📊 Full opportunity report: Outcome-First Decisions: Keep, Change, or Kill on ThorstenMeyerAI.com — validation score, market gap, and execution plan.

TL;DR

Outcome-First Decisions is a framework that helps organizations determine whether to keep, change, or kill projects based solely on current outcomes. It emphasizes pruning and outcome measurement to improve portfolio health.

The Outcome-First Decisions framework, now publicly available as open source, provides a structured method for organizations to evaluate whether ongoing initiatives should be kept, changed, or terminated based on their current outcomes.

The framework centers on the Worth Filter, which assesses each initiative solely by its present and projected outcomes relative to ongoing costs. It advocates for rigorous pruning of projects that no longer justify their expense, emphasizing that past investments and effort should not influence the decision. The process aims to prevent portfolio bloat caused by projects that continue out of inertia or emotional attachment. The framework is provider-agnostic, runs locally, and is licensed under AGPL-3.0, ensuring openness and adaptability. It is positioned as the final decision node in a portfolio management cycle, closing the loop on idea generation, planning, and review. However, experts warn that outcome measurement can be gamed or misinterpreted, and emotional biases may still hinder tough decisions, such as killing promising but slow-starting initiatives.
Outcome-First Decisions — Keep, Change, or Kill · Built in Public Day 8/19
Built in Public · Day 8 / 19 ThorstenMeyerAI.com · the operator portfolio
The Decision Layer · Day 08 Dispatch

Outcome-First Decisions — keep, change, or kill

The hardest decision isn’t what to start — it’s what to stop. Judge every initiative by the outcome it produces now, not the effort already spent.

01 The Worth Filter
The Worth Filter
is the outcome worth the ongoing cost?
judged forward (outcome) — not backward. Ignored: sunk cost · effort spent · identity
✓ Keep
Affiliate cluster A
compounding revenue
Channel E
reach still growing
↻ Change
Product C
right problem, wrong shape
alter deliberately — don’t drift
✕ Kill
Experiment B
flat · high upkeep
Side project D
zero traction · sunk cost
3verdicts: keep · change · kill outcomesthe only input that counts AGPLopen source · local-first
02 Why stopping is the leverage
kill
the verdict everything in human nature avoids — made normal, not a failure.
forward
judge what it will produce next, not what you’ve already spent. Sunk cost is gone either way.
capacity
killing dead work reclaims the focus and capital trapped in it — the cheapest growth there is.
03 The thesis the whole series inherits
01
Local-first
Reviews run on owned compute — cheap enough to run as often as honesty requires.
02
Provider-agnostic
The reasoning isn’t welded to one model. Swap freely; no lock-in.
03
Non-developer build
A small, opinionated framework — AGPL-3.0, open so the method stays inspectable.
04
Edit by subtraction
The whole product is subtraction — killing what no longer earns its place.
04 The operator constellation
18 products · one foundation
Today: Outcome-First lit — the keep/change/kill review that closes the loop. The Decision layer is complete: validate → plan → review.
Content
DojoClaw
RoundupForge
Stenvrik
ChannelHelm
IdeaNavigator
Decision
IdeaClyst
Threlmark
Outcome-First
Platform
Grimfaste
Delvasta
Open / Reg
Glasspane
QAtrial
Markets
Polybot
TradingAgents
Defense / Intel
Argus
VigilSAR
VigilSAR-Bench
Diagnostic
World Model Readiness
Local-first · Provider-agnostic foundation

Independent commentary, produced with AI assistance under human editorial oversight. The views are the author’s own and may change. Outcome-First Decisions is open source under AGPL-3.0, provided “as is” without warranty; see the repository LICENSE. The framework’s verdicts are reasoning aids based on the inputs given and may be wrong — decision support, not decisions; verify independently before acting. Product and company names are trademarks of their respective owners; mention does not imply endorsement.

ThorstenMeyerAI.com · Built in Public · Day 8 of 19 · © 2026 Thorsten Meyer

Why Outcome-First Decisions Reshape Portfolio Management

This framework offers a disciplined approach to portfolio pruning, which is often overlooked or avoided due to emotional or sunk-cost biases. By focusing on current outcomes, organizations can free up resources and attention, leading to more agile and effective operations. It encourages a culture of honest assessment and decisive action, reducing waste and increasing the likelihood of strategic success. However, its effectiveness depends on accurate outcome measurement and organizational willingness to make difficult decisions.

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The Evolution of Portfolio Decision-Making

Traditional portfolio management often struggles with the ‘continuing but not succeeding’ problem, where projects persist due to past effort or emotional attachment. Existing methods tend to focus on starting new initiatives rather than trimming existing ones. Outcome-First Decisions address this gap by providing a clear, outcome-based criterion for termination, aligning with modern needs for agility and resource optimization. The framework builds on ongoing discussions about effective decision-making in complex, multi-initiative environments.

“The hardest decision in any portfolio isn’t what to start. It’s what to stop.”

— Thorsten Meyer, creator of the framework

Innovation Portfolio Management: Linking Strategy to Execution

Innovation Portfolio Management: Linking Strategy to Execution

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Challenges in Implementing Outcome-First Decisions

It is not yet clear how organizations will adapt their cultures to embrace outcome-based pruning, especially in environments resistant to killing projects. The effectiveness of the Worth Filter depends heavily on organizations’ ability to measure outcomes objectively and avoid biases. Additionally, the framework’s impact on slow-starting but valuable initiatives remains uncertain, as premature killing could stifle innovation.

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Next Steps for Adoption and Refinement

Organizations interested in Outcome-First Decisions are expected to pilot the framework within their portfolios, monitor outcomes carefully, and refine their metrics. Further development may include tools for better outcome measurement and case studies demonstrating successful pruning. The open-source nature encourages community contributions and shared best practices to improve decision accuracy and organizational buy-in.

The Decision Intelligence Handbook: Practical Steps for Evidence-Based Decisions in a Complex World

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Key Questions

How does Outcome-First differ from traditional portfolio management?

It focuses solely on current outcomes and ongoing costs to decide whether to keep, change, or kill initiatives, rather than relying on past investments or effort.

What are the main risks of using this framework?

Outcome mismeasurement, gaming metrics, premature killing of slow-starting but valuable projects, and organizational resistance to tough decisions.

Can this framework be applied to all types of projects?

While designed to be provider-agnostic and flexible, its success depends on accurate outcome measurement and organizational willingness to act decisively.

Is Outcome-First suitable for large organizations?

Yes, especially for organizations managing multiple initiatives, but it requires cultural alignment and robust outcome metrics to be effective.

Where can I learn more or access the framework?

The framework is available as open source on GitHub under the AGPL-3.0 license. Further resources and community discussions are expected to develop over time.

Source: ThorstenMeyerAI.com

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