Live in the future, then build what's missing
This article dissects the AI super‑cycle economics by breaking down the full‑stack cost of AI factories, revealing that GPUs account for only half of expenses while power infrastructure, labor, and cooling dominate, and examines how token value, bottlenecks, and competitive strategies shape the market.
The article analyzes how the immutable physical cost of each AI token and the exponential rise in inference demand outpace hardware improvements, driving a shift toward heterogeneous compute architectures, disaggregation, and ultimately an inference foundry model exemplified by NVIDIA's rapid acquisition of Groq.
Apoorv Agrawal’s AI supercycle analysis introduces an A‑shaped three‑layer value‑chain (Semiconductor → Infrastructure → Apps), shows how AI revenue grew from $90 B in 2024 to $435 B in 2026, why the semiconductor layer now captures most profit, and what conditions could flip the structure.
Agent systems often repeat the same failures, like missing historical calendar data or miscalculating time zones, but Garry Tan’s Skillify framework turns each error into a testable skill with a ten‑step checklist—including contracts, deterministic scripts, unit and integration tests, LLM evals, resolver checks, DRY audits, smoke tests, and knowledge‑base filing—to make agents structurally unable to repeat mistakes.
When AI eliminates execution bottlenecks, the real constraint becomes information flow, prompting a shift from hierarchical information‑routing to AI‑driven world models, intelligence layers and interfaces; the article analyses Block’s four‑layer architecture, its preconditions, challenges for mid‑level managers, and offers a step‑by‑step path for small teams to begin the AI‑native transformation.
The article dissects OpenDev’s open‑source AI coding agent, comparing its scaffolding‑vs‑harness architecture, cognitive‑flow design, context‑compression strategies, tool‑reliability mechanisms and safety layers with Claude Code, Cursor, Codex and Augment, and shows that harness‑level engineering remains the biggest performance lever even for frontier models.
The article examines how Anthropic’s Claude Cowork with plugins lets lawyers build custom AI workflows that cut operating costs dramatically, contrasts this DIY approach with vertical AI platforms like Harvey, and analyzes how these dynamics are reshaping the legal market and its competitive landscape.
Amid headlines proclaiming SaaS’s demise, Intercom’s CEO returned, killed the legacy business, and drove the AI‑first Fin product to $100 M ARR, cutting $60 M of old revenue while reshaping culture, pricing, organization, and technology to secure irreversible growth.
Giga, an AI agent startup founded by IIT Kharagpur alumni, pivoted to AI customer service, leveraging a Python-as-Primitive architecture and the Atlas multi‑agent system to automate FDE work, achieve 98% resolution rates, and position itself against competitors through speed, complex‑scenario handling, and a reusable Skills library.
The article analyzes how Pace, a tiny AI‑driven insurance BPO startup, aims to capture the $70 billion insurance BPO market with outcome‑based pricing and 100% POC success, while positioning itself for a longer‑term expansion into the $400 billion BFSI sector through reusable assets and a Constellation‑style acquisition strategy.
