Will 2026 Be The Year AI Decision Intelligence Goes Mainstream?
In this episode of Next Frontiers of AI, Scott Hebner and Joel Sherlock, CEO of Causify, argue that 2026 will be the year AI Decision Intelligence goes mainstream. Following GenAI and the rise of AI agents and agentic workflows, enterprises are facing a reality check, as a recent Carnegie Mellon study found — AI agents can act, but they often cannot justify, explain, or audit the decisions that matter most. Scott and Joel unpack why causal AI and knowledge graphs are emerging as the enabling layer for decision-grade AI.
Closing the Enterprise AI Value Gap
Why only 5% of enterprises realize AI value—and what it takes to move from experimentation to scalable, revenue-driving AI.
My Predictions: Why 2026 Will Be the Year AI Has to Prove Its ROI
2026 will be the year enterprise AI must prove its ROI. After massive spending on GPUs, data platforms, and agentic AI, cost optimization is now the top priority.
From Digital to Physical: How End-User-Centered AI Is Transforming Industrial Work
At CES 2026, Oshkosh revealed how physical AI is moving artificial intelligence out of the cloud and into the real world, powering refuse trucks, airport security robots, and job-site systems that directly support frontline workers. By combining edge AI, robotics, and human-in-the-loop autonomy, Oshkosh is showing how end-user-centered AI can deliver safer, more productive, and more trusted operations across fleets, airports, and industrial environments.
301 | Breaking Analysis | Nvidia Resets the Economics of AI Factories, Again
At CES 2026, Jensen Huang once again reset the economics of AI factories. In particular, despite recent industry narratives that Nvidia’s moat is eroding, our assessment is the company has further solidified its position as the hardware and software standard for the next generation of computing. In the same way Intel and Microsoft dominated the Moore’s Law era, we believe Nvidia will be the mainspring of tech innovation for the foreseeable future. Importantly, the previous era saw a doubling of performance every two years. Today Nvidia is driving annual performance improvements of 5X, throughput of 10X and driving token demand of 15X via Jevons Paradox.
The bottom line is that ecosystem players and customers must align with this new paradigm or risk a fate similar to that of Sisyphus, the beleaguered figure who perpetually pushed a rock up the mountain.
Slop Squatting, Defensive UX, and Governing LLMs in the Enterprise
Slop squatting exposes new LLM risks. Learn why defensive UX, human oversight, and governance are critical for safe enterprise AI use.
Low-Code, Fusion Teams, and the Rise of the “Orchestrator” Developer
AI is reshaping low-code, fusion teams, and developer roles—shifting engineers from coding to orchestration and governance.
Vibe Coding, AI Code Review, and the New Trust Gap in AI-Generated Code
AI-generated code boosts velocity but raises trust issues. Explore vibe coding, AI code review, and governance in modern DevOps.
300 | Breaking Analysis | Why NVIDIA Maintains its Moat and Gemini Won’t Kill OpenAI
Two prevailing narratives are driving markets right now. The first is that NVIDIA’s moat is eroding primarily due to GPU alternatives like TPUs and other ASICs. The second is that Google generally and Gemini specifically is gaining share, will dominate AI search and ultimately beat OpenAI. We believe both of these propositions are unlikely to materialize as currently envisioned at least. Specifically, our research indicates that NVIDIA’s GB300 and the follow on Vera Rubin will completely reset the economics of AI. Furthermore, NVIDIA’s volume lead will make it the low cost producer and, by far, the most economical platform to run AI at scale.