Rudina has a {:badgeType} account
Boston, Massachusetts, United States
Contact info
- 19,561 followers
About
Rudina Seseri is Founder and Managing Partner of Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI and frontier technology companies. With over 24 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in enterprise SaaS, software and data applications and intelligent verticals. Rudina’s investments include Asepha, Basetwo, Celtra (Acquired by STG), CrowdTwist (Acquired by Oracle), Featurebyte, Inrupt, Iris Finance, SocialFlow (Acquired by Piano), Verusen, Reprise and ShipAngel. Rudina is a Harvard Business School Executive Fellow, after having served as a Rock Venture Capital Partner and Entrepreneur-In-Residence for the School, and an MIT Connection Science Fellow. She is also a Member of the Business Leadership Council of Wellesley College.
Rudina's experience spans public company board roles to early-stage start-up board and advisory roles. She serves as an Independent Director for M&T Bank (NYSE: MTB) and MSC Industrial Supply Co (NYSE: MSM).
Rudina has been named to Entrepreneur Magazine’s Top 100 Women Entrepreneurs List, Startup Boston’s 2022 Investor of the Year, Boston Business Journal Power 50: Newsmaker, a Women to Watch honoree by Mass High Tech, a Business Insider Most Important VC in Boston, and Top VC investing in AI and Machine Learning, and a Boston Business Journal 40-under-40 honoree for her professional accomplishments and community involvement. She graduated magna cum laude from Wellesley College with a BA in Economics and International Relations and holds an MBA from HBS. She is a member of Phi Beta Kappa and Omicron Delta Epsilon honor societies.
Featured
Rethinking AI’s Attention Span: Recursive Language ModelsRudina Seseri on LinkedInDespite being powerful tools for decision-making at scale, every LLM still shares a frustrating limitation: they are fundamentally short-term thinkers. The amount of input that an LLM can handle at once is known as its context window, and even as new developments stretch this into the hundreds of thousands of tokens, models still struggle when asked to work across enormous codebases or long-running operational workflows. With larger inputs, LLMs’ decision-making degrades, they take shortcuts, and important details begin disappearing. Most enterprise deployments work around this constraint by summarizing or retrieving slices of information on demand. I have also explored alternative model architectures in previous AI Atlases, such as Mamba and Hyena, that take fundamentally different approaches to language modeling in order to bypass traditional context limitations. These methods can be effective for less demanding workflows, such as summarizing long documents, but they still break down when a task requires sustained engagement with large, interconnected bodies of data, such as what you would see when integrating multiple databases as context for a company-wide body of agents. This is where we come to the topic of today’s AI Atlas: a very recent breakthrough from MIT, known as Recursive Language Models, has introduced an extremely exciting solution. This time, rather than trying to make models “remember more,” RLMs are changing how models interact with information entirely. 🗺️ What are Recursive Language Models? Recursive Language Models (RLMs) are an inference framework that allows existing language models to work against large inputs, viewing them as an external environment rather than a single prompt. This is not a new class of foundation model, but an entirely new paradigm for how we can leverage LLMs. Instead of ingesting all information at once, a model operated via RLM analyzes data piece-by-piece, reasons over each section, and then is able to reflexively inspect itself in order to refine or verify conclusions. In practice, this allows an RLM to operate over datasets that are orders of magnitude larger than any fixed context window. More importantly, the model is no longer forced into a one-shot interaction, where you send a single prompt and hope everything important is captured therein. The RLM can loop and revisit earlier assumptions, aggregating insight across multiple passes, similar to how a human analyst would work through a complex problem. 🤔 What is the significance of RLMs and what are their limitations? If you take away nothing else from this writing, know that RLMs do not make AI dramatically smarter by themselves. Instead, you should pay attention to how they are making AI persistent. AI systems are moving from one-shot tools that simply answer questions to ongoing operators that stay with a problem, revisit information, and refine understanding over time. Your competitive advantage will not come from who adopts the largest model, but from who builds AI systems that can remain engaged inside real business processes. Change in behavior: RLMs avoid the trap of ever-larger prompts by making persistence a property of the system, rather than focusing on the model’s limited attention span. Long context length: RLMs' design allows AI systems to stay engaged with a problem over time and across adaptations, rather than collapsing everything into a single response. Scalability: Productivity gains from RLMs are driven by better orchestration and inference strategies, rather than just larger or more expensive models. RLMs are also constrained, however, by some current limitations: Complexity: RLMs require careful, intentional design of execution environments and defined guardrails, increasing the initial engineering overhead to operationalize. Variability: Recursive reasoning (where the RLM reflects on its own inputs) makes runtime and costs more difficult to predict, which can be hard to control in production. Scope: For short or well-bounded tasks, standard LLMs remain the faster and simpler option (using an RLM here is almost like using a flamethrower to cook your food). RLMs are best for information-dense, multi-step scenarios. 🛠️ Applications of RLMs Recursive Language Models are best suited for business problems where value is created through sustained engagement with large, interconnected bodies of information rather than fast, one-off answers. This includes: Persistent AI copilots: RLMs enable agents that can stay engaged across long conversations and workflows, validating and refining outputs rather than responding once and losing track of objectives. Knowledge systems: RLMs can reason directly across internal knowledge bases with higher fidelity, enabling the creation of more sophisticated agent suites such as swarm agents, which are collections of autonomous AI agents that operate in coordination toward a common goal. Cybersecurity: In domains like cybersecurity or incident response, RLMs can trace patterns across massive logs and historical data without losing context mid-analysis.
Activity
19,561 followers
Loaded 9 Posts posts
- Rudina Seseri reposted this
I'll ask one question: "In the world of AI what do you know will exist 3 years from now?"
Businesses and people are going to change so much in the next 3 years and it's investors jobs to know how AI is changing things.
During MATCH HOUSE at SXSW I'm hosting a panel to discuss this question and many more about tech and VC in a post AI world.
And this panel is STACKED:
Kleida Martiro MD at Glasswing (they've been investing in AI since way before it was cool).
Eunice Ajim Founding Partner who's investing in the Africa diaspora.
Arzu Tekir GP at Treeo VC who's investing AI Infra and immigrant founders.
They are all crazy talented and experienced!!
1:15pm CST. BE THERE!
Experience
-
-
-
Glasswing Ventures is an early-stage venture capital firm dedicated to investing in next generation of artificial intelligence, machine learning, cybersecurity, cloud computing, Internet of Things (IoT), and supply chain tech.
-
-
-
-
-
-
The Harvard Business School Executive Fellows Program seeks to leverage the expertise of outstanding practitioners to enhance teaching and learning at the School. The fellows contribute to the School by working with faculty and MBA students on curricular and co-curricular activities. These include, but are not limited to, co-teaching course sessions in the elective curriculum, offering career counseling and development coaching, co-leading Short Intensive Programs (SIPs), and delivering workshop sessions.
-
-
-
-
-
The Venture Partners program offers a unique opportunity for Harvard University students to connect one-on-one with VCs and have meaningful engagement on a wide range of topics from new ventures and ideas to entrepreneurial career advice.
-
-
-
That is what excites me about GEPA, a new optimization framework out of University of California, Berkeley, Stanford University, Massachusetts Institute of Technology, and Databricks that promises to change how we think about self-improving AI. GEPA teaches AI systems to learn from failure by reading what went wrong and writing better rules for next time, much like a human professional would.
I dive further into GEPA in today’s AI Atlas, as well as why it matters for enterprise deployments and where it might have the most immediate impact. Read more here: