Capability Commoditization Tracking

---
name: capability-commoditization-tracking
description: "Tracks which of the organization's capabilities are becoming commoditized by AI (cheap, reproducible by a prompt) versus which are becoming newly complementary (valuable precisely because AI makes them combinable with other domains), and redirects investment away from the former toward the latter before the market forces the move."
---

# Capability Commoditization Tracking

## Purpose

A recurring capability-investment check, distinct from a one-time
roadmap: AI doesn't commoditize or elevate a company's capabilities
once and then stop — it's an ongoing shift, and capabilities that were
genuinely differentiating eighteen months ago can quietly become
table-stakes while the organization keeps defending them as if they
were still a moat. This skill names which capabilities are moving in
which direction, on a recurring basis, so investment gets redirected
before a competitor or a new entrant makes the shift obvious the hard
way.

## Anchored in research

Two independent named sources converge on the same underlying point.
Sangeet Paul Choudary's *Reshuffle* (2025): AI "unbundles expertise
from individual experts, turning specialized knowledge into scalable,
rentable, and recombinable capabilities" — a "building block economy"
where competitive advantage shifts from what a company owns to how
effectively it assembles blocks that are themselves increasingly
commoditized; and "the complement has to be commoditized to capture
value at the complementary layers" — meaning value doesn't disappear
when a capability commoditizes, it MOVES to whatever remains genuinely
hard to replicate alongside it. Howard Yu's LEAP thesis (IMD, 2023
Thinkers50 Strategy Award), reached independently: competitive
longevity depends not on refining a company's current knowledge base,
but on deliberately crossing into adjacent disciplines before
copycats commoditize what already exists — the same capability-flight
logic from a different angle.

## Method

1. **List the organization's current capabilities** — the specific
   things it's good at that customers or the market currently pay for
   or value, not a generic skills inventory.
2. **For each capability, ask the commoditization question:** could a
   competent competitor, or even a customer directly, now get
   equivalent output from a well-prompted AI system, with no
   organization-specific advantage required? If yes, this capability is
   commoditizing or already commoditized — regardless of how much
   internal pride or historical investment is attached to it.
3. **For each capability, separately ask the complementarity question:**
   is AI making this capability newly COMBINABLE with capabilities from
   other, previously separate domains — turning what was a narrow,
   siloed specialty into something that connects to a wider system? If
   yes, this capability's value is rising, not falling, even if it
   doesn't feel more differentiated in isolation — its value now comes
   from what it connects to.
4. **Place every capability on a 2x2**: commoditizing vs. not,
   complementary vs. not. The two axes are independent — a capability
   can be commoditizing AND complementary at once (its standalone
   execution is cheap, but it's newly valuable as connective tissue to
   other domains); don't collapse them into a single "still valuable /
   not valuable" score.
5. **Redirect investment based on the placement, not on habit or sunk
   cost:**
   - Commoditizing + not complementary → stop defending this as a
     moat; either divest attention from it or keep it purely as a cost
     center, not a growth investment.
   - Commoditizing + complementary → keep the capability, but stop
     investing in doing it better in isolation — invest instead in the
     connections and integration that make it complementary (the actual
     source of remaining value).
   - Not commoditizing + not complementary → a genuine, currently
     defensible specialty; protect it, but watch it, since today's
     defensible specialty is a common candidate for tomorrow's
     commoditization.
   - Not commoditizing + complementary → the highest-priority
     investment category — genuinely rare AND newly connectable.
6. **Re-run this on a cadence, not once.** Commoditization and
   complementarity both move over time as AI capability advances — a
   capability correctly placed today can shift quadrants within a year.
   Pair with `../ai-capability-roadmap/SKILL.md`'s three-horizon view
   to schedule when to re-check each capability, rather than treating
   this as a single static assessment.
7. **Use the Shutterstock case as the reference example** for a
   deliberate move away from a commoditizing capability toward a
   complementary one, independently confirmed: image licensing itself
   was becoming commoditized by free, unlimited AI-generated images, so
   Shutterstock redirected investment toward a complementary layer
   instead — a six-year training-data agreement with OpenAI, a
   Contributor Fund compensating artists, and positioning itself for
   enterprises around content-usage governance and legal safety, not
   just image supply.

## What this skill does NOT do

- Doesn't replace `../ai-capability-roadmap/SKILL.md`'s three-horizon
  scheduling — it identifies WHICH capabilities to move investment
  toward or away from; the roadmap skill schedules WHEN.
- Doesn't make the specific reinvestment decision (what exactly to
  build in the complementary space) — it identifies the direction, not
  the destination.
- Doesn't claim commoditization is always bad news — a capability
  commoditizing can free real resources to redirect toward a
  complementary opportunity, which is the more useful reframe than
  treating commoditization purely as a threat.

## Refinement notes

- What's a real capability you've seen a client defend well past the
  point it had commoditized — what would have caught it earlier?
- Which of the four 2x2 quadrants do clients most often misjudge, in
  your experience?
- How often should this actually be re-run in practice — is an annual
  cadence enough, or does it need to be more frequent for
  fast-moving categories?

## Continue from here

- Use alongside: `../ai-reshuffle-opportunity-framing/SKILL.md` — the
  same underlying reshuffle logic applied to opportunities rather than
  existing capabilities.
- Feeds into: `../ai-capability-roadmap/SKILL.md` for scheduling the
  resulting investment shifts across horizons.
- Related case: `../ai-output-curation-and-quality-control/SKILL.md` —
  the Shutterstock case above also illustrates that skill's
  creator-to-curator shift.
- This pack's shared guardrails: `../../CLAUDE.md`

## References

- `../../references/ai-native-reshuffle-heuristics-research.md` —
  selection and grounding notes for this skill and its siblings
- `../../references/` — the pack's shared background material
- `../../CLAUDE.md` — the pack's shared guardrails