Dell Technologies Capital sees AI changing SaaS pricing, not ending SaaS

Dell Technologies Capital managing director Daniel Docter says the firm is still backing deep-tech startups before conventional financial metrics are fully visible, leaning on a technical investing team and Dell’s enterprise network to assess early markets. The Palo Alto-based corporate venture firm has invested $1.8 billion since its 2012 launch, but did not disclose a new fund, valuation target or current deployment pace.

In an interview with Crunchbase News, Docter described an investment approach built around early technical conviction: what a product can change, which existing markets it might pressure and whether the underlying technology works. That bias reflects his own background in electrical engineering, computer science and research funding, as well as a Dell Technologies Capital team with degrees and operating experience across engineering, data science, large technology companies and startups.

The firm’s corporate connection is central to its pitch. Docter said Dell Technologies Capital uses access to Michael Dell’s network and Dell’s broader customer base to understand what large enterprises are asking for, including Fortune 500 technology needs. He framed that network as both an input for investment decisions and a way to help portfolio companies reach buyers.

Deep tech still has a timing problem

Docter said the hardest deep-tech investments are companies that may be directionally right years before buyers are ready. In those cases, he said the investor decision starts with founders rather than spreadsheets, including whether a team can change course, accept outside input and avoid being trapped by its first technical thesis.

Survival is the harder part. Docter said deep-tech companies facing five-, seven-, 10- or longer-year adoption cycles need spending discipline and investors willing to keep supporting the company over time. He added that the timeline for building companies has compressed, making that endurance harder.

He also separated two types of startup timing. For companies creating a new category, the first entrant may spend heavily teaching the market what the product is and why it should exist. For companies disrupting an existing large category, he said being early can be more useful because budgets, buyers and reference points already exist.

AI pressure on SaaS is pricing pressure

Docter rejected the idea that AI agents will wipe out SaaS companies as a group, while saying AI will change how software is built, used and sold. His sharper prediction was on pricing: the per-seat model, he said, is likely to give way to consumption-based or outcome-based models.

That does not mean incumbents lose by default. Docter pointed to SaaS companies’ brands and existing customer relationships as advantages, naming Salesforce, Intuit and Oracle as examples of companies with wide market recognition. His caveat was that management teams have to use AI to alter their products and commercial models. Some will not, he said.

For AI startups, Docter said distribution has become one of the most important questions in early-stage diligence. Technical quality alone is less likely to be enough if many companies can build strong AI products. Startups need a credible answer for how they will reach customers.

That pressure could also drive M&A. Docter said some SaaS incumbents may need acquisitions over the next six to 24 months to adapt faster than they could by building internally. In that version of the market, incumbents get technology, while startups get the distribution they would otherwise have to build from scratch.

Durability over AI pilots

Dell Technologies Capital is also looking harder at whether AI revenue is durable, according to Docter. He said many AI companies are moving away from annual or multiyear contracts toward project-based revenue, sometimes with large customers but without the same contractual predictability.

For Series A and B companies, he said evidence that customers return for additional projects can matter more than a single initial win. His example was a startup that can show a first deal with Anthropic followed by repeat deals in later months. The point is not just that a logo signed once, but that the buyer kept spending.

Docter said recent Dell Technologies Capital liquidity came from companies that matched the firm’s longer-horizon view. Netskope and SingleStore took more than a decade to develop before their markets caught up, he said, while Rivos reached a significant exit in under five years as AI workloads increased pressure on data center infrastructure. He also named LayerX and Entro Security among recent outcomes, while saying the firm does not claim it can time exit markets.

This story draws on original reporting from Crunchbase News.