Enable distributed AI adoption: let teams pick tools, run many small experiments, and share what works—govern with light guardrails instead of top‑down mandates.
Skip rigid pilots: embed AI inside existing workflows people already use, and adoption will follow the value.
Blend remote scale with in‑person speed: colocate small AI squads inside a remote org to accelerate iteration without losing async strengths.
Win with open ecosystems: infuse AI into extensible architectures like WordPress to compound value with existing reach.
Adopt bottom‑up tooling: let teams pick AI tools, then spread proven stacks through organic sharing and light governance.
Run on-sync cycles: prototype in the meeting, observe impact, and add LLM ops practices to keep speed from eroding product quality.
Win with applied AI: amplify existing distribution and domain strengths while partnering on foundation models.
Fix the three‑speed mismatch: pair 10× development with faster discovery and marketing, update planning cadence, and retire legacy Agile rituals that assume equal speeds.
Stop slicing—solve the whole job: use AI to deliver complete workflows, not narrow features, and raise the ambition bar.
Delegate entire workflows to silicon: deliver outcomes, not dashboards, by automating end‑to‑end jobs.
Operate without phases: discover and build in parallel, shorten decision cycles, and validate ideas live as teams prototype while they learn.
Explore new categories: build lightweight, focused apps that fill gaps traditional software never served.
Ship ‘clip software’: build lightweight, niche apps that were previously uneconomic, and capture new demand unlocked by AI.
Speed requires specification: invest in crisp requirements so AI can implement in hours, not months.
Redesign your org for AI: inventory tasks, automate what’s ready, and regroup the remaining human work into new roles built around judgment and orchestration.
Start and scale with AI as a true team member: launch services fast, let models handle production work, and reserve human effort for strategy, taste, and client impact.
Prepare for hybrid intelligence: align HR and IT to onboard digital workers, redefine roles, and orchestrate humans plus AI as one team.
Compete when intelligence is cheap: automate entire functions, revisit unit economics, and reinvest savings into differentiation.
Leverage analog foundations: use manual craft to judge quality, guide AI output, and know what should not be automated.
Build better judgment: train with analog methods (notes, paper prototypes) so you can evaluate and steer AI output with a sharper critical eye.
Rebuild around models: treat LLMs as programmable building blocks and redesign systems for probabilistic behavior.
Operationalize prompts: version them, test for drift, and treat changes like code releases.
Move AI from edge to core: make models the primary logic layer, with code orchestrating prompts, tools, and safeguards.
Automate outcomes, not steps: hand full jobs to AI where possible so products deliver finished work rather than dashboards.
Expose the hidden work: orchestrate thousands of AI calls behind simple UX, with monitoring to ensure reliability at scale.
Combine buy and build: wrap third‑party models with your logic and UX, and write code only where it compounds advantage.
Use AI gains to out‑ship competitors: keep teams intact, raise throughput goals, and funnel the 10× lift into more experiments, features, and customer value.
Build from human intent: move beyond code to directing digital employees, defining roles, guardrails, and desired outcomes.
Architect for AI attention limits: know when to switch tools as complexity grows, and add evals plus regression checks to catch breakage.
Use your remote muscle: document decisions, share openly, and plug AI into strong written‑culture workflows.
Control AI‑created code quality: require reviews and tests, refactor messy AI output, and set clear thresholds before shipping.
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