Over the past months, I have argued that we are living through a structural inversion: execution is becoming cheap, while coordination, judgment, and verification are becoming the dominant constraints. That argument has appeared in different forms — in discussions of no tests, no merge, in the inversion thesis, in the critique of dashboard-driven management, and in the broader coordination shift.

This post is not a repetition of that thesis. It is a consequence of it.

If AI collapses the marginal cost of building, then intent, constraints, and proof are no longer secondary engineering concerns. They become organizational infrastructure. Frameworks that once felt aspirational — measurable outcomes, evidence-based steering, falsifiability — become mandatory operating conditions.

The interesting question is not whether this directional shift is correct. It is what kind of operating model can survive under those new physics.

From Cultural Preference to Structural Requirement

For years, discussions around OKRs, outcome thinking, and evidence-driven product management have existed as cultural improvements. They competed with feature roadmaps, velocity metrics, and KPI dashboards. Organizations could adopt them partially, or superficially, without immediate consequences.

That tolerance is disappearing.

When implementation throughput is bounded by human effort, vague intent produces waste slowly. When AI increases throughput by an order of magnitude, vague intent produces waste at scale. Poorly specified objectives are not merely inefficient; they become destabilizing.

In this environment:

  • Ambiguity compounds faster.
  • Local optimization accelerates.
  • Vanity metrics are gamed sooner.
  • Coordination debt accumulates invisibly.

The bottleneck shifts from "can we build?" to "should we build?" and "how will we know whether it worked?" Specification, verification, and constraint management move from the margins of engineering to the center of organizational design.

This is the context in which the Centaur Manifest and OBAF should be understood.

The Centaur Manifest: Execution Under High Throughput

The Centaur Manifest is optimized for small, AI-augmented delivery units. Its central assumption is that execution is cheap and therefore cannot be the primary gating mechanism. Verification becomes the dominant constraint.

The practical implications are specific:

  1. Intent must be canonical. A single artifact defines the end-state, the purpose, the constraints, and the observable signals of change. Without a canonical intent, AI acceleration increases divergence.
  2. Progress must be hypothesis-driven. Work proceeds through small, explicit micro-experiments. Each experiment includes a falsifier — a clear condition under which the hypothesis is abandoned or revised. Large, unfalsifiable initiatives are structurally incompatible with high-speed iteration.
  3. "Done" must include proof. Integration requires passing guardrails: tests, checks, observability, and rollback mechanisms. Automation enforces constraints; meetings do not. Governance becomes executable rather than conversational.
  4. Evidence alignment replaces status reporting. The unit regularly reviews signals, not task completion. If signals do not move, the course of action changes. Learning loops are short and tied to observable effects.
  5. Conceptual integrity must be protected deliberately. High change velocity erodes shared mental models. The unit must invest in keeping boundaries, contracts, and architectural intent coherent.

The Centaur approach is not a cultural appeal to "be outcome-focused." It is a runtime discipline designed to prevent high-throughput execution from degrading system integrity.

OBAF: Portfolio Coherence Under Acceleration

OBAF was developed to counter agile theater: process rituals without learning, feature delivery without outcome validation, and compliance replacing accountability. Its emphasis on observable change, evidence as arbiter, constraints over scope, and blameless learning remains directly applicable.

The difference in the AI era is urgency.

At portfolio scale, the risks are not limited to a single team’s drift. They include:

  • Competing local optimizations.
  • Fragmented interpretation of strategy.
  • Outcome ownership diluted across teams.
  • Metrics that measure symptoms rather than value.

OBAF addresses these risks by:

  • Framing work around observable outcomes rather than outputs.
  • Assigning singular ownership per outcome.
  • Requiring signals tied to real-world behavior.
  • Institutionalizing After Action Reviews.
  • Guarding explicitly against Goodhart’s Law and vanity metrics.

In a layered model:

  • OBAF defines north-star intent and portfolio alignment.
  • Centaur units execute within that intent at high speed.
  • Guardrails enforce constraints locally.
  • Evidence determines continuation or course correction.

Centaur without portfolio coherence fragments.
OBAF without execution discipline slows.
Together they form a consistent operating stack.

The End of KPI Theater

One of the more significant consequences of AI acceleration is the exposure of vanity metrics. In slower environments, dashboards could remain green while underlying value eroded. Execution latency masked drift.

Under AI-augmented throughput, this buffer disappears.

Metrics that do not correspond to observable change will diverge quickly from reality. Symptom KPIs will be optimized while outcomes remain unchanged. Local teams will appear productive while the system as a whole loses coherence.

Both Centaur and OBAF incorporate defenses:

  • Signals are treated as learning instruments, not performance targets.
  • Quantitative indicators are paired with qualitative evidence.
  • Falsifiers are explicit.
  • Constraints are revisited and versioned.
  • "Kill metrics" are defined to prevent silent harm.

The requirement is not simply to measure more. It is to measure what changes decisions.

If a metric cannot alter a course of action, it is ornamental. Ornamental metrics are fragile under acceleration.

Implementation Without Bureaucratization

The correct response to AI acceleration is not more ceremony. It is tighter epistemics (stronger standards of evidence).

For Small AI-Augmented Units

Adopt the Centaur kernel fully:

  • Establish a canonical intent artifact.
  • Encode constraints and proof in CI/CD.
  • Limit work in progress aggressively.
  • Require verification as part of integration.
  • Conduct short, event-driven micro-reviews.
  • Update guardrails as part of weekly governance.

Avoid adding planning layers that replicate portfolio concerns. Focus on verification and coherence.

For Mid-Sized Organizations

Layer OBAF above Centaur:

  • Define north-star outcomes with clear ownership.
  • Replace feature steering with outcome framing.
  • Review evidence rather than status.
  • Require explicit mapping between unit intent and portfolio outcomes.
  • Treat cross-team interfaces as contracts with automated checks.

Standardize proof expectations, not implementation details.

For Large Organizations

Prioritize coherence:

  • Define what constitutes acceptable evidence.
  • Clarify decision rights explicitly.
  • Encode constraint authority.
  • Reduce shared ownership without accountability.
  • Maintain cross-unit compatibility through guardrail standards.

Execution can remain decentralized. Verification standards cannot.

Conclusion

The current shift is not about AI replacing labor. It is about a reallocation of scarcity.

When execution cost approaches zero, judgment becomes the primary constraint. When generation is abundant, verification is the bottleneck. When output is easy, correctness becomes expensive.

Frameworks centered on measurable outcomes, falsifiability, and evidence were once aspirational improvements. They are now structural requirements.

Centaur provides the runtime discipline for AI-augmented units.
OBAF provides the alignment layer for portfolios of such units.

Execution is cheap. Correctness is not. Intent, therefore, must be treated as infrastructure.