Definition
AI-Augmented Engineering
A development discipline that uses AI tools to accelerate boilerplate, pattern-matching, and code generation while maintaining full human ownership of architecture, security decisions, and every committed line of code. Contrasts with vibe coding in that the human developer is accountable for understanding what they commit — AI is a tool for speed, not a substitute for judgment. Woz's internal standard: every pull request receives two developer reviews, regardless of whether AI wrote the initial code. The LLM-era risk is engineers who never developed deep architectural intuition because the easy out was always available.
From the episode “AI That Turns Ideas Into Real Companies”Hear it on the podcast1 episode
Learn the language of AI-native founders
Every week: the new terms, tools, and workflows founders are using, straight from the podcast.
Related terms in Product & Engineering
Access controlRules determining which users can reach which parts of a system.Account takeover (ATO)A fraudster gaining control of an existing user's account, often to drain funds.ACL (Access Control List)Rules for who can see or change which data.AI factoryFraming a data center as a facility that manufactures intelligence rather than storing data.AI-Native ProductSoftware whose core value proposition changes week over week as new model capabilities become available. The tell: customers receive meaningful new capabilities on a near-weekly basis without requesting them.AI-Ready DataData that has been cleaned, structured, deduplicated, and formatted to a standard that allows AI systems to consume and process it without producing unreliable outputs. The concept reflects the difference between data that exists in a system and data that an AI can trust. Key characteristics: consistent formatting across records, no significant duplication, complete fields for critical values, and a schema that maps to the downstream AI task. David Carmel's framing at DataRockit: 'Data is just data until it is clean enough for AI.' Most enterprise datasets - including those stored in modern cloud systems - are not AI-ready without deliberate transformation.Algorithmic OptimizationProduct design that prioritizes engagement metrics over depth of meaning.Ambient recordingCapturing the consultation naturally without the clinician changing behavior, no dictation or commands required.