HR AI Index

Index › Vendors › Uber · September 2026 Edition

2 categories · Named, not ranked

Uber

5Judge labels

0First choices

5Negative labels

4 of 12Models named it

2Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.

Standing

5 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Uber was named 5 times in Contingent workforce man and 1 other category, where SimpleVMS led with 35%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market97 of 97

under the label threshold

Enterprisenot named

No model named it for this buyer.

In contingent workforce man · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Uber for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
Contingent workforce management	Workforce planning and analytics	0%	97 of 97	100%	3	under 10 labels · led by SimpleVMS at 35%
Compensation management	Compensation and total rewards	0%	184 of 189	100%	2	under 10 labels · led by Pave at 18%

Movement

This is the first edition on this tier, so no move can be computed for Uber yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Uber across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Negative	Labels
Claude Haiku 4.5	1	1
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	0	0
Mistral Small	0	0
DeepSeek V4 Flash	1	1
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	2	2
GLM 4.7 FlashX	0	0
MiniMax M2.5	1	1

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative5 labelsNone

First choiceAlternativeMentionNegative5 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

No positive label carried a quote.

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“identified seven companies it says systematically misclassify workers, pay below minimum wage in some cases, and use opaque algorithms” DeepSeek V4 Flash · Contingent workforce man · negative prompt · hard negative

“Multiple lawsuits for wage theft, underpayment, and misclassification - Uber alone has been sued at least 38 times” Kimi K2 · Compensation management · negative prompt · hard negative

“Uber - Multiple lawsuits over worker misclassification, wage theft, and tip stealing” MiniMax M2.5 · Contingent workforce man · negative prompt · hard negative

“with DoorDash and Uber drivers reporting stagnant or declining base pay while companies shift more costs onto workers” Claude Haiku 4.5 · Compensation management · negative prompt · soft negative

Named alongside

The products named in the same answers as Uber, over the 5 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Uber was named but was not.

ProductSame answerTook the first choice insteadHead to head

DoorDash5 of 50Not in the top three

Instacart4 of 50Not in the top three

Lyft4 of 50Not in the top three

Amazon Flex3 of 50Not in the top three

Shipt3 of 50Not in the top three

Favor2 of 50Not in the top three

Fiverr2 of 50Not in the top three

Freelancer.com2 of 50Not in the top three

Grubhub2 of 50Not in the top three

Upwork2 of 50Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Uber. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 5 of the 5 answers that named Uber and are not a share of its labels.

Domains cited

bbb.org4

hrw.org4

nelp.org3

trustpilot.com3

compens.ai2

complaintsboard.com2

lawreview.law.ucdavis.edu2

linkedin.com2

reddit.com2

worksome.com2

Twenty-six of the twenty-six domain citations in answers naming Uber came from somebody else's page.

Pages cited

hrw.org/report/2025/05/12/the-gig-trap/algorithmic-wage-and-labor-exploitation-in-platform4

compens.ai/en/articles/gig-economy-class-action-guide-20252

complaintsboard.com/upwork-b1225232

lawreview.law.ucdavis.edu/sites/g/files/dgvnsk15026/files/2024-04/57-4_McPeak.pdf2

nelp.org/app/uploads/2019/08/Fact-Sheet-Gig-Companies-Facing-Dozens-Lawsuits.pdf2

nelp.org/insights-research/gig-companies-facing-dozens-lawsuits-workplace-violations2

worksome.com/blog/contingent-workforce-management-software2

ainvest.com/news/uber-1-09-billion-share-volume-ranks-56th-15m-seattle-settlement-labor-la1

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Uber's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Uber, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at uber.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.