Instacart’s AI Pricing Tools Raise Grocery Prices

Instacart, the leading grocery‑delivery platform, is using artificial‑intelligence pricing tools that have resulted in shoppers seeing different prices for the same product at the same retailer, according to a new study released Tuesday.

The study, conducted by the advocacy group Groundwork Collaborative in partnership with Consumer Reports and the news outlet More Perfect Union, enlisted 437 participants in four U.S. cities. Each participant added identical items to their Instacart carts from the same physical store.

Researchers focused on a range of major grocery partners that operate on Instacart’s marketplace, including Target, Costco, Albertsons, Kroger and Safeway.

Findings reveal that nearly three‑quarters of the items tested were priced differently for different shoppers. In one striking example, a dozen‑egg carton at a Safeway in Washington, D.C. displayed five distinct price points within a short time frame.

The price variance translated into an overall basket‑level swing of roughly 7 percent. Groundwork estimates that this could equate to an annual “cost swing” of about $1,200 for a typical household.

Instacart responded in a blog post, noting that the observed pricing differences stem from “limited online pricing tests” run by a small subset of its retail partners. The company emphasized that these experiments do not rely on personal, demographic, or individual‑level behavioral data, and prices are not adjusted in real time in response to supply‑and‑demand signals.

According to Instacart, the purpose of short‑term, randomized tests is to help retailers gauge category‑level price sensitivity. The insights enable partners to allocate promotional spend more efficiently, potentially lowering prices in high‑elasticity categories while protecting margins elsewhere.

In 2022, Instacart acquired Eversight, an AI‑driven pricing and promotions platform, to bolster its data‑science capabilities. The acquisition underscores a broader industry trend: leveraging machine learning to optimize pricing, inventory placement and promotional timing across omnichannel retail.

The study arrives amid escalating scrutiny of AI‑enabled pricing across sectors. Lawmakers and consumer‑advocacy groups argue that algorithmic price discrimination can erode consumer trust, especially when shoppers are unaware that personalized pricing is occurring.

Recent regulatory activity highlights the growing concern. New York State enacted the nation’s first law mandating businesses to disclose the use of personal data for algorithmic pricing. The Federal Trade Commission has also launched a study to investigate “surveillance pricing” practices across multiple industries. In the transportation space, airlines and ride‑hailing firms have faced congressional hearings over dynamic fare algorithms.

Senator Ruben Gallego (D‑AZ) introduced legislation aimed at prohibiting companies from charging different prices for identical goods or services based on a consumer’s personal data. If enacted, the bill would impose transparency and fairness requirements that could reshape how platforms like Instacart deploy AI pricing.

From a business perspective, the tension between revenue optimization and regulatory compliance presents a strategic dilemma. While AI can unlock incremental margin gains—estimates suggest that dynamic pricing can boost retailer profits by 1‑3 percent—excessive price variability risks backlash, brand damage, and potential fines.

Technologically, the next wave of pricing algorithms is expected to incorporate richer contextual signals, such as real‑time inventory levels, local competitor pricing, and macroeconomic indicators, while increasingly relying on federated learning to protect consumer privacy. Companies that strike the right balance between data‑driven precision and transparent consumer communication are likely to gain a competitive edge in the rapidly evolving grocery‑delivery market.