In 2025 businesses will seek to get real returns on their AI investmemts

The rise of AI in 2024 marked a shift from theoretical possibilities to practical applications, as companies focused on developing useful products from existing AI models.

Key industry shift: The technology sector pivoted from releasing powerful AI models to creating practical applications that deliver tangible value to users.

• Princeton University computer science professor Arvind Narayanan noted a transformation from model development to product creation
• AI technology became increasingly embedded in everyday services, from Google search results to photo editing tools
• The pace of fundamental AI model improvements slowed after the release of GPT-4 in March 2023, leading to more realistic expectations about AI capabilities

Financial realities: The substantial costs associated with AI development and deployment emerged as a significant concern for investors and companies.

• Tech companies faced scrutiny from Wall Street analysts regarding the return on massive AI investments
• The high energy requirements of AI systems led tech giants to explore nuclear power solutions
• Goldman Sachs analyst Kash Rangan noted hundreds of billions in capital investment, while maintaining optimism about AI’s incremental productivity benefits

Workplace impact: AI tools began reshaping various industries, raising both opportunities and concerns about job displacement.

• Borderless AI demonstrated practical applications by using AI chatbots for creating international employment contracts
• Screen Actors Guild members struck partly over AI-related concerns about performance replication
• Musicians and authors expressed worry about AI systems copying their creative works
• Experts emphasized AI’s current limitations in truly creative and reasoning tasks

Medical advances: Healthcare emerged as a promising field for AI applications, with notable developments in research and diagnostics.

• AI-related research earned a Nobel Prize in chemistry for work that could accelerate drug discovery
• The technology helped bridge the gap between traditional laboratory research and data analysis
• AI tools improved diagnostic efficiency by quickly identifying potential areas of concern for medical professionals
• Some AI tools, like OpenAI’s Whisper transcription service, showed limitations including accuracy issues

Future developments: The next phase of AI development is focusing on creating more sophisticated “agent” systems that can work collaboratively.

• Cisco’s Vijoy Pandey predicts AI agents will evolve to work together like human teams
• Future applications may include specialized AI agents for various tasks in cryptocurrency software
• The focus is shifting toward developing AI systems with better reasoning capabilities and practical problem-solving skills

Looking ahead: While early excitement about AI’s revolutionary potential has moderated, the technology continues to demonstrate incremental but meaningful improvements across industries, suggesting a future characterized by practical applications rather than dramatic breakthroughs.