How to Build AI That Actually Delivers with Dr. Arjun Jain (Fast Code AI) скачать в хорошем качестве

How to Build AI That Actually Delivers with Dr. Arjun Jain (Fast Code AI)

In this episode, Dr. Arjun Jain - Founder of Fast Code AI - reveals why the AI industry's trillion-dollar bet on bigger models is failing, and what's replacing it. Dr. Arjun Jain isn't your typical AI founder. After training under Turing Award winner Yann LeCun at NYU, working on Apple's secretive autonomous vehicle project, and leading Mercedes-Benz's robotaxi AI, he returned to India to bootstrap Fast Code AI with zero venture capital. In just two years, his company grew 8x by doing what the AI giants won't: charging for outcomes instead of software seats, deploying Small Language Models that outperform GPT-4 for specific tasks, and building agents that actually work in production. He shared this contrarian journey in this candid conversation with host Akshay Datt. From explaining why "we have but one internet and we've used it all" (quoting OpenAI's Ilya Sutskever) to revealing how procurement agents train by negotiating with themselves millions of times, this episode dismantles the AI hype and shows what enterprise automation actually looks like. Whether you're a founder evaluating AI vendors, an engineer choosing between foundation model labs and application companies, or an investor trying to separate signal from noise, this is the reality check the industry needs. What You'll Learn: 👉Why scaling laws have stagnated and what test-time compute and reinforcement learning mean for enterprise AI's future 👉How Fast Code AI captures "Salary TAM" (30-70% of revenue) through outcome-based pricing instead of traditional SaaS seat licenses 👉The real reason AI engineers command $10-100 million salaries, and why this won't last as foundation models commoditize 👉Why Project Athena (Mercedes-Bosch's multi-billion euro robotaxi venture) failed, and what end-to-end learning beats modular approaches 👉How Small Language Models fine-tuned on company data outperform massive generic models at 1/10th the inference cost 👉The "self-play" reinforcement learning methodology that makes Fast Code's agents reliable in production, not just impressive in demos CHAPTERS 00:00 - Dr. Arjun Jain's Journey from Yann LeCun to Fast Code AI 05:20 - Deep Learning Revolution: From AlexNet to ChatGPT Explained 12:44 - How Machine Learning Actually Works: Data-Driven Algorithms 19:30 - Attention Is All You Need: Understanding Transformers and Context 25:27 - Why AI Scaling Laws Have Stagnated: The Data Cliff 31:03 - Building Foundational Models: The 8 Steps Explained 38:02 - Outcome-Based Pricing: From Software TAM to Salary TAM 42:08 - Why AI Engineers Earn $100 Million Salaries 46:31 - Small Language Models vs Large Language Models for Enterprise 51:41 - From Tintin's Snowy to Mercedes: Computer Vision Journey 1:00:20 - Why He Left Apple and Chose Entrepreneurship 1:08:12 - Project Athena Failure: Modular vs End-to-End Autonomous Driving 1:16:12 - Fast Code AI: Bootstrapped 8x Growth Without VC Money 1:21:02 - Building AI Agents with Reinforcement Learning Self-Play 1:33:01 - Procurement Automation: Fast Code's Flagship Use Case 1:37:25 - Capturing the Long Tail: AI Agents for Contracts Under $10K 1:40:09 - Value-Based Pricing vs Outcome-Based Pricing in AI Services 1:46:42 - Enterprise AI Adoption: Overcoming Change Management Resistance #DrArjunJain #FastCodeAI #AgenticAI #AIScalingLaws #EnterpriseAI #SmallLanguageModels #OutcomeBasedPricing #ReinforcementLearning #AIAgents #YannLeCun #BootstrappedStartup #IndiaAIStartups #AIServices #TestTimeCompute #FoundationModels #LLMLimitations #AIForEnterprise #ProcurementAutomation #AIEngineers #AutonomousDriving #ProjectAthena #SalaryTAM #AIInference #AIDeployment #BangaloreAI #AIConsulting #MachineLearningExplained #DeepLearning #TransformersAI #FounderThesisPodcast Disclaimer: The views expressed are those of the speaker, not necessarily the channel