What We Learned This Year From 6 Active AI Investors Backing Startups Across The Stack

Editor鈥檚 note: In 2025, 附近上门 News heard from six active startup investors in artificial intelligence. Below, we publish highlights from those interviews or presentations. Read the full stories with Accel, Dell Technologies, Foundation Capital, GV, AI Fund and Sierra Ventures, as well as highlights from interviews in 2023 and 2024.

AI investment accelerated in 2025, as startups and investors alike angled for market share in this tech wave. By Q3, nearly half of all startup funding globally went to AI companies, according to 附近上门 data. Global venture funding overall was up 38% year over year in the third quarter, powered largely by megadeals for AI giants.

All told, AI startups raised around $100 billion in the first half of 2025 alone, roughly matching 2024鈥檚 full-year total.

Against that backdrop, six active AI investors shared their insights with us this year and offered a ground-level view of how the playbook is evolving, from compute and data moats to new models for co-founding companies. Here鈥檚 what we learned.

Accel鈥檚 Boterri: How startups can compete against behemoths

Philippe Botteri, partner at Accel

The 鈥淪uper Six鈥� companies 鈥� , , , , and 鈥斕齡enerate hundreds of billions in operating cash flow, much of which is being poured straight back into AI infrastructure. partner and his firm鈥檚 2025 Globalscape report highlighted the new AI power map and how startups can compete.

The firm is one of the three most-active investors on the The 附近上门附近上门, which has surged in value this year amid the AI boom.

Accel in particular has backed a wave of native AI startups at both the model and application layer. Those startups include , small model developer , publicly traded AI infrastructure provider , and application-layer companies including Cursor-maker , , and security startup .

While the incumbents are capturing enormous share, there鈥檚 still room for focused, fast-growing AI-native companies to wedge themselves into new categories or reinvent old ones, according to Boterri.

鈥淚f you don鈥檛 think that GenAI is going to generate a 1%-2% increase in the global GDP, then I鈥檓 not sure why we鈥檙e doing all this,鈥� he said last month, speaking onstage at Web Summit in Lisbon, where 附近上门 News was also in attendance.

Where Foundation see opportunities in physical tech

Steve Vassallo, general partner at Foundation Capital

But AI鈥檚 massive acceleration and computing needs also raise an existential question for the industry: Will the physical infrastructure keep up?

As many of the investors we spoke with this year noted, AI鈥檚 bottlenecks are increasingly physical 鈥� power, chips and data centers 鈥� and those bottlenecks are spawning some of the most interesting startup opportunities.

Botteri鈥檚 analysis points to an estimated 117-gigawatt energy shortfall over the next five years needed to serve projected AI demand 鈥� roughly equivalent to powering three large European economies combined.

It鈥檚 a problem that , general partner at , is also coming at from the venture side. The firm incubated AI chipmaker back in 2016, well before today鈥檚 enthusiasm around AI infrastructure, and has gone on to back more than 100 AI startups.

In our interview, Vassallo recalled that betting on semiconductors in the mid-2010s 鈥渨as a recipe for losing a lot of money鈥� 鈥� but the team believed AI workloads were growing so fast that traditional architectures would eventually hit a wall. That thesis now looks prescient as Cerebras has signaled plans to go public, and chip giant 鈥檚 market cap has exploded to above $4 trillion.听

Vassallo argued that the companies that matter most in this cycle will be those that both harness AI and are mindful of how humans can be 鈥渉acked鈥� 鈥� in other words, products that respect psychology as much as physics. He pointed in particular to reinforcement learning with human feedback, where people help close the loop on AI behavior and, in the process, become more adept at working alongside the systems they鈥檙e training.

The firm鈥檚 AI investments include seed rounds into , a company automating authorization healthcare workflows from often convoluted and paper-based processes, and , an app that assists with writing built on top of 鈥檚 GPT-3. It also invested in the Series A for , whose product predicts and debugs software failures in AI-written code before it is deployed.

鈥淲e love working with founders who are living right at that edge,鈥� Vassallo said.

Why Dell鈥檚 venture arm invests at the silicon level

Dell Technologies Capital managing director Daniel Docter — Daniel Docter, managing director at Dell Technologies Capital

At , or DTC, managing director and partner sit at the intersection of infrastructure and enterprise demand.

Dell, and its venture arm has logged six exits since June 鈥� one IPO and five acquisitions 鈥� even as exits elsewhere in venture have been harder to come by.

As we discussed in our interview, Dell鈥檚 position as a leading GPU server provider means its venture arms sees nearly every serious enterprise AI buyer and builder up close.

partner Elana Lian — Elana Lian, partner at Dell Technologies Capital

And like other investors we spoke with, DTC noted that the current pace of investment in hot AI startups feels unprecedented. 鈥淲e鈥檒l meet with a company on a Tuesday for the first time and sometimes by Thursday, they have a term sheet that they鈥檝e already signed,鈥� Docter said.

The firm鈥檚 infrastructure-level investments include AI chipmaker , which plans to for an undisclosed amount. (The deal is pending regulatory approval.) It has also backed , which makes a chip for embedded edge use cases including in automobile, drone and robot technologies, and , an AI developer software layer with on-demand access to GPUs.

DTC invests at the silicon level because you 鈥渃an be incredibly disruptive to the ecosystem,鈥� Docter told us.

At the application level, DTC鈥檚 investments include , which provides customer support for complex and high-compliance enterprise use cases, and , a GenAI platform for game development that aims to drastically shorten deployment timelines.

Sierra Ventures鈥� layer-cake approach

Tim Guleri, managing partner, Sierra Ventures

If compute is the bottleneck, data is the differentiator.

Lian at DTC put it bluntly: 鈥淎I is almost a data problem.鈥� For models to keep improving, she argued, you need high-quality, domain-specific data 鈥� not just more parameters.

At , managing partner is also focused on data. As he explained in our interview, his firm tends to seek out startups that share a pattern: They attack big, painful workflows, promise order-of-magnitude productivity improvements, and sit on top of rich datasets.

Sierra鈥檚鈥渓ayered cake鈥� framework breaks AI investing into five levels: infrastructure; applied infrastructure on top of foundational models; horizontal applications; vertical applications; and entirely novel innovations that wouldn鈥檛 exist without AI.

The firm isn鈥檛 trying to compete in the most capital-intensive infrastructure layer, but is leaning instead into applied infrastructure and applications where proprietary data and clever distribution can create durable moats, Guleri told us.

Global GDP is , he noted, with roughly $6 trillion in agriculture 鈥� leaving more than $100 trillion in services and industries where he expects AI-driven efficiency gains to accrue.

AI is 鈥渢he wave that鈥檚 lifting everything on top of it,鈥� he said. 鈥淭here鈥檚 going to be a tremendous amount of value creation in the coming decades.鈥�

How a Google Brain co-founder builds and backs AI startups

Andrew Ng, managing general partner, AI Fund

Brain and co-founder is taking a more hands-on route to unique data via corporate partnerships at , his venture studio launched in 2018.

Corporate LPs including , , , and others bring Ng and his AI Fund into highly specialized sectors 鈥� renewable energy, large-scale industrial operations, insurance and more 鈥� where internal data is both hard to access and critical to building defensible AI products.听

Many of AI Fund鈥檚 startup ideas, Ng said, come directly from these partners spotting gaps in huge but under-digitized markets.

鈥淚t turns out a meaningful fraction of our startup ideas come from corporate partners that have spotted a market need, often in some sector of the economy, which is very large, very important but completely foreign to the typical consumer, or completely foreign to the typical AI engineer. I find that it鈥檚 been interesting how often we get to play in these spaces,鈥� he said in our interview. 鈥淲e think it鈥檚 wildly exciting, while no one else cares.鈥�

Venture firms and are also investors in the fund, but Ng said his fund鈥檚 strategy is differentiated from the traditional venture approach. 鈥淯nlike a traditional VC, our primary business activity is not to compete for deal flow,鈥� he said. 鈥淥ur primary business activity is to identify promising startup ideas, validate the market need and the customer need. Then we recruit a CEO to work alongside us to build a company.鈥�

Ng said he sees continued opportunities in specific verticals such as visual and voice AI: 鈥淚t feels like AI is not one thing; it is many different things that are creating new opportunities.鈥�

GV on being willing to invest at AI鈥檚 premium valuations

Dave Munichiello, managing partner at GV

Then there鈥檚 , which has quietly become one of the most-active and -flexible corporate investors in AI.

With as its sole LP but independent on investment decisions, GV (formerly Google Ventures) has no qualms about backing startups that directly compete with Google鈥檚 own products 鈥� as it once did with and is now doing with AI companies that go head-to-head with Alphabet鈥檚 internal initiatives.

Tom Hulme, managing partner and head of Europe at GV

Managing partners and told us in our interview that they鈥檙e writing checks across the stack 鈥� from chips and compilers to applications 鈥� at both early and late stages.

And, they鈥檙e willing to accept premium AI valuations when they believe the opportunity warrants it.

鈥淲hen we look at companies that are coming in to raise, the revenue run rate is insane. These companies are growing incredibly fast, faster than ever before,鈥� Munichiello said. 鈥淎nd it鈥檚 very hard to spend a lot of time looking at AI applications companies, and then go back to looking at other companies.鈥�

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