Here’s how the 100 most promising AI startups in 2025 compare by the numbers

by | Jun 26, 2025 | Market Analysis & Trends | 0 comments

In April, we identified 100 of the most promising emerging AI companies — here’s how they stack up against each other.

The 9th annual AI 100 list highlighted the most promising AI startups selected from over 17K companies. 

Now, we’re examining the critical metrics behind these winners, revealing potential acquisition targets, partnership opportunities, and emerging competitors before they reshape the market.

Below, we analyzed the 100 winners to understand how the cohort stacks up, the markets we’re seeing emerge, top investors in AI, and more.

Some highlights from our analysis: 

  • AI infrastructure shows a maturity gap despite massive funding. Despite the already enormous amount of capital raised in this category, AI infrastructure still has overall low Commercial Maturity Scores and sees a lot of early-stage activity with a specific focus on efficiency. These AI 100 winners are betting on next-generation solutions like specialized AI chips, novel computing architectures with reduced energy consumption and optimized inference, and infrastructure designed for multimodal workloads that current systems can’t efficiently handle. 
  • Autonomous vehicles are accelerating beyond the hype cycle. The auto & mobility market ranks third by Mosaic score, with companies gaining significant commercial traction following Waymo‘s recent success in scaling its robotaxi operations. This momentum validates years of R&D investment and suggests we’re entering a new phase of AV deployment. Read more in our recent autonomous vehicle analysis.
  • Multimodal AI is driving the biggest breakthroughs. Voice AI platform Cartesia leads the largest year-over-year Mosaic score jump (+321), alongside other companies pushing beyond text-only models toward integrated voice, vision, and reasoning capabilities. This shift represents the next evolution of AI, especially for embodied AI systems like humanoids, moving from single-modality tools toward systems that can understand and generate across multiple forms of media simultaneously. 

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