Research & Insights
Deep analysis of autonomous AI systems, enterprise deployments, and market intelligence.
Deep analysis of autonomous AI systems, enterprise deployments, and market intelligence.
Multi-agent systems consume 15x more tokens than single agents. Here's how to make them sustainable: the 40% Smart Zone, four optimization techniques, and agent-specific budgets.
Building AI agents costs money. Running them costs more. But the economics favor scale: once an agent works, the marginal cost of the next task approaches zero.
LLM tokens cost $3-15 per million. But minimizing tokens is the wrong goal. Here's how to think about token economics as value creation, not cost reduction.
Do large language models genuinely understand language, or are they sophisticated mimics? The 'stochastic parrots' debate and what it means for AI trust.
The symbol grounding problem in AI: when LLMs use words like 'cat' or 'water,' do they refer to anything real—or just to other words?
The epistemology of AI: examining whether LLMs possess genuine knowledge or something fundamentally different—and why the answer matters for trust.
AI makes it easy to generate content. Too easy. How do we maintain quality when the cost of production approaches zero?
Sierra hit $100M ARR in 21 months. Cursor crossed $1B. What separates AI companies that make money from those that don't?
