Generative AI is experiencing a rapid commoditisation across its layers, from data and models to infrastructure and applications. This trend is due to the proliferation of open-source datasets and models, the rise of cloud platforms, and the increasing adoption of AI applications.
In the data layer, commoditisation is driven by the availability of large, open-source datasets. The model layer, on the other hand, is becoming commoditised through the release of pre-trained models and the standardisation of model architectures.
The infrastructure layer is experiencing commoditisation due to the emergence of cloud platforms that offer AI-as-a-Service. These platforms provide scalable, cost-effective solutions for training and deploying AI models, reducing the need for in-house infrastructure.
The application layer is the last to be commoditised, as it requires domain-specific knowledge and expertise. However, the rise of AI applications in various industries, from healthcare to finance, is leading to the commoditisation of this layer as well.
Despite the commoditisation, certain areas are expected to accrue the most value. These include proprietary datasets, specialised AI processors, and niche AI applications. Proprietary datasets offer unique insights that are not available in open-source datasets. Specialised AI processors provide superior performance for AI workloads. Niche AI applications cater to specific needs and use cases, offering a competitive edge over generic AI solutions.
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