Why AI Startups Burn More Than Their Non-AI Peers

Artificial intelligence is often associated with greater efficiency: automation, faster workflows, and leaner teams. Yet Silicon Valley Bank’s State of the Markets: H2 2025 report suggests that the companies building AI tools are not experiencing those efficiency gains themselves. When benchmarked against non-AI startups, AI-focused companies appear to be operating with higher burn rates, lower profit margins, and lower revenue per employee across most stages of growth.

Rather than contradicting the potential of the technology, the data highlights the realities of building companies in an emerging market. It shows that the path to efficiency inside AI startups looks materially different from the efficiency that AI products may offer their customers. The technology may compress costs for end users, but those gains do not automatically translate into leaner operations for the firms creating the systems, especially when markets are young, competition is intense, and infrastructure demands are unusually high.

What the Data Shows: Efficiency Isn’t the Current Priority

SVB’s analysis compares AI and non-AI technology companies across several operational metrics. One of the clearest patterns is in revenue efficiency. At every stage from Seed through Series D, median revenue per employee is lower for AI companies. Even at later stages, where operating discipline typically improves, non-AI companies continue to generate more revenue per headcount.

Profitability follows a similar pattern. Median profit margins for AI companies remain more negative than those of non-AI peers through most of the funding lifecycle. At the seed and Series A stages, the gap is particularly wide, reflecting the capital-intensive nature of building data pipelines, training models, and supporting early-stage infrastructure. Many AI companies are also in markets where pricing is still in flux, making it harder to convert usage into revenue at the same pace as more established software sectors.

These differences become especially visible when looking at burn multiples such as the ratio of net burn to net new revenue. At Series A, the median AI company burns about $5 for every $1 of new revenue generated. That is roughly $1.40 more than the burn level for non-AI companies at the same stage. Even at Series C, where companies generally have some repeatable revenue motion in place, AI startups show a higher median burn multiple than their non-AI counterparts.

Taken together, these numbers point to an operating environment where AI startups are pursuing growth and experimentation ahead of efficiency. The report does not frame this as a flaw, but as a reflection of how early the commercial AI market still is. When markets are still taking shape, companies tend to optimize for speed and relevance rather than margin structure, and AI is following that familiar pattern.

Why AI Companies Burn More: Capital, Competition, and Cost Structure

One of the most consistent explanations offered in the report is access to capital. Over the past three years, AI companies have raised larger rounds at higher valuations, with substantial interest from crossover funds, corporate investors, and dedicated AI vehicles. This capital availability influences how companies operate.

Larger early teams, substantial compute spending, and faster product iteration all contribute to higher burn. At a time when model training, inference optimization, and data acquisition are core parts of the business, spending is not concentrated in one area but rather is spread across headcount, infrastructure, and go-to-market efforts. For many AI startups, the cost of building and refining the product remains structurally high. Even companies leveraging hosted foundation models often incur meaningful costs through usage-based compute, rapid prototyping, and the integration of new model capabilities.

This dynamic also affects incentives. When companies have significant cash reserves and strong investor demand, the pressure to optimize burn multiples or maximize revenue per employee is lower. In a competitive landscape where differentiation depends on speed of development, hiring engineering talent earlier or experimenting more broadly can feel like a rational trade-off. This becomes especially relevant when competitors are moving quickly; choosing not to invest aggressively may risk falling behind in a market where technological advantage compounds.

SVB’s benchmarking reflects this. While non-AI companies show a more gradual scaling pattern in spending relative to revenue, AI companies front-load investment in pursuit of market share, technical advantage, or platform positioning. This pattern mirrors previous technology cycles but on a larger scale, particularly because AI companies often operate in categories where the addressable market is still expanding and customer expectations evolve rapidly.

A More Nuanced View of AI’s Operating Model

The report’s findings highlight a broader point: efficiency inside a company depends on its stage, cost structure, and competitive environment, not solely on the nature of the technology it builds. As AI markets evolve, some of these patterns may shift. More mature companies may eventually benefit from automation in their internal workflows or from standardized infrastructure that reduces costs.

For now, though, the median data presents a consistent picture. AI startups are spending more, scaling teams earlier, and operating with lower efficiency metrics than non-AI peers. This may reflect the strategic realities of a rapidly evolving sector rather than a permanent feature of AI company building.

Interpreting the 2025 Efficiency Gap

SVB’s analysis does not claim that one type of company is inherently better positioned than the other. Instead, it offers a snapshot of how AI and non-AI companies differ in operating behavior at this moment in the technology cycle. It captures the early economics of an industry still in formation, where competitive pressure and technological complexity shape day-to-day operating decisions.

Some factors, such as compute intensity, competition, and capital inflows, are likely to evolve. Others, such as the need to invest ahead of revenue in emerging markets, are familiar patterns in technology transitions.

The main takeaway is straightforward: while AI may improve efficiency for end users, AI startups themselves are currently operating with higher burn and lower median efficiency. Understanding this gap helps contextualize the unique demands of building companies in the AI era, without assuming that traditional metrics will look the same for every category of technology. The report underscores that early-stage inefficiency does not necessarily contradict long-term potential; it simply reflects where the industry is in its development curve.

As competition increases, infrastructure stabilizes, and customer adoption patterns become clearer, the efficiency profile of AI companies may shift. For now, the available data provides a grounded view of how these companies operate today and why their cost structures differ so visibly from those of traditional software peers.

Chief Brand & Investor Relations Officer

Sneha Dadrwal

Frequently Asked Questions

Q: What does the SVB report say about AI startup efficiency?

A: The State of the Markets: H2 2025 report finds that AI startups, on median, have lower revenue per employee, more negative profit margins, and higher burn multiples than non-AI tech companies.

Q: Why do AI companies have lower revenue per employee?

A: AI startups tend to hire earlier, spend more on infrastructure, and operate in markets where pricing and revenue models are still developing.

Q: What is a burn multiple, and how do AI companies compare?

A: A burn multiple measures how much cash a company spends to generate new revenue. SVB’s data shows median AI burn multiples are higher than those of non-AI peers across most stages.

Q: Are AI companies less profitable than non-AI companies?

A: According to the report’s benchmarking, AI companies have more negative median profit margins through most funding stages due to higher costs in compute, talent, and product development.

Q: Does this mean AI startups can’t become efficient?

A: No. The report highlights current patterns in an emerging market. As AI matures and infrastructure becomes more standardized, efficiency metrics may shift.

Sources

1. State of the Markets: H2 2025. Silicon Valley Bank, 2025.