Pith Number

pith:AYL2EDHU

pith:2026:AYL2EDHU47FUHN4BREFDWUB53Q

not attested not anchored not stored refs pending

The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

Michael Caosun, Sinan Aral

Rational decision-makers adopt AI despite anticipating skill erosion, leaving workers less productive in the long run.

arxiv:2604.03501 v3 · 2026-04-03 · cs.HC · cs.AI

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\usepackage{pith}
\pithnumber{AYL2EDHU47FUHN4BREFDWUB53Q}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded productivity gains outweigh long-run skill costs, producing steady-state loss: the worker ends up less productive than before adoption.

C2weakest assumption

The model assumes a specific functional form for how AI usage intensity directly erodes worker skill over time and that productivity decomposes into expertise-independent and expertise-dependent channels.

C3one line summary

A dynamic model shows rational AI adoption can trap workers in lower productivity via skill erosion, with five regimes separating beneficial from harmful deployments.

Receipt and verification

First computed	2026-05-22T01:04:01.462731Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0617a20cf4e7cb43b781890a3b503ddc3ef3e96471e44bb43624819443e6eb28

Aliases

arxiv: 2604.03501 · arxiv_version: 2604.03501v3 · doi: 10.48550/arxiv.2604.03501 · pith_short_12: AYL2EDHU47FU · pith_short_16: AYL2EDHU47FUHN4B · pith_short_8: AYL2EDHU

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AYL2EDHU47FUHN4BREFDWUB53Q \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0617a20cf4e7cb43b781890a3b503ddc3ef3e96471e44bb43624819443e6eb28

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "170f2f8f7c0d85492f5281283183ce7c735175d45ab8b2182ccb84d95859066d",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-04-03T22:50:32Z",
    "title_canon_sha256": "b0783be2ffec3376e16ac9cb18adf0f777bd6dc25f72c9e0fe267263f4ceca31"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.03501",
    "kind": "arxiv",
    "version": 3
  }
}