We’ve reviewed a fair number of AI-coded stores by now, and the pattern is depressingly consistent. The homepage looks sharp, the product pages are clean, the cart animation is genuinely nice, and then checkout falls over the moment two people try to buy the last item in stock. AI ecommerce app bugs don’t usually show up in a demo. They show up on launch day, when real traffic and real money hit the parts of the system that were never properly tested.
This isn’t a knock on the tools. Bolt, Lovable, Cursor and the rest are excellent at producing a store that looks finished. The problem is that “looks finished” and “handles money correctly” are two completely different bars, and checkout is where that gap becomes expensive.
Why stores are harder than they look
A blog or a portfolio site can be wrong in small ways and nobody notices. An ecommerce store can’t. Every checkout involves at least three systems that have to agree with each other in real time: your inventory count, your pricing and tax logic, and a payment processor that doesn’t care about your deadlines. Get any one of those out of sync and you’ve either sold something you don’t have, charged the wrong amount, or taken someone’s money without giving them an order.
AI coding tools are good at writing the happy path. Add to cart, go to checkout, enter card details, confirm, done. What they’re much weaker at is the unhappy paths that make up a huge share of real checkout traffic: two customers buying the same item at once, a customer who refreshes the page mid-payment, a discount code that’s expired but still visible, a shipping address in a state with different tax rules than the one the cart calculated. None of that is exotic. It’s just Tuesday for an online store.
We wrote about why a vibe-coded app breaks in production in general terms a while back. Ecommerce is that problem concentrated into one page of your site, with a payment form sitting in the middle of it.
The checkout failure points
There are four places we see AI-generated checkouts go wrong again and again, and they tend to cluster.
Stock. The cart shows an item is available because it checked stock when the page loaded, not when the order was placed. If two customers add the last unit to their carts within the same minute, both checkouts can succeed. Now you’ve sold one item twice and you get to have an awkward email conversation with whoever loses that race.
Pricing. Prices get calculated in more than one place: the product page, the cart, the checkout summary, and sometimes a separate discount engine. AI-generated code often duplicates this logic instead of centralising it, so a coupon applies correctly on the cart page but not at final checkout, or a currency conversion rounds differently depending on which component did the maths.
Tax. Tax is genuinely difficult, and it’s the part most likely to be stubbed out with a flat rate “for now” that never gets revisited. A flat 20% VAT assumption breaks the moment you sell to a customer in a state or country with different rules, and most AI tools won’t flag this as a risk because from a code-correctness standpoint, nothing is actually broken. It’s just wrong for the real world.
Payment race conditions. This is the one that costs the most. A customer clicks “pay” once, the network is slow, they click again, and now two payment intents fire against the same order. Or the payment succeeds with the processor but the webhook confirming it never gets processed correctly, so the customer’s card is charged and your system still thinks the cart is abandoned. We go into more depth on this specific failure mode in our guide on trusting AI with Stripe integrations, because it’s common enough to deserve its own article.
Cart and inventory bugs that cost real money
The failures above aren’t theoretical edge cases, they’re the ones that actually show up in support tickets. We’ve seen a store overselling a limited drop because stock checks happened client-side and could be bypassed by anyone who opened dev tools. We’ve seen abandoned carts hold stock hostage for hours because nothing released the reservation, quietly strangling a bestseller’s availability during its busiest week. We’ve seen discount codes stack when they were only supposed to apply once, turning a 10% off promo into an effective 40% loss on every order that found the trick.
None of these bugs are dramatic to look at in the code. They’re usually one missing check, one race condition, one assumption that only one person would ever be checking out at a time. But the financial impact is direct and immediate, not the vague “technical debt” kind of cost. It’s refunds, chargebacks, and stock you can’t fulfil.
What to test before a sale
Before you point traffic at a store built this way, run through checkout with intent to break it, not just to confirm it works.
Open two browser sessions and try to buy the last unit of the same product at the same time in both. See what happens. Apply a discount code twice in one order. Start checkout, then deliberately let your session time out or your connection drop, and see whether the order or the charge exists in a half-finished state. Change your shipping address to a different tax region partway through and check the total updates correctly. Refresh the confirmation page after payment and make sure it doesn’t submit the order again.
If any of that makes you nervous just reading it, that’s useful information. It means these paths haven’t been tested, which means they haven’t been fixed either.
When to get the money path reviewed
Not every part of a vibe-coded store needs a professional audit before launch. A slightly awkward about page or a clunky filter on the product listing is a fine thing to ship and improve later. Checkout isn’t in that category, because bugs there don’t cost you polish, they cost you money and customer trust in the same afternoon.
If you’ve built your store with an AI tool and haven’t stress-tested the checkout flow specifically, that’s the one area worth a proper look before you run a sale, a launch campaign, or anything that drives a spike of traffic. It’s also worth reading the real cost of free AI-generated code if you want the fuller picture of how these small gaps compound once real customers are involved.
Selling online? Let us harden your checkout before launch day. Free. Get in touch and we’ll review it before you find out the hard way.