The 'D' Got Deleted: How VC Funding Broke the Innovation Ecosystem Last week's whitepaper isn't production-ready. But someone's already pitching it to your board. Kence Anderson has deployed 100+ autonomous AI systems for Fortune 500 companies—and watched venture capital create a research-to-PR pipeline that skips development entirely. The 'D' in R&D got deleted. Hype cycles got amplified. Rule-based AI—systems encoding expertise as decision logic—was the 1980s breakthrough. Overhyped, then abandoned when it couldn't do everything. But engineers kept deploying it where codified rules excel: industrial controls, diagnostics, compliance. It's running critical infrastructure today. Every AI wave follows this arc. For leaders, the lesson: stop asking which technology wins. Ask what each does well—and build modular systems that match capabilities to tasks. The fix: if AI can learn, someone should teach it the right way. Machine teaching—goals, scenarios, strategies—creates modular agents that compound capability through orchestration. Paradigm Shifts: 📌 Components > Algorithms: LLMs excel at language. Reinforcement learning excels at practice. Engineering matches superpowers to tasks. 📌 Methodology Before Platform: Databases required relational algebra before SQL scaled. Autonomous AI requires machine teaching before platforms compound. 📌 Teaching > Training: Every intelligence requires instruction. Practice without pedagogy is noise. 📌 Swarms Beat Battleships: In an AI naval competition, one giant ship won—then got banned. The algorithm responded with 100,000 tiny ships and overwhelmed everyone. Distributed beats concentrated. Shopify vs. Amazon. 📌 Distributed but Interoperable: Winning economies build decentralized, self-healing innovation units. Losing economies calcify around monoliths. Operational Impact: 📌 Research-to-PR Pipeline: When government labs led innovation, development preceded deployment. VC filled the gap but deleted the rigor. 📌 Hierarchical Orchestration: Supervisor agents directing specialized agents produce explainability swarms can't. Top-down orchestration enables traceability. 📌 Human-AI Teaming: Teams of agents and humans beat both alone. Experts teach agents; agents teach novices. Capability compounds bidirectionally. 📌 Space Forces the Issue: Harsh environments demand self-healing, modular systems. Manufacturing principles translate to orbital operations. Strategic Reframe: What's the superpower of each component? How do modular pieces orchestrate into systems that perform? Restore the 'D.' Ecosystems that develop escape the trough. Hype machines fall in. Guest: Kence Anderson , CEO & Founder, AMESA | Author, Designing Autonomous AI Series Hosts: Dyan Finkhousen , Founder & CEO, Shoshin Works | Vikram Shyam, Futurist, NASA Ecosystemic Futures is the Shoshin Works foresight series with NASA heritage.
