How Founders Use AI

How people use AI, and the tool for each job.

The AI map of which tool is for which job, checked on 29 October 2026, what changed since July, and every video, thread, guide, podcast and tool behind 360 answered questions on putting AI to work.

The AI map: which tool is for which job, as of 29 October 2026

Models and assistants AI assistants Frontier models Open-weight models Decision models

Building with AI Coding agents App builders Design and presentations Spreadsheets, data and internal tools

Agents and research Agents that do tasks for you AI browsers No-code automation with AI steps Research and search Meeting notes and everyday productivity

Making media Video generation Images Avatars and voice Music and audio Video editing

AI at work Prospecting and sales Marketing, ads and AI search Customer support agents Legal and contracts Finance and accounting Hiring and HR

What changed since July

  1. 28 Oct 2026 GitHub began including Copilot with GitHub Enterprise licences and added projects to the Copilot app, where several agents work across repositories. Coding agents github.blog
  2. 27 Oct 2026 n8n released the 3.0 beta, a reliability and security release with breaking changes; self-hosting now runs on Docker only and the stable release is due in early December. No-code automation with AI steps blog.n8n.io
  3. 27 Oct 2026 Hex released Hex 4.0, rebuilt for analysts and AI agents working together, with a $100 million raise. Spreadsheets, data and internal tools hex.tech
  4. 26 Oct 2026 Replit launched Replit for Business, and the next day Cloud Agents that let non-engineers propose changes to a company codebase. App builders blog.replit.com
  5. 26 Oct 2026 OpenAI released GPT-6.1 Terra at $1 / $5 per million tokens, half the price of GPT-6.1 Sol. Frontier models openai.com
All 108 changes

What founders use most, ranked by how many answers lean on it.

  1. An Asia-market VC's nuanced answer: data moats work in specialized domains, not generically. review.insignia.vc Article Is Proprietary Data Still a Moat in the AI Race? Insignia Ventures (Southeast Asia)
  2. A growth-stage investor's checklist of which classic moats survive AI and which do not. insightpartners.com Article Building a moat in the age of AI Insight Partners
  3. What investors actually screen for when they hear the word wrapper. techcrunch.com Article VCs say AI companies need proprietary data to stand out TechCrunch
  4. A skeptical, technical take on when data actually compounds into advantage. v7labs.com Article Data Moats: Do They Exist In The Age of AI? V7 Labs
  5. Actionable moves for capturing data your competitors structurally cannot get. thestrategystack.substack.com Article How to Build a Proprietary Data Moat in AI (7 Practical Moves) The Strategy Stack
  6. Heller sold Casetext for $650M; he explains exactly where the defensibility lived. creators.spotify.com Podcast Why Vertical LLM Agents Are The New $1 Billion SaaS Opportunities Y Combinator with Jake Heller (Casetext)
  7. When everyone has the same models, taste and product quality become the differentiator. open.spotify.com Podcast Figma's CEO: Why AI makes design, craft, and quality the new moat Lenny's Podcast with Dylan Field
  8. A crisp synthesis of the Gil and Tavel schools applied to today's AI stack. ntkris.substack.com Article Defensibility in AI Krishna's Newsletter
  9. The contrarian 2026 update: distribution, not technology, decides who wins. forbes.com Article Every Company Is Now An AI Wrapper So GTM Is The New Moat Josipa Majic (Forbes)
  10. A taxonomy of AI-era moats with named startup examples for each. eoncodes.substack.com Article The Startup Moats Defining the AI Era Andre J (Substack)
  11. How systems design makes usage data inseparable from the product, the subtlest moat. latitudemedia.com Article In the age of AI, can startups still build a moat? Latitude Media
  12. Practitioners on building with LLMs without getting outmaneuvered by the labs. oreilly.com Article What We Learned from a Year of Building with LLMs (Part III): Strategy Eugene Yan, Hamel Husain, et al.
  13. Why speed itself is now a moat, and what that demands of a small team. a16z.com Podcast How AI Created the Fastest Product Cycle in History a16z (Anish Acharya)
  14. The manifesto that successful agents are mostly well-engineered software with LLM sprinkled at key points. github.com Guide 12-Factor Agents: principles for production LLM software Dex Horthy (HumanLayer)
  15. A working engineer's decision criteria with cost and reliability tradeoffs spelled out. towardsdatascience.com Article A Developer's Guide to Building Scalable AI: Workflows vs Agents Towards Data Science
  16. Short and sharp: if the steps are known in advance, a workflow wins on every axis. redis.io Article AI Agents vs Workflows: When to Use Each Redis
  17. A neutral reference explainer to align your team on terminology before the build debate. promptingguide.ai Guide AI Workflows vs AI Agents Prompt Engineering Guide (DAIR.AI)
  18. How a regulated enterprise makes the call, useful discipline for any founder. medium.com Article AI agents vs. predefined workflows: practical decision guide Capital One Tech
  19. Brings the production data: only 23% of orgs have scaled an agent anywhere. intuitionlabs.ai Article AI Agents vs. AI Workflows: Why Pipelines Dominate IntuitionLabs
  20. The founder-flavoured argument against reaching for the harder, smarter-sounding option. buildmvpfast.com Article AI Workflows vs Agents: Why Workflows Win BuildMVPFast
  21. Video Everything Gets Rebuilt: The New AI Agent Stack Harrison Chase (LangChain)
  22. The Sierra founder on where agents genuinely outperform, from thousands of deployments. open.spotify.com Podcast Transforming Customer Service through Company Agents, with Bret Taylor No Priors (Sarah Guo and Elad Gil)
  23. The next interaction pattern: agents that act on events, with humans approving at checkpoints. inferencebysequoia.substack.com Article Ambient Agents and the New Agent Inbox Sequoia Capital
  24. The developer-facing detail on what is stored, for how long, and how to reduce it. developers.openai.com Guide Data controls in the OpenAI platform OpenAI

Show the next 24

Playbooks

A goal, and the route through it a step at a time.

Start using AI well Pick an assistant, learn with it, hand it real work, and keep yourself and your data safe. 5 steps Build your product with AI From a prompt to a shipped product: prototype, build, and wire AI into what you ship. 6 steps Code and ship with AI Write, ship, and maintain real software as a founder, with AI as your pair. 4 steps Design and prototype with AI Go from idea to a clickable prototype without a designer. 3 steps Build AI agents and automations Wire up agents and internal tools that do real work for you. 3 steps Put AI inside your product Ship AI features users actually feel, not a bolted-on chatbot. 3 steps Research your market with AI Size the market, study competitors, and read data in hours, not weeks. 3 steps Market and grow with AI Content, creative, and distribution: fill the top of the funnel without a big team. 5 steps Create content with AI Write, film, and design a content engine that runs without a team. 3 steps Get found in AI search Show up when founders and buyers ask AI, not just Google (AEO/GEO). 3 steps Run ads with AI Brief creatives, read the numbers, and optimize spend with AI. 3 steps Find and serve customers with AI Research the market, prospect, close, and support, with AI doing the heavy lifting. 4 steps Sell and prospect with AI Find leads, personalize outreach, and run better sales calls. 3 steps Support customers with AI Answer faster and deflect tickets without losing the human touch. 3 steps Run the back office with AI The unglamorous grind: workflows, finance, legal, and hiring, automated where it counts. 5 steps Automate your workflows Stitch your tools together so the busywork runs itself. 3 steps Hire and manage your team with AI Source, screen, and onboard faster, and keep the team in sync. 3 steps Handle legal and finance with AI Draft contracts and stay on top of the numbers without a big spend. 2 steps Be more productive with AI Get more done and think sharper with AI as a daily habit. 3 steps Become an AI-native founder The meta-skills: build your stack, prompt well, and use AI to research, think, and decide. 6 steps Build your AI stack Pick the tools worth paying for and the prompts worth reusing. 3 steps

The questions, by topic

Building & Product

Vibe Coding 20 Databases & Internal Tools 11 AI Agents 15 Design & Prototyping 14 AI in Your Product 15

Research & Knowledge

Search & Research 16 Market Research 13 Data Analysis 13

Marketing & Growth

Content Creation 14 Video, Image & Audio 27 Getting Found in AI Search (AEO/GEO) 12 Ads & Performance Marketing 14 Social & Community 12

Sales & Customers

Sales Prospecting 13 Customer Support 6 CRM & Sales Calls 12

Operations & Back-office

Legal & Contracts 13 Finance & Accounting 3 Hiring & HR 3 Productivity 5 Workflows & Automation 3

Strategy & The AI-Native Founder

Fundraising 6 Prompting 4 Building Your AI Stack 5 AI as a Thinking Partner 5

Everyday AI

Choosing Your AI Assistant 15 Learning with AI 8 Working with AI Agents 8 Using AI Safely 9 AI in Indian Languages 8 Quick Fixes for Files, Photos and Video 26 AI for Your Line of Work 12
eChai Partner Brands The partnership →