Amazon Optimisation
Why Amazon Is Poised to Win AI Search + Retail
AI at retail is a two-engine game: data to train on and compute to process it. Amazon has both at world scale, its retail graph (first-party + marketplace signals) and AWS (the world’s leading cloud). Add a native shopping model (Rufus) that already lives inside the store, and Amazon’s path to winning AI Search + Retail looks structurally advantaged.
11 November 2025
8 min read
Unmatched retail data to train on
Amazon’s commerce surface is gigantic: independent estimates place ~2.3–2.8B monthly visits to Amazon.com in 2025, orders of magnitude more than most competitors. That volume produces dense query→view→cart→purchase→review loops that are ideal for AI training and evaluation.
The marketplace is a data flywheel: 60%+ of store sales come from third-party sellers, creating broader SKU coverage, richer attributes, and faster content iteration than single-retailer catalogs.
Why it matters: Training modern retail models isn’t just about tokens; it’s about behavioural chains (what people asked, viewed, compared, returned) tied to structured PDP data. Amazon has the deepest, freshest chains.
Compute at scale (and on tap)
AWS remains the #1 cloud by market share, powering the industry’s AI boom and offering specialised silicon (Trainium/Inferentia) plus partner GPUs i.e., the compute substrate to train, fine-tune, and serve retail models at global scale.
Why it matters: Building state-of-the-art AI shopping requires sustained, flexible compute for training + inference + experimentation. Amazon owns the stack from chips to shopping sessions.
The model is already inside the store
Rufus is Amazon’s generative shopping assistant, trained on Amazon’s selection and blended with web information, embedded directly in the app and desktop buying flow. That’s answer→cart with minimal friction.
Why it matters: Assistants that live outside the cart must hand off to retailers. Rufus starts where intent becomes purchase, so it learns faster and converts smoother.
Why others will struggle to match the stack
Walmart, eBay, etc. can (and will) partner with foundation-model providers, but they don’t combine Amazon-scale traffic and a hyper scale cloud under the same roof. Walmart.com, for example, sees ~0.5B monthly visits—a huge number, but far shy of Amazon’s multi-billion baseline—meaning sparser behavioural chains for training and validation.
Without an integrated data+compute+retail loop, alignment lags: stitching external models, smaller graphs, and fragmented infrastructure slows iteration compared with Amazon’s in-house flywheel.
The big picture: AI is data + compute
You need massive, high-quality retail data and massive, flexible compute. Amazon has both, at native scale, and ~2.5B+ monthly visits keep the data fresh. That’s why its learning and deployment cycles compound.
Yes, challengers are improving, and AWS’s lead is closely pursued by Microsoft and Google but today, Amazon is the only player that controls the full stack (retail data, shopping assistant, and hyper scale cloud), which is exactly what AI search + retail rewards.
What this means for brands (practical takeaways)
Optimise for Rufus (answer inclusion): titles, bullets, structured attributes, on-image spec callouts, proof-based claims, and seeded Q&A.
Keep stories consistent across D2C, PR/reviews, and Amazon PDPs; contradictions reduce model confidence.
Measure AI visibility: track Share of Model inside assistants (including Rufus), then tie changes to add-to-cart and ordered units on Amazon.
Bottom line: AI retail rewards whoever unifies data, compute, and checkout. Today, that’s Amazon. If you want to win the new shelf-space, start by making your products the obvious answer inside Amazon’s own AI.
Amazon’s commerce surface is gigantic: independent estimates place ~2.3–2.8B monthly visits to Amazon.com in 2025, orders of magnitude more than most competitors. That volume produces dense query→view→cart→purchase→review loops that are ideal for AI training and evaluation.
The marketplace is a data flywheel: 60%+ of store sales come from third-party sellers, creating broader SKU coverage, richer attributes, and faster content iteration than single-retailer catalogs.
Why it matters: Training modern retail models isn’t just about tokens; it’s about behavioural chains (what people asked, viewed, compared, returned) tied to structured PDP data. Amazon has the deepest, freshest chains.
Compute at scale (and on tap)
AWS remains the #1 cloud by market share, powering the industry’s AI boom and offering specialised silicon (Trainium/Inferentia) plus partner GPUs i.e., the compute substrate to train, fine-tune, and serve retail models at global scale.
Why it matters: Building state-of-the-art AI shopping requires sustained, flexible compute for training + inference + experimentation. Amazon owns the stack from chips to shopping sessions.
The model is already inside the store
Rufus is Amazon’s generative shopping assistant, trained on Amazon’s selection and blended with web information, embedded directly in the app and desktop buying flow. That’s answer→cart with minimal friction.
Why it matters: Assistants that live outside the cart must hand off to retailers. Rufus starts where intent becomes purchase, so it learns faster and converts smoother.
Why others will struggle to match the stack
Walmart, eBay, etc. can (and will) partner with foundation-model providers, but they don’t combine Amazon-scale traffic and a hyper scale cloud under the same roof. Walmart.com, for example, sees ~0.5B monthly visits—a huge number, but far shy of Amazon’s multi-billion baseline—meaning sparser behavioural chains for training and validation.
Without an integrated data+compute+retail loop, alignment lags: stitching external models, smaller graphs, and fragmented infrastructure slows iteration compared with Amazon’s in-house flywheel.
The big picture: AI is data + compute
You need massive, high-quality retail data and massive, flexible compute. Amazon has both, at native scale, and ~2.5B+ monthly visits keep the data fresh. That’s why its learning and deployment cycles compound.
Yes, challengers are improving, and AWS’s lead is closely pursued by Microsoft and Google but today, Amazon is the only player that controls the full stack (retail data, shopping assistant, and hyper scale cloud), which is exactly what AI search + retail rewards.
What this means for brands (practical takeaways)
Optimise for Rufus (answer inclusion): titles, bullets, structured attributes, on-image spec callouts, proof-based claims, and seeded Q&A.
Keep stories consistent across D2C, PR/reviews, and Amazon PDPs; contradictions reduce model confidence.
Measure AI visibility: track Share of Model inside assistants (including Rufus), then tie changes to add-to-cart and ordered units on Amazon.
Bottom line: AI retail rewards whoever unifies data, compute, and checkout. Today, that’s Amazon. If you want to win the new shelf-space, start by making your products the obvious answer inside Amazon’s own AI.