Implementation Abundance Inverts Product Work

Sources#

• OpenAI Codex lead on the new shape of product work

Summary#

Andrew Ambrosino's (OpenAI Codex) framing of what agentic coding does to product process: when anyone can stand up any feature by talking to a model, implementation stops being the scarce, expensive, derisk-it-up-front step — so the whole process runs backwards. The old process spent documents, research, and prototypes to derisk implementation before building, because building was expensive and "you can really only afford to build once." Now implementation is abundant across every medium, so "everybody's building everything" — Ambrosino estimates a needed feature has "90 different uncoordinated teams" implementing it at once. The costly work migrates downstream to curation: "of those 90 attempts, what's good about these? What should we fold into other aspects? How should we frame this?" That curation is taste — "dare I say taste" is his answer to what replaces implementation as the expensive part. This is the OpenAI-side, process-level statement of the shift the wiki tracks as Verification as the New Bottleneck and "deciding what to build is the bottleneck skill".

Evidence note. practitioner-opinion — a frontier-lab product leader's account, not measurement. But it converges with empirical sources (Returns to Expertise in Agentic Coding, Planning / Execution Division of Labor) that find domain/product judgment, not coding, predicts success.

The inversion, precisely#

The two states Ambrosino contrasts:

	Old process	Inverted process
Expensive step	Implementation	Curation / taste
Why	You could only afford to build once, so you derisked up front	Building any feature is ~free; the 90 builds already exist
Up-front work	Docs, research, prototypes to derisk before building	Skip straight to many parallel builds
Scarce input	Engineering capacity	Judgment: what's good, what to fold in, how to frame, what medium

"It's backwards, and it's not that people are doing fundamentally different roles or that skill sets have vanished — it's that it's backwards. The implementation is actually not the expensive part anymore."

Curation is not the same as prototyping-instead-of-PRDs#

Ambrosino is careful to separate his claim from the popular "PRDs are dead, prototypes are in" slogan — which he says he does not believe (see the pushback recorded on Prototype Over PRD). The inversion is not "always jump to a prototype." It is: because every medium's implementation got cheap, the load-bearing skill is picking the right medium for the point you're making and then curating what the cheap builds produce. Sometimes the right medium is still a document. The abundance is what makes curation — not creation — the constraint.

What "taste" means here (the curation faculty)#

Taste, in this thesis, is the curation faculty applied under abundance, and Ambrosino explicitly de-couples it from aesthetics (citing the "Paul Graham has great taste and wears cargo shorts" line). Its components:

• Systems thinking — how a build fits the whole; what theme it belongs to.
• Direction — "where are we going," what the goal is "if we can build anything."
• Presentation — how to frame and present the information.
• Semantic fit — whether an interaction/animation matches the meaning it's supposed to convey ("too snappy for what it's trying to say").
• Medium selection — which artifact makes the point.

Because taste is now the binding constraint, it becomes the hiring bar: "high agency, high taste" people who can "take an idea from idea to done." Ambrosino's steering test for an IC given unlimited tokens: "determine what's signal, what's noise, in a world of infinite content."

Connections#

• Andrew Ambrosino — articulates the inversion
• Verification as the New Bottleneck — the general form: when generation is cheap, judgment/verification is the scarce resource; this is its product-process face
• Engineer PM Convergence — "as code becomes cheaper, deciding what to write becomes more valuable" (Cat Wu) is the same bottleneck-shift; this page is its OpenAI-side, process-level statement
• Research Taste as the Human Bottleneck — the AI-research cousin (taste as the residue AI can't yet absorb); this is the product-work cousin
• Building Is Cheap, Arguing Is Expensive — the same "cheap building relocates the hard part" logic, applied to settling debates; here applied to the whole process
• Prototype Over PRD — the position Ambrosino refines: not "prototype replaces PRD," but "abundance makes medium-choice + curation the skill"
• Polish No Longer Signals Readiness — a direct consequence: the 90 cheap builds all look prod-ready, so polish stops signaling stage
• Role Averaging, Not Role Elimination — who does the curating, and why "zone defense" coverage matters when 90 uncoordinated builds appear
• Dogfooding as Product Discipline — how the curating taste is trained: relentless first-hand use
• Compute Allocator — curating 90 builds is allocation at the level of a whole feature exploration
• Harness Shrinkage as Models Improve — implementation abundance is harness-shrinkage seen from the product-process side
• Returns to Expertise in Agentic Coding — empirical support: domain/product understanding, not coding, is what predicts who succeeds once building is cheap

Open Questions#

• Curation of 90 uncoordinated builds is itself expensive and doesn't obviously scale — is there a point where the cost of curating parallel exploration exceeds the cost it replaced? ("zone defense" is Ambrosino's partial answer.)
• If taste is the bottleneck and taste is "just another capability" AI eventually masters, does the inversion invert again — does curation migrate into the model?
• The 90-uncoordinated-builds picture assumes abundant tokens and an agentic culture; how much of the inversion survives outside a frontier lab that gives everyone "unlimited tokens"?

Sources#

• OpenAI Codex lead on the new shape of product work — Ambrosino: "the implementation is actually not the expensive part anymore… it's taste"; the 90-uncoordinated-teams picture