AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models. To understand this challenge, consider the foundation of the web itself. The web was not designed…
The rapid expansion of AI use cases is creating an urgent need for scalable and usable data infrastructure, highlighted by the current limitations in accessing unstructured web data.
Enterprises will only be able to fully capitalize on AI's potential if they can overcome the fundamental challenge of accessing and structuring vast amounts of relevant web information.
The focus is shifting from pure AI model development to the underlying data infrastructure required to feed these models, making data accessibility a primary bottleneck and innovation driver.
- · Web data infrastructure providers
- · AI model developers with robust data pipelines
- · Enterprises leveraging AI for complex tasks
- · Data engineering firms
- · AI companies reliant on proprietary or limited datasets
- · Enterprises without data structuring capabilities
- · Legacy AI solutions with poor data integration
Demand for specialized tools and platforms that can efficiently extract, clean, and structure web data for AI models will surge.
New competitive advantages will emerge for companies that master web data as an AI input, leading to more sophisticated and performant AI applications.
The development of 'AI-native' web data standards and protocols may accelerate to facilitate seamless ingestion of information by autonomous systems.
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Read at MIT Technology Review — AI