Verification Complexity
Verification complexity grows exponentially with interconnected components. As AI drives code generation costs toward zero, this barrier becomes the dominant constraint in software engineering.
Verification complexity grows exponentially with interconnected components. As AI drives code generation costs toward zero, this barrier becomes the dominant constraint in software engineering.
AI-generated code optimizes for speed but can't address the deeper problem: building software well at scale. The gap between 'good enough' and 'craft' is widening.
Tracking the landscape of truly open and offline-capable AI models. Open weights alone aren't enough - training data transparency is the real differentiator for security-critical applications.
How work changes when AI commoditizes code and compresses timelines. Collects external sources alongside ThinkNimble's original thinking on tours of duty, value pricing, and what's actually hard.
AI fails not by coherently pursuing wrong goals but by being incoherent and unpredictable. Incoherence compounds over extended reasoning, with implications for safety research and the verification complexity barrier.
AI is a general-purpose technology whose economic impact is structural, not incremental. Token costs drop fast but total costs stay high, and the productivity gains remain elusive at enterprise scale.
Building production AI agents is still mostly unsolved engineering. The core loop is simple (~200 lines), but production requires context management, agent coordination, trust, and infrastructure that's still being figured out.
The narrative that AI is an environmental catastrophe rests on shaky empirical ground. Multiple analyses show datacenter water usage is dramatically overstated relative to agriculture and other industries.
The 'size' of a coding task is not intrinsic to the task itself. It exists between tasks in the graph of their relationships. That graph is mostly hidden and changes over time. This is why estimating software work is so hard, and why AI doesn't magically fix it.
AI-Native Schools: Alpha School and the New Education Movement
The real difference between traditional and GenAI software is not determinism, but instead the much large state spaces GenAI can represent. Products are now explorable territories rather than fixed paths.
GenAI fundamentally changes information access by breaking down barriers between technical and non-technical knowledge, enabling new forms of synthesis.
Our commitment to transparent use of AI through clear attribution levels: AI-generated, AI-supported, and human-written content.
In the AI era, value comes not from writing ability but from having something worth writing about! Humans provide the essential information that makes AI output valuable.
Chatbots are the new command line interface - making complex computer operations accessible through natural language conversation.
A new way of thinking about your career path.
Pricing not just time, but value.
How to extend deep, specific learning into broad applications in consulting work.