The marginal cost of generating a first draft has approached zero. The marginal cost of generating ten first drafts, or a hundred, has similarly collapsed. What has not become cheaper is knowing which draft is worth developing, which idea has merit, and which direction leads somewhere valuable. In a world of AI-generated abundance, judgment becomes the scarce resource.
The Cost Structure Revolution
For most of software history, creating artifacts has been expensive. Writing code, generating content, producing designs: these required significant human time and expertise. AI has fundamentally altered this cost structure.
Consider what has changed. Code generation now allows first drafts of functions, classes, and even entire modules to be generated in seconds. Content creation means articles, marketing copy, and product descriptions flow from AI systems at near-zero marginal cost. Design iteration enables AI to generate hundreds of UI variations in the time a human designer creates one. Data analysis allows initial exploration and visualization of datasets to be largely automated.
Principle: Judgment Has Become More Valuable
The abundance of AI-generated artifacts makes discernment more precious, not less. The person who can identify which AI output is worth developing, which direction is promising, and which goal is worth pursuing has become more valuable, not less.
You now have access to 100 first drafts in 5 minutes but still spend 3 hours deciding which one to develop. AI gave us writer's block at scale.
What Remains Expensive
While artifact creation has become cheap, certain things remain genuinely expensive:
Judgment: Knowing What Matters
Determining what to build, what direction to pursue, what problem is worth solving: these require judgment that AI cannot replicate. AI can generate options; humans must decide which options are worth pursuing. This judgment comes from understanding context, values, and consequences in ways that are difficult to formalize.
Data: Training and Ground Truth
High-quality training data, curated evaluation sets, and ground truth labels remain expensive. The performance ceiling of AI systems is often determined by data quality, not model architecture. Creating and maintaining data assets is increasingly a competitive moat.
Trust: User Confidence
Earning and maintaining user trust is expensive. It requires consistent performance, transparent communication, reliable fallback mechanisms, and ongoing investment in user education. Trust can be destroyed in moments and takes time to rebuild.
Integration: Making Things Work Together
Connecting AI capabilities into cohesive products that work with existing systems, meet compliance requirements, and fit into user workflows remains expensive engineering work. The "last mile" of AI product development often costs more than the AI capability itself.
Implications for Product Strategy
The economics of abundance change product strategy fundamentally:
The New Value Chain
Value has shifted from creation to curation, from generation to selection, from production to judgment:
Creation Is Commoditized
First drafts, prototypes, and initial implementations are low-value because they can be generated cheaply. Competing on raw generation capability is a race to the bottom.
Curation Is Valuable
The ability to sort through AI-generated options, identify promising directions, and develop them into polished products is increasingly valuable. This is where human judgment adds the most.
Integration Is a Moat
Deep integration into user workflows, enterprise systems, and existing tools creates switching costs that pure AI capabilities cannot match. Build where you can own the workflow.
Trust Is Competitive Advantage
Users will pay for, and remain loyal to, AI products they trust. Trust is built through consistent performance, transparent communication, and handling failures gracefully.
Worked Example: AI Writing Product Evolution
An AI writing assistant company recognized the shift. In Phase 1, they competed on generation quality where users chose the AI that wrote the best sentences, but eventually all major providers achieved similar quality. In Phase 2, they competed on features such as more templates, better integrations, and more languages, but features became commodities too. Now in Phase 3, they compete on judgment, asking which AI understands your brand voice, which helps you develop ideas not just polish prose, and which knows what kind of content performs for your audience. The winning products are those that help humans make better decisions about content strategy, not just generate content faster.
Product Strategy Framework
Apply this framework to evaluate AI product opportunities:
Where Is Judgment Required?
Identify where human judgment is the bottleneck. These are area