2024-10-13-PitchBook-新兴技术未来报告_更新我们的人工智能展望（英）_47页_1mb

Emerging Tech Future Report Summary: GenAI Outlook Update

Core Content Overview

This report provides an update on the progress and impact of Generative AI (GenAI) across various sectors, including Enterprise Applications, Crypto, Data Analytics, Enterprise SaaS, and Fintech. It highlights both the anticipated and actual developments in the GenAI landscape, focusing on adoption rates, investment trends, and challenges.

Main Points and Key Insights

GenAI Overview

• GenAI's Impact: The rise of GenAI has transformed several industries, particularly in AI & Machine Learning (AI/ML), with significant investment and innovation.
• Adoption Challenges: Despite the hype, adoption remains limited due to high compute costs, data security concerns, and system complexity.
• Market Trends: The GenAI infrastructure layer is seeing more investment and innovation, while application-level startups face challenges in demonstrating commercial viability.

Enterprise Applications

• Prior Expectations: Expected impacts included the maturation of the LLMOps industry, enhanced AI agents, and code generation.
• Reality Check: GenAI has had a limited impact on enterprise applications, with legacy ML models still prevalent. AI agent startups are growing, but face challenges in differentiation and adoption.
• Infrastructure Growth: Infrastructure companies such as semiconductor startups and cloud providers have seen significant unicorn valuations, with companies like CoreWeave, Crusoe, and Lambda achieving high valuations.
• VC Activity: VC deal count for GenAI operations software has doubled in 2023, with expected 50% growth in 2024. However, deal value has not kept pace with infrastructure layer growth.

Crypto

• Prior Expectations: Anticipated AI impacts included smart contract development, auditing, and blockchain data querying.
• Reality Check: Adoption of AI in crypto has been slower than expected, with developers still reliant on traditional tools. AI-driven tools have started to simplify data querying.
• Decentralized AI (DeAI): There has been a notable rise in DeAI startups, leveraging blockchain to create transparent and decentralized AI systems. These startups aim to counter the centralized nature of current AI models.

Data Analytics

• Prior Expectations: Expected GenAI use cases included SQL generation, data transformation, and business intelligence visualization.
• Reality Check: GenAI-native analytics software is growing, with $2.1 billion in 2024 spending. However, the technology is not yet reliable for structured data analysis.
• Adoption Barriers: Industrial customers are lagging due to challenges in integrating tabular data with LLMs and a lack of data science expertise. Only 6% of supply chain and 4% of manufacturing organizations use GenAI extensively.

Enterprise SaaS

• Prior Expectations: Anticipated major impacts on customer service, enterprise search, and generative design.
• Reality Check: Enterprise SaaS has seen progress in conversational AI and emotion AI, with many startups and incumbents developing AI-driven solutions.
• Adoption Trends: While adoption is widespread, traditional cost concerns are limiting investment. Incumbents are proceeding cautiously, but some have made strategic AI acquisitions.

Fintech

• Prior Expectations: Anticipated use of GenAI for operational efficiencies and hyperpersonalization.
• Reality Check: Fintech companies have been quicker to deploy GenAI solutions, while traditional financial institutions are more cautious. GenAI is being used in lending, banking, wealthtech, and regulation tech.
• Strategic Acquisitions: Several fintech firms have made AI-related acquisitions, such as Ramp acquiring Cohere.io and Venue, and Nubank acquiring Hyperplane, to enhance their AI capabilities.
• Future Outlook: A lower-interest-rate environment is expected to increase AI acquisition activity in the fintech sector.

Key Figures and Trends

• GenAI Software Spending (2024): Estimated at $14.5 billion, representing 14.3% of AI software spending.
• GenAI Spending Growth (2024 vs 2023): 61.6% increase in spending on GenAI-native analytics & business intelligence software.
• LLMOps Unicorn Valuations: 25 of 39 tracked unicorns in the LLMOps space are infrastructure-related.
• VC Deal Activity: A doubling of VC deal count for GenAI operations software in 2023, with expected 50% growth in 2024.
• Notable Acquisitions:
  • Character.AI acquired by Alphabet
  • Adept AI acquired by Amazon
  • Inflection AI acquired by Microsoft
  • DarwinAI acquired by Apple

Summary of Real-World Progress

• Conversational AI: Significant progress in conversational AI and emotion AI, with startups like Hugging Face, Kore.ai, and Ada leading the charge.
• AI in Software Development: AI code generation has seen widespread adoption, with coding assistants raising over $1.0 billion in H1 2024.
• Decentralized AI: DeAI startups are leveraging blockchain to create more transparent and decentralized AI systems.
• Data Science Tools: While some startups are integrating AI into their platforms, many data scientists still rely on native LLM capabilities like OpenAI's Code Interpreter.
• Market Barriers: The dominance of hyperscalers in data analytics creates challenges for new entrants, requiring successive versions of GenAI models to compete.

Conclusion

GenAI has had a transformative impact on certain industries, but adoption remains constrained by technical, economic, and regulatory challenges. The landscape is evolving rapidly, with significant investment in infrastructure and a growing number of startups exploring AI applications. While some areas show promising progress, such as conversational AI and DeAI, others like generative media and industrial data analytics are still in early stages. The report underscores the importance of continued innovation and strategic investment to overcome these barriers and realize the full potential of GenAI.