OpenAI’s first sales lead, Aliisa Rosenthal, has found a new career: venture capital. He participates Acrew Capital As a general partner, working with 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 stint at the AI lab that launched DALL·E, ChatGPT, ChatGPT Enterprise, Sora and other products. “I wasn’t originally looking to join a VC fund,” he told TechCrunch. “I was out there meeting a lot of AI startups.”
But after growing OpenAI’s enterprise sales team from two people to hundreds, she saw the appeal when Kolodny pitched her to venture capital. Instead of helping a startup with its go-to-market strategy, it could help a portfolio of them.
In her time at OpenAI, “I learned a lot about behavior, both from the buyer side, how people think about these markets, and the gap between what most organizations think is possible and what they can actually deploy today,” she said.
For example, he has first-hand knowledge of what kind of moat an AI startup can build that won’t leave it vulnerable when model makers like OpenAI launch competing products.
Will OpenAI just create everything and put every company out of business? You know, they’re already doing a lot: they’re consumer, they’re business, they’re building a device. I don’t think they’re going to follow every possible business application,” he says.
So a moat is for AI startups to offer expertise.
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The framework as a trench
Additionally, he believes the key to a good boot moat will be “context” — or the information the AI stores in the context window’s memory as it operates on requests.
“The framework is dynamic. It’s adaptable. It’s scalable. And I think what we’re seeing is going beyond basic RAG to this idea of a context graph, which is persistent,” he says, referring to Retrieval-Augmented Generation (RAG) the de facto method as of 2025 (training source minimization and halluc minimization. LLM lists them).
However, there is still a lot of technology to be developed for this area, from memory to logic, beyond pattern recognition.
“I expect real innovation here. I think this year we’ll see new approaches—the idea of context and memory,” says Rosenthal.
But beyond startups working directly on environment engineering, Rosenthal believes enterprise apps that bake it will have the advantage.
“Ultimately, when we’re talking about a moat, I think who owns and manages that layer of the environment is going to become a big asset for AI products,” he says.
Another opportunity he sees: startups aren’t built on the state-of-the-art models of a big lab, with their high prices.
“I think there is room in the market for cheaper models that are lighter and innovate on the cost of inference,” he says. These are models that are not, perhaps, at the top of the scoreboards of various benchmarks, but “are still very useful” and more affordable.
“What I’m really excited to invest in is at the application level. I’m really interested in what the durable applications are going to be based on all these different models, not just the base models,” he says. It’s looking for startups with “interesting use cases” or that use artificial intelligence to help business employees work more efficiently.
As for where she will find these startups, she will initially work with her network among OpenAI alums. Now that the AI outfit is 10 years old, the alum network has grown. Many have already founded startups that have raised big money at high valuations, from OpenAI’s biggest competitor, Anthropic, to buzzy early-stage companies like Safe Superintelligence.
There is also a growing precedent for high-profile ex-OpenAI people to become early-stage investors. About a year ago, Peter Deng, formerly head of consumer products at OpenAI, joined Felicis. He’s been crushing it ever since and clearly having fun, participating in 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 for winning deals. He also has deep contacts among enterprise AI users—the type of buyers and beta testers these early AI startups need.
Businesses still don’t understand how much AI can do for them. “There’s a very big gap that I’m very optimistic can be filled. It leaves a huge green field for apps and companies.”
