2024年二季度人工智能和机器学习公共报表和估值指南2024

Q2 2024 Artificial Intelligence & Machine Learning Public Company Summary and Valuation Guide

Key Takeaways:

1. Semiconductor Leaders Outperform: Semiconductor firms experienced significant growth, driving their EV/TTM revenue multiples past 9.0x, exceeding pure-play vertical applications. This trend is attributed to upward revisions in earnings estimates, particularly for AMD, Intel, NVIDIA, and Qualcomm.

2. Share Price Growth in AI Core and Semiconductors: In Q2, share prices for AI core conglomerates and semiconductors showed the highest growth, with notable increases for NVIDIA (+37%), Arm (+31%), and Oracle (+14%). Oracle's valuation growth was attributed to improved cloud pricing enhancing its competitive position.

3. Uneven Revenue Growth: Only about 28 out of 60 companies are expected to accelerate revenue growth in FY2024, and 32 are projected for FY2025. The majority of these companies are in the hardware sector, specifically within the semiconductors and autonomous machines segments.

4. SaaS Business Model Adjustments: SaaS companies, adapting to generative AI, saw significant share price declines, including UiPath (-44%), MongoDB (-30%), Snowflake (-16%), and Salesforce (-15%). These declines reflect the tension between reaccelerating existing product lines and investing in alternative AI-based solutions.

5. Notable Valuation Improvements: Recently VC-backed companies, such as Arm, Matterport, and CrowdStrike, experienced substantial valuation growth, with Arm showing a 170% increase in its valuation multiple.

6. Best and Worst Performing Stocks: Rackspace Technology (+61%), Arm (+36%), Palantir (+21%), C3.ai (+21%), and Adobe (+16%) stood out as top performers. Arm’s performance might be misleading compared to NVIDIA’s earnings growth. Conversely, ECARX (-68%), Veritone (-57%), and UiPath (-44%) were among the worst performers, reflecting slower integration of generative AI.

7. Private Valuations Disconnected from Public Companies: Foundation model infrastructure and application companies have revenue multiples exceeding 30x ARR multiples, stretching up to 100x for high-flying research labs. These companies' valuations differ significantly from those of public companies.

Valuation and Performance Indicators:

• Revenue and EBITDA: Revenue and EBITDA figures for various segments show the performance trends across AI core, generative AI vertical applications, AI semiconductors, and AI autonomous machines.
• Geographical Perspective: Analysis covers global performance with specific data points as of June 30, 2024.
• Interactive Excel Data Pack: Accessible to PitchBook clients, offering detailed financial data for over 55,000 public companies and over 4 million private companies.

This summary encapsulates the key dynamics affecting the AI and machine learning sectors, highlighting both the leading performers and the challenges faced by different segments within the industry.