The rapidly evolving field of artificial intelligence is seeing a shift in leadership as former OpenAI sales executive Aliisa Rosenthal transitions to venture capital. Rosenthal has joined Acrew Capital as a general partner, bringing with her valuable insights into the AI market and a network cultivated during a pivotal three-year period at the AI research and deployment company. This move signals a growing trend of experienced AI professionals seeking to shape the future of the industry through investment.
Rosenthal’s departure from OpenAI occurred approximately eight months ago, following the successful launches of groundbreaking products like DALL·E, ChatGPT, ChatGPT Enterprise, and Sora. While initially exploring opportunities to advise AI startups directly, she was ultimately drawn to the broader impact possible through venture capital, according to a report by TechCrunch.
Understanding the AI Startup Landscape
Rosenthal’s time at OpenAI, where she scaled the enterprise sales team from a small unit to a substantial operation, provided her with a unique perspective on the challenges and opportunities facing AI companies. She observed a significant disconnect between the perceived potential of AI and the actual implementation capabilities of many organizations.
A key question she grapples with is the extent to which OpenAI itself will dominate the AI application space. However, Rosenthal believes that OpenAI is unlikely to pursue every potential enterprise application, leaving room for specialized startups to thrive. She suggests that building a sustainable “moat” – a competitive advantage – for these startups will be crucial.
Specialization as a Competitive Advantage
One way to establish this moat, Rosenthal argues, is through specialization. Rather than attempting to compete directly with OpenAI’s broad capabilities, startups can focus on niche applications and develop deep expertise in specific areas. This targeted approach can offer unique value propositions that differentiate them from larger, more generalized AI providers.
The Importance of Contextual AI
Another critical area for differentiation lies in “context” – the ability of AI systems to store and utilize information from past interactions. Rosenthal emphasizes that context is “dynamic, adaptable, and scalable,” and represents a significant step beyond basic Retrieval-Augmented Generation (RAG) techniques. RAG, currently a standard practice in 2025, aims to reduce AI “hallucinations” by grounding responses in trusted data sources.
She anticipates innovation in context engineering, including advancements in memory and reasoning capabilities. Ultimately, Rosenthal believes that ownership and management of the “context layer” will be a major advantage for AI products in the future. This suggests a potential market for companies focused on building and maintaining these contextual frameworks.
Beyond the foundational models, Rosenthal is particularly excited about investment opportunities in the application layer of AI. She’s looking for startups developing innovative use cases and tools that enhance employee productivity within enterprises. This focus reflects a belief that the long-term value of AI will be realized through practical applications rather than solely through advancements in core model technology.
Interestingly, Rosenthal isn’t alone in this transition. Peter Deng, former head of consumer products at OpenAI, recently joined Felicis, and has reportedly found success as a seed-stage investor. Deng’s experience influenced Rosenthal’s decision to pursue a similar path, highlighting a growing trend of ex-OpenAI leaders entering the venture capital arena. This influx of talent brings valuable operational experience and a deep understanding of the AI landscape to the investment world.
Rosenthal’s extensive network within OpenAI’s alumni community will likely be a valuable asset in identifying promising startups. The OpenAI alumni network has grown significantly over the past decade, with many former employees now founding their own companies, some of which have already attracted substantial funding. Furthermore, her connections with AI enterprise users provide access to potential customers and beta testers, crucial for early-stage companies.
A significant challenge remains in bridging the gap between the potential of artificial intelligence and the understanding of enterprise users. Rosenthal believes there’s a “huge green field” for applications and companies that can effectively demonstrate the value of AI to businesses. This suggests a continued demand for solutions that simplify AI adoption and integration.
Rosenthal also noted the potential for startups to succeed by leveraging cheaper, lighter-weight AI models, rather than solely relying on the most advanced – and expensive – offerings from major labs. These models, while not necessarily leading benchmark scores, can still provide significant utility at a more accessible price point, opening up opportunities for a wider range of applications. This focus on cost-effective machine learning solutions could be particularly appealing to smaller businesses and organizations with limited resources.
Looking ahead, Rosenthal will be actively seeking out and evaluating startups in the artificial intelligence space, leveraging her network and expertise to identify promising investment opportunities. The success of her venture capital efforts will depend on her ability to accurately assess the long-term viability of AI companies and to navigate the rapidly changing dynamics of the AI market. The coming months will be crucial as she builds her portfolio and establishes herself as a key player in the AI investment landscape.
