Wonder how much of this is due to economics since computer vision tech never reached the expected performance + outsourced workers got (relatively) much more expensive after COVID.
Wonder how much of this is due to economics since computer vision tech never reached the expected performance + outsourced workers got (relatively) much more expensive after COVID.
[Disclaimer: Former Amazon employee and not involved with Go since 2016.]
I worked on the first iteration of Amazon Go in 2015/16 and can provide some context on the human oversight aspects.
The system incorporated human review in two primary capacities:
1. Low-confidence event resolution: A subset of customer interactions resulted in low-confidence classifications that were routed to human reviewers for verification. These events typically involved edge cases that were challenging for the automated systems to resolve definitively. The proportion of these events was expected to decrease over time as the models improved. This was my experience during my time with Go.
2. Training data generation: Human annotators played a significant role in labeling interactions for model training-- particularly when introducing new store fixtures or customer behaviors. For instance, when new equipment like coffee machines were added, the system would initially flag all related interactions for human annotation to build training datasets for those specific use cases. Of course, that results in a surge of humans needed for annotation while the data is collected.
Scaling from smaller grab-and-go formats to larger retail environments (Fresh, Whole Foods) would require expanded annotation efforts due to the increased complexity and variety of customer interactions in those settings.
This approach represents a fairly standard machine learning deployment pattern where human oversight serves both quality assurance and continuous improvement.
The news story is entertaining but it implies there was no working tech behind Amazon Go which just isn't true.
no idea how much they make on it, but it's a game changer in that small area.
One employee as a stocker/chef can support higher throughput in automat style than in counter style fast food service because you have a much more focused task (put food in empty cubby, repeat) than the normal process of "Take order, take money, get order, give customer, deal with mistakes"
They can have an entire wall full of panels for the same item, so that purchases are heavily parallelized. There's usually only a single digit number of items available.
Automats seemingly died because inflation made it hard to accept payment, but that has been a solved problem in vending machines since then.
Japan and some other places still do a lot of vending machine food, but the specific "Wall of items" Automat format enables great logistics that you don't get from vending machines. Weirder still, there are places in asia I have seen that have a AutomatWall style setup, but cook food to order, so you end up waiting!
You can't use an Automat for beer though, without some sort of external system to only allow use by "adults". But surely that's true of a vision system?
I imagined, at the time, future goals would be to scale store size and product variety while reducing the cost of the technology, but I have no insight into how that progressed. I am sorry to learn it's been shut down.
Training is part of any AI project, but it sounds like Amazon wasn’t making much progress, even after years of working on the project. “As of mid-2022, Just Walk Out required about 700 human reviews per 1,000 sales, far above an internal target of reducing the number of reviews to between 20 and 50 per 1,000 sales,” the report said.
The report said Amazon’s team “repeatedly missed goals” to cut down on human reviews, and “the reliance on backup humans explains in part why it can take hours for customers to receive receipts.”
https://arstechnica.com/gadgets/2024/04/amazon-ends-ai-power...
If so, is the reason why it is not used related to cost?
If the customer is accidentally billed for an orange instead of a tangerine 1% of the time, the consumer probably won't notice or care, and as long as the errors aren't biased in favour of the shop, regulators and the taxman probably won't care either.
With that in mind, I suspect Amazon Go wasn't profitable due to poor execution not an inherently bad idea.
In addition to that, you'll have the problem of inventory differences, which is often cited as being an even bigger problem with store theft than the loss of valued product. If the inventory numbers on your books differ too much from the inventory actually on the shelves, all your replenishment processes will suffer, eventually causing out of stock situations and thus loss of revenue. You may be able to eventually counter that by estimating losses to billing inaccuracies, but that's another complexity that's not going to be free to tackle, so the 1% inaccuracy is going to cost you money on the inventory difference front, no matter what.
It is unlikely the tech would be frozen when an acceptable accuracy threshold is reached:
1. There is a strong incentive to reduce operational costs by simplifying the hardware infrastructure and improving the underlying vision tech to maintain acceptable accuracy. You can save money if you can reduce the number and quality of cameras, eliminate additional signal assistance from other inputs (e.g., shelves with load cells), and generally simplify overall system complexity.
2. There is business pressure to add product types and fixtures which almost always result in new customer behaviors. I mentioned coffee in my prior post. Consider what it would mean to add support for open-top produce bins and the challenge of complex customer rummaging. It would take a lot of high-quality annotated data and probably some entirely new algorithms, as well.
Both of those require maintaining a well-staffed annotation team working continuously for an extended time. And those were just the first two things that come to mind. There are likely more reasons that aren't immediately apparent.
They trusted their tech enough to accept the false-positive rate, then worked to determine / validate their false positive rate with manual review, and iterate their models with the data.
From a consumer perspective the point is that you can "just walk out". They delivered that.
I get that this is a message board for YC, so lying about your company's tech is considered almost a virtue but that is an unreasonably big lie to tell without getting your hand-slapped by some regulatory body or investor backlash.
No one cared what I was doing. Is this what it feels like to shop when you're not black?
Turns out people did care what she was doing.Indians, AI, whatever, meh.
It would be a shame if this shared experience was taken over by third worlders.
Meanwhile the distinction between the US and the so-called "third world" seems to become less apparent and less relevant every day. Indian teenagers need jobs too, don't they? More power to them.
Still, would love to see a breakdown of why it didn't improve. Regardless of the accuracy at launch, I'd think that advances in AI would have been massively to their advantage. I wonder if security degradation hit them hard.
The entire system depends on a level of social trust that doesn't exist in American cities today. Similarly, the "Dash Cart" seems like a cheaper and easier way to accomplish the same thing.
At the end of the day, there's also a mismatch in the use case. If I'm going to a smaller format store, like they had, I'm not buying a ton of stuff. Self checkout is great, and minimal friction.
I'd think that improving the UX of self-checkout gets 80% of the way there with way less fraud, way less theft, and way less technology.
Still, I think it's wicked cool they took a big shot.
I know someone that worked on the project in the early days. It was always incredibly difficult technology, they were always behind on their accuracy targets, and the solutions were increasingly kludgy as they layered more and more complex systems on top. An honorable failure.
A lot of smart people really tried to make it work.
But this is tech and you just lie because hardly anyone in the investor class knows enough to call you out on it or they are just going with the lie to make a buck off of other rubes.
Privacy concerns aside, I thought it was a cool project. I agree that “convenience store” was probably not the best target but I think it was an effective enough proof of concept (creating a decent sized chain of them probably wasn’t the best idea) . I’ve seen the system used more effectively in smaller situations like stadium concessions, where the duration of the transactions needs to be very short to facilitate throughout.
For a full fat grocery store. With zero change or adjustment to the rest of the grocery store. And customers weirdly like self checkouts even when they are a dramatically worse outcome (compared to the highish bar of well trained cashiers)
An idle self-checkout machine costs the store almost nothing. An idle cashier costs the store wages. So the stores will always skimp on cashiers, leading to lines, wasting my time.
Example: Tesla Cybercab with safety drivers, or Starship Technologies "autonomous" robots, which are remote controlled delivery robots.
And in fact, Tesla was sued for securities violations by shareholders over the cybercab.
https://kehoelawfirm.com/wp-content/uploads/2025/08/Tesla-Cl...
Starship Technologies is a private company.
Regulation lags so far behind that you can get away with bad behavior long enough that, by the time regulation catches up, you can buy your way out of consequences.
It's not hard to imagine edge scenarios for which the models aren't trained, like a customer dropping an item, or putting an item back in a random shelf instead of the one it's intended for, or someone picking up that previously randomly placed item, etc.
I think people just think that they must be misunderstanding something; that nobody could claim one thing while offering evidence of its opposite. 1/5 of purchases lose their significance.
Business Insider also reached out to Amazon at the time and a spokesperson denied that actually reviewed any transactions.
This "proven false" thing is just another anonymous source claiming that actually it was only 20%.
So you actually have no proof of anything, you just have three persons claiming three different things (0%, 20% and 70%).
[1] https://www.businessinsider.com/amazons-just-walk-out-actual...
It’s reasonable to expect a system like Amazon’s to use human feedback in training, and to quote the article linked on Wikipedia:
> Amazon said that the India-based team only assisted in training the model [and validating] a small minority of shopping visits.
It certainly felt like it could have been sent off to a lower paid country for a human to tot up.
Also consider you're in the store for what, 10 mins - that's a lot of video processing presumably using state of the art CV models. It's quite possibly cheaper to pay a human than rent the H100 to do it.
The supply/demand curve shifted and now those workers are becoming more expensive while domestic workers are becoming cheaper.
Scale is beyond comprehension though, there were 250 million people on strike one day last summer. This is not ever really covered in western media or mentioned on HN for reasons that are surely not interesting or worth pondering at all.
Americans have the nearly the highest nominal and PPP income of OECD countries as of 2024, only behind Luxembourg, Iceland, and Switzerland [1].
India experiences substantially higher shelter and food insecurity and poverty rates than the United States.
However, tech workers in Bangalore are paid an order of magnitude higher than prevailing local wages in other sectors, at around ₹2M (₹20 lakh) [2]. Median annual rents for 2BHK (2 bedroom) apartments appear to be around 1/10th of that figure at ₹3 lahk in desirable neighborhoods [3].
It appears to be reasonable for a technology worker to be able to perform a sustained strike. I have never personally traveled to Bangalore, though I have lived in places where cost of living is under a tenth of median American income.
I invite correction by people with first hand knowledge about cost of living in Bangalore.
1. https://www.oecd.org/en/data/indicators/average-annual-wages...
2. https://timesofindia.indiatimes.com/city/bengaluru/median-te...
3. https://www.birlaevara.org.in/best-areas-in-bangalore-for-re...
I don't think the strikes are done by tech people at all. Just normal workers.
This is a fact.
I think they might have added all the trade union workers across India and came up with that number.
250 million people striking in India isn't mainly “tech workers in Bangalore”, or mainly tech and other elite workers at all. It’s about 40% of Indian workers, and most articles I've seen about it centered on widespread participation of workers in coal, construction, and agricultural sectors.
When India "shut down" for Covid, day labourers suddenly had no income, and no government support - they had to walk all the way to their home province (can't remember if the trains were even running).
But oh well, Uberizing employment means the run-of-the-mill American worker can also live like that in the future... progress!
I'm not an economist either, but I also assume that as the country attracts more local talent for local companies, the competition for outsourcing becomes harder. (i.e, you now have to pay more than the local companies).
All just speculation on my part though, I really have no clue either.
Theoretically if it was 99% computer and 1% human, that's enough to mess up the economics but it's not a bait and switch like some companies have done.
(Not to refute your point, of course, I am just curious)