There is an increasing emphasis on explainability in AI, with professionals seeking transparency to build trust and support effective decision-making.

The demand for quality assurance roles is rising, with humans reviewing AI-generated content to ensure accuracy and reliability.

This shift towards expert involvement is also fueled by a growing recognition of data's critical role in AI development, beyond just computational resources.

Leaders like Jonathan Siddharth of Turing AI emphasize the importance of using real human data for training AI, especially for complex scenarios.

Olga Megorskaya, CEO of Toloka, highlights that data quality has been overlooked but is now gaining industry focus, reflecting a broader shift towards prioritizing data in AI training.

Companies like Turing are paying experts 20-30% higher salaries to attract skilled professionals, underscoring the increasing value placed on expert analysis.

Organizations such as Scale AI, Turing, and Toloka are hiring specialists in fields like biology and finance to create more sophisticated and complex training datasets.

Major AI companies are shifting from relying on low-cost data labeling workers in Africa and Asia to hiring skilled professionals to improve data quality and accelerate AI model training.

This transition is driven by the need for larger volumes of refined data and faster task completion, which are crucial for developing more advanced AI models.

Experts are essential for developing advanced problem-solving features in AI, as they demonstrate how to break down complex tasks into manageable components.

Significant investments follow this strategic shift, including Meta's $15 billion investment in Scale AI, raising its valuation to $29 billion, and Turing AI securing $111 million at a $2.2 billion valuation, with Toloka receiving $72 million from Bezos Expeditions.

The demand for AI expertise is expanding into industries like cybersecurity, data management, and recruitment, where AI is improving operational efficiency.

This strategic shift towards expert involvement and high-quality data reflects a broader movement to leverage advanced expertise for AI innovation across various sectors.