# Early 2026 AI Notes By [Jayme Hoffman](https://newsletter.jaymehoffman.com) · 2026-01-20 --- Over the past few weeks, I read through the best end-of-year and early 2026 AI posts to get a sense of where things are headed. Here are my favorites with tldr notes, tools, and takes. * [Read on X](https://x.com/jaymehoffman/status/2013623076831875557) * [Read on Linkedin](https://www.linkedin.com/pulse/early-2026-ai-notes-jayme-hoffman-mx1ic/) * * * [**2025 LLM Year in Review by Andrej Karpathy**](https://x.com/karpathy/status/2002118205729562949?s=20) * Reinforcement Learning from Verifiable Rewards (RLVR): New major training stage added to the LLM production stack after pre-training → SFT → RLHF. Uses automatically checkable rewards (math/code/etc.) to induce reasoning strategies. * Ghosts vs. Animals: LLMs (unlike humans) are optimized for text + puzzles, not survival. Capabilities spike in verifiable domains but remain uneven elsewhere. Benchmarks became unreliable “benchmaxxing” * Cursor / New Layer of LLM Apps: Cursor popularized the LLM app pattern: bundling context, multi-call orchestration, vertical GUI, autonomy slider. Are there green pastures for apps? Yes. Labs are generalists. Apps are specialists in verticals by using private data and feedback loops. * Claude Code / AI That Lives on Your Computer: First convincing demo of what an LLM agent looks like. Runs locally with your environment/data instead of cloud. Analogy: a little spirit/ghost that "lives" on your computer. * Vibe Coding: Natural language → code crossed usability threshold. Non-programmers can build apps; programmers can build far more. Code becomes ephemeral, free, disposable. “Vibe coding will terraform software and alter job descriptions.” * LLM GUI: Text is efficient for machines but not for humans; we prefer visual/spatial formats. Who is “actually going to build the LLM GUI?” Nano Banana hints at this by combining text + images + world knowledge [**Standing Out in 2026 by Lulu Cheng Meservey**](https://x.com/lulumeservey/status/2008187268922536109) * Everything is fake now: Fake content by fake influencers with fake engagement from fake followers, launching fake products with fake testimonials. Real has never been more precious. * For comms, 2024 was going direct and 2025 was winning attention. 2026 will be about doing real things. * Doing real things means: Putting in real effort, Showing real evidence, Real world events and artifacts, Showing up as real humans, Forming real relationships * “_Once you are Real you can’t become unreal again. It lasts for always_.” [**The GPT-9 Test by Michael Bloch (Quiet Capital)**](https://michaelxbloch.substack.com/p/the-gpt-9-test) * What happens to your business when GPT-9 ships? * Does your biz depend on AI being bad at something? * Do you have networks effects where the product improves as more people use it? * Does your biz require physical presence that can’t be automated away? * Does better AI make your product more valuable, or less? * Most biz today are “arbitraging a temporary capability gap” * Build something that lasts. [**The Last Moat Standing by fintechjunkie**](https://x.com/fintechjunkie/status/2008185723849982010) * If anyone (kid in dorm room) can build your product in a weekend, what's actually defensible? * Last real moat: _An opinionated perspective on the solution_ * Building is now easy and fast. Having an informed opinion is hard and takes time. * Copying opinionated teams is like hitting a moving target. [**The disappearing middle of software work by Karri (Linear)**](https://x.com/karrisaarinen/status/2007534281011155419) * Middle of software = opening the codebase, booting up the environment, and writing the code * For a long time, this has been the most important work and where most time was spent. * This middle is disappearing/thinning thanks to coding agents. * Understanding the problem, gathering context, and directing agent work become the most important work. [**The End of Reusable Software by Sherwood**](https://x.com/realshcallaway/status/2007944295693918659) * Code is now free. No longer need to use existing software. Claude can create from scratch. * Why create reusable programs? Why not just write one-off for every scenario? Coding agents already do this. [**Observability's Past, Present, and Future by Sherwood**](https://x.com/realshcallaway/status/2009326186833281390) * Observability emerged to tame cloud and microservice complexity: distributed tracing + a new reliability mindset that actually worked at first. * Today we over-collect telemetry and obsess over dashboards, but the real bottleneck is humans making sense of the data, not generating more of it. * AI is about to flood the world with wayyy more (and messier) software, so we’ll need a new kind of observability that helps us reason about and operate this infinite codebase. * h/t [1 in every 5 founders I meet in the Bay Area is building an observability platform for agents](https://x.com/0xDevShah/status/2010435036584333514) [**Founding of Claude Code + Cowork by Boris Cherny**](https://x.com/bcherny/status/2010923222813065308) * Initially launched Claude Code to Anthropic team to dogfood * Started with Sonnet 3.5 before model was good at agentic coding * Couple months later, non-eng (research, data sci, design) started using CC daily * Now people are using CC to “control their oven, recover wedding photos from a busted hard drive, analyze their DNA and medical records, haggle with customer support.” * Realized they needed to “make it easier for people that want to use the Claude agent for things that are not coding” → Introducing Claude Cowork [**How I use Claude Code by Boris Cherny, creator of Claude Code**](https://x.com/bcherny/status/2007179832300581177) * Run 1-5 Claudes locally, run 5-10 Claudes on the web ([claude.ai](http://claude.ai)) * Opus 4.5 for everything * Team shared and updated [claude.md](http://claude.md) * Start most sessions in plan mode * Create slash commands for repeat work * Create subagents for automating common workflows * PostToolUse hook to clean up code * Use /permissions > dangerous skip permissions * Allow Claude Code to use all your tools (MCP) for you * Verify and/or ralph long-running tasks * Give Claude a way to verify its work [**Scaling long-running autonomous coding by Wilson Lin (Cursor)**](https://cursor.com/blog/scaling-agents) **+** [**Leerob summary**](https://x.com/leerob/status/2012938056043565333) * Cursor ran ”hundreds of concurrent agents on a single project, coordinating their work, and watching them write over a million lines of code and trillions of tokens.” * Single agents are good for focused tasks but slow for complex projects * Flat structure of agents failed bc agents became risk-averse and avoided difficult tasks * Separating into planner and worker roles and judging agents solved coordination problems and allowed cursor to scale to very large projects * Lessons deploying trillions of tokens on long-running tasks: Model choice (GPT-5.2) matters, removing complexity (unnecessary roles) but the prompts matter most [**Coding agents need product agents by Jordan**](https://x.com/jsngr/status/2008584545671508123) * Coding is cheap. Decision-making is still expensive. * More pressure on the part teams have always struggled with most: deciding what to build, why it matters, and staying aligned. * Coding agents help teams ship faster. Product agents (Async) can help teams ship the right thing. [**Shipping at Inference-Speed by Peter Steinberger**](https://steipete.me/posts/2025/shipping-at-inference-speed) * You can now ship code now at a speed that seems unreal. Now limited by inference time and hard thinking. * Important decisions have become languages, ecosystem, and dependencies. * It’s getting harder and harder to trust benchmarks. Try multiple models/tools to understand. [**Notes on AI Apps in 2026 by Anish Acharya (a16z)**](https://x.com/illscience) * Thinking tools vs Making tools: Many execution tools exist, but more exploration tools are needed. * Software eats all the “service” functions in the organization: Agents will replace human service functions (legal, finance, HR) * Compounding AI apps: Apps that benefit from multi-modal data, proprietary datasets, networks, and ecosystems (e.g., thick apps) will compound. * Humans discover “the rest” of AI: UX/UI will improve and more consumers will create with AI. * Notes for (incumbent) CEOs: Collapse customer-facing roles, software-first everywhere, and price boldly. For most enterprise tasks, AGI is here. [**LLMs vs. Marketplaces by Dan Hockenmaier**](https://www.danhock.co/p/llms-vs-marketplaces?utm_source=post-email-title&publication_id=1618498&post_id=184240494&utm_campaign=email-post-title&isFreemail=true&r=168g3&triedRedirect=true&utm_medium=email) * LLMs are on a collision course with marketplaces * Collision = User → AI interface → DoorDash → Sandwich delivery * This is a problem because marketplaces pay back CAC from repeat transactions and would have to spend more per tx because orders are coming from ChatGPT rather than their app * Marketplace defensibility to LLMs comes from: * Difficulty of supply aggregation: hotels (easy for LLM) vs airbnb (hard for LLM) * Degree of management: search and tx (easy for LLM) vs risk and service (hard for LLM) * Nature of customer engagement: low frequency and high consideration/research (good for llm) vs high frequency and low consideration (bad for llm) * What marketplaces should do? Do things LLMs wont or can’t. [**Consumer AI predictions by Eugenia (Wabi)**](https://x.com/ekuyda/status/2008611849189945662) * Screenless AI devices will flop: voice is good secondary interface but bad primary, hard to fight our addition to feeds/screens * “Always listening” devices won’t work either: most things dont matter to record and things that do you wont dare record. granola is good. * Mini-apps will unlock UGC personal software: full apps are hard to make and heavy to onboard and use, ai coding + mini-apps = UGC software * By 2030 there will be two big general purpose AI chatbots: today we have cGPT and lots of niche bots, tomorrow cGPT-like assistant and AI friend will be the big ones * Performance marketing for apps is dead: saturated channels and copycats will push margins to zero. paid acquisition is a boost, not a biz model * The fastest consumer product to reach $1B ARR will be an AI webcam girl: dropping video generation cost will result in a hyper-personalized 24/7 super OnlyFans * Whoever solves AI discovery wins: normal people use text input for chat and search. consumer AI winners will unlock hidden beyond search/chat use-cases [**Human data will be a $1 trillion/year market by Ali Ansari**](https://x.com/aliniikk/status/2009347948816335031) * All functions (digital and physical) will be automated. * Automation pushes humans towards higher-value creative work. * Frontier AI requires structured human data to learn. * A lot of time and money will be spent on “expert human data creation or structured human judgment” [**21 Lessons From 14 Years at Google by Addy Osmani**](https://addyosmani.com/blog/21-lessons/) * The best engineers are obsessed with solving user problems.\*\* — “User obsession means spending time in support tickets, talking to users, watching users struggle, asking “why” until you hit bedrock.” * Bias towards action. Ship. You can edit a bad page, but you can’t edit a blank one. — “First do it, then do it right, then do it better.“ * Your code doesn’t advocate for you. People do. — “In large organizations, decisions get made in meetings you’re not invited to, using summaries you didn’t write, by people who have five minutes and twelve priorities.” * The best code is the code you never had to write.\*\* — “The problem isn’t that engineers can’t write code or use AI to do so. It’s that we’re so good at writing it that we forget to ask whether we should.” * Focus on what you can control. Ignore what you can’t. — “Dwelling on these creates anxiety without agency.” * Writing forces clarity. The fastest way to learn something better is to try teaching it. — “The act of making something legible to someone else makes it more legible to me.” * The work that makes other work possible is priceless — and invisible. — “Glue work - documentation, onboarding, cross-team coordination, process improvement - is vital.” * When a measure becomes a target, it stops measuring. — “The goal is insight, not surveillance.” * Admitting what you don’t know creates more safety than pretending you do. — “When a leader admits uncertainty, it signals that the room is safe for others to do the same.“ * Your network outlasts every job you’ll ever have. — “Your job isn’t forever, but your network is. Approach it with curiosity and generosity, not transactional hustle.” * Most performance wins come from removing work, not adding cleverness. — “Before you optimize, question whether the work should exist at all.” [**2025 Year in review by Paul Stamatiou**](https://paulstamatiou.com/2025-year-in-review) * Rewind lessons: “prioritize task-based workflows over pure recall, and explore using lightweight visual models for data classification.” * Limitless pendant lessons: AI wearables have real uses-cases but some “take privacy extraordinarily seriously and lean introverted” and arent excited to wear recording devices * Who to work with? “exceptionally talented team, at the forefront of AI, with leadership and a CEO who genuinely care about quality, and as little organizational friction as possible between us and an outstandingly well-crafted product.” * How to be successful? “if successful, will make the rest of my career look like a footnote.” * Lessons exploring: Designers who code aren't a nice-to-have anymore: they're the norm. People “who weren't already working closely with AI were thinking about leaving their companies.” * Sesame (hiring) is building lifelike personal agents via software + hardware * Restraint with coding agents: “Just because you can, doesn't mean you should.” The hard part now is restraint. **New AI tools** * [Scott AI](https://www.tryscott.ai/): agnetic workspace for eng alignment * [Claude Cowork](https://x.com/claudeai/status/2010805682434666759): Claude Code for non-technical tasks. * [Ralph Wiggum](https://github.com/anthropics/claude-code/blob/main/plugins/ralph-wiggum/README.md): self-referential AI development loops in Claude Code. * [Worktrunk](https://x.com/max_sixty/status/2006077845391724739?s=20): git worktree manager for running AI agents in parallel * [CallMe](https://x.com/boredGenius/status/2009035664860565525): plugin that allows claude code to call you on your phone. * [Async](https://x.com/jsngr/status/2008584545671508123): slack-based product agent that learns about your company's product, customers, codebase, and team from existing work. * [Universal Commerce Protocol (UCP)](https://developers.google.com/merchant/ucp) — enable commerce inside Google AI products like Gemini **Good AI takes** * [Solve problems that won't get solved with the next model update — Maor Shlom (Base44)](https://x.com/MS_BASE44/status/2011490401442517078) * [Claude Code is the Gutenberg press moment of software — Sergey Karayev](https://x.com/sergeykarayev/status/2007899893483045321) * [Figma is a bottleneck](https://x.com/hvpandya/status/2013240464879894786) + [1 month of Figma in 5 days. — Ryan Lu (Cursor)](https://x.com/ryolu_/status/2011808864153280563) * [Claude Code built all of Claude Cowork — Boris Cherny (Anthropic)](https://x.com/bcherny/status/2010813886052581538?s=20) * [the era of humans writing code is over. — Ryan Dahl (Nodejs)](https://x.com/rough__sea/status/2013280952370573666) * [Bros building Claude Code setups — John Palmer (Area)](https://x.com/johnpalmer/status/2012911338276720852) * [Curse of LLM interfaces — David Holz (Midjourney)](https://x.com/DavidSHolz/status/2012652653990314030) * [Personal software is incredible — Issac](https://x.com/roundrobin42/status/2012365229213065468) --- *Originally published on [Jayme Hoffman](https://newsletter.jaymehoffman.com/early-2026-ai-notes)*