Complete book · Free to read
Rewrite the DNA
How to Build an AI-Native Organization
- 00Front MatterRewrite the DNA: How to Build an AI-Native Organization4 min
- 01Preface: The Last ScarcityIn digital knowledge work, AI is driving down the cost of execution and of retrieving knowledge that already exists. As getting work done and looking things up get cheaper, adaptive insight becomes the last scarcity. An AI-native organization exists to settle that scarcity into shared standards and shared context.8 min
- 02Chapter 1: Execution Approaches ZeroWhen AI drives down both the cost of execution in digital knowledge work and the cost of retrieving knowledge that already exists, the hierarchies, approvals, reports, and output metrics built around "people are slow and know less" lose the economic case they were drawn on.26 min
- 03Chapter 2: The Paradox of AbundanceThe spread of AI tools does not automatically produce an order-of-magnitude change at the company level. Models amplify the judgments and practices an organization already uses. They do not fill in goals, facts, or the duty to correct.26 min
- 04Chapter 3: The Age of Adaptive InsightAs the cost of retrieving existing knowledge and of completing much digital execution keeps falling, the gap between people will depend more on adaptive insight: whether they can find a missing standard, borrow one that has already been verified, judge whether it transfers, and correct it quickly against results.23 min
- 05Chapter 4: Judgment: Organizational Adaptive InsightOrganizational judgment is personal adaptive insight after it has been settled, unified, and reused as an organizational asset. To get judgment out of a few heads, the organization must complete two unifications: unified judgment standards, and unified context.26 min
- 06Chapter 5: Limiting Resources: Money, Time, and JudgmentAt the company, look first at money. At the person, look first at time. At an AI-native organization, look first at the capacity to produce reliable judgment. None of the three is a permanent identity; whichever resource is currently blocking output migrates with the stage. Before you allocate it, hold the baseline, then use a value hypothesis written in advance and staged acceptance to see whether value can cover the outlay.30 min
- 07Chapter 6: No Standard, Stop ExecutionA standard is judgment written as executable, checkable, revisable clauses. When no usable standard is on hand, stop unstandardized scaled execution and turn to finding, borrowing, or verifying a standard.30 min
- 08Chapter 7: The Clarity Method: Expand Your OptionsThe Clarity Method splits resources into refining a standard (10), collecting data (10), and seeking a better solution by means (80). The split is resource discipline, not a statistically proven ratio: in most cases, borrowing a verified candidate standard is a more reliable start than starting from scratch, but the angle of observation, the scale of judgment, and the conditions of use have to come with it, and then be tested against reality.29 min
- 09Chapter 8: The Interference Method: Narrow the Other Party's OptionsThe Interference Method splits resources into analyzing the consensus users already hold (10), finding a suitable scene (10), and forming new consensus (80). The split is a principle of resource allocation, not a statistically proven ratio. Without concealing material facts, it helps the other party see and understand the plan that fits them best; it does not decide for them.23 min
- 10Chapter 9: The Consensus Cycle: How Unified Context Is CreatedThe work an AI-native organization can least afford to outsource is this: the people in the same decision share the judgment standard, the facts that belong to the decision, the disagreements already known, and the action commitments. Humans are responsible for that consensus. AI takes the rest of the work that can be standardized, automated, accepted by a result, and kept inside a controllable risk. When people outside the organization also begin to use the same standards and language on their own, the consensus cycle produces cultural spillover.33 min
- 11Chapter 10: The Collapse of the Execution Layer: Evolving the Talent StructureCollapse happens first in the pure-execution tasks inside a job. Tasks in S1 and S2 that can be delivered digitally or remotely, that have a usable standard, available context, an acceptable result, and a controllable risk, will move step by step to AI or to outsourcing. Jobs recombine as the mix of tasks changes. S3 judgment and accountability for results stay with people. Decision rights then migrate by the concrete task, the risk, and the judgment already shown.35 min
- 12Chapter 11: A New Mix of Hours: Evolving Personal RhythmFor a principal-leader or a core job that sits in a relatively stable stage, is mainly responsible for judgment, and has some autonomy over its own calendar, an ordinary workday can give 1–2 hours to short-term urgency and about 6 hours to long-term importance. That mix is a reference to be adjusted with the organization's stage and the job's main task. It is not a uniform hours mandate for everyone.32 min
- 13Chapter 12: Reverse Iteration from Consensus: Evolving the Product PathA product need not start from the most frontier demand. Locate the target demand first, then locate your own capital, channels, technology, and organizational capability. When the conditions match, you can first use a new technology on a problem that already has a mature consensus, verify payment, delivery, and survival, and then, on the results, widen product capability and the radius of innovation.30 min
- 14Chapter 13: No Right or Wrong, Only Stages: How Relationships Evolve"No right or wrong" applies only to a mismatch in which there is no clear deception, harm, coercion, breach, or neglect of duty. Inside that range, stage is a way of seeing where both sides currently sit. It is not the only reason a relationship ends. To operate a relationship, look first at duty, then locate the other party and yourself from observable behavior, judge whether the important matters are compatible enough, and finally choose among screening, negotiation, adjusting a boundary, layered coexistence, and exit after remaining duties have been met.30 min
- 15Chapter 14: Ninety Days: Completing the First Cycle of Gene EvolutionAI-native transformation is not the deployment of more tools. It is the shift of an organization from depending on a few people's experience, custom, and private information to depending on shared standards and decision-relevant context. What ninety calendar days can promise is finishing, inside a controlled scope, the first cycle that can be observed, reviewed, and iterated. It does not promise to complete the whole transformation, and it does not prove long-term ROI.38 min