Factors Driving Growth in AI Stocks

We’ve been tracking the performance of publicly traded AI companies since the beginning of the year. Publicly traded companies with AI products or strategies trade at about twice the forward multiple of non-AI peers. Within the private markets, the same is true within the Series A. GenAI startup companies raise at about 1.5-2x the post-money valuations of all software companies. These businesses represent about 30% of Series As in 2024. The rationale behind these higher prices rest in the idea that AI companies have signficant future growth & likely faster growth than their non-AI peers both public & private. Most of the time, the private tends to lead the public market with trends & valuations. Not this time. The markets are moving in parallel. This is likely because the major AI publics like NVIDIA & Microsoft have spurred the market forward first. Should the multiples remain roughly the same in both arenas this means that there is no kink in the valuation curve between public & private markets. During the last decade, the private markets often applied higher multiples to privates than the publics & this has created an overhang - a need for private companies to grow into their valuations as they approach IPO. Forward multiple is the enterprise value divided by the forward revenue estimate. Pitchbook Series A data as of publication date.

“If it’s not AI, I don’t want it” – a VC headed to Monaco for summer Q2'25 data* shows AI companies are securing significantly larger rounds across sectors, with median deal sizes hitting $4.6M – over $1M above the broader market. In Q2’25, the AI premium was strongest in Auto Tech which saw AI companies securing deals $20.6M larger than traditional peers (lead by Applied Intuition's $600M Series F at $15B valuation), followed by Robotics and Cybersecurity with median deal premiums of $10.7M and $6.4M respectively. The AI premium extends beyond funding to company performance and trajectory metrics. AI companies consistently score higher on our Mosaic Score (success probability) and Commercial Maturity (ability to compete and partner) metrics, proving their fundamentals justify investor confidence. Why are AI companies commanding these premiums? 1) Capital-intensive development cycles AI companies often require dramatically more upfront investment for compute infrastructure, data acquisition, and model training before achieving product-market fit, necessitating larger initial rounds to reach meaningful milestones. 2) Longer runway to defensibility Unlike traditional SaaS where competitive advantages emerge quickly, AI companies need 12-18 months of continuous model refinement and data collection to build meaningful moats, requiring sustained funding through extended R&D phases. 3) Premium for hybrid expertise The most successful AI companies combine rare AI/ML talent with deep domain expertise (like automotive engineers for autonomous driving), creating interdisciplinary teams that command higher compensation. 4) Infrastructure-first business models AI companies often build foundational platforms (like simulation environments or data processing pipelines) that require significant upfront investment but can later support multiple product lines and customer segments. The AI premium continues to reflect investors' "go big or go home" approach; making concentrated bets on AI teams they believe can capture outsized market share. The AI premium signals more than just funding enthusiasm – it's recognition that AI-first companies are simultaneously disrupting the last two decades of companies and building the infrastructure for tomorrow's economy. *Data from CB Insights’ State of Venture Q2’25 report. Explore the latest data on what happened last quarter across the startup ecosystem at the link in the comments.

GenAI is the biggest swing factor in SaaS valuations today—doubling multiples for some, leaving others unchanged. Here’s where things stand: 1. SaaS baseline vs. GenAI uplift Most public SaaS names trade around 9× trailing revenue. But companies with a credible GenAI story are seeing multiples in the 17–28× range: • CrowdStrike trades at 28×, with AI powering threat detection and automation. • Snowflake and ServiceNow hover near 17–18×, positioning AI as central to their platform strategy. • Adobe, despite heavy investment in generative tools, has dropped closer to 7× following cautious signals on monetization. The median for AI-forward software companies is around 17×, nearly double the broader SaaS average. 2. Private AI startups are even more aggressively valued Recent deals in the GenAI space are pricing at 23–26× revenue, well above the private SaaS norm of 7–9×. This reflects investor belief in future expansion, even when current usage or monetization is early. 3. Why GenAI adds 8–10 turns The valuation premium isn’t just buzz—it’s grounded in investor conviction around: • Revenue acceleration through new SKUs, pricing power, and AI-led land-and-expand • TAM expansion, transforming point products into full platforms • Scarcity premium, with few scaled GenAI-native players in the market • Margin tailwinds, based on improving inference efficiency and pricing dynamics 4. But the premium is fragile Without clear, monetized AI traction, the multiple deflates quickly. Adobe’s recent dip is a case in point—investors want results, not just vision. In categories like cybersecurity, we’re already seeing a sharp divergence in multiples: those with visible GenAI differentiation are trading 4–5× higher than peers still early in their AI journey. 5. What to watch next • GenAI-specific revenue reporting: More companies will need to show AI’s direct business impact. • Inference cost curves: If infrastructure costs don’t drop fast enough, margin expansion assumptions will need to be revisited. • Platform consolidation: The long-term winners will become the embedded AI layer for enterprise workflows, agents, and copilots—not just feature vendors. Bottom line: GenAI is adding 8–10 full turns to SaaS valuations, but that uplift is fragile. Investors are no longer rewarding potential—they’re rewarding proof.