentic AI, semiconductor infrastructure, data centres, energy systems and sector-specific applications. WIPO’s recent analysis, “Beyond the Chatbot: What Patent Data Reveals About the Next Phase of GenAI,” captures this shift through global patent activity. [1]
The numbers are striking: published GenAI patent families increased from 18,862 in 2024 to 37,808 in 2025, while GenAI’s share of overall AI patenting rose from 6.1% in 2023 to 8.7% in 2025. [2] Patent volume, however, should be read carefully. It indicates where investment and research effort are being directed, but it is not, by itself, proof of technological leadership, commercial success or patent quality. [1]
The more useful question is therefore not who has filed the most GenAI patents, but what those filings reveal about the next phase of AI competition.
From Chatbots to an AI Value Chain
WIPO’s analysis shows that GenAI innovation is no longer centred only on LLMs. LLM-related patent families increased from 881 in 2023 to more than 14,100 in 2025. At the same time, patenting around diffusion models, multimodal systems, reasoning-oriented models and agentic systems is expanding. [1]
This suggests that AI competition is becoming distributed across several connected layers. Model capability remains important, but access to advanced chips, cloud infrastructure, computing capacity, data and specialised technical talent may be equally decisive.
The AI value chain now extends from semiconductors and data centres to foundation models, application layers and deployment. Competitive advantage may therefore come not only from building a better model, but from controlling the infrastructure and implementation pathways through which AI is commercialised.
Infrastructure Is Becoming an IP Question
The composition of leading GenAI applicants reinforces this shift. WIPO identifies SoftBank, Nvidia, State Grid Corporation of China, China Southern Power Grid, Inspur and Bosch among significant GenAI patent applicants. [1] The presence of energy, semiconductor and industrial infrastructure players shows that AI innovation is increasingly tied to the physical systems required to train, deploy and operate it.
Energy is a clear example. The International Energy Agency estimates that electricity consumption by data centres could increase from approximately 485 TWh in 2025 to around 950 TWh by 2030. More computationally intensive applications, including reasoning, video generation and agentic tasks, are likely to add further pressure. [1]
At the same time, AI is also part of the response. WIPO notes patent activity by electricity-grid operators in grid optimisation, predictive maintenance and infrastructure monitoring. [1] This creates a practical and legal point: future AI portfolios may cover not just models, but the systems that make those models usable at scale.
Small AI and the Shift Towards Deployment
WIPO’s discussion of “Small AI” is particularly relevant from a deployment perspective. The expression refers to AI systems that are affordable, resource-efficient and adapted to specific operational environments, rather than designed only to compete with frontier models.
The commercial significance lies in practical use. AI may increasingly be embedded into agricultural diagnosis, industrial maintenance, healthcare, education and enterprise workflows, instead of existing only as a general-purpose chatbot interface.
For emerging economies, this distinction matters. Countries and companies need not necessarily build frontier models to participate in the GenAI economy. Specialised applications developed around local languages, sectoral needs, datasets and regulatory environments may create meaningful value.
Patent Quantity Is Not Patent Leadership
The SoftBank–OpenAI comparison usefully shows why patent quantity should not be equated with patent leadership.
WIPO identifies SoftBank as the largest GenAI patent holder, with 2,985 patent families, reflecting ambitions across AI models, semiconductors, data centres, power infrastructure and robotics. [1]
OpenAI presents a different approach. WIPO identified only 35 patent filings globally as of late 2025, mainly relating to multimodal interfaces, code generation, image generation and text editing. [1] OpenAI has also indicated that its patents are intended to serve a defensive function.
The contrast does not establish that one company is technologically superior. It shows that patenting is a strategic choice. Some businesses seek broad protection across the AI value chain; others may rely more heavily on trade secrets, proprietary systems, data, infrastructure and speed to market.
The Indian Position: From Patent Trends to Patentability
These trends are especially relevant in India, where AI-related inventions increasingly intersect with the patentability framework for computer-related inventions. The Indian Patent Office’s Guidelines for Examination of Computer Related Inventions (CRIs), 2025 expressly address technologies such as AI, machine learning, deep learning, blockchain and quantum computing. [3]
Section 3(k) of the Patents Act, 1970 excludes computer programmes per se and algorithms from patentability. The analysis, however, does not end merely because software is involved. The 2025 CRI Guidelines place emphasis on technical effect or technical contribution, consistent with recent judicial developments. [3]
This is where the move beyond the chatbot becomes important for Indian practice. An AI invention directed only to an abstract computational method may continue to face objections under Section 3(k). By contrast, an AI implementation that produces a demonstrable technical effect or solves a technical problem in an industrial, infrastructure or sector-specific setting may present a stronger patentability position. [3]
For applicants, this means that claim drafting and specification strategy will be critical. Patent applications should not simply describe an AI model in functional terms; they should clearly articulate the technical problem, the technical means used to solve it and the resulting technical contribution.
Conclusion
WIPO’s patent data points to a GenAI landscape that is broader, more infrastructure-dependent and increasingly application-oriented. The next phase of competition may not be determined solely by the most capable model, but by the ability to combine models, infrastructure, deployment, technical expertise and IP strategy.
For India, this presents both an opportunity and a drafting challenge. As GenAI moves into technically implemented, sector-specific use cases, applicants may have greater scope to seek patent protection, provided they can demonstrate a real technical contribution and satisfy the statutory requirements.
The key takeaway is straightforward: the future of GenAI will not be defined only by the chatbot at the interface, but by the technical architecture, infrastructure and intellectual property strategy behind it.
References
[1] World Intellectual Property Organization, Beyond the Chatbot: What Patent Data Reveals About the Next Phase of GenAI (2026).
[2] WIPO, Patent Trends Update in GenAI (2026).
[3] Indian Patent Office, Guidelines for Examination of Computer Related Inventions (CRIs), 2025.