Daniel Docter, managing director at Dell Technologies Capital, began his career as a technologist. He holds degrees in electrical engineering and computer science, as well as a Ph.D., but early on found himself gravitating away from purely technical work toward translating technology into business and commercial use cases.

Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital’s broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm’s affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with Crunchbase News, Docter also discussed how AI is reshaping SaaS and why he doesn’t believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

Crunchbase News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don’t get at any other VC firm. I’m using my words carefully because I’m not saying we’re better. I’m just saying we’re unique.

That unique network is that we have access to Michael Dell’s network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what’s going on in the world and understand technology and how it’s being used. What do Fortune 500 companies want or need? What is Goldman Sachs asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

Warren Buffett classically said, “Invest in what you know.” The way I look at it is that we’re trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that’s also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there’s a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn’t changed from how we’ve always thought about it and how venture capital always thinks about it. First and foremost, you’re really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It’s not purely about the technical capability of the founders. There’s definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder’s ability to be agile — to understand when they’re wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that’s qualitative. It’s EQ more than IQ, but a lot of times that determines success. I don’t think this AI era has changed that. That’s consistently true.

The answer to the second question is even harder. How do you know if you’re betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It’s really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don’t overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, “Play nice but win.” We always try to play nice but win.

It takes a village for these things to work, so it’s important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the “sleeping giants” thesis suggests the second wave — the companies with the foundational architecture in place when a catalyst like generative AI hits — may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation — which means it’s creating a brand-new category of business or software product that doesn’t exist today and is going to be huge — or category disruption, meaning there’s already a very large category that exists and I’m going to disrupt it with my technology. I’m doing something much better, faster, cheaper or stronger.

It’s important to have a sense of whether a company is doing category disruption or category creation. If you’re doing category creation, being first means you have to educate everybody. It’s a heavy lift. It’s a daunting amount of work, capital and effort that goes into explaining something that doesn’t currently exist and why it’s going to be needed in the future.

A lot of times, first-mover advantage isn’t an advantage there. Category creation is often where the second, third or fourth company hasn’t had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there’s value in first-mover advantage. You’re disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There’s a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It’s disruptive because it will change how software is built and consumed. Maybe even more importantly, it’s going to change how it’s priced. The per-seat pricing model is probably outdated and going to die. It’s going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don’t believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They’re going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They’re not going to go away.

How they charge and price might be different, but they’re still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows Salesforce 1, Intuit and Oracle.

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they’ve sold to for years and years and have long-standing relationships with. If — and it’s a big if — they understand how to embrace the AI transformation that’s going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don’t make the turn. But is that any different from any other technological or industrial revolution? It’s always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren’t.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: “What is your distribution strategy?” That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it’s clear that some SaaS companies won’t be able to transform themselves organically. They’re going to need to undergo an inorganic transformation, meaning they’ll have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you’re trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year — including massive liquidity events like Netskope, Rivos and SingleStore — right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I’d love to say we saw it all coming, but the reality is we can’t time the market. It just doesn’t work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, LayerX and Entro Security.

We just try to stay really focused on backing great founders with deeply technical ideas. We’re investing early and know that sometimes it can take years for the market to fully catch up to what’s being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You’ve talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Dockter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is “Is their revenue durable?” Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we’re also seeing is a huge shift away from recurring revenue to something I’m calling  “re-occuring” revenue. I know that’s not really a word. What I mean by “re-occuring” is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say “we got our first deal with Anthropic in October, and they did a second deal with us in January, and we already did our third deal in March” is very powerful.

Given DTC’s unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Dockter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. “You don’t get what you don’t ask for.” I know that’s an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don’t be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it’s management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

Related Crunchbase query:

• Global Venture Funding To AI Startups In 2026  

Related reading:

• Dell Technologies Capital On The Next Generation Of AI — And The Data Fueling It
• Corporate Venture Capital Is Splitting In Two

Illustration: Dom Guzman