Adoption, exposure, and the cost of using AI

A weekly Norwegian vacancy tracker — and why user cost, not capability, shapes what we see

Utgitt

3. juni 2026

The empirical object

Sammendrag

Denne siden skiller mellom AI-ens kapasitet (eksponering, fra Eloundou m.fl.) og den faktiske bruken (adopsjon). Hovedfunnet er at gapet mellom dem først og fremst bestemmes av brukerkostnaden ved å ta AI i bruk — ikke av hvor god modellen er. Brukerkostnaden er kostnaden ved å bruke AI trygt og pålitelig: menneskelig tid til å kontrollere og godkjenne resultatet, pluss etterlevelse, personvern og integrasjon. Høy lønn trekker AI inn, mens høy brukerkostnad holder den ute.

I norske stillingsannonser (NAV) er AI fortsatt sjelden nevnt (rundt 1 %) og konsentrert i få yrker. Eksponering forklarer bare svakt hvilke yrker som faktisk etterspør AI; det er lønnseffekten som overlever ut-av-utvalg-testing, mens en oppgave-for-oppgave-splitt av brukerkostnaden ennå ikke lar seg identifisere med så tynne data. SSBs egne tall peker samme vei: bruken stiger mens kostnadsbarrieren faller.

Den estimerte brukerkostnaden er ikke målt direkte, men et estimat fra en økonomisk strukturell modell basert på Lindenlaub og medforfattere (Lindenlaub, Oh, Rodríguez & Veldkamp 2026, «LORV»), der komparative fortrinn — ikke kapasitet alene — avgjør hvilke yrker som tar AI i bruk.

Two numbers get attached to every occupation in the AI-and-jobs debate:

Exposure — how much of the work AI is technically capable of doing (Eloundou et al.).
Adoption — how much AI is actually being used or asked for.

They are not the same object, and the gap between them is the interesting one. This page tracks both for the Norwegian labour market — exposure from Statistics Norway (SSB) occupational data, adoption from a weekly snapshot of open vacancies at arbeidsplassen.nav.no — and argues that what drives the gap is the user cost of putting AI to work.

Why user cost? A comparative-advantage story

The same 200-year-old logic that governs trade governs this. A firm puts AI on a task only when AI is cheaper per unit of output than the worker — productivity weighed against price — not whenever AI is merely able:

\[\underbrace{\frac{Z^{\text{AI}}_k}{r_k}}_{\text{AI's value for money}} \;>\; \underbrace{\frac{Z^{\text{worker}}_k}{w}}_{\text{the worker's value for money}}\]

The denominator on the left, the user cost $r_k$, is the whole story. It is not the price of compute alone — it is the cost of using AI safely and reliably: the human time to check and sign off on the output, plus compliance, privacy and integration:

\[r_k \;\approx\; \rho_k \;+\; w\, t^{V}_k .\]

So a task can be highly exposed yet barely adopted because $r_k$ is high (verification, accountability, regulation); and high wages $w$ pull AI in, because they lower the worker’s value-for-money on the right. Capability is half the story; the cost of using AI is the other half — and it is an organisational and institutional variable, not a property of the model. This is the central finding of Lindenlaub, Oh, Rodríguez & Veldkamp (2026, “LORV”): across occupations, user cost — not capability — explains most of who actually adopts AI. The exposure measure is from Eloundou et al. (2024), and the task-chain logic below from Demirer, Horton, Immorlica & Lucier (2026).

How the cost cascades

The user cost is not fixed. When consecutive tasks are handed to AI, they chain — the interior steps run machine-to-machine and a human verifies only the end of the chain. So verification becomes roughly a fixed cost per chain, not per step: as AI grows reliable and chains lengthen, the cost per task falls, and augmentation can tip toward replacement.

Figur 1: A job is a sequence of tasks. AI is used on a task when its value-for-money beats the worker’s; the binding cost is verification, which is paid once per AI chain, not per step. After Demirer, Horton, Immorlica & Lucier (2026), “Chaining Tasks.”

What the Norwegian vacancies show

Load the latest weekly snapshot + SSB exposure, and merge

import json, re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

KOJA = {"fjord": "#5d79a0", "amber": "#cf9a5c", "mist": "#b1abb0",
        "muted": "#8292ad", "heading": "#e7ecf3"}
plt.rcParams.update({
    "figure.facecolor": "none", "axes.facecolor": "none", "savefig.facecolor": "none",
    "savefig.transparent": True, "text.color": KOJA["mist"], "axes.labelcolor": KOJA["mist"],
    "axes.edgecolor": "#3a4860", "xtick.color": KOJA["muted"], "ytick.color": KOJA["muted"],
    "axes.titlecolor": KOJA["heading"], "axes.grid": True, "grid.color": "#8292ad",
    "grid.alpha": 0.15, "font.size": 11,
})

D = "adoption-tracker/data"
latest = json.load(open(f"{D}/latest.json"))
weekly = pd.read_csv(f"{D}/adoption_weekly.csv")
occ = pd.read_csv(f"{D}/adoption_by_occupation.csv")
occ = occ[occ.date == occ.date.max()].copy()

def norm(s):
    s = str(s).lower().strip().replace("–", "-"); s = re.sub(r"\s+", " ", s)
    return re.sub(r"\s*([/-])\s*", r"\1", re.sub(r"[.;]+$", "", s))

ex = pd.read_csv(f"{D}/exposure_reference.csv")
ex["key"] = ex.occupation_title.map(norm); ex = ex.dropna(subset=["ai_exposure"]).drop_duplicates("key")
occ["key"] = occ.styrk.map(norm)
m = occ.merge(ex[["key", "ai_exposure", "wage_nok_monthly"]], on="key", how="inner")

As of the latest snapshot (2026-06-03), 10,000 open vacancies were collected (of 13,791 open), of which 92 mention AI (0.9%). AI demand is still rare and concentrated — which is exactly what a high-user-cost world looks like.

Kode

d = m[m.n_ads >= 15].copy()
x, y, w = d.ai_exposure.values, d.ai_share.values * 100, d.n_ads.values.astype(float)
W = w / w.sum(); xb, yb = np.average(x, weights=W), np.average(y, weights=W)
b1 = np.sum(W * (x - xb) * (y - yb)) / np.sum(W * (x - xb) ** 2); b0 = yb - b1 * xb
r2 = 1 - np.sum(W * (y - (b0 + b1 * x)) ** 2) / np.sum(W * (y - yb) ** 2)

fig, ax = plt.subplots()
ax.scatter(x, y, s=np.sqrt(w) * 7, alpha=.45, color=KOJA["fjord"], edgecolor="none")
xs = np.linspace(x.min(), x.max(), 50); ax.plot(xs, b0 + b1 * xs, color=KOJA["amber"], lw=2)
ax.set_xlabel("AI exposure (Eloundou index, 0–1)")
ax.set_ylabel("Vacancy-adoption: % of ads mentioning AI")
ax.set_title(f"Exposure vs vacancy-adoption  (N={len(d)} occ, R²={r2:.2f})")
plt.tight_layout(); plt.show()

Figur 2: Does exposure predict adoption? Each dot is a Norwegian occupation (≥15 vacancies), sized by vacancy count; vacancy-adoption is the share of its ads mentioning AI. Exposure predicts adoption only weakly — the bulk of the variation is left to user cost.

The slope is positive but the fit is loose (R² ≈ 0.18): exposure orders occupations only roughly. In our companion analysis, once capability is controlled the wage pull is the piece that survives out-of-sample testing, while a task-by-task user-cost split does not — Norway’s vacancies are too thin and too uniform in task mix to identify it yet. That is the case for tracking adoption over time and, ultimately, for worker-level data.

The weekly series (it grows from here)

Kode

wk = weekly.sort_values("date")
fig, ax = plt.subplots()
ax.plot(pd.to_datetime(wk.date), wk.ai_share * 100, "o-", color=KOJA["amber"], lw=1.6, ms=7)
ax.set_ylabel("AI-mention share of vacancies (%)"); ax.set_xlabel("snapshot date")
ax.set_title("AI demand in Norwegian vacancies, weekly")
ax.set_ylim(0, max(1.2, wk.ai_share.max() * 100 * 1.4))
plt.tight_layout(); plt.show()

Figur 3: AI-mention share of open Norwegian vacancies, by weekly snapshot. The series starts with this reading and extends every week the collector runs.

What firms say: adoption rising, the user cost falling (SSB)

The vacancy signal is demand-side and thin. Statistics Norway’s own AI surveys give the complementary picture — and they say exactly what the comparative-advantage story predicts: use is rising while the cost of using AI is the thing holding it back, and that cost is falling. (Pulled reproducibly from SSB by adoption-tracker/’s rebuild — firm AI-use table 13265, barriers 13272, individual genAI use 14365.)

Kode

ssb = pd.read_csv("ssb-data/processed/ssb_ai_public_data_focus_extract.csv")
bar = ssb[ssb.table_id == 13272].copy()
yr = bar.Tid_code.max()
b = (bar[bar.Tid_code == yr].sort_values("value").tail(7))
fig, ax = plt.subplots()
ax.barh(range(len(b)), b.value, color=KOJA["fjord"])
ax.set_yticks(range(len(b)))
ax.set_yticklabels([s[:42] for s in b.ContentsCode_label], fontsize=9)
ax.set_xlabel(f"share of firms (%), {yr}")
ax.set_title("Barriers to AI use = the user cost")
for i, v in enumerate(b.value):
    ax.text(v, i, f" {v:.0f}", va="center", fontsize=8, color=KOJA["mist"])
plt.tight_layout(); plt.show()

cost = bar[bar.ContentsCode_label.str.contains("kostnad", case=False, na=False)].sort_values("Tid_code")
use = ssb[(ssb.table_id == 14365) & (ssb.ContentsCode_label.str.contains("Har brukt", na=False))].sort_values("Tid_code")

Figur 4: Why Norwegian firms hold back on AI (SSB table 13272, share of firms, latest year). These barriers are the ‘user cost’ r — and the cost barrier itself has fallen sharply since 2021.

Two trends make the point. The cost barrier (“Høge kostnader”) fell from 42% (2021) to 21% (2025) — using AI got cheaper and safer. Over the same window, individual generative-AI use rose from 36% to 54%. Falling user cost, rising adoption — the mechanism, in SSB’s own numbers.

Implications: a new occupation inside the chain

Look again at Figur 1. As tasks chain, the human does not simply disappear from the interior — they re-enter at the second-to-last task (review & edit) as the in-the-loop validator: the person who catches the chain’s errors before sign-off. That hybrid human-+-AI step is a plausible new occupation, and where it sits is a policy choice:

Lower the user cost and you move the validator earlier and deeper into the chain — the AI dividend is unlocked by training validators and building safe-to-use infrastructure, not by waiting for a smarter model.
It is also the natural entry point for younger workers if the new validation work grows faster than the old rungs disappear — the open empirical question behind Brynjolfsson, Chandar & Chen (2025).

Data & method

Adoption — collect.py pulls the open vacancies from NAV’s public search API once a week and flags AI with an audited bilingual lexicon (Norwegian + English phrases, brand names, and uppercase-only abbreviations so “ML” ≠ millilitre). One timestamped snapshot is archived per run; the share by occupation feeds the figures above.
Exposure / employment / wages — from Statistics Norway (SSB): occupational employment (table 12542) and monthly earnings (table 11418). ssb_check.py runs weekly and flags when SSB refreshes either table, so the bundled occupational panel can be re-pulled.

Read these as descriptive, directional facts

Vacancy AI-mentions are demand-side signals (what employers advertise), not realised worker use; the snapshot is a recency-capped slice of open postings; mentions are rare (~1%) and concentrated in a few occupations. So the cross-section orders occupations loosely and the weekly series is what to watch. The structural “user cost by task” object needs worker-level / register adoption to identify — this tracker is the first ingredient.

References

Lindenlaub, I., Oh, R., Rodríguez, M. A. & Veldkamp, L. (2026). Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage (“LORV”).
Demirer, M., Horton, J., Immorlica, N. & Lucier, B. (2026). Chaining Tasks: AI Automation and the Division of Labor.
Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2024). GPTs are GPTs: Labor Market Impact Potential of LLMs.
Brynjolfsson, E., Chandar, B. & Chen, R. (2025). Canaries in the Coal Mine: Early Labor-Market Effects of Generative AI. Stanford Digital Economy Lab.
Humlum, A. & Vestergaard, E. (2026). Still Waters, Rapid Currents: Early Labor-Market Transformation under Generative AI.
Statistics Norway (SSB), StatBank via PxWebApi v2 — tables 12542, 11418 (occupational employment & earnings) and 13265, 13271, 13272, 14365, 14364 (AI use, purposes, barriers, individual genAI use/non-use).

--- title: "Adoption, exposure, and the cost of using AI" subtitle: "A weekly Norwegian vacancy tracker — and why user cost, not capability, shapes what we see" date: today date-format: "D. MMMM YYYY" lang: nb --- ## The empirical object ::: {.callout-note title="Sammendrag"} Denne siden skiller mellom AI-ens *kapasitet* (eksponering, fra Eloundou m.fl.) og den faktiske *bruken* (adopsjon). Hovedfunnet er at gapet mellom dem først og fremst bestemmes av **brukerkostnaden** ved å ta AI i bruk — ikke av hvor god modellen er. Brukerkostnaden er kostnaden ved å bruke AI trygt og pålitelig: menneskelig tid til å kontrollere og godkjenne resultatet, pluss etterlevelse, personvern og integrasjon. Høy lønn trekker AI inn, mens høy brukerkostnad holder den ute. I norske stillingsannonser (NAV) er AI fortsatt sjelden nevnt (rundt 1 %) og konsentrert i få yrker. Eksponering forklarer bare svakt hvilke yrker som faktisk etterspør AI; det er **lønnseffekten** som overlever ut-av-utvalg-testing, mens en oppgave-for-oppgave-splitt av brukerkostnaden ennå ikke lar seg identifisere med så tynne data. SSBs egne tall peker samme vei: bruken stiger mens kostnadsbarrieren faller. Den estimerte **brukerkostnaden** er ikke målt direkte, men et estimat fra en **økonomisk strukturell modell** basert på Lindenlaub og medforfattere (Lindenlaub, Oh, Rodríguez & Veldkamp 2026, «LORV»), der komparative fortrinn — ikke kapasitet alene — avgjør hvilke yrker som tar AI i bruk. ::: Two numbers get attached to every occupation in the AI-and-jobs debate: - **Exposure** — how much of the work AI is technically *capable* of doing (Eloundou et al.). - **Adoption** — how much AI is *actually* being used or asked for. They are not the same object, and the gap between them is the interesting one. This page tracks both for the Norwegian labour market — exposure from Statistics Norway (SSB) occupational data, adoption from a **weekly snapshot of open vacancies** at [arbeidsplassen.nav.no](https://arbeidsplassen.nav.no) — and argues that what drives the gap is the **user cost** of putting AI to work. ## Why user cost? A comparative-advantage story The same 200-year-old logic that governs trade governs this. A firm puts AI on a task only when AI is **cheaper per unit of output** than the worker — productivity *weighed against price* — not whenever AI is merely *able*: $$\underbrace{\frac{Z^{\text{AI}}_k}{r_k}}_{\text{AI's value for money}} \;>\; \underbrace{\frac{Z^{\text{worker}}_k}{w}}_{\text{the worker's value for money}}$$ The denominator on the left, the **user cost** $r_k$, is the whole story. It is *not* the price of compute alone — it is the cost of using AI *safely and reliably*: the human time to **check and sign off** on the output, plus compliance, privacy and integration: $$r_k \;\approx\; \rho_k \;+\; w\, t^{V}_k .$$ So a task can be highly *exposed* yet barely *adopted* because $r_k$ is high (verification, accountability, regulation); and high wages $w$ *pull* AI in, because they lower the worker's value-for-money on the right. **Capability is half the story; the cost of using AI is the other half** — and it is an organisational and institutional variable, not a property of the model. This is the central finding of **Lindenlaub, Oh, Rodríguez & Veldkamp (2026, "LORV")**: across occupations, user cost — not capability — explains most of *who actually adopts* AI. The exposure measure is from **Eloundou et al. (2024)**, and the task-chain logic below from **Demirer, Horton, Immorlica & Lucier (2026)**. ### How the cost cascades The user cost is not fixed. When consecutive tasks are handed to AI, they **chain** — the interior steps run machine-to-machine and a human verifies only the *end* of the chain. So verification becomes roughly a **fixed cost per chain, not per step**: as AI grows reliable and chains lengthen, the cost *per task* falls, and augmentation can tip toward replacement. ![A job is a sequence of tasks. AI is used on a task when its value-for-money beats the worker's; the binding cost is verification, which is paid once per AI chain, not per step. After Demirer, Horton, Immorlica & Lucier (2026), "Chaining Tasks."](assets/task-chain.svg){#fig-chain width="100%"} ## What the Norwegian vacancies show ```{python} #| label: setup #| code-summary: "Load the latest weekly snapshot + SSB exposure, and merge" import json, re import numpy as np import pandas as pd import matplotlib.pyplot as plt KOJA = {"fjord": "#5d79a0", "amber": "#cf9a5c", "mist": "#b1abb0", "muted": "#8292ad", "heading": "#e7ecf3"} plt.rcParams.update({ "figure.facecolor": "none", "axes.facecolor": "none", "savefig.facecolor": "none", "savefig.transparent": True, "text.color": KOJA["mist"], "axes.labelcolor": KOJA["mist"], "axes.edgecolor": "#3a4860", "xtick.color": KOJA["muted"], "ytick.color": KOJA["muted"], "axes.titlecolor": KOJA["heading"], "axes.grid": True, "grid.color": "#8292ad", "grid.alpha": 0.15, "font.size": 11, }) D = "adoption-tracker/data" latest = json.load(open(f"{D}/latest.json")) weekly = pd.read_csv(f"{D}/adoption_weekly.csv") occ = pd.read_csv(f"{D}/adoption_by_occupation.csv") occ = occ[occ.date == occ.date.max()].copy() def norm(s): s = str(s).lower().strip().replace("–", "-"); s = re.sub(r"\s+", " ", s) return re.sub(r"\s*([/-])\s*", r"\1", re.sub(r"[.;]+$", "", s)) ex = pd.read_csv(f"{D}/exposure_reference.csv") ex["key"] = ex.occupation_title.map(norm); ex = ex.dropna(subset=["ai_exposure"]).drop_duplicates("key") occ["key"] = occ.styrk.map(norm) m = occ.merge(ex[["key", "ai_exposure", "wage_nok_monthly"]], on="key", how="inner") ``` As of the latest snapshot (**`{python} latest['date']`**), `{python} f"{latest['n_ads']:,}"` open vacancies were collected (of `{python} f"{latest['n_total_open']:,}"` open), of which **`{python} latest['n_ai']` mention AI (`{python} f"{latest['ai_share']*100:.1f}"`%)**. AI demand is still rare and concentrated — which is exactly what a high-user-cost world looks like. ```{python} #| label: fig-adopt-exposure #| fig-cap: "Does exposure predict adoption? Each dot is a Norwegian occupation (≥15 vacancies), sized by vacancy count; vacancy-adoption is the share of its ads mentioning AI. Exposure predicts adoption only weakly — the bulk of the variation is left to user cost." d = m[m.n_ads >= 15].copy() x, y, w = d.ai_exposure.values, d.ai_share.values * 100, d.n_ads.values.astype(float) W = w / w.sum(); xb, yb = np.average(x, weights=W), np.average(y, weights=W) b1 = np.sum(W * (x - xb) * (y - yb)) / np.sum(W * (x - xb) ** 2); b0 = yb - b1 * xb r2 = 1 - np.sum(W * (y - (b0 + b1 * x)) ** 2) / np.sum(W * (y - yb) ** 2) fig, ax = plt.subplots() ax.scatter(x, y, s=np.sqrt(w) * 7, alpha=.45, color=KOJA["fjord"], edgecolor="none") xs = np.linspace(x.min(), x.max(), 50); ax.plot(xs, b0 + b1 * xs, color=KOJA["amber"], lw=2) ax.set_xlabel("AI exposure (Eloundou index, 0–1)") ax.set_ylabel("Vacancy-adoption: % of ads mentioning AI") ax.set_title(f"Exposure vs vacancy-adoption (N={len(d)} occ, R²={r2:.2f})") plt.tight_layout(); plt.show() ``` The slope is positive but the fit is loose (R² ≈ `{python} f"{r2:.2f}"`): exposure orders occupations only roughly. In our companion analysis, once capability is controlled the **wage pull** is the piece that survives out-of-sample testing, while a task-by-task user-cost split does not — Norway's vacancies are too thin and too uniform in task mix to identify it yet. That is the case for tracking adoption *over time* and, ultimately, for worker-level data. ## The weekly series (it grows from here) ```{python} #| label: fig-weekly #| fig-cap: "AI-mention share of open Norwegian vacancies, by weekly snapshot. The series starts with this reading and extends every week the collector runs." wk = weekly.sort_values("date") fig, ax = plt.subplots() ax.plot(pd.to_datetime(wk.date), wk.ai_share * 100, "o-", color=KOJA["amber"], lw=1.6, ms=7) ax.set_ylabel("AI-mention share of vacancies (%)"); ax.set_xlabel("snapshot date") ax.set_title("AI demand in Norwegian vacancies, weekly") ax.set_ylim(0, max(1.2, wk.ai_share.max() * 100 * 1.4)) plt.tight_layout(); plt.show() ``` ## What firms say: adoption rising, the user cost falling (SSB) The vacancy signal is demand-side and thin. Statistics Norway's own AI surveys give the complementary picture — and they say exactly what the comparative-advantage story predicts: **use is rising while the cost of using AI is the thing holding it back, and that cost is falling.** (Pulled reproducibly from SSB by `adoption-tracker/`'s rebuild — firm AI-use table 13265, barriers 13272, individual genAI use 14365.) ```{python} #| label: fig-ssb-barriers #| fig-cap: "Why Norwegian firms hold back on AI (SSB table 13272, share of firms, latest year). These barriers are the 'user cost' r — and the cost barrier itself has fallen sharply since 2021." ssb = pd.read_csv("ssb-data/processed/ssb_ai_public_data_focus_extract.csv") bar = ssb[ssb.table_id == 13272].copy() yr = bar.Tid_code.max() b = (bar[bar.Tid_code == yr].sort_values("value").tail(7)) fig, ax = plt.subplots() ax.barh(range(len(b)), b.value, color=KOJA["fjord"]) ax.set_yticks(range(len(b))) ax.set_yticklabels([s[:42] for s in b.ContentsCode_label], fontsize=9) ax.set_xlabel(f"share of firms (%), {yr}") ax.set_title("Barriers to AI use = the user cost") for i, v in enumerate(b.value): ax.text(v, i, f" {v:.0f}", va="center", fontsize=8, color=KOJA["mist"]) plt.tight_layout(); plt.show() cost = bar[bar.ContentsCode_label.str.contains("kostnad", case=False, na=False)].sort_values("Tid_code") use = ssb[(ssb.table_id == 14365) & (ssb.ContentsCode_label.str.contains("Har brukt", na=False))].sort_values("Tid_code") ``` Two trends make the point. The **cost barrier** ("Høge kostnader") fell from `{python} f"{cost.value.iloc[0]:.0f}"`% (`{python} str(cost.Tid_code.iloc[0])`) to `{python} f"{cost.value.iloc[-1]:.0f}"`% (`{python} str(cost.Tid_code.iloc[-1])`) — using AI got cheaper and safer. Over the same window, individual generative-AI use rose from `{python} f"{use.value.iloc[0]:.0f}"`% to `{python} f"{use.value.iloc[-1]:.0f}"`%. Falling user cost, rising adoption — the mechanism, in SSB's own numbers. ## Implications: a new occupation inside the chain Look again at @fig-chain. As tasks chain, the human does not simply disappear from the interior — they **re-enter at the second-to-last task** (review & edit) as the *in-the-loop validator*: the person who catches the chain's errors before sign-off. That hybrid human-+-AI step is a plausible **new occupation**, and where it sits is a policy choice: - **Lower the user cost and you move the validator earlier and deeper into the chain** — the AI dividend is unlocked by *training validators and building safe-to-use infrastructure*, not by waiting for a smarter model. - It is also the natural **entry point for younger workers** if the new validation work grows faster than the old rungs disappear — the open empirical question behind Brynjolfsson, Chandar & Chen (2025). ## Data & method - **Adoption** — `collect.py` pulls the open vacancies from NAV's public search API once a week and flags AI with an audited bilingual lexicon (Norwegian + English phrases, brand names, and uppercase-only abbreviations so "ML" ≠ millilitre). One timestamped snapshot is archived per run; the share by occupation feeds the figures above. - **Exposure / employment / wages** — from **Statistics Norway (SSB)**: occupational employment (table [12542](https://www.ssb.no/en/statbank/table/12542)) and monthly earnings (table [11418](https://www.ssb.no/en/statbank/table/11418)). `ssb_check.py` runs weekly and flags when SSB refreshes either table, so the bundled occupational panel can be re-pulled. ::: {.callout-note title="Read these as descriptive, directional facts"} Vacancy AI-mentions are **demand-side** signals (what employers advertise), not realised worker use; the snapshot is a recency-capped slice of open postings; mentions are rare (~1%) and concentrated in a few occupations. So the cross-section orders occupations loosely and the weekly series is what to watch. The structural "user cost by task" object needs worker-level / register adoption to identify — this tracker is the first ingredient. ::: ## References - Lindenlaub, I., Oh, R., Rodríguez, M. A. & Veldkamp, L. (2026). *Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage* ("LORV"). - Demirer, M., Horton, J., Immorlica, N. & Lucier, B. (2026). *Chaining Tasks: AI Automation and the Division of Labor.* - Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2024). *GPTs are GPTs: Labor Market Impact Potential of LLMs.* - Brynjolfsson, E., Chandar, B. & Chen, R. (2025). *Canaries in the Coal Mine: Early Labor-Market Effects of Generative AI.* Stanford Digital Economy Lab. - Humlum, A. & Vestergaard, E. (2026). *Still Waters, Rapid Currents: Early Labor-Market Transformation under Generative AI.* - Statistics Norway (SSB), StatBank via PxWebApi v2 — tables 12542, 11418 (occupational employment & earnings) and 13265, 13271, 13272, 14365, 14364 (AI use, purposes, barriers, individual genAI use/non-use).