HR AI Index

Index › Products › Uber · September 2026 Edition

2 categories · Named, not ranked

Uber

6Judge labels

0First choices

6Negative labels

4 of 12Models named it

2Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.3, every buyer segment counted.

Standing

6 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Uber was named 6 times in Compensation management and 1 other category, where Pave led with 18%. 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-market183 of 186

under the label threshold

Enterprisenot named

No model named it for this buyer.

In compensation management · 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
Compensation management	Compensation and total rewards	0%	183 of 186	100%	3	under 10 labels · led by Pave at 18%
Contingent workforce management	Workforce planning and analytics	0%	97 of 97	100%	3	under 10 labels · led by SimpleVMS at 35%

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 and per category, 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.

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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	2	2

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

Negative6 labelsNone

First choiceAlternativeMentionNegative6 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

“Uber & UberEats - Scored only 2/10 in South Africa” MiniMax M2.5 · Compensation management · negative prompt · hard negative

Named alongside

The products named in the same answers as Uber, over the 6 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.

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ProductSame answerTook the first choice insteadHead to head

DoorDash5 of 60Not among the top eight

Amazon Flex4 of 60Not among the top eight

Instacart4 of 60Not among the top eight

Lyft4 of 60Not among the top eight

Fiverr3 of 60Not among the top eight

Freelancer.com3 of 60Not among the top eight

Shipt3 of 60Not among the top eight

Upwork3 of 60Not among the top eight

Amazon Mechanical Turk2 of 60Not among the top eight

Favor2 of 60Not among the top eight

A head-to-head page exists where both products are among a category's top eight. 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 6 of the 6 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

painonsocial.com2

reddit.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

painonsocial.com/blog/freelance-platform-complaints-reddit2

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

Pages are listed as the models cited them.

Names read as Uber

What the judge wrote, as written, with how often. The vendor table decides that these count as Uber; a claim can dispute any of them.

Uber & UberEats 1

Follow Uber

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

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.

With a new claim: the vendor brief

A new claim receives the current edition's vendor brief for Uber by email, built from the raw record of the edition. It shows:

where Uber is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name Uber, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.

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.