"Almost 20 years ago, a startup named Jigsaw pioneered a new crowdsourcing model where users would contribute data to a platform in exchange for access to its services."

"Jigsaw is largely forgotten today, but its so-called “give-to-get” model could be perfect for AI startups that need to obtain rich proprietary datasets to train their models."

"These datasets are crucial to improve accuracy and performance of AI models, to provide a competitive advantage over rivals, to allow customization and specialization for industry-specific needs, and to reduce reliance on third-party data sources."

"Users could create a free account on Jigsaw’s platform by contributing their own business contact information. They could also add new contacts to the database to earn points, which they could then spend to see contacts posted by others."

"Jigsaw also encouraged users to verify the accuracy of contact information in the database by rewarding them with points for each correction made."

"In many industry verticals, obtaining a rich proprietary dataset will be the key challenge to producing a differentiated AI model."

"Users could then spend points by asking the AI to design new plans."

"Incentivizing users to crowdsource could be a cost-effective way to acquire large amounts of data, as it leverages the efforts of a community rather than relying on paid data collection services."

"A give-to-get, crowdsourced data collection approach could be applied to a number of industry verticals where the target users are in possession of the training data."

"Medical and health data: AI models can greatly benefit from access to diverse patient data, such as electronic health records, medical imaging, and genomic data."

"Legal document analysis: Law firms and legal professionals often have access to large collections of legal documents, such as contracts, court rulings, or patent filings."

"Art and creative work: Artists and designers may possess large collections of their own artwork, sketches, or designs."

"Finance and investment: Financial professionals and investors may have access to proprietary trading algorithms, portfolio data, or market analysis reports."

"Scientific research data: Researchers in various fields might have access to valuable datasets generated through experiments or simulations."

"Manufacturing and production data: Companies involved in manufacturing and production may possess proprietary data on production processes, quality control, and equipment performance."

"Obtaining rich proprietary training datasets will be the key challenge for startups looking to create AI models for industry verticals."

"Moreover, crowdsourcing should create a flywheel: as users contribute data to the model, the model gets smarter and more capable, which draws in the next set of users, who provide the next set of data."