The Artificial Intelligence Podcast

TVN Reddy, CEO of Aptean, breaks down why the entire software industry is selling "bigger is better" AI while the real goal is the opposite: people spending 80% less time in systems and more time doing the work that actually drives the business. He explains why general purpose models hand every competitor the same 95% while the deep vertical 5% drives the outsized gains, why enterprise AI is unusable past roughly a half percent error rate, and why validation agents beat the instinct to hire three people to check the AI's work. TVN also digs into the quiet security crisis of employees pasting P&Ls and proprietary recipes into public LLMs that never had training turned off, and why agents bolted onto generic models won't scale without truly vertical models underneath. Finally, he shares why the winners won't be the companies with the hottest models but the ones with the courage to re-engineer their processes, because every big technology removes old constraints only to create new ones.

Bill Franks, President of Analytics Advisory Partners, breaks down why a degree no longer proves much and employers now hire on what you can show, not what you know. He explains the pipeline trap companies are walking into - handing every junior, tactical task to AI the way firms offshored it back in 2000, then finding no one qualified to step into the senior roles - and argues teams should deliberately do one in ten tasks by hand just to keep their skills sharp. Bill also digs into the boring physical limits that could throttle AI long before the algorithms do, from power and water to grids so backed up that wiring a data center now takes longer than building it. Finally, he unpacks model collapse, where models trained on AI output slowly degrade until the only fix may be retraining on human content from 2022 or earlier.

Vishnu Hari, CEO of Ego AI, breaks down why the labs with billions in compute missed the product hiding in plain sight: character AI quietly tops every consumer retention leaderboard while the serious labs write it off as a sideshow. He argues humanness itself is the hard problem - dangerous, uncontrollable, and unchained - so the giants retreat to safe coding agents and ship characters that collapse into therapy or role-play because they never actually live lives. Vishnu takes apart the memory paradox (an agent that remembers everything is unusable; one that remembers nothing is forgettable) and points to the Nemesis system in Shadow of Mordor, a feature so good Warner Bros trademarked it, as proof you can build real relationships without superintelligence. He also gets into the edges no one wants to touch: who inherits the AI you've talked to for a decade, and why the new "I'm not human" disclosure laws are pure theater.

Carter Huffman, CTO of Modulate, breaks down why voice AI keeps failing in production despite years of "solved" transcription claims. He explains the "transcript trap" that fools dev teams into shipping voice agents that crumble on real calls, why bigger language models actually make latency worse, and how ensemble approaches with smaller specialized models outperform monolithic systems. Carter also dives into the explosion of deepfake voice fraud - including the $25M Hong Kong heist - and why passive monitoring plus red teaming are now essential to a modern voice security stack. Finally, he shares why even Gen Z still defaults to voice for high-stakes issues, and what contact centers must get right over the next three years to build genuine trust with callers.

Join Babak Hodjat, Chief AI Officer at Cognizant, as he unpacks what's actually happening at the cutting edge of agentic AI — from agents that run continuously without being asked, to the governance crisis quietly unfolding inside enterprises right now. Babak draws on decades of AI research to break down multi-agent architecture, the TerraLingua experiment that let AI agents form their own societies, and a breakthrough in evolutionary fine-tuning that could change how the entire industry thinks about customizing large language models.