The Production AI Manifesto
The POC graveyard is full. Impressive demos, celebrated launches, quietly abandoned. This manifesto declares a different path - 12 commitments for building AI that survives contact with reality.
Introduction
Happy New Year, everyone. This is 2026.
AI capabilities have never been more powerful. Foundation models can reason, generate, and act. Every vendor promises transformation. Every conference declares a new era with a bunch of announcements.
And yet.
Exposed in these fields of progress lies an enormous graveyard. It holds the remains of the AI projects that were satisfactory in demos, celebrated at launch, and abandoned within a year. No one satisfactorily explained why they failed. No one actually could say whether they had ever really worked.
We have watched this cycle repeat across industries, company sizes, and technology stacks. The pattern is not random. The failures are not inevitable.
They are the result of building with “tools-first“ mindset.
This manifesto declares a different path. Not a new framework, not a new tool, not a new model. A discipline. A set of commitments about how production AI must be built.
I offer these principles to every practitioner who is tired of demos that don’t become products - and ready to build AI that survives contact with reality.
The Principles
I. Most AI initiatives fail not from lack of capability, but from lack of clarity about what success looks like.
II. You cannot automate a decision that was never defined. If the logic lives only in people’s heads, AI will not extract it. It will invent something worse.
III. Data built for humans breaks when machines consume it. Reports and dashboards tolerate ambiguity. AI amplifies it.
IV. Intelligence on top of inconsistency produces confident nonsense. Confident nonsense is more dangerous than no answer at all.
V. Three debts block the path to production: Data Debt, Decision Debt, Evaluation Debt. Your data is not ready, your logic was never specified, you cannot tell if the system works. These are not technical problems. They are clarity problems. No model will solve them for you.
VI. Evaluation is not a final checkbox. It is the architecture that determines whether your system can learn, improve, and survive.
VII. Define success before you build. Design measurement before you design systems. Choose tools only after you know how you will judge them.
VIII. If you cannot measure it, you cannot improve it. If you cannot prove it works, you cannot defend the investment.
IX. Observability is not overhead. It is the difference between debugging in hours and debugging never. Every decision your AI makes must be traceable to the data, logic, and context that produced it.
X. AI does not transform organizations. It reveals how untransformed they already are. Every gap in data, every inconsistency in decisions, every missing feedback loop - AI makes them visible and makes them hurt.
XI. The teams that succeed do not have bigger budgets or better models. They have clearer definitions of success and the infrastructure to measure it.
XII. We reject the theater of impressive demos. We commit to building AI that works - in production, under pressure, at scale, over time.
Conclusion
This manifesto is a line in the sand.
On one side: AI as spectacle. Measured by applause in meeting rooms. Validated by executive excitement. Declared successful at launch and never examined again.
On the other side: AI as engineering discipline. Measured by outcomes. Validated by evidence. Improved continuously because the architecture demands it.
We stand on the second side.
In the weeks and months ahead, I will publish the frameworks, patterns, and hard-won lessons that make this real. How to assess whether your data is ready. How to implement evaluation-first development. How to build systems you can observe, debug, and improve.
This is not theory. This is the work.
Join us if you are building AI that has to survive contact with reality.
Thanks,
Sandi.
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