Generative AI Applications: You Have No Moat...Or Do You?
Summary
With the recent advances in Generative AI technology and the new generation of LLMs, many products can now layer “AI” features very quickly into existing and new products to provide domain-specific ChatGPT-like experiences fairly quickly. There are countless new startups racing to market with products, while incumbents are adding GenAI features at a breakneck speed. But does that mean that there’s essentially no moat around these AI features?
While the no-moat was a prevailing perception in the early GenAI days, it’s becoming clear, at least to us at Lexion, that this is not entirely true, and that a lot of data, intellectual property and technology investments are still very much necessary to build compelling GenAI applications, especially for quality-sensitive domains. In this presentation, Emad shares his team’s experience in building and evolving one of the first GenAI applications and the first in the end-to-end contract lifecycle management space.
Speakers
Emad Elwany is the CTO and co-founder of Lexion. Lexion is a powerfully simple operations workflow and contracting platform that helps teams get deals done faster. Lexion streamlines and centralizes the end-to-end contract lifecycle with intuitive email-driven intake and workflows, simple no-code automation, best-in-class AI, and more. Lexion was one of the first AI companies to leverage LLMs in building production quality applications. The company was founded in 2018 at the Allen Institute for Artificial Intelligence, is backed by an iconic Silicon Valley law firm, and recently raised a $20M Series B with support from top-tier VC firms.
Prior to co-founding Lexion, Emad held principal engineering roles at Microsoft Research, working on Microsoft's core AI products, specifically as founding and lead engineer on their core conversational AI and NLP platform as well as their AI scheduling assistant.
Emad holds a Bachelor’s degree in Computer Engineering from Alexandria University and a Master’s degree in Computer Science from Stanford University. He’s an active member of special interest groups in machine learning and artificial intelligence and has published research papers in major computer science conferences like CHI, NeurIPS and KDD.
