OpenAI’s former sales leader joins VC firm Acrew: OpenAI taught her where startups can build a ‘moat’ 

OpenAI’s first sales leader, Aliisa Rosenthal, has found a new career: venture capital. She’s joining Acrew Capital as a general partner, working alongside founding partner Lauren Kolodny and the firm’s other partners, Rosenthal and Kolodny tell TechCrunch.

Rosenthal left OpenAI about eight months ago after a three-year sprint at the AI lab that saw the launch of DALL·E, ChatGPT, ChatGPT Enterprise, Sora, and other products. “I wasn’t initially looking to join a VC fund,” she told TechCrunch. “I was out there meeting with lots of AI startups.” 

But after growing OpenAI’s enterprise sales team from two people to hundreds, she saw the appeal when Kolodny pitched her on venture capital. Instead of helping one startup with its go-to-market strategy, she could help a portfolio of them.

In her time at OpenAI, “I learned a lot about behavior, both on the side of the buyers, how people are thinking about these purchases, and the gap between what most organizations think is possible and what they can actually deploy today,” she said.

For instance, she has firsthand insight into what kind of moat an AI startup can build that won’t leave it vulnerable when model makers like OpenAI launching competing products.  

Will OpenAI “just build everything and put every company out of business? You know, they are doing a lot already: they’re in consumer, they’re in enterprise, they’re building a device. I don’t think they are going to go after every potential enterprise application,” she says. 

So one moat is for enterprise AI startups to offer specialization. 

Context as moat

Additionally, she thinks the key to a good startup moat will be “context” — or the information the AI stores in its context window memory as it works on requests.  

“Context is dynamic. It’s adaptable. It’s scalable. And I think what we’re seeing is going beyond sort of the basic RAG towards this idea of a context graph, which is persistent,” she says referring to Retrieval-Augmented Generation (RAG) the de facto method as of 2025 to minimize hallucinations by training LLMs on trusted, specific sources (and having the LLM cite them). 

There’s still a lot of tech that needs to be developed for this area, though, from memory to reasoning beyond pattern recognition. 

“I expect real innovation here. I think this year we will see new approaches — the idea of context and memory,” Rosenthal says. 

But beyond startups working directly on context engineering, Rosenthal thinks enterprise apps that bake it in will have the advantage. 

“Ultimately, when we talk about moat, I think who owns and manages this context layer will become a large advantage for AI products,” she says. 

Another opportunity she sees: startups not building atop a major lab’s state-of-the-art models, with their high prices.  

“I think there is room in the market for cheaper models that are lighter weight and innovate on inference costs,” she says. These are models that are not, perhaps, at the top of the leaderboards of various benchmarks but “are still very useful” and more affordable. 

“Where I’m really excited to invest is on the application layer. I’m really interested in what will be the durable applications built on all of these different models, not just on the foundational models,” she says. She’s seeking startups with “interesting use cases” or that use AI to help enterprise employees work more efficiently.  

As for where she’s going to find these startups, she’ll be working her network among OpenAI’s alums for starters. Now that the AI outfit is 10 years old, the alums network has grown. Many have already founded startups that have raised big bucks at high valuations, ranging from OpenAI’s biggest competitor, Anthropic, to buzzy early-stage companies like Safe Superintelligence.

There is also a growing precedent for high-level ex-OpenAI folks to become seed-stage investors. About a year ago, Peter Deng, OpenAI’s former head of consumer products joined Felicis. He’s been crushing it ever since, and clearly having fun, getting in on big deals for hot startups like LMArena and Periodic Labs.  

“I actually had a call with Peter a few months ago, and he helped me make the decision,” Rosenthal said of her choice to become an investor. 

But Rosenthal may have a secret weapon to win deals. She also has deep contacts among AI enterprise users – the type of buyers and beta testers these early AI startups need. 

Enterprises still don’t understand how much AI can do for them. “There’s a really large gap that I am very optimistic can be filled. It leaves a huge green field for applications and companies.”