A writer who describes himself as AI-skeptical but not anti-AI argues there is no acceptable "right amount" of LLM use in software, citing Overcast's Claude-generated code and Big Walk's disclosure that under a percent of a percent of its code was touched by LLMs. He contends that using LLMs means participating in an exploitative system, and points to growing evidence of "de-skilling" among developers who lean on coding agents.
AI can accelerate all three of gaming's core archetypes — indie developers, prophetic toolmakers and world-building studios — rather than displacing them, according to a16z's Anish, Jon and the author. Coding agents now help solo developers bridge skill gaps: one agent built a Habbo Hotel-inspired record shop in Blender and an engine, then added pet-adoption mechanics and kept the codebase backward-compatible. Jon used ChatGPT and Codex to build a Minecraft mod inspired by Ghost of Tsushima for his eight-year-old son.
Software engineer Neil Kakkar reports shipping 200+ pull requests per month at AI-native startup Tano, roughly 11x his PostHog rate and about tenfold his earlier output. He attributes the jump to AI agents replacing code review and communication overhead, while flagging new costs like context thrashing, lost shared context, and AI plan limits.
Titus Winters, lead author of Software Engineering at Google, discusses on The Pragmatic Engineer podcast how a dozen engineers refactored Google's roughly 250M-line C++ codebase in 2010 using parsing, matching and code generation, long before LLMs existed. He also explains why Google made automated tests mandatory after Search slipped to quarterly releases, and why he now argues most of today's CS curriculum should be rebuilt.
OpenAI's Codex, once a scrappy cloud effort that failed to find product-market fit, became the centerpiece of the company's AI coding comeback after pivoting to a terminal and code-editor version. In summer 2025 testing, GPT-5 inside Codex felt fast but overly cautious compared with Anthropic's Claude Code, which let users delegate entire tasks to Opus 4 and walk away.
A nontechnical writer vibe coded Tastemaker, an app that builds writing style guides, and added an MCP connector so agents like Claude Code and Codex could pull style guides directly. After deploying, GPT-5.6 Sol found the live connector had a public registration route anyone could walk through; no evidence of data access was found, and the connector was shut down.
Ethan Mollick published an opinionated guide arguing that using AI now means running agentic systems that combine a model with tools, not just chatting with a chatbot. He recommends ChatGPT (Work and Codex modes) or Claude (Cowork and Code modes) at $20/month for real work, starting with GPT-5.6 Sol or Fable/Opus at High thinking levels, and keeping approval prompts on to guard against prompt injection. He also says Google currently has no leading frontier model or Codex/Code equivalent, though Gemini Notebook and Gemini Omni remain useful for research and video editing.
Cursor published a follow-up to its original "Problems" post, listing the research questions it considers most critical for the next phase of AI coding. The company has extended Copilot++ to predict the next cursor position, and combining that with next-edit prediction lets the model "play" sequences of low-entropy changes—a demo required 11 Tab presses versus 3 presses of every other key, which Cursor calls Cursor Flow. Other directions include action prediction across the codebase, perfect edits via more inference-time compute, hallucinated pseudocode, multi-file editing, optimal context, multi-hop context, and bug detection and debugging.
Cursor published a list of problems it is working on, including training a custom fast reranker that filters 500k tokens from all sources down to the most relevant 8k per request. The list also covers an edit-oriented copilot, constrained in-flow agents for large codebases, bug finding, and larger-scale edits. As of Oct 12, 2023, Cursor had indexed 1.4B vectors and 150k codebases.