INSIGHTS ¦ Generative AI Predictions for 2024


The CB Insights report for 2024 highlights the rapid evolution and impact of generative AI across various sectors, predicting significant advancements in infrastructure, foundation models, and human-machine interactions. It delves into the challenges of escalating energy demands, data scarcity, and security concerns, alongside the burgeoning roles of multimodal AI and the shift towards smaller, more efficient models.

Key Points

  1. Generative AI infrastructure is becoming more power-intensive, with predictions of significant energy consumption.
  2. The industry faces a GPU shortage, driving up costs and fostering chip rivalries.
  3. Security concerns are growing with the increased targeting of language models by bad actors.
  4. Multimodal AI is becoming essential, pushing companies to adapt to new market demands.
  5. Smaller AI models are gaining popularity for their efficiency and reduced costs.
  6. Open-source models are challenging the dominance of closed, proprietary models.
  7. Advances in human-machine interactions suggest a shift in social dynamics, including the potential impact on human relationships.
  8. The rise of AI-driven devices, including wearables, is predicted to revolutionize consumer technology.
  9. Energy and environmental concerns are pushing the search for more sustainable AI operations.
  10. The market is seeing consolidation in machine learning operations (MLOps) to address fragmentation and inefficiency.
  11. New AI model architectures are emerging to address the limitations of current technologies.
  12. Data scarcity is prompting a search for new sources and the development of synthetic data initiatives.

Key Statistics

  • Predicted massive energy consumption by AI chips, comparable to small nations.
  • GPU shortages leading to significant markups in resale prices.
  • Open-source models have seen nearly $1B in combined funding in recent months.
  • Multimodal AI applications are expanding into healthcare, search, and more.
  • The shift towards smaller models and the exploration of new architectures to reduce carbon footprint and computational requirements.
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Key Takeaways

  • The energy demands and environmental impact of generative AI infrastructure are becoming critical considerations.
  • Chip shortages and the high cost of GPUs are significant challenges for the industry.
  • Security vulnerabilities in language models pose risks to users and organizations.
  • The advancement of multimodal and smaller AI models is opening new possibilities for efficiency and application.
  • Open-source models are gaining ground against proprietary solutions, driven by community support and lower costs.
  • Human-machine interactions are evolving, with implications for social relationships and consumer technology.

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