Live in the future, then build what's missing
The Sequoia AI Ascent 2025 keynote outlines a trillion‑dollar AI market, compares its scale to cloud computing, explains why the moment is ripe, highlights application‑layer value, and warns of emerging challenges from foundation models and agent economies.
The article analyses Nvidia’s shift from a chip supplier to a full‑stack AI infrastructure provider called AI Factory, explains the token‑economics framework that measures intelligent output, details the hardware‑software stack and network fabric, quantifies token consumption of advanced agents, and evaluates the strategic opportunities and risks for Nvidia.
The article analyzes the emerging "Era of Experience" in AI, arguing that reliance on static human data limits progress and that reinforcement learning‑based experiential learning—exemplified by AlphaZero—offers a path toward surpassing human knowledge, while outlining the technical, safety, and ethical challenges ahead.
The article analyzes Google’s Agent‑to‑Agent (A2A) protocol, explaining how it addresses the fragmentation of LLM‑driven agents, outlines its architecture, design principles, core components, and compares it with Anthropic’s MCP, while discussing strategic implications and remaining challenges for large‑scale multi‑agent ecosystems.
The article analyses Google’s Gemini 2.5 Pro as a decisive shift toward a “Reasoning Model”, detailing its architectural focus on inference, benchmark breakthroughs such as Humanity’s Last Exam and GPQA Diamond, long‑context capability, multimodal strengths, Vibe‑coding experience, and the roadmap for future Gemini models.
The podcast recap dissects a year of rapid AI change, highlighting surprise‑fast open‑source model releases, shifting foundation‑model dynamics, the rise of GPT wrappers, over‑hyped agents, undervalued memory, product‑market fit debates, infrastructure opportunities, and lingering mysteries like RL in non‑verifiable domains.
A reflective analysis shows how the 1999 predictions in *The Sovereign Individual* about AI, decentralized finance and the diminishing relevance of geography closely match today’s rapid advances in large‑language models, cryptocurrency adoption, and the empowerment of individuals over traditional institutions.
The article examines whether the surge of general‑purpose large language models such as Manus, Claude Sonet, and Qwen undermines the Bessemer Venture Partners claim that Vertical AI will grow tenfold, by analysing market size, use‑case demand, technical challenges, emerging business models, and competitive moats.
The Model Context Protocol (MCP) introduced by Anthropic provides a standardized, TCP/IP‑like communication layer that unifies resources, tools, and prompts, enabling seamless integration of large language model agents with external systems, reducing fragmentation, and accelerating AI agent development.
The article explains what Deep Research agents are, walks through a concrete example of investigating the $6 million training cost controversy of DeepSeek V3, details the multi‑step plan‑edit‑execute workflow, and discusses broader implications for AI efficiency, market dynamics, and product design.
