Samir Mehta is the Founder and CEO of CR3SCENDO AI, the operating platform for startups that automates core business operations such as formation, banking, tax, invoices, and compliance. A six-time founder with two exits (4thpass to Motorola, Keetli to Nagra), his teams shipped the first Java microbrowser and the first telco content-delivery system. He previously served as CEO at CarAdvise, COO at Conviva, CPO at Wrench, and Vice President of Digital Content and Commerce at Amazon. He is also an Affiliate Assistant Professor at the University of Washington Information School, teaching Entrepreneurship.                                                                                                                              

The AI-Native Startup: How Deep Learning is Rewriting the Way Companies are Built and Run                                                                                                                                                                       Across every region, a new generation of startups is being founded differently. While the previous generation adopted AI as a feature bolted onto an existing product, AI-native companies place deep learning models at the core of how the business itself is planned, operated, and scaled—the model functions as a co-worker rather than a plug-in. Drawing on two years of building and operating an AI-native company, this talk examines what genuinely changes when large language models and neural networks move from the demo stage to the critical path.This presentation contrasts three approaches to common operational problems (customer onboarding, bookkeeping, compliance, and quality assurance): traditional rule-based software, classical machine learning, and modern generative deep learning. The emerging pattern is neither "AI replaces everything" nor "AI is a chatbot on the side." Probabilistic models excel at extraction, classification, synthesis, and conversation, but fail when trusted directly with money, legal filings, or customer data. The successful AI-native architecture works as a sandwich: deterministic preparation, probabilistic generation, and deterministic validation, with human approval scaled to the stakes of each action.The talk will share concrete, transferable lessons on routing work across model tiers by cost and difficulty, redacting personal data before it reaches external models, versioning prompts and schemas as code, enabling AI agents to test and monitor their platform, and designing capabilities so autonomous agents can call them. We conclude with implications for founders, investors, and policymakers, including smaller teams shipping larger scope, new failure modes, and governance requirements from day one