Adoption, exposure, and the cost of using AI

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

Utgitt

8. 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.”

Data & method

Adoption, narrow — 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) on each ad’s title + NAV tag fields. One timestamped snapshot is archived per run; the share by occupation feeds the figures below.
Adoption, broad — the search API does not expose ad bodies, so fetch_bodies.py retrieves each ad’s full text from its public posting page (fetch-once per ad, cached) and applies the same lexicon. A title/tag mention means the role is about AI; a body-only mention is usually AI as a tool or workplace fact (“vi bruker KI-verktøy”) — closer to exposure-in-practice than to AI being the job.
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.

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

def md(s):
    from IPython.display import Markdown
    return Markdown(s)

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"))
# Headline measure = AI mentioned anywhere in the FULL AD TEXT (body), not just the title/tags.
wbody = pd.read_csv(f"{D}/adoption_weekly_body.csv").sort_values("date")   # full-ad-text weekly series
wlast = wbody.iloc[-1]
# occupation cross-section on the full-ad-text measure (share_body = AI mentioned in the ad body)
occ = pd.read_csv(f"{D}/adoption_by_occupation_body.csv")
occ = occ[occ.date == occ.date.max()].copy()
occ["ai_share"] = occ.share_body.astype(float)     # full ad text, per occupation
occ["n_eff"] = occ.n_matched.astype(int)           # ads whose body was retrieved (the denominator)

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 (2026-06-08), 10,000 open vacancies were collected (of 13,146 open). Scanning the full ad text, 333 mention AI (3.3%) of the 10,000 ads whose body was retrieved — versus only 84 (0.8%) counting the title and tags alone. AI demand is still rare and concentrated — which is exactly what a high-user-cost world looks like.

Kode

d = m[m.n_eff >= 15].copy()
x, y, w = d.ai_exposure.values, d.ai_share.values * 100, d.n_eff.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 (full text)")
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 ads with retrieved text), sized by ad count; vacancy-adoption is the share of its ads mentioning AI **anywhere in the full ad text**. 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 ≈ 0.26): 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

dts = pd.to_datetime(wbody.date)
fig, ax = plt.subplots()
ax.plot(dts, wbody.share_body * 100, "o-", color=KOJA["amber"], lw=1.8, ms=7, label="full ad text")
ax.plot(dts, wbody.share_title * 100, "o--", color=KOJA["muted"], lw=1.2, ms=5, alpha=.75, label="title/tags only")
ax.set_ylabel("share of vacancies mentioning AI (%)"); ax.set_xlabel("snapshot date")
ax.set_title("AI demand in Norwegian vacancies, weekly (full ad text)")
ax.set_ylim(0, max(2.0, wbody.share_body.max() * 100 * 1.35)); ax.legend(frameon=False)
plt.tight_layout(); plt.show()

Figur 3: Share of open Norwegian vacancies mentioning AI **anywhere in the full ad text**, by weekly snapshot; the faint line is the narrower title/tags-only count, for reference. The series extends every week the collector runs.

Narrow vs. broad: what the full ad text adds

The ~1% headline above counts an ad only when AI appears in its title or tag fields — the role is about AI. Scanning the full ad body (same lexicon) catches the much larger set of employers who merely mention AI: tools in use, “KI-nysgjerrig” wish-list items, AI-product employer branding. The gap between the two lines is itself informative — it is the difference between AI as the job and AI in the workplace, and the comparative-advantage story predicts the broad measure should move first as user cost falls.

Kode

wb = pd.read_csv(f"{D}/adoption_weekly_body.csv").sort_values("date")
fig, ax = plt.subplots()
dts = pd.to_datetime(wb.date)
ax.plot(dts, wb.share_any * 100,   "o-", color=KOJA["fjord"], lw=1.6, ms=7, label="either (any)")
ax.plot(dts, wb.share_body * 100,  "s-", color=KOJA["mist"],  lw=1.6, ms=6, label="broad: body mention")
ax.plot(dts, wb.share_title * 100, "o-", color=KOJA["amber"], lw=1.6, ms=7, label="narrow: title/tags")
ax.set_ylabel("share of vacancies (%)"); ax.set_xlabel("snapshot date")
ax.set_title("AI as the job vs AI in the workplace")
ax.set_ylim(0, max(2.0, wb.share_any.max() * 100 * 1.3)); ax.legend(frameon=False)
plt.tight_layout(); plt.show()
last = wb.iloc[-1]

Figur 4: Three readings of the same vacancies: AI in the title/tags (narrow — the role is about AI), AI anywhere in the ad body (broad — AI is mentioned at all), and either. Body shares are computed over ads whose full text could be retrieved.

In the latest snapshot (2026-06-08), 3.3% of ads with retrievable text mention AI somewhere in the body (333 of 10,000), against 0.8% in the title/tags — a factor of 4×. Most Norwegian employers who talk about AI are not hiring for AI; they are signalling that AI is part of how the workplace runs. The narrow series is the adoption measure; the broad series is the leading indicator to watch.

Kode

ob = pd.read_csv(f"{D}/adoption_by_occupation_body.csv")
ob = ob[(ob.date == ob.date.max()) & (ob.n_matched >= 30)].nlargest(12, "share_body")
fig, ax = plt.subplots(figsize=(8, 4.6))
ax.barh(range(len(ob)), ob.share_body[::-1] * 100, color=KOJA["fjord"])
ax.set_yticks(range(len(ob)))
ax.set_yticklabels([s[:46] for s in ob.styrk[::-1]], fontsize=9)
ax.set_xlabel("share of ads mentioning AI in body (%)")
ax.set_title("Broad AI mentions by occupation")
plt.tight_layout(); plt.show()

Figur 5: Where the broad (body-mention) measure concentrates: occupations by share of ads mentioning AI anywhere in the text (latest snapshot, ≥30 ads with retrieved text).

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 6: 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).

Take this study into the AI Impact Analyst →

The analyst opens with every exhibit of this study loaded in the Exhibit picker — including the NAV vacancy-adoption sample, the weekly tracker series, and the SSB cost-barrier and genAI-use surveys, all deployed as datasets — and can edit each one on request: change the specification, the threshold, or the variables, and the revised figure or table appears directly in the chat.

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 format: html: code-tools: true code-links: false grid: body-width: 1040px --- ## 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%"} ## Data & method - **Adoption, narrow** — `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) on each ad's **title + NAV tag fields**. One timestamped snapshot is archived per run; the share by occupation feeds the figures below. - **Adoption, broad** — the search API does not expose ad bodies, so `fetch_bodies.py` retrieves each ad's **full text** from its public posting page (fetch-once per ad, cached) and applies the *same* lexicon. A title/tag mention means the role is *about* AI; a body-only mention is usually AI as a tool or workplace fact ("vi bruker KI-verktøy") — closer to exposure-in-practice than to AI being the job. - **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. ::: ## 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 def md(s): from IPython.display import Markdown return Markdown(s) 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")) # Headline measure = AI mentioned anywhere in the FULL AD TEXT (body), not just the title/tags. wbody = pd.read_csv(f"{D}/adoption_weekly_body.csv").sort_values("date") # full-ad-text weekly series wlast = wbody.iloc[-1] # occupation cross-section on the full-ad-text measure (share_body = AI mentioned in the ad body) occ = pd.read_csv(f"{D}/adoption_by_occupation_body.csv") occ = occ[occ.date == occ.date.max()].copy() occ["ai_share"] = occ.share_body.astype(float) # full ad text, per occupation occ["n_eff"] = occ.n_matched.astype(int) # ads whose body was retrieved (the denominator) 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") ``` ```{python} #| output: asis #| echo: false md(f"As of the latest snapshot (**{wlast['date']}**), {int(wlast['n_ads']):,} open vacancies were " f"collected (of {latest['n_total_open']:,} open). Scanning the **full ad text**, " f"**{int(wlast['n_ai_body'])} mention AI ({wlast['share_body']*100:.1f}%)** of the " f"{int(wlast['n_matched']):,} ads whose body was retrieved — versus only {int(wlast['n_ai_title'])} " f"({wlast['share_title']*100:.1f}%) counting the title and tags alone. AI demand is still rare and " f"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 ads with retrieved text), sized by ad count; vacancy-adoption is the share of its ads mentioning AI **anywhere in the full ad text**. Exposure predicts adoption only weakly — the bulk of the variation is left to user cost." d = m[m.n_eff >= 15].copy() x, y, w = d.ai_exposure.values, d.ai_share.values * 100, d.n_eff.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 (full text)") ax.set_title(f"Exposure vs vacancy-adoption (N={len(d)} occ, R²={r2:.2f})") plt.tight_layout(); plt.show() ``` ```{python} #| output: asis #| echo: false md(f"""The slope is positive but the fit is loose (R² ≈ {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: "Share of open Norwegian vacancies mentioning AI **anywhere in the full ad text**, by weekly snapshot; the faint line is the narrower title/tags-only count, for reference. The series extends every week the collector runs." dts = pd.to_datetime(wbody.date) fig, ax = plt.subplots() ax.plot(dts, wbody.share_body * 100, "o-", color=KOJA["amber"], lw=1.8, ms=7, label="full ad text") ax.plot(dts, wbody.share_title * 100, "o--", color=KOJA["muted"], lw=1.2, ms=5, alpha=.75, label="title/tags only") ax.set_ylabel("share of vacancies mentioning AI (%)"); ax.set_xlabel("snapshot date") ax.set_title("AI demand in Norwegian vacancies, weekly (full ad text)") ax.set_ylim(0, max(2.0, wbody.share_body.max() * 100 * 1.35)); ax.legend(frameon=False) plt.tight_layout(); plt.show() ``` ## Narrow vs. broad: what the full ad text adds The ~1% headline above counts an ad only when AI appears in its **title or tag fields** — the role is *about* AI. Scanning the **full ad body** (same lexicon) catches the much larger set of employers who merely *mention* AI: tools in use, "KI-nysgjerrig" wish-list items, AI-product employer branding. The gap between the two lines is itself informative — it is the difference between **AI as the job** and **AI in the workplace**, and the comparative-advantage story predicts the broad measure should move first as user cost falls. ```{python} #| label: fig-narrow-broad #| fig-cap: "Three readings of the same vacancies: AI in the title/tags (narrow — the role is about AI), AI anywhere in the ad body (broad — AI is mentioned at all), and either. Body shares are computed over ads whose full text could be retrieved." wb = pd.read_csv(f"{D}/adoption_weekly_body.csv").sort_values("date") fig, ax = plt.subplots() dts = pd.to_datetime(wb.date) ax.plot(dts, wb.share_any * 100, "o-", color=KOJA["fjord"], lw=1.6, ms=7, label="either (any)") ax.plot(dts, wb.share_body * 100, "s-", color=KOJA["mist"], lw=1.6, ms=6, label="broad: body mention") ax.plot(dts, wb.share_title * 100, "o-", color=KOJA["amber"], lw=1.6, ms=7, label="narrow: title/tags") ax.set_ylabel("share of vacancies (%)"); ax.set_xlabel("snapshot date") ax.set_title("AI as the job vs AI in the workplace") ax.set_ylim(0, max(2.0, wb.share_any.max() * 100 * 1.3)); ax.legend(frameon=False) plt.tight_layout(); plt.show() last = wb.iloc[-1] ``` ```{python} #| output: asis #| echo: false md(f"""In the latest snapshot ({last.date}), **{last.share_body*100:.1f}%** of ads with retrievable text mention AI somewhere in the body ({int(last.n_ai_body):,} of {int(last.n_matched):,}), against **{last.share_title*100:.1f}%** in the title/tags — a factor of {last.share_body/max(last.share_title,1e-9):.0f}×. Most Norwegian employers who talk about AI are not hiring *for* AI; they are signalling that AI is part of how the workplace runs. The narrow series is the adoption measure; the broad series is the leading indicator to watch.""") ``` ```{python} #| label: fig-body-occ #| fig-cap: "Where the broad (body-mention) measure concentrates: occupations by share of ads mentioning AI anywhere in the text (latest snapshot, ≥30 ads with retrieved text)." ob = pd.read_csv(f"{D}/adoption_by_occupation_body.csv") ob = ob[(ob.date == ob.date.max()) & (ob.n_matched >= 30)].nlargest(12, "share_body") fig, ax = plt.subplots(figsize=(8, 4.6)) ax.barh(range(len(ob)), ob.share_body[::-1] * 100, color=KOJA["fjord"]) ax.set_yticks(range(len(ob))) ax.set_yticklabels([s[:46] for s in ob.styrk[::-1]], fontsize=9) ax.set_xlabel("share of ads mentioning AI in body (%)") ax.set_title("Broad AI mentions by occupation") 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") ``` ```{python} #| output: asis #| echo: false md(f"""Two trends make the point. The **cost barrier** ("Høge kostnader") fell from {cost.value.iloc[0]:.0f}% ({cost.Tid_code.iloc[0]}) to {cost.value.iloc[-1]:.0f}% ({cost.Tid_code.iloc[-1]}) — using AI got cheaper and safer. Over the same window, individual generative-AI use rose from {use.value.iloc[0]:.0f}% to {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). ```{=html} <div style="border:1px solid #3a4860;border-radius:10px;background:rgba(111,174,142,.06);padding:1rem 1.1rem;margin:0.2rem 0 1.6rem 0"> <a href="analysis.html?expert=ai-impact&study=adoption" style="display:inline-block;background:#cf9a5c;color:#070b12;font-weight:600;padding:.55rem 1.1rem;border-radius:8px;text-decoration:none"> Take this study into the AI Impact Analyst →</a> <p style="color:#8292ad;font-size:.86rem;margin:.5rem 0 0 0"> The analyst opens with <b>every exhibit of this study loaded</b> in the Exhibit picker — including the NAV vacancy-adoption sample, the weekly tracker series, and the SSB cost-barrier and genAI-use surveys, all deployed as datasets — and can edit each one on request: change the specification, the threshold, or the variables, and the revised figure or table appears directly in the chat.</p> </div> ``` ## 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).