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.
Where to place your bets: prioritize applied AI that leverages your existing distribution and data, and partner on foundation models instead of competing to build them.
Skip rigid pilots: embed AI inside existing workflows people already use, and adoption will follow the value.
Build less, enable more: standardize on best‑in‑class external AI tools, then add thin, workflow‑specific integrations where they create clear leverage.
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.
Recalculate the economics: when two people can do the work of ten, rethink team structures, cost models, and where human judgment adds the most value.
Stop slicing—solve the whole job: use AI to deliver complete workflows, not narrow features, and raise the ambition bar.
Make meetings productive: prototype live with stakeholders, validate assumptions in hours, and capture decisions immediately.
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.
Front‑load precision: write detailed specs and context early so AI builds the right thing fast and avoids downstream rework.
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.
Launch faster with an AI co‑founder: spin up brand, site, and ops in weeks while reserving human effort for client outcomes.
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.
Think in layers, not fads: stack generative, agentic, and spatial AI capabilities to compound value over time.
Make AI an all‑hands change: build organization‑wide literacy, embed AI into owned workflows, and avoid treating it as an IT rollout.
Merge people and platform thinking: align HR and IT to hire, onboard, and manage hybrid human‑digital teams.
Adopt a 2–3 year horizon: plan architectures and bets that survive rapid AI change, and avoid over‑committing beyond that window.
Add spatial understanding: design systems that reason about objects, people, and places—not just text.
Why problem framing beats code: shift from writing functions to expressing clear intent, decomposing problems, and orchestrating solutions that AI can implement.
Design for near‑zero marginal intelligence: when digital workers cost electrons, automate end‑to‑end tasks and rethink pricing models.
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.
Practice AI judgment: pick problems where AI fits, and prefer simpler methods when they work better.
Classify by meaning, not keywords: apply LLMs to text‑heavy cases where context, not terms, drives the right categorization.
How to apply the 80/90 rule: target error‑tolerant problems, define “good‑enough” accuracy, and ship 80% capability at ~90% lower cost without over‑engineering edge cases.
Leverage analog foundations: use manual craft to judge quality, guide AI output, and know what should not be automated.
Collapse review cycles: let PMs prove feasibility with V0 in minutes, then iterate with engineering on what’s production‑ready.
Move beyond forms: build conversational interfaces that understand messy input and adapt flows to each user.
Detailed specs accelerate delivery: invest more up front so AI halves build time and avoids costly misalignment.
Keep humans in the room: use AI for synthesis and logistics, but rely on facilitators for deep questions and emotional insight.
Where AI helps and where it doesn’t in design: use models for ideation and variations, but rely on human taste for final decisions when quality bars are high.
Reinvest saved time: shift AI efficiencies into deeper collaboration, richer workshops, and more meaningful client touchpoints.
Validate your best guesses: use AI simulation to predict customer reactions with ~80% accuracy before you commit to a direction.
Build better judgment: train with analog methods (notes, paper prototypes) so you can evaluate and steer AI output with a sharper critical eye.
Screen participants fairly and fast: let AI shortlist by criteria to reduce bias, then apply expert judgment on the final selection.
Do research without hallucinations: use Gemini Notebook LM to constrain sources, cite correctly, and keep client insights trustworthy.
How to compress weeks of whiteboarding into hours: use AI-powered “vibe coding” to build clickable prototypes fast, align stakeholders early, and reduce costly iteration.
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.
Adopt the new stack: UserDoc for requirements → V0 for prototypes → export code → Cursor/Windsurf to finish the last 20%.
Expose the hidden work: orchestrate thousands of AI calls behind simple UX, with monitoring to ensure reliability at scale.
Expect ~40% AI‑written code in production: keep human ownership of architecture, enforce reviews and tests, and reserve complex logic for engineers.
Combine buy and build: wrap third‑party models with your logic and UX, and write code only where it compounds advantage.
Design digital employees: specify goals, permissions, tools, and escalation rules instead of database tables and forms.
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.
Unlock text at scale: mine RFPs, contracts, and news to build capabilities that numbers‑only analytics miss.
Architect for AI attention limits: know when to switch tools as complexity grows, and add evals plus regression checks to catch breakage.
How to operate in a non‑deterministic world: move from unit-level certainty to system-level observability, evals, and human-in-the-loop checks when orchestrating natural‑language driven components.
Design for non‑determinism: add guardrails, fallbacks, and manual overrides where probabilistic AI cannot be fully trusted.
Measure what matters for AI: track time‑to‑value, output volume, and ‘tweak time’ to ensure automation actually saves effort.
Harden AI for production: detect out‑of‑distribution inputs, add human‑in‑the‑loop, and monitor outcomes continuously.
Choose the right tool: don’t force LLMs on math; use simpler analytic methods where numbers beat language models.
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.
Plan for the ‘repair’ tier: when vibe coding hits its limits, transition to specialists who stabilize, refactor, and productionize.
Generate living docs from code: reverse‑engineer requirements and flows so teams can ship changes with confidence.
Turn legacy into leverage: use AI to explain undocumented code, scope migrations, and reduce the cost of tackling technical debt.
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