Anaïs Monlong

Venture Principal

IRIS

Artificial Intelligence

Connectivity, Cloud & Infrastructure

Growth & Investment

About Me

Anais Monlong is a Venture Principal at IRIS, focusing on Seed and Series A investments across DevOps, DataOps, MLOps, Applied AI, Proptech, Engineering software, Digital Health, Legal Tech, and financial data platforms. She also leads the development of IRIS’s internal data platform, contributing as product owner and Python developer. Her investments include Weefin, Adaptive ML, and Hopia. Previously, she was Senior Associate at AXA Venture Partners, where she combined venture investing with Python-based automation for sourcing and internal tools. She has experience in M&A at HSBC, covering Media & Tech sectors in Europe and Australia. Anais holds degrees from Sciences Po Paris and Korea University, and has lived in France, South Korea, and Australia. She works in English, French, Spanish, Italian, and Korean.

Hear My Insights

Frugal AI: Optimizing AI Infrastructure for Efficiency and Scale

As AI models grow in size and complexity, scaling infrastructure efficiently becomes a critical challenge. This panel will explore strategies for optimizing AI systems, focusing on reducing the high costs and energy consumption associated with training and running large language models (LLMs). The discussion will cover recent innovations that improve performance while optimizing cost and energy efficiency. The conversation will also address the future of AI infrastructure, examining how it can evolve to meet the growing demands of machine learning while prioritizing cost-effectiveness and sustainability.

GenAI: Rewriting the Rules of Copyright

Centuries-old copyright laws, already strained by the internet's global reach, are now buckling under the weight of generative AI. LLMs and image models, trained on scraped internet data, are exposing the widening gap between existing legal frameworks and technological reality. This talk argues that current copyright law is obsolete and dives into the fundamental technological reasons why adaptation is impossible. We urgently need a new paradigm for data, authorship, and consumption to ensure AI's continued progress; can copyright law survive this disruption, and what will the future of content creation look like?