domain · ai-native
Build for an AI-native operating model
How operating changes when the agent is on the team. Verification loops, evals, AI-mediated workflows, agent infrastructure.
Strongest claims
- Measure AI search on three layers: Presence, Readiness, Business Impact Aleyda Solis
- Run agent-first GTM as a three-stage flywheel with one named agent per job Yamini Rangan
- Agent-first content has six platform layers, access, discovery, capability, format, token, UX bridge Addy Osmani
- Agents are first-class product users; design for output reliability, not navigation Elena Verna
- Agents work when treated as a team, not a single super-tool Claire Vo
- A single seeded fake claim can self-confirm in AI Overviews Lily Ray
Adjacent domains
- product · 42 co-occurrences
- engineering · 36 co-occurrences
- pmm · 35 co-occurrences
- leadership · 30 co-occurrences
- gtm · 29 co-occurrences
- growth · 15 co-occurrences
- strategy · 11 co-occurrences
- founder-craft · 11 co-occurrences
Synthesis patterns in ai-native
- Agent-first GTM (rebuild, don't bolt-on)
- Agents are first-class users, design for output, not navigation
- Agents mapped 1:1 to JTBD with named human checkpoints
- AI defensibility comes from non-AI moats
- Build for the next model, not the current one
- Context, not capability, is the bottleneck
- The Economic Turing Test, outcomes pricing, agent labor, revenue per employee
- Evals are data analysis, single judge, binary rubrics, error analysis first
- Execution is becoming free; judgement is the part that doesn't compress
- Generalists with taste, shipping end-to-end
- LLM-as-OS, post-training as moat
- Make the implicit explicit
- Research preview, frontier programs, seasons not roadmaps
- Sales is an engineered system, not individual art
- Strip what does not compound: AI abundance makes judgment the scarce resource
- Substrate runs the loop; humans run alignment and taste
- Verification, not execution, is the irreplaceable human job
153 insights in ai-native
- 10-80-10: human direction, AI execution, human polish · Arvid Kahl
- An automation that works 95% of the time is not an automation · Cat Wu
- Advisor-tool replaces ensemble-of-3 stability hacks at near-Sonnet rates · Cat Wu
- AEO is a GTM capability, not an SEO experiment · Maja Voje
- Measure AI search on three layers: Presence, Readiness, Business Impact · Aleyda Solis
- Run agent-first GTM as a three-stage flywheel with one named agent per job · Yamini Rangan
- Agent-washing is the buyer-confusion surface PMM should attack · Gartner
- Agent-first content has six platform layers, access, discovery, capability, format, token, UX bridge · Addy Osmani
- Agents are first-class product users; design for output reliability, not navigation · Elena Verna
- Agents work when treated as a team, not a single super-tool · Claire Vo
- At agent scale, security governance must shift from humans approving humans to automated agent-identity and policy systems · Jonathan Jaffe
- AI adoption creates an asymmetric information problem where wins are public and costs are private, requiring deliberate feedback loop design · Charity Majors
- AI is the co-pilot, not the autopilot, automate the 80% you understand, hold the 20% that needs taste · Ben Tossell
- A 7-step AI prompt chain beats one-shot resume rewrites for job search · Nishchal Dua
- AI cold-call compliance follows the prospect's residence, not the seller's HQ · Gartner
- AI replaces PMMs whose job was launch emails, recap docs, and slide tweaks · Sachin Jha
- The number one enemy of innovation is efficiency, AI is collapsing the moats specialization built · Leah Tharin
- AI has crossed the threshold to something indistinguishable from judgment and taste, winners will know what to build, not how · Matt Shumer
- A single seeded fake claim can self-confirm in AI Overviews · Lily Ray
- Per-employee AI tool spending caps anchored to compensation signal an implicit enterprise ROI model, and are more sustainable than consumption competitions · Simon Willison
- "We're using AI" is not a business strategy, defensibility comes from domain expertise, customer relationships, and data, not from the model layer · Sam Altman
- When intelligence is abundant, taste, judgment, relationships, and the ability to identify what is worth doing become the scarce resources · Sam Altman
- The singularity is one smooth curve, vertical looking forward, flat looking backward, never the disruptive shock people expect · Sam Altman
- Building production ML systems at scale · Anand Karunan
- Claude Code subagents run in isolated context windows with model routing, enabling cost-optimized bulk delegation without polluting the orchestrator session · Anthropic
- Audience first, Community second, Product last, and AI makes the inversion much faster · Greg Isenberg
- Battle cards become workflow primitives, not Notion pages · Gartner
- Being crisp and concise is underrated. The lack of it signals something more concerning · Chantal Cox
- Appoint one trusted-taste expert as the eval benevolent dictator, committees stall the loop · Hamel Husain
- Data foundation determines AI agent value delivery. · Bogdan Tyskyy
- When the agent isn't doing what you want, fix the context, not the model · Sherwin Wu
- Frontier labs are baking native harness preferences into model weights, not layering them on top · Drew Breunig
- Build for the model six months out, not the one that ships today · Boris Cherny
- Build for the model six months out, the current model will eat your scaffolding · Sherwin Wu
- Build products at the edge of what does not yet work · Cat Wu
- In AI products, capability overhang is the central growth problem · Amole Naik
- AI capability is not evenly distributed, it spikes where labs have data, rewards, and verification loops · Andrej Karpathy
- Automate the four stages of a growth experiment; keep humans on alignment · Amol Avasare
- Continuous Calibration, Continuous Development (CCCD) is the operating loop for AI products · Aishwarya Naresh Reganti
- AI is changing everything. Faster than most people are ready for · Dan Murphy
- 65 to 85 percent of ChatGPT prompts are invisible to keyword tools · Kevin Indig
- AI tools combine with CRMs through orchestration; they do not replace them · Maja Voje
- Frictionless agentic AI triggers cognitive surrender, where people stop evaluating outputs critically even when the AI is wrong · Ethan Mollick
- AI labs are running the commoditize-the-complement playbook; tag features as core or complement quarterly · Tomasz Tunguz
- AI clears the runway for competitive strategy, 8-10 hour teardowns compressed to under an hour · Vivian Jordan
- Context documents are reusable battery packs that need named owners · Aatir Abdul Rauf
- Context engineering beats prompt engineering for marketing AI workflows · Aatir Abdul Rauf
- The block to AI adoption is the start, not the depth, design 30-day ladders, not deep-dive bootcamps · Hilary Gridley
- The dark factory: nobody reads the code, gated by a simulated QA swarm · Simon Willison
- The design role's time mix shifted from 60% mocking to 30% mocking, 30% pairing, 20% code · Jenny Wen
- Give the model tools and a goal; do not hard-code the workflow · Boris Cherny
- Treat `.claude/` as a deployable artifact with versioning and rollback · Pawel Huryn
- The Economic Turing Test, would you hire the agent if you didn't know it was a machine? · Benjamin Mann
- Claude Skills let non-technical PMMs build a reusable library of AI tools. · Emily Pick
- Evals are systematic data analysis on your LLM application, start with error analysis, not tests · Hamel Husain
- Cross-functional pod (product + eng + data + ops + marketing) is the org unit for agentic GTM · Kieran Flanagan
- In an AI-native team, hire generalists with one deep dimension, not specialists · Anton Osika
- Citation rate and mention rate are different metrics; comparative content closes the gap · Kevin Indig
- AI prose can't violate expectation because it IS expectation, protect the smallest deliberate rule-break from every polish pass · Ann Handley
- In an AI-flooded content market, voice is the only defensible advantage, distinct, authentic, sounds like one source · Ann Handley