When software becomes easier to generate, product teams risk losing conviction about what should endure. The scarce skill is deciding what should not change.
Conviction Collapse Is the Real AI Product Risk
The easier software becomes to generate, the harder it becomes to know what should endure.
That is the product problem hiding underneath much of the AI software debate.
The O'Reilly Radar idea file points to , a phrase that captures the mood of the moment even when the details vary by team. that AI does not end programming so much as change what programming is, just as previous abstraction layers changed the work without eliminating the need for skilled practitioners.
The product consequence is blunt:
when building gets cheaper, conviction gets more expensive.
Abundance Weakens the Old Product Discipline
Traditional product discipline was shaped by scarcity.
Engineering time was limited. Releases were slow. Opportunity cost was visible. A product manager could not pursue every idea because every idea consumed scarce build capacity.
AI changes that pressure.
If a team can generate interfaces, workflows, prototypes, tests, integrations, and variants at much higher speed, then the cost of saying yes appears to fall. More experiments feel possible. More customer-specific adaptations feel reasonable. More internal tools can be created. More workflows can be automated.
This sounds like liberation.
It can also destroy focus.
The organization starts producing optionality faster than it produces clarity.
A Product Is Not Every Possible Feature
The mistake is to confuse generative capacity with product strategy.
A product is not the sum of everything the system can generate. It is a durable point of view about which problems matter, which workflows deserve structure, which tradeoffs are acceptable, and which behaviors should remain coherent over time.
AI can help explore the option space.
It cannot absolve leaders from choosing.
In fact, it raises the bar. If software can be continuously modified, assembled, and personalized, then someone has to define which parts of the experience are allowed to vary and which parts must remain stable.
That is product architecture, not feature management.
Conviction Is Not Stubbornness
There is a bad version of conviction: defending old assumptions because they are familiar.
That is not what companies need.
Useful conviction is evidence-aware. It holds a strong view while remaining willing to update when reality changes. It distinguishes between the core promise of the product and the implementation details that can be regenerated.
In an AI-assisted product organization, leaders need conviction about:
the customer problem,
the quality bar,
the operating model,
the trust boundary,
the data model,
and the evidence required to change direction.
Everything else can be more fluid.
Without that hierarchy, teams will drown in generated alternatives.
The New Product Skill Is Constraint Design
As generation gets cheaper, constraints become more valuable.
Good constraints tell agents and teams what not to do. They encode brand, safety, data access, workflow rules, customer promises, and business priorities. They define when personalization is useful and when it erodes coherence.
This is where product management becomes more architectural.
The product leader is no longer only prioritizing a backlog. They are designing the boundaries inside which human and machine builders can safely generate.
Where Gaia Fits
Gaia matters here because agentic systems need a place where intent, constraints, runtime behavior, evaluation, and delivery evidence stay connected. The platform view is useful precisely because AI product work cannot be reduced to faster feature production.
Relevant follow-up resources are the , , and . They connect product intent to governed execution, which is where conviction has to survive.
Practical Takeaway
Before using AI to generate more product surface area, define the stable core:
What customer promise must remain true?
Which workflow rules are non-negotiable?
Which parts of the experience may vary by context?
Which evidence is required before changing the product direction?
What should the team stop generating?
AI makes it easier to create.
That makes it more important to decide.
About the author
Kostas Karolemeas
Product and Technology Lead of Gaia, two-time founder, and software product executive with more than three decades of experience building and scaling products across healthcare, architectural and mechanical engineering software, logistics and supply chain, financial services and banking, enterprise resource planning (ERP), and visual effects (VFX) for television.
