Amit Bachbut writes about ecommerce marketing, with a focus on how AI is changing product discovery, visibility, and checkout. Bachbut's posts explain practical implications for online stores, including Shopify settings and measurement trade-offs.
Amit Bachbut is most useful for explaining AI-driven ecommerce discovery and Shopify checkout changes to store and agency teams.
Best for
A Shopify agency or ecommerce platform team preparing clients for AI discovery and checkout.
Not for
A consumer brand campaign needing shopper-facing content to drive direct seasonal sales.
Key takeaways
AI is taking the decision, not the checkout.
The default setting may already have enrolled the store.
Measurement is what breaks in AI-native checkout.
Check first: Ask for webinar details and verify the 752% AI referral figure before client-facing use.
Main ideas
AI is taking the decision, not the checkout · The default setting may already have enrolled the store · Measurement is what breaks in AI-native checkout
Amit Bachbut writes about how AI is changing ecommerce discovery and buying.
In three LinkedIn posts, the focus ranges from the product information and brand visibility that shape AI recommendations to practical Shopify checkout settings.
The posts make the subject useful to follow for store and agency teams trying to understand what changes before a shopper reaches a website—and what new checkout paths may mean for measurement.
Bachbut connects broad shifts in AI commerce to concrete questions teams can act on.
One post reframes the issue from whether to sell inside ChatGPT to whether a brand is visible during discovery; another gives a Shopify admin path to review agentic checkout settings and explains the tracking trade-offs.
A seasonal post makes the case for preparing AI visibility before BFCM and points readers to a webinar; ask for the webinar details.
Amit Bachbut writes about ecommerce marketing and how AI is changing what happens before a purchase.
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Across their posts, the recurring argument is that AI is winning the decision more than the checkout: discovery, recommendations, try-on and the wishlist move into assistants, while the sale still lands on the brand's own site. Their priorities follow: check platform defaults that may already be switched on, verify whether measurement still works on each new commerce surface, and start visibility work well before peak trading.
AI is taking the decision, not the checkout
Amit Bachbut argues that the contest over AI commerce is not about who processes the payment.
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In-chat buying came and went, but shopping before a purchase — discovery, inspiration, the fitting room, the wishlist — kept moving into AI assistants.
The click still leads to the brand's own site, but by then the choice is largely made somewhere the brand cannot watch, A/B test or retarget.
That shifts the practical question from "should we sell inside ChatGPT?" to "are we winning the conversation that happens before the click?"
Bachbut's answer points to product data, imagery, reviews and AI visibility, each with an owner and a budget.
Four dated events carry the argument: Instant Checkout launched in September 2025 so shoppers could buy without leaving ChatGPT; Walmart listed about 200,000 products in November 2025; Walmart reported in March 2026 that in-chat purchases converted at roughly a third the rate of click-outs, after which OpenAI retired Instant Checkout; and October 2026 brought virtual try-on and Favourites, with Walmart as launch partner.
The framing narrows over the year. A question about selling inside chat becomes a question about pre-click visibility: the checkout experiment was retired while pre-purchase features expanded.
The default setting may already have enrolled the store
On Google's native checkout inside AI Mode and Gemini, Amit Bachbut's point is that inclusion is opt-out rather than opt-in.
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Google emailed some Merchant Center users to say eligible products are automatically included with nothing to set up, and direct checkout is on by default for eligible Shopify stores.
Bachbut also flags that leaving Shopify's management setting on enrols the store automatically in future agentic storefront channels.
Check the settings now rather than plan around them later, and treat automatic enrolment as something the store should knowingly consent to.
The review path they give is Shopify admin, then Sales channels, then Agentic: turn off "Allow Shopify to manage for me," select Google AI Mode and Gemini, choose the Direct checkout setting, and save. Eligibility currently requires a US store selling to US customers, and the rollout is still under way.
Their recurring test for any new commerce surface is whether the store can still see what happened.
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Inside the AI Mode checkout, Shopify says Google Analytics and custom pixels do not fire; only server-to-server checkout-start and checkout-completion events do.
The order still arrives in Shopify with channel or referrer attribution, so the sale is visible while the journey is not.
Reporting or retargeting built on browser pixels quietly loses data.
Bachbut also lists other losses — upsells and custom fields may be missing, and subscriptions and bundles are not supported — and weighs fewer steps to purchase against the measurement trade-off instead of declaring the feature good or bad.
A store that relies on pixel-based retargeting would keep receiving orders in Shopify while its audiences stopped being refreshed by shoppers who came through the AI Mode path, because no browser pixel fires there.
Amit Bachbut argues that during a peak trading season, a brand's biggest competitor can be whichever brand AI recommends instead.
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They say AI answers are shaped by brands' visibility months earlier, not during the sale period, making AI search visibility pre-season work with a calendar rather than a November reaction. They frame it as a trust question — what AI engines actually trust and where brands lose visibility — and point readers to a longer discussion for the detail.
They cite AI referrals to ecommerce brands rising 752% during the previous BFCM and point to a recorded session on where brands lose visibility in AI search, what AI engines trust, and what to prioritise before the season. Guests Jenna Vaccaro and Sandy Wolff Eisen joined the session.
Two platforms, opposite directions on the same idea
Read together, the posts show a tension Bachbut does not smooth over.
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OpenAI retired its in-chat checkout after weak conversion, while Google's native checkout inside AI Mode and Gemini expanded with eligible products included automatically. They assess each platform separately and on its own terms, making the case for treating agentic commerce as a set of platform-specific pilots with defined measurement rather than one trend to adopt wholesale.
The same period contains OpenAI's March 2026 retirement of Instant Checkout and Google's rollout to eligible Merchant Center and Shopify stores, where direct checkout is on by default.
The two observations were published months apart. Before acting on the contrast, check what has changed since.
Amit Bachbut’s post introduces the shift they’re writing about.
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It answers the question brands and agencies usually ask first — should we sell inside ChatGPT? — and shows why the answer changed.
Amit Bachbut lays out four dated events: Instant Checkout launched in September 2025, allowing shoppers to buy without leaving ChatGPT; Walmart listed roughly 200,000 products in November 2025; Walmart reported in March 2026 that in-chat purchases converted at about a third of the rate of click-outs, after which OpenAI retired Instant Checkout; and in October 2026, virtual try-on and Favourites arrived with Walmart as a launch partner.
The conclusion: AI is not taking over the checkout; it is taking over everything before it — discovery, inspiration, the fitting room, the wishlist.
The click still returns to the brand’s own site, but by then the decision is largely made somewhere the brand cannot see, A/B test, or retarget.
The post replaces the question “Should we sell in ChatGPT?” with “Are we winning the conversation that happens before the click?”
It names what that requires: product data, imagery, reviews, and AI visibility, each with an owner and a budget.
For brands planning a campaign
Use this framing in a client conversation about AI commerce: rather than scoping a selling-in-chat project, scope the pre-click layer — product feed and data quality, image and review coverage, and who inside the client owns AI visibility.
The conversion and retirement figures are Amit Bachbut’s reporting on Walmart and OpenAI developments, not independently verified in the posts.
Present them as his reporting and confirm the numbers before repeating them to a client.
It answers "what do I actually do in the admin today?" and gives a step-by-step path with the measurement caveat that most teams miss.
Google has emailed some Merchant Center users to say their products are eligible for native checkout inside AI Mode and Gemini, and that eligible products are automatically included with nothing to set up.
For eligible Shopify stores, direct checkout is on by default; Shopify currently requires a US store selling to US customers, and the rollout is still under way.
The measurement detail is the point: Shopify says Google Analytics and custom pixels do not fire inside this checkout, and only server-to-server checkout-start and checkout-completion events fire.
The order still lands in Shopify with channel or referrer attribution, so teams relying on browser pixels for reporting and retargeting should check what they are no longer capturing.
Some checkout features may also be absent, including upsells and custom fields; subscriptions and bundles are not supported.
The review path: Shopify admin, then Sales channels, then Agentic.
Turn off “Allow Shopify to manage for me,” select Google AI Mode and Gemini, choose the Direct checkout setting, and save.
Leaving Shopify’s management setting on also enrols the store automatically in future agentic storefront channels.
For brands planning a campaign
Turn this into a standard client audit item: a short settings review plus a measurement warning.
For any client whose reporting or retargeting depends on browser pixels, the AI Mode checkout path will be invisible unless server-side events are in place, and default settings may already be enrolling them in channels they have not agreed to.
The steps are specific enough to follow, but settings and eligibility rules are changing during rollout.
Re-check the admin before promising a client a particular state.
A short post that answers a timing question — when should AI visibility work start relative to the buying season? — and points to a longer recording for more detail.
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The argument: during peak season, a brand’s biggest competitor may be whichever brand AI recommends instead.
Amit Bachbut cites AI referrals to ecommerce brands surging 752% in the previous BFCM period and argues that the brands appearing in those answers are being determined now, not in November, so the work has to start early.
Most of the post points to a webinar recording with Jenna Vaccaro and Sandy Wolff Eisen covering where brands lose visibility in AI search, what AI engines trust, and what to prioritise before BFCM.
For brands planning a campaign
Use this as a timing argument in a seasonal proposal: budget AI visibility work ahead of the peak, not during it. The 752% figure has no cited source in the post; trace it to its origin before using it in client-facing material.
Explaining platform change through timelines and settings paths3
One platform retreats while another expands2
Checking whether agentic checkout is already on1
Measurement breaks inside AI-native checkout1
AI takes the decision before the click1
AI search visibility and the pre-season deadline1
Checking whether agentic checkout is already on
Shows store owners how to use Google's native checkout inside AI Mode and Gemini.
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Google has emailed some Merchant Center users to say eligible products are included automatically, with nothing to set up.
For eligible Shopify stores, direct checkout is on by default.
The rollout is limited to US stores selling to US customers and is still under way.
The post gives the exact path — Shopify admin, Sales channels, Agentic — and notes that leaving Shopify's management setting on also enrols the store in future agentic storefront channels.
For brands planning a campaign
A Shopify agency can turn this into a short, repeatable client audit: confirm the current setting, then ask whether automatic enrolment in future channels is acceptable.
Shopify says Google Analytics and custom pixels do not fire inside the AI Mode checkout; only server-to-server checkout-start and checkout-completion events do.
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The order still arrives in Shopify with channel or referrer attribution, but reporting and retargeting built on browser pixels will not capture the journey.
Some checkout features are also absent, including upsells and custom fields, and subscriptions and bundles are not supported.
The post weighs the upside of fewer steps to purchase against that measurement trade-off without declaring the feature good or bad.
Uses the platform's own setup language, then turns each limitation into a question about the store's existing stack.
The detail applies to Shopify stores selling in the US through Google's channels. Ask how other platforms and markets behave.
For brands planning a campaign
Gives campaigns a concrete hook around attribution and retargeting risk, with a clear client ask: check server-side event coverage before peak trading.
Traces OpenAI's shopping moves through four dated moments: Instant Checkout launching in September 2025; Walmart listing around 200,000 products; Walmart reporting in-chat purchases converting at a third of the rate of click-outs, followed by OpenAI retiring Instant Checkout in March 2026; and virtual try-on plus Favourites launching in October 2026 with Walmart as launch partner.
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The takeaway: AI is not taking checkout but taking discovery, inspiration, the fitting room and the wishlist. The click still returns to the store, but the decision is largely made somewhere the brand cannot see, A/B test or retarget.
For brands planning a campaign
Reframes the question of whether to sell inside chat: is the brand winning the conversation before the click? The post points budget towards product data, imagery, reviews and AI visibility, and names an owner for each.
Within roughly the same period, OpenAI retired Instant Checkout after weak in-chat conversion, while Google's native checkout inside AI Mode and Gemini rolled out with eligible products included automatically and direct checkout on by default for eligible Shopify stores.
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The two posts describe opposite directions for the same idea.
The two observations were published months apart. Check what has changed before making a live decision.
For brands planning a campaign
Argues against treating agentic commerce as a single trend to adopt wholesale. Per-platform pilots with defined measurement will serve clients better than one blanket programme.
Reports that AI referrals to ecommerce brands surged 752% during the previous peak season.
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The post argues that brands appearing in AI answers are decided well before November, not during it. It points to a recorded session with Jenna Vaccaro and Sandy Wolff Eisen covering where brands lose visibility in AI search, what AI engines trust and what to prioritise for the season.
Pairs a headline statistic with a named practitioner discussion, then turns it into a seasonal deadline.
For brands planning a campaign
Sell and schedule visibility work months before peak trading, not during it. The post also offers a workable format for a co-hosted pre-season briefing or webinar.
Explaining platform change through timelines and settings paths
Posts follow one of two patterns: a dated sequence of platform events followed by a reframed question, or a step-by-step settings path with the trade-offs spelled out.
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Claims rest on vendor statements and product behaviour, and each post ends with a decision or action a store owner can take.
Timeline then reframe for strategy posts; settings path then trade-off for operational posts.
For brands planning a campaign
Best suited to explainer briefs, checklists and short internal briefings rather than lifestyle or product-led creator content.
Amit Bachbut's posts explain ecommerce platforms rather than announce commercial partners.
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They analyse or cite Shopify and Google on agentic checkout, OpenAI and Walmart on in-chat shopping, and include a Yotpo-branded hashtag on one post. Ask whether any of these brands has a sponsorship, client, affiliate, employment or ownership arrangement with Bachbut.
Shopify
Mention
Bachbut's post on agentic checkout is written for Shopify merchants.
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It explains that direct checkout inside Google AI Mode and Gemini is switched on by default for eligible US stores selling to US customers; Google Analytics and custom pixels do not fire in that flow, only server-to-server checkout-start and checkout-completion events do; and upsells, custom fields, subscriptions and bundles may be missing.
Leaving Shopify's management setting on also enrols a store in future agentic channels.
The post gives step-by-step admin instructions for reviewing the setting and states no commercial relationship with Shopify.
The post reports that Google is emailing some Merchant Center users to say their products are eligible for native checkout inside AI Mode and Gemini, quoting the message: "Eligible products are automatically included.
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There's nothing for you to set up."
Bachbut frames this as a measurement problem: customers can find and buy a product without ever reaching the merchant's site.
They advise merchants to check their reporting and retargeting assumptions.
Bachbut's post lays out OpenAI's shopping strategy as a four-date timeline: Instant Checkout launched in September 2025 so shoppers could buy without leaving ChatGPT; Walmart listed about 200,000 products in November 2025; in March 2026, Walmart reported that in-chat purchases converted at a third of the rate of click-outs, and OpenAI retired Instant Checkout; in October 2026, virtual try-on and Favourites launched.
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Bachbut concludes that AI is capturing discovery, inspiration and consideration rather than checkout, and that brands need someone accountable for product data, imagery, reviews and AI visibility. No relationship with OpenAI is stated.
Walmart features in the same timeline as the retailer testing in-chat commerce: roughly 200,000 products listed in ChatGPT in November 2025, a reported one-third conversion rate against click-outs by March 2026, and a launch partnership for virtual try-on and Favourites in October 2026.
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Bachbut cites this as evidence that the buying decision increasingly happens before the click, in a place merchants cannot A/B test or retarget. No relationship with Walmart is stated.
Amit Bachbut's posts are worth following to track how AI assistants change the steps before an online purchase.
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Start with the operational walkthrough of Shopify's agentic checkout settings, which shows whether a store may already be enrolled and what breaks in tracking once buyers complete a purchase inside AI Mode.
Then read the four-date timeline of OpenAI's shopping moves, which argues that AI is winning discovery, try-on and the wishlist rather than the payment.
Both connect to the argument that AI search visibility is decided months before peak trading.
The work suits readers who want platform-specific consequences and a decision at the end of each post rather than general commentary about AI in retail.
Tom Shivers explains the agentic commerce standards that let AI agents shop, covering OpenAI and Stripe's Agentic Commerce Protocol and Google's Universal Commerce Protocol.
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Both writers conclude that structured product data now determines whether a brand can be recommended or checked out at all, and translate the technical change into a short list of fixes for retailers.
Shivers stays with the protocols, comparing what ACP and UCP allow and who keeps the customer relationship.
Amit Bachbut starts with what a specific store will find in its admin and how its reporting changes.
Shivers is the better follow for understanding the standards; Bachbut is the better follow for what to do in a Shopify account this week.
Ben Salomon works on SEO and AI visibility in ecommerce and makes the same timing argument as Amit Bachbut: AI visibility compounds through reviews, citations and re-crawling, so it has to be built well before peak trading rather than during it.
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Both cite the same large rise in AI referrals to ecommerce brands during the previous peak season and turn it into a deadline.
Salomon writes from inside a commerce platform, launching loyalty placement and a BFCM visibility toolkit with an audit, a dated plan and a review-email step.
The posts are closer to product and campaign material.
Bachbut's posts offer an independent reading of platform behaviour, with settings paths and measurement trade-offs; they also cover AI checkout as well as AI search visibility.
Jade Beard-Stevens argues that as ChatGPT becomes a shopping destination, product reviews must be structured for machine learning, brand presence has to be optimised for AI-driven search and content must be findable through conversation rather than only through search rankings.
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Amit Bachbut makes the same pre-purchase argument; both write short, first-person LinkedIn posts that end with a practical point for brands.
Beard-Stevens mixes AI shopping commentary with hands-on store operations, including a Square-to-Shopify inventory sync problem and card-fee trade-offs, and writes from a personal, occasionally anecdotal angle. Bachbut focuses on platform policy and measurement consequences and more consistently names what a store should check or change.
For a campaign about AI-driven discovery and checkout in ecommerce, Amit Bachbut is worth a first conversation, but the audience case rests on one measured number and a lot of inference.
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The only published audience figure is 11,441 LinkedIn followers, observed 8 October 2026; Bachbut has a single-channel LinkedIn presence.
Their posts cover agentic checkout settings inside Shopify, browser pixels that do not fire during AI Mode checkout, a 752% jump in AI referrals to ecommerce brands over the previous BFCM, and OpenAI's retreat from in-chat purchasing.
That content is written for store operators, ecommerce marketers, and agencies who have to act on platform changes, but it does not establish who follows them.
Before committing budget, ask for recent results on posts closest to AI checkout mechanics, measurement, and AI search visibility in the same short how-to format, plus any audience survey or platform analytics reporting follower geography, age, employer, occupation, and seniority.
Geography matters because the Shopify and Google AI Mode checkout rollout described in the posts is limited to US stores selling to US customers.
LinkedIn followers
11,441
LinkedIn profile for Amit Bachbut (platform profile record) · 2026-10-08
Amit Bachbut's posts do not describe a sponsorship offer.
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They publish no rate card, media kit, sponsorship policy, or partnership inquiry page or address.
The three posts read as analysis for ecommerce operators rather than pitches to brands, and none discloses a paid brand arrangement or its terms.
One points to a recorded webinar hosted with two named guest speakers, showing that longer-form, collaborative formats feature in their work, but not how a brand could commission, sponsor, or co-host one.
A brand considering a paid engagement should ask which formats they accept, whether sponsored posts and webinars are labelled as paid, what the lead times and turnaround are, what usage, exclusivity and republishing rights apply, and how Shopify- or measurement-specific questions are scoped.
A Shopify agency, or an ecommerce platform team, preparing clients for AI-driven discovery and checkout is the strongest match for Amit Bachbut's work.
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Their posts offer exactly that: a step-by-step walkthrough of Shopify's agentic checkout settings, the measurement gap it opens, a dated account of how OpenAI's and Google's shopping moves have diverged, and a timing argument about starting AI visibility work before peak season.
A campaign aimed at Shopify clients fits naturally with those posts.
The formats line up too.
The operational post follows a settings path and then spells out the trade-offs, which converts directly into a client checklist; the strategy posts run a timeline and then reframe the question, which converts into a short internal briefing.
Each post ends on a decision or an action, so an agency can hand the output to a client without rewriting it into instructions.
The subject matter is aimed at store operators, marketers and agencies rather than shoppers, and the recurring asks — who owns product data, imagery, reviews and AI visibility, and who holds the budget — are agency-side questions.
Only three posts are available, so this is a small recent selection, not a survey of the whole output.
The 752% AI referral figure and the Walmart conversion and OpenAI retirement details are their reporting of platform developments; verify those numbers before putting them in client-facing material.
The audience case is thin: one measured figure, 11,441 LinkedIn followers on 8 October 2026, on a single channel, with no posts describing where those followers are based or what they do.
Do not promise an agency reach into a particular client segment.
There is no published sponsorship offer, rate card or inquiry route, so scope and price an engagement directly — a briefing, an explainer series, or a co-hosted session — rather than booking through a media kit.
When to choose a specialist
A consumer brand buying creator content to drive direct sales during a seasonal push is a poor match.
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Nothing in the three posts is shopper-facing.
There is no product styling, lifestyle framing, unboxing or personal recommendation, and nothing shows sales, conversion or reach outcomes attributable to the content.
The posts read as analysis for people who run stores, not as persuasion aimed at people who buy from them, so a campaign that needs a creator to move a consumer audience has no demonstrated basis here.
The accountability side fails for the same reason.
A performance campaign usually needs audience demographics — geography, age, occupation, seniority — and a documented commercial format with disclosure and usage terms.
The only measured number is a LinkedIn follower count of 11,441 observed 8 October 2026; there is no audience survey, demographic breakdown or second channel to cross-check.
There is no rate card, media kit, sponsorship policy or inquiry page, and no post discloses a paid brand arrangement.
Before committing spend, ask whether sponsored posts are labelled, what lead times apply, and who holds usage and exclusivity rights.
A campaign that wants a strong personal verdict, a contrarian take or entertainment value would not be served either.
The posts work by reading platform statements and product behaviour back to store owners and then weighing both sides — the AI Mode checkout post deliberately refuses to call the feature good or bad, and the strategy post replaces one question with a better one rather than declaring a winner.
A brand wanting heat, personality or a decisive endorsement should look elsewhere.
The checkout mechanics discussed are also limited to US stores selling to US customers, so a brand outside that market cannot assume the operational detail transfers.
A worked campaign example
Take the brief an agency is most likely to bring: help clients prepare for AI-driven discovery and checkout, and produce a short internal briefing on the risks. A workable angle is a pre-peak "agentic checkout readiness" session, built from the two halves of Amit Bachbut's approach — the operational walkthrough and the timing argument.
Part one is a settings audit the agency can run with each client.
Walk the Shopify admin path: Sales channels, then Agentic, turn off "Allow Shopify to manage for me," select Google AI Mode and Gemini, choose the direct checkout setting, and save.
Add the detail clients usually miss: leaving Shopify's management setting on also enrols the store in future agentic storefront channels automatically, so the choice is not only about today's checkout.
Flag that eligibility is currently limited to US stores selling to US customers and that the rollout is still under way, so the admin should be re-checked rather than described from a slide.
Part two is the measurement warning, which is the reason to run the session at all.
Shopify says Google Analytics and custom pixels do not fire inside the AI Mode checkout; only server-to-server checkout-start and checkout-completion events do.
Orders still arrive with channel or referrer attribution, so the gap shows up in reporting and retargeting rather than in the order count.
Ask each client which dashboards, audiences and lookalikes depend on browser pixels, and note that upsells and custom fields may be missing and that subscriptions and bundles are not supported.
That produces a concrete pre-peak task list instead of a trend presentation.
Part three reframes the brief for the client.
The strategy post's move is to stop asking whether to sell inside chat and start asking whether the brand is winning the conversation before the click.
Applied here, the session closes on ownership: who holds product data, imagery, reviews and AI visibility, and whose budget pays for it.
The BFCM post supplies the deadline argument — that the brands appearing in AI answers are settled well before November — which is what justifies scheduling the work now rather than during peak trading.
Format and delivery: a 20-minute internal briefing plus a one-page client checklist, with an optional co-hosted session for the agency's own audience following the recorded-webinar model.
Sequence the strategy section as timeline-then-reframe and the operational section as settings-path-then-trade-off, matching how the source posts are built.
Before it goes out, confirm the 752% figure and the Walmart conversion and retirement details, which are reported but not verified here, and agree the commercial terms — format, labelling, lead time and usage — directly, because no rate card, media kit or sponsorship policy is documented.
Choose this for a campaign about putting a loyalty or retention programme in front of customers at the point of decision, or when the agency needs a dated programme rather than a settings walkthrough.
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The posts cover a Shopify-native loyalty placement on the product page and in the cart, with named results (30% uplift in logins, 70% of engaged shoppers completing the login flow, 20–40% more redemptions, and a quarter of purchases landing within minutes of a redemption).
They also lay out a 16-week AI visibility plan with three deliverables: a prompt-set audit scoring the brand against competitors, a dated task plan, and a review-capture email aimed at the 72 hours after peak.
The plan is a client-ready artefact; Amit Bachbut's posts provide the argument and admin path, not the schedule.
The material centers on one vendor's products and a lead-generation toolkit, so the recommendation points toward buying that suite and the plan assumes it.
The 752% AI referral figure appears in both creators' posts, but neither traces it to a source.
For clients focused on checkout tracking rather than loyalty placement, ask how measurement changes when checkout moves inside AI Mode, including pixel and server-to-server details—the gap Salomon leaves open and Amit Bachbut documents.
This is the wrong fit for a client whose priority is checkout tracking.
Use this when a client needs a plain-language account of ChatGPT becoming a shopping destination: real-time product picks, reviews and prices inside the chat; buy-now links to merchant sites; and recommendations personalised from earlier conversations.
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It also suits clients facing a retail-operations problem: a store running Square at the till and Shopify online, with stock drifting between the two and card fees jumping from 0.8% to 2.4% under the alternative. The pet-store post candidly describes a live sync failure at up to 80 customers a day—a worked example that can prompt a client to describe their own setup honestly.
Both posts focus on implications and questions.
The shopping post assembles platform reporting and OpenAI material without testing, an implementation path or measurement detail.
It dates from April 2025, before the checkout experiments Amit Bachbut traces, so it cannot describe the current state.
The operations post is a crowdsourcing request: it states the problem but confirms no fix.
It can open a client conversation, but not answer it.
Agencies needing to know what to change in Shopify admin and what happens to attribution should ask for an implementation path and measurement detail.
Choose this when the briefing needs the standards behind agentic checkout: how OpenAI and Stripe's Agentic Commerce Protocol and Google's Universal Commerce Protocol let an agent compare products, check inventory, build a cart and complete checkout while the retailer stays merchant of record.
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The posts explain what that requires: a structured product feed, checkout endpoints, delegated payments, consistent pricing and inventory and fulfilment data, and clean feed and schema data for eligibility. The 'structured feed is the new homepage' framing and the Merchant Center action list translate directly into a data-readiness workstream.
This is protocol-level analysis, not store operations.
The June 2026 posts describe the protocol as already live across free listings, ads and AI shopping in Search and Gemini, with Google extending it to Maps and YouTube.
That trajectory needs checking before it is repeated.
The announced feed-readiness checklist has not yet been delivered.
Before peak, ask for a Shopify admin path, confirmation of US-store eligibility limits on the current rollout, and guidance on browser pixels, which stop firing inside AI Mode checkout—the details Amit Bachbut's posts supply.
This page is built from three LinkedIn posts by Amit Bachbut, read in full.
They cover Google's native checkout inside AI Mode and Gemini and the tracking that stops working inside it, OpenAI's shopping timeline from September 2025 to October 2026, and the argument that AI search visibility has to be prepared before peak trading.
A LinkedIn follower count of 11,441, observed on 8 October 2026, is the only audience figure used.
The platform developments the posts describe are Amit Bachbut's own reporting: the underlying announcements and the webinar recording linked from one post were not available to check, so figures such as the 752% rise in AI referrals and Walmart's reported in-chat conversion rate are unverified here.
No media kit, rate card, sponsorship policy, audience demographics or second channel was available, and nothing in the material states whether sponsored posts are labelled or what usage, lead time or exclusivity terms apply.
The selection is small and recent, so it does not establish earlier signature work or the full range of Amit Bachbut's output.
