Human By Design

About Human By Design

We are seeking to answer the core question of 'how do traditional enterprises become AI-native' by talking to the innovation leaders, CTOs, and founders who are shipping AI to production - not just experimenting in labs. Hosted by Dr. Arjun Jain - researcher, builder, founder. From working with Yann LeCun at NYU to shipping computer vision systems for Mercedes-Benz, Arjun now leads Fast Code AI, helping enterprises navigate the gap between AI potential and AI reality. No AI theatre. Just the real story of making machines intelligent.

Publisher

Arjun Jain

Category

technology

Language

en

Explicit

No

First episode

12 Feb 2026

Latest episode

21 Sept 2026

Latest episodes

6 episodes in the feed.

1. 

   21 Sept 2026

   Why Schneider Electric's AI Chief Philippe R Refuses to Train His Own LLM

   Most enterprise AI dies somewhere between the demo and production. Philippe Rambach, the first Chief AI Officer at Schneider Electric, breaks down the enterprise AI strategy that gets past the pilot stage: five stage gates, one shared platform, and a flat refusal to treat AI as innovation at all.Rambach spent fifteen years running sales and P&L inside Schneider Electric before the board handed him AI in mid 2021, a year before ChatGPT existed, and that business background shapes everything about how he built the function. Schneider Electric is a forty billion euro energy technology company whose AI organisation now spans four hundred core specialists and roughly nine hundred people at any given moment, split evenly between internal efficiency and customer facing products. He argues that industrial AI stalls because companies manage it as research instead of product development, that a learning module sitting on a chip costing a couple of dollars delivers more building energy savings than any large language model could, and that new token economics have quietly reversed his build vs buy rule in the last six months. Speaking with host Arjun Jain, he also explains why Schneider Electric shut down its own internal ChatGPT after eighteen months rather than run two tools in parallel.👉Why Schneider Electric deliberately moved AI out of the innovation lab and into a five gate product development process with quarterly portfolio reviews and joint business sign off.👉How the hub and spoke model actually works, with four hundred core AI specialists plus an equal number embedded in business squads that own delivery from idea through to production support.👉What the enterprise AI build vs buy rule looks like in practice, and why new token pricing pushed Rambach back toward building things he would previously have bought off the shelf.👉How Schneider Electric built its own internal ChatGPT called JoeChatGPT in December 2022, ran it for eighteen months, then killed it completely instead of letting people choose between two tools.👉Why a small learning model embedded in a low cost room controller beats a large language model, delivering up to twenty percent additional energy savings with no LLM anywhere in the loop.👉What single question Rambach asks before approving any agentic AI project, and why answering yes to it usually means the team should go find a different approach. Subscribe to Human by Design for weekly founder conversations and follow Arjun Jain on LinkedIn https://www.linkedin.com/in/arjunjain/ for daily insights.00:00 - Meet Schneider Electric's First Chief AI Officer 05:57 - Taking Enterprise AI Out Of Innovation 07:09 - Inside The 400 Person AI Hub 13:37 - Enterprise AI Build vs Buy Explained 17:37 - Why Schneider Electric Killed Its Own ChatGPT 23:29 - Avoiding AI Vendor Lock In 27:47 - What Industrial AI Actually Looks Like 41:07 - The Question That Kills AI Agents 48:02 - Will AI Cut Your Workforce 52:09 - Physical AI Is Already Here#PhilippeRambach #SchneiderElectric #ArjunJain #HumanByDesign #ChiefAIOfficer #EnterpriseAI #IndustrialAI #AIatScale #EnterpriseAIStrategy #AIBuildVsBuy #AIAgents #DigitalTransformation #AITransformation #HowToScaleEnterpriseAI #WhyAIPilotsFail #AIInManufacturing #EnergyManagement #PhysicalAI #AIOrgDesign #TokenEconomics

2. 

   6 Jul 2026

   The 15-Year-Old Dreamer who became the Father of Modern AI | Jürgen Schmidhuber

   The man whose team laid the foundations of ChatGPT explains why the systems passing today's Turing tests might be nothing more than fancy text editors - and why real intelligence, what he calls physical AI, only begins when machines leave the screen and start acting in the world.At fifteen, Jürgen Schmidhuber set himself one goal - build a machine smarter than himself and then retire - and he has spent five decades chasing it. He has been called a father of modern AI, and for good reason: his Munich team in 1990 - 91 published the roots of today's trillion-dollar AI boom - the first Transformer (the "T" in ChatGPT, as an unnormalized linear Transformer), Pre-Training (the "P"), neural network distillation now central to systems like DeepSeek, and deep residual learning that underpins LSTM and the most-cited neural nets of both the 20th and 21st centuries. Today he directs AI research at KAUST, where a sovereign-scale supercomputer backs his next bets. Across this conversation with host Arjun Jain, he argues that the universe itself may be a short computer program, that almost every breakthrough in the history of deep learning traces back to a handful of underfunded academic labs and one relentless trend - 10x cheaper compute every five years - and that today's chatbots are not real intelligence at all. His case for physical AI and embodied robots reframes what artificial general intelligence will actually demand, and why the real race has barely started.👉Why Schmidhuber believes the entire universe could be generated by a program just ten lines long, and what observation would prove him wrong.👉How 1991 in Munich quietly seeded today's AI - the linear Transformer, pre-training, distillation, and deep residual learning - decades before the compute existed to make any of it useful.👉How one trend, computing power getting ten times cheaper every five years, explains why powerful 1990s ideas only became useful two decades later.👉What the generative adversarial networks of the 1990 artificial curiosity system actually did, with one network inventing actions and another racing to predict their consequences.👉Why he dismisses today's screen-based AI as "nothing" and insists real intelligence must do what a plumber or a seven-year-old with a football can do.👉How self-replicating, self-improving robots could open what he calls an age of abundance that expands far beyond Earth.Subscribe to Human by Design for weekly conversations on the future of intelligence, and follow Arjun Jain on LinkedIn [https://www.linkedin.com/in/arjunjain/] for daily insights.Chapters 00:00 - Building AI smarter than himself 08:25 - Is the universe a program? 16:22 - Why quantum randomness may be illusion 22:18 - Lugano to KAUST and compute 28:00 - Artificial curiosity, world models, and PMAX (1992) vs JEPA (2022) 48:35 - Why compute, not ideas, unlocked AI 50:58 - 1991, the year deep learning opened up 01:30:23 - Japan, GPUs and the CNN story #HumanByDesign #PhysicalAI #EmbodiedAI #ArtificialGeneralIntelligence #AGI #DeepLearning #Transformers #AIResearch #KAUST #FutureOfAI #AIRobots #MachineLearning #ComputationalUniverse #AIpodcast #WhatIsPhysicalAI #IsTheUniverseAComputer #WillRobotsReplaceHumans #HistoryOfDeepLearning #Munich1991

3. 

   8 Apr 2026

   Forget the Algorithm - the Data Is Your Real AI Problem | Simon Jagers, Samotics

   Simon Jagers, Co-Founder of Samotics, has built the world's most scalable sensorless industrial AI platform, raising $44M and claiming roughly 50% of the global condition monitoring market for hard-to-reach assets.Up to 40% of critical machines in heavy industry operate in places where traditional sensors simply cannot survive - inside blast furnaces hitting 1,500 degrees Celsius, submerged in sewage networks, or buried 3,000 metres underground in oil wells. Simon Jagers spent 11 years solving exactly that problem. Starting as a data science consulting firm and pivoting through failed vibration sensor pilots, Simon built Samotics from the ground up - designing custom hardware, proprietary signal processing, and an AI platform that analyses high-frequency electrical data to detect mechanical and electrical faults across tens of thousands of industrial machines worldwide. The platform now serves clients in steel, oil and gas, wastewater, chemicals, and mining - and is embedded directly into ABB variable speed drive firmware as native infrastructure. Simon shared the full journey, including the near-death pivot at Heineken and the decision to burn every bridge, in this candid conversation with host Dr. Arjun Jain.What you will learn in this episode,👉How Simon Jagers built Samotics into the global leader in sensorless AI condition monitoring, covering roughly 50% of the market for hard-to-reach industrial assets👉The Heineken pilot that almost ended the company - and the exact decision that saved it👉Why up to 40% of factory machines have never been monitored, and how Electrical Signature Analysis (ESA) changes that permanently👉The counterintuitive case for keeping humans in the AI loop - and why one engineer managing 2,000 assets is a scale strategy, not a workaround👉Why most industrial AI pilots fail before a single algorithm is written, and the one question every AI team must ask about their data👉Simon's contrarian take on co-founder trust, cultural inertia, and why the best idea must always winIf this episode gave you one insight you can use, subscribe to Human by Design for weekly conversations with founders and operators who are making AI work in the real world. Follow Dr. Arjun Jain on LinkedIn and X for daily posts on AI, entrepreneurship, and what it actually takes to build at the frontier.Chapters:00:00 - Simon Jagers and the Samotics origin story04:30 - From IT sales to industrial AI founder08:45 - The 40% of machines nobody can monitor13:20 - Why sensors burn, drown, and fail in factories17:40 - Turning every motor into an AI sensor20:30 - The Dutch railway that started everything24:00 - The Heineken pilot that almost killed Samotics29:00 - Human-in-the-loop as a scale strategy35:00 - How ESA detects faults without touching the machine41:00 - Predicting machine failure, the art vs. science debate48:00 - Burning the boats - no plan B, no off ramp55:30 - Industries Samotics serves globally01:00:00 - Claiming 50% of a new AI market category01:03:00 - Co-founder trust and company culture#SimonJagers #Samotics #IndustrialAI #PredictiveMaintenance #ElectricalSignatureAnalysis #SensorlessMonitoring #ConditionMonitoring #AIStartup #HeavyIndustryAI #AIDisruption #IndustrialIoT #ManufacturingAI #AITransformation #PredictiveMaintenanceAI #DigitalTransformation #FounderStory #AIFunding #HumanByDesign #DrArjunJain #EnterpriseAI #Industry40 #MachineLearningManufacturing #AIScaleup #IndustrialAutomation #OperationalTechnology

4. 

   25 Mar 2026

   The Algorithm That Powers ChatGPT, Tesla & AlphaGo - Max Li on Reinforcement Learning's Hidden Role

   This episode unpacks the full story of reinforcement learning with AI researcher and educator Max Li.Reinforcement learning is one of the most misunderstood and underestimated technologies in modern AI. The theory has existed since the 1980s, yet it only entered mainstream conversation with the rise of large language models and RLHF. So what took so long, and why does it matter so much now?In this deeply technical yet accessible conversation with host Arjun Jain, Max Li, who has been working in reinforcement learning since 2016, traces the journey from Sutton's foundational textbook to DeepMind's landmark 2016 Nature paper, the rise of AlphaGo Zero, and how RL quietly powers everything from Tesla's autopilot to the fine-tuning of today's most powerful AI models. Max also shares his honest take on whether breakthroughs like DeepSeek's GRPO represent true innovation or just incremental progress. If you have ever wondered how AI agents actually learn to make decisions in a changing world, this episode gives you the real picture, from first principles to frontier applications.What you will learn in this episode:👉Why reinforcement learning remained a niche research field for decades before LLMs finally brought it into the spotlight👉How AlphaGo Zero taught itself to master Go from scratch and why no human wants to play the game anymore👉Where Tesla uses RL in its autopilot stack to learn individual driver behavior👉The honest verdict on PPO vs GRPO and which one actually deserves to be called an innovation👉How to think about reward function design and why Max says it is more art than science👉Which real-world problem classes are the best fit for reinforcement learning and which ones are notIf this episode gave you a clearer picture of where AI is really headed, subscribe to the podcast and follow Arjun Jain on LinkedIn and X for more conversations at the frontier of AI, robotics, and autonomous systems.Chapters00:00 - Why RL Was Ignored for Decades 02:30 - Sutton's Book and the 1990s Foundations 05:10 - DeepMind's Atari Paper Changed Everything 09:00 - AlphaGo Zero Killed a Thousand-Year-Old Game 13:20 - How Tesla Autopilot Uses Reinforcement Learning 16:45 - RLHF Explained - The Secret Behind LLM Fine-Tuning #reinforcementlearning #RLHF #machinelearning #artificialintelligence #deeplearning #AlphaGo #LLM #largelanguagemodels #autonomousdriving #Teslaautopilot #PPO #GRPO #DeepMind #AIresearch #rewardfunction #AIfundamentals #roboticsAI #deepseek #AIfuture #MaxLi

5. 

   18 Mar 2026

   Proof of Honesty: OORT's Dr. Max Li on Solving AI's Quality Control Problem at Scale

   What if the data powering your AI is biased, brittle, and owned by three companies in Silicon Valley?Dr. Max Li, Founder of OORT and Columbia University Professor, is building the decentralised alternative - and 350,000 people across 130 countries are already part of it.Dr. Max Li is not your typical AI founder. With a PhD in Information Theory, 200+ patents from Qualcomm Research, and a front-row seat at the birth of 5G, he spent years designing the invisible infrastructure of the modern internet before turning his attention to its next frontier: AI data. In this episode, he pulls back the curtain on OORT, the decentralised AI data cloud he founded in 2021 that is quietly challenging AWS, Google Cloud, and Scale AI at the same time. From his patented Proof of Honesty algorithm that guarantees quality from anonymous global contributors, to his Multi-Agent System that cut OORT's own burn rate by 60 to 70 percent, Max shares the engineering breakthroughs, business lessons, and contrarian bets that have made OORT a trusted data partner for Dell, Lenovo, and Wall Street financial firms. He shared the full journey in this wide-ranging, technically rich conversation with host Arjun Jain.Here is what you will learn in this episode:👉How Dr. Max Li scaled OORT from zero to 350,000 data contributors across 130 countries in under a year, without a mega funding round, by building permissionless Web3 infrastructure that no centralised competitor can replicate👉Why the bias inside today's large language models is a data sourcing problem, not a model problem, and how OORT's decentralised RLHF approach is the only structural fix that works at global scale👉The Proof of Honesty algorithm, OORT's US-patented quality control mechanism that uses hidden test questions and token staking to guarantee honest contributions from completely anonymous participants worldwide👉How OORT deployed its own Multi-Agent System internally first, achieved a 60 to 70 percent reduction in burn rate and 10x productivity improvement, and is now selling that same system to finance, real estate, and enterprise IT clients👉Why decentralised GPU training and inference is still years away from enterprise readiness, and why data collection and storage is the only layer of DePIN that is genuinely production-ready todayChapters00:00 - Dr. Max Li's Journey From 5G to AI 08:18 - Building the World's Largest Decentralised Data Network 19:19 - The Proof of Honesty Algorithm Explained 32:13 - Earning From AI With Just a Smartphone 47:20 - Why DePIN for AI Training Is Not Ready Yet 53:34 - Multi-Agent Systems Cutting Costs by 70 Percent #DrMaxLi #OORT #DecentralisedAI #DePIN #RLHFData #AIDataCloud #Web3AI #AIAgents #MultiAgentSystems #HumanbyDesign #AIStartups #DataLabelling #ScaleAI #BlockchainAI #AIBias #LargeLanguageModels #OORTDataHub #DecentralisedInfrastructure #AIFounders #DeepSeek #AlphaGo #ProofOfHonesty #AIDataBias #EnterpriseAI

6. 

   12 Feb 2026

   Arjun Jain (Fast Code AI) Explains How To Build A Foundation AI Model

   This inaugural episode of Humans by Design is a special release: a candid, long-form conversation originally recorded for the Founder Thesis (hosted by Akshay Datt) featuring Dr. Arjun Jain (Founder, Fast Code AI).Before we bring guests onto Humans by Design, this episode sets the baseline - cutting through AI hype to explain what today’s models actually do, where they break in production, and what’s replacing the “bigger models will solve everything” thesis.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.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, Dr. Arjun Jain 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. 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 #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 #FounderThesisPodcastDisclaimer: The views expressed are those of the speaker, not necessarily the channel

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