3 Founders Disrupting Military AI, Sports & Enterprise Data | Seattle Deep Tech Summit Fireside Chat

What happens when you bring together a former cage fighter building AI for the military, a tech executive reshaping pro sports operations, and an ex-Uber platform chief creating next-gen causal world models? You get one of the most dynamic tech panels ever recorded. In this session hosted by 161 Ventures in Seattle, three distinct founders share how they are moving past standard, generalized LLMs to build highly specialized, domain-specific AI solutions for defense, sports entertainment, and massive enterprise architectures. Meet the 3 Panelists: Tyler (Co-Founder, Edgerunner): Former Penn State wrestler, boxer, and MMA cage fighter turned defense-tech founder. Previously head of supercomputing at Stability AI (where he helped train Stable Diffusion), Tyler is now building secure, on-device LLMs that act as an "AI battle buddy" to reduce cognitive load for the warfighter. Vamsi (Co-Founder, Arkero AI): A fintech veteran who spent 9 years in financial tech before transitioning to the sports enterprise space. His team recently raised a $6M pre-seed round to bring Silicon Valley-level operational excellence, predictive analytics, and marketing tools to pro sports teams across the MLS and beyond. Hasibul (Founder, Causal Dynamics Lab): Former university professor turned corporate executive who scaled the ranks at Microsoft, VMware, and Uber (where he served as Head of Engineering Platform). He has now returned to frontier research, building an enterprise world model that out-performs GPT-5.5 on enterprise data with 98% less token consumption. Key Highlights: Specialized vs. Generalized AI: Why public models are hitting a wall and why private data is the ultimate moat. Building for the DOD: The logistics of selling AI to the military, navigating rejections, and partnering as a force multiplier with incumbents like Palantir. The Blueprint for Lean Funding: How to leverage team credibility to raise millions without bloated team sizes. Inverting the Developer Problem: How a sidecar, GPS-like context system allowed a lean research lab to beat Claude and Codex at bug localization. #DefenseTech #SportsAI #EnterpriseAI #Founders #VentureCapital #161Ventures #StartupSeattle #MachineLearning