# Early 2026 AI Notes

**Published by:** [Jayme Hoffman](https://newsletter.jaymehoffman.com/)
**Published on:** 2026-01-20
**URL:** https://newsletter.jaymehoffman.com/early-2026-ai-notes

## Content

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 XRead on Linkedin2025 LLM Year in Review by Andrej KarpathyReinforcement 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 knowledgeStanding Out in 2026 by Lulu Cheng MeserveyEverything 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)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 fintechjunkieIf anyone (kid in dorm room) can build your product in a weekend, what's actually defensible?Last real moat: An opinionated perspective on the solutionBuilding 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)Middle of software = opening the codebase, booting up the environment, and writing the codeFor 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 SherwoodCode 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 SherwoodObservability 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 agentsFounding of Claude Code + Cowork by Boris ChernyInitially launched Claude Code to Anthropic team to dogfoodStarted with Sonnet 3.5 before model was good at agentic codingCouple months later, non-eng (research, data sci, design) started using CC dailyNow 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 CoworkHow I use Claude Code by Boris Cherny, creator of Claude CodeRun 1-5 Claudes locally, run 5-10 Claudes on the web (claude.ai)Opus 4.5 for everythingTeam shared and updated claude.mdStart most sessions in plan modeCreate slash commands for repeat workCreate subagents for automating common workflowsPostToolUse hook to clean up codeUse /permissions > dangerous skip permissionsAllow Claude Code to use all your tools (MCP) for youVerify and/or ralph long-running tasksGive Claude a way to verify its workScaling long-running autonomous coding by Wilson Lin (Cursor) + Leerob summaryCursor 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 projectsFlat structure of agents failed bc agents became risk-averse and avoided difficult tasksSeparating into planner and worker roles and judging agents solved coordination problems and allowed cursor to scale to very large projectsLessons deploying trillions of tokens on long-running tasks: Model choice (GPT-5.2) matters, removing complexity (unnecessary roles) but the prompts matter mostCoding agents need product agents by JordanCoding 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 SteinbergerYou 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)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 HockenmaierLLMs are on a collision course with marketplacesCollision = User → AI interface → DoorDash → Sandwich deliveryThis 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 appMarketplace 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)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 softwareBy 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 onesPerformance marketing for apps is dead: saturated channels and copycats will push margins to zero. paid acquisition is a boost, not a biz modelThe 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 OnlyFansWhoever solves AI discovery wins: normal people use text input for chat and search. consumer AI winners will unlock hidden beyond search/chat use-casesHuman data will be a $1 trillion/year market by Ali AnsariAll 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 OsmaniThe 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 StamatiouRewind 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 devicesWho 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 + hardwareRestraint with coding agents: “Just because you can, doesn't mean you should.” The hard part now is restraint.New AI toolsScott AI: agnetic workspace for eng alignmentClaude Cowork: Claude Code for non-technical tasks.Ralph Wiggum: self-referential AI development loops in Claude Code.Worktrunk: git worktree manager for running AI agents in parallelCallMe: plugin that allows claude code to call you on your phone.Async: slack-based product agent that learns about your company's product, customers, codebase, and team from existing work.Universal Commerce Protocol (UCP) — enable commerce inside Google AI products like GeminiGood AI takesSolve problems that won't get solved with the next model update — Maor Shlom (Base44)Claude Code is the Gutenberg press moment of software — Sergey KarayevFigma is a bottleneck + 1 month of Figma in 5 days. — Ryan Lu (Cursor)Claude Code built all of Claude Cowork — Boris Cherny (Anthropic)the era of humans writing code is over. — Ryan Dahl (Nodejs)Bros building Claude Code setups — John Palmer (Area)Curse of LLM interfaces — David Holz (Midjourney)Personal software is incredible — Issac

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