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LinkedIn is planning to develop a product targeted towards delivering advertising on its streaming services. The strategic move follows LinkedIn’s recent introduction of AI features that assist advertisers in crafting compelling ad content, a crucial step in expanding its advertising business amidst the uncertain economic climate affecting ad budgets.

Penry Price, LinkedIn’s Vice President of Marketing Solutions, expressed enthusiasm about the potential impact of in-stream video ads. He highlighted their ability to transform how brands connect with and captivate their audiences.

The introduction of this new advertising avenue aligns with LinkedIn’s impressive trailing 12-month revenue. It surpassed $14 billion, which meant an 8% year-over-year increase in the third quarter of the fiscal year 2023.

LinkedIn’s application of generative AI

LinkedIn’s revenue largely comes from advertising sales and subscriptions targeting recruiters, job seekers, and sales professionals. This all creates a solid foundation for its financial success.

On another front, LinkedIn’s Head of Data and AI, Ya Xu, discussed the company’s innovative Generative AI Playground with VentureBeat. This internal developer sandbox empowers engineers to delve into LinkedIn’s rich data using advanced generative AI models from notable sources like OpenAI.

Moreover, LinkedIn recently hosted its largest-ever internal Hackathon, attracting thousands of participants and fostering collaboration among engineers.

Committing to excellence

Xu emphasized the engineering team’s commitment to an exploration-based philosophy, prioritizing understanding existing problems before creating a mature final product. By placing generative AI technology in the hands of engineers and product managers who express interest, LinkedIn aims to cultivate a profound comprehension of the challenges.

This approach ensures that they can proactively address potential pitfalls. In addition, they can deliver optimal solutions when they develop products utilizing these models.

“We need a comprehensive understanding of the problems we face,” Xu explained, “so that when we build the product with these models, we can preemptively mitigate any issues that may arise.”