Street Vendors (excluding Food)

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Street Vendors (excluding Food) (ISCO-08 9520) score an average of 0.20 on a 0–1 exposure scale — more exposed than about 34% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Receiving immediate payment.”

Scores 0.35 on the 2025 scale. The task of "Receiving immediate payment" consists of handling transactions which can involve both cash and non-cash methods, and is similar in nature to tasks like "Collecting payments for sold products" (0.375) and "Accepting payments and making payouts," which involve transaction processing. Generative AI can largely facilitate digital payment processes by automating much of the data entry, real-time payment processing, and potentially generating receipts, but human oversight is necessary for handling physical currency, ensuring security, and managing exceptions or customer service aspects. The task is less physically complex than handling goods in a store ("Serving customers purchasing goods," 0.215) yet has more automated potential compared to purely manual and interactive roles due to the structured nature of payment processing. Considering the context of a high-income country like Poland, where digital payment infrastructure is widely used, a moderate potential for automation is apparent. This aligns the adjusted score slightly above tasks requiring significant human interaction but below those fully relegated to routine automation.

Moving fastest, 2023 → 2025

“Receiving immediate payment.”

Model capability on this task changed by +0.15 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 9520, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Door-to-Door Sales Workers, News and Street Vendors, and Related Workers

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Street Vendors (excluding Food) sit at the 34th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Street Vendors (excluding Food) rank in the 34th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Receiving immediate payment.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Street Vendors (excluding Food)". https://singulariki.com/gradient/9520-street-vendors-excluding-food.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.