A shopper grabs five tomatoes and walks out. Who knows how much they paid when the cameras guess wrong? A pizza bakes in a moving truck. Who stops the cheese from sliding? A remastered Gone with the Wind hits streaming. Who approves it when the faces look off? Automation works well when there is an army of people behind it. Today's story is about five failed AI pivots where product bets were hit with physics, market reality, and margins. We will study five big cases where the unit economics snapped and the product failed and figure out what we can learn from them. Let's dive in.

Amazon's just walk out was supposed to be a checkout free project at grocery and convenience stores. The idea was to let customers enter a store, pick up the items, and leave without scanning anything or stopping to pay at the till. The project launched in 2018, and later on they rolled it out in Amazon Go and Amazon Fresh stores. The idea was to create a new age frictionless shopping experience. The Just Walk Out project was the first example of a massive investment in AI retail automation, but nevertheless, the company retired the system from its stores in 2024.

Let's understand why. Amazon reportedly spend around $1 million annually on Just Walkout between 2019 and 2020. This investment covered all R&D and capital expenses. Now, how much is it? a billion dollars. Is it a lot or is it a little? The implementation of this system in large supermarkets required from 10 to 15 million per store, which is far above traditional checkout systems. Behind the automation curtain, Amazon hired more than 1,000 people in India labeling videos in real time. That human layer kept things accurate, but it also destroyed the margins. The cost of processing data and fixing mistakes outpaced a normal retail operation, especially in big box stores with complex product mixes.

It is a lot easier to implement a system like this at a convenience store like Amazon Go or Amazon Fresh. And the reason for that is because the product mix and the possible combination of products at those stores are quite limited. Now, if you think about a store like Walmart, for example, you can get a few tomatoes that need to be weighted. You can get a box of tomatoes that needs to be scanned and perhaps a pillow that is 25% off if you get a toaster by the end of the week. When you work with a product mix like that, and that's just a tiny example, Amazon's just walk out starts to produce a lot of errors, which is why they struggled with the adoption among the large stores. It was estimated that up to 70% of transactions required human intervention. The labor cost savings expected from removing cashiers were wiped out by the substantial off-site human operation. And if you think about it, the need to have the humans behind it really defeats the purpose of automation in the first place.

The project also required lots of hardware, thousands of cameras, shelf sensors, a backend infrastructure for computer vision and AI, all of which resulted in high setup and maintenance costs. In this business model, stores would have needed to dramatically increase sales to offset capital and operational expenses. And that was the threshold that Amazon never met. The end result, Amazon continues using this project and smaller Amazon Go stores, but they have abandoned the grocery store application.

You know how every meeting ends in the same way. 10 people nod, three people take notes, and only one remembers what got decided. If you're lucky, you might say, "But costs get recorded." Now, sure, if you're paying for those recordings, and even if you do, what happens after? You've got a pile of text that you still need to do something with, creating action items, updating tickets, drafting follow-ups, or cleaning up the chaos. And this is why we partnered with Radiant, because in a world where you're paying for every single app, the smartest move is to pay for one that replaces five.

And Radiant is actually totally free while it's in beta, so you don't have to pay for anything at all. I personally love tools that work across multiple platforms. It's a lot cleaner. It gets you the best value for your money, and you don't have to remember which app handles what. Radiant is an AI meeting assistant that captures your calls, converts them into notes, and then turns those notes into completed actions. Slack messages, project updates, backlog tickets, drafted documents, and even prototypes. And it works across Zoom, Teams, Google Meet, Notion, Cursor, Lovable, Asana, ClickUp, and so many more. So your follow-ups appear exactly where you already work.

Radiant is not a bot that joins your calls. It runs silently in the background and stores your transcripts locally for privacy so that they never leave your device. Radiant takes care of the admin work that drains your time. And instead of dumping a pile of text on you after the meeting, it immediately takes action. It's available for M1 and newer Macs. And it already feels like a glimpse of what future of workplace AI should feel like. Multi-functional, crossplatform, invisible, and secure. So, if you want to know what it feels like to have your meetings end and your work is already done, check out Radiant using the link below. No bots, no setups, just results. Huge thanks to Radiant for sponsoring this part of the video.

Moving on to IBM's Watson Health. IBM's Watson Health is perhaps the most expensive AI pivot failure in corporate history. The one that has burned through four to five billion dollars in cash over a decade. And in the end, it ended up being sold for a fraction of the money that was invested. IBM is an example of how AI marketing can outpace technological reality. IBM's failure is rooted in extremely high operational costs, deeply flawed unit economics, and glaring gaps between AI promise and practical medical results. IBM positioned its product Watson as a revolutionary cancer diagnosis technology. They also partnered with MD Anderson Cancer Center. Now, if you aren't familiar with the world of cancer care, MD Anderson is a pretty big deal.

The promise was ambitious. AI that could analyze vast amounts of medical literature and patient data to recommend personalized and tailored cancer care. Onto the unit economics and costs. IBM spent an estimated $5 billion on healthcare acquisitions. And that was done with a goal to feed Watson's AI engine with data. At its peak, Watson Health employed more than 7,000 people of various medical and scientific specialties, which meant a massive fixed cost spent in salaries, technical teams, and support staff. Watson's cost per one oncology diagnosis or support case was reportedly much higher than usual. And on the consumer side, consumer being a hospital, the product ended up being very expensive to purchase and maintain with no clear ROI and most importantly no proven clinical benefit.

The MD Anderson Cancer Care Partnership alone consumed $62 million without producing a deployable product. Watson Health's implementation at hospitals was often in the millions per contract but required additional budget for ongoing support customization and integration with electronic health records and electronic health records is the backbone of any hospital. Most hospital deployments required custom configuration because Watson struggled with unstructured patient data. System costs grew faster than revenues. IBM never scaled Watson Health to profitability. Their peak financial performance was when they broke even by slashing jobs and eventually selling the unit for about $1 billion, which is the loss of about 80% of investment.

So why did it fail? What's in health is an example of a fundamental misalignment between AI expectations and the reality of a medical practice. IBM's marketing promised revolutionary breakthroughs the technology couldn't deliver. They did not account for realworld messy patient records and contextrich doctor's notes. Watson could only analyze well ststructured data. A minority at most hospital databases. Doctors, the ultimate users of Watson, found it hard to use and often preferred their own expertise. All of this resulted in many abandoned deployments after months of frustration with complicated interfaces and most importantly unreliable recommendation system.

And on top of that, privacy concerns and multiaceted healthcare laws were not addressed by the product. In the meantime, Oracle and Microsoft took advantage of the fact that IBM was distracted by Watson's complexity and issues and decided to make large and focused acquisitions that yielded clear enterprise value. End result, by 2021, Watson Health became unprofitable, which prompted IBM to sell it. All in all, what's in health enormous losses were a result of unrealistic expectations from AI, unchecked spending, hight touch implementations, and AI that never matched its marketing narrative, and the needs of clinical medicine.

Moving on to Netflix, AI content upscaling. In early 2025, Netflix pushed to use AI for content enhancement and upscaling, which draw a lot of attention and multiple controversies, especially around the mangled restoration of classic shows. Customer backlash has been intense and on top of this, their use of AI triggered broader questions raised about the role of artificial intelligence in media and specifically in media preservation, content quality, and authenticity. Netflix is a prime example of PR crisis around AI. Let's look at what happened. Netflix used AI upscaling to convert older, lower resolution television shows to high definition, notably A different world.

What they did is that they bypassed traditional manual remastering that requires original film negatives and detailed human restoration work. The goal behind this move was rapid, cost-effective restoration of old film. But as it turned out, AI really struggled with grainy source materials. It produced lots of distorted visuals, blurred faces, overly smooth textures, and extremely distorted and unrealistic backgrounds.

Netflix also faced massive backlash when fans discovered AI generated promotional posters for Arcane season 2. Those posters included telltale signs of artificial generation included distorted anatomy and unnatural details. The controversy reached 6 million viewers and forced Netflix to remove the content. In Netflix's case, unlike Amazon Just Walk Out or IBM Watson, it's less about lost revenue or burned cash and a lot more about damaged customer trust and brand perception.

Let's go through the unit economics of this move. If we put ourselves in the shoes of Netflix and look at it from the business perspective, it makes sense to try AI and use it for content upscaling because manual remastering is very expensive and slow. And if there is a part of it that can be restored by a model so you can delegate the most delicate work to an experienced professional, why not do it? AI upscaling can process large libraries at scale with minimum labor. Netflix invested heavily in machine learning for video restoration and the key goal was to reduce the per title cost. But in my opinion, it's one of those things that need to be done in a waterfall sequence. It only works when it's done well because when it fails, the brand damage and customer dissatisfaction offset those savings 10fold.

So why did it fail? The models that Netflix deployed couldn't faithfully recreate the artistic intent, colors, or subtle details of the original footage. Instead of enhancing nostalgia, that is actually very much a trend, especially among Gen Z, with non-conforming anti-perfect aesthetics, it alienated longtime fans. Another curious thing is that the critical flaws with the upscaled content like garble text, cut off scenes, blurred faces, all of them pass Netflix's quality checks. And that in turn signals extreme over reliance on algorithmic solutions and the absolute need for human oversight. AI was used not just in upscaling but also to manipulate actors mouth movements and dubbed films which in turn raised broader ethical concerns over artistic integrity and performer rights. This was not the first time when Netflix faced backlash for AI generated visuals in original documentaries and marketing which they did not adequately address.

All in all, to me, this is a PR failure, even more so than the technical. Technical, too, for sure, but mostly PR. This entire situation has triggered industry-wide debate on the limits of automation in creative media. So, this is a lot more than just Netflix, meaning this whole situation had an industry-wide impact.

Moving on to Zoom Pizza. Zoom Pizza was an ambitious softbankbacked startup whose ambition was to revolutionize pizza delivery. Their vision was to cook pizzas inside trucks and deliver them to customers homes much faster than any other traditional pizza chain. Trucks were filled with dozens of internet connected ovens. Orders were placed through the app. Ingredients and toppings were robotically assembled, although some human workers did some prep. And pizzas were cooked just before delivery arrival. The goal was maximum freshness and efficiency. But there was one painfully simple problem that sort of killed the business. Cheese kept sliding off pizzas while they were cooked and moving trucks.

If you're thinking that it doesn't sound like a big problem, why close the entire business because of the cheese? I'd like to argue and say that on the surface it actually does sound like a good idea. I understand why they got funding, but I would like to invite you to play product management 101 with me and really understand the problem. But before we go there, unit economics and cost breakdown. Zoom's entire automation bet was based on staggering capital and operational costs in an industry known for very tight margins. They raised $445 million from Soft Bank, but they did not do a due diligence calculating operational costs.

Zoom had one fundamental flaw. Despite hundreds of millions in funding and teams of engineers, Zoom forgot about the physics of cooking pizza and moving vehicles. And when the business began operating, the cheese was simply sliding off. They had teams of engineers trying to solve the cheese problem after years of R&D. But the entire operation got essentially defeated by simple physics and realworld conditions that robots could not overcome. Zoom's trucks cost millions to design and build. with each truck fitting 56 mini ovens and was supplied with highly advanced GPS-driven scheduling.

Now, I haven't found a lot of data on the whole thermo isolation piece of it. But can you imagine having a truck full of gas with 56 working ovens inside of it? Ingredient costs were around $6 per pizza and the selling price was set at $18 or more per pizza. Now, the price, in my opinion, is actually quite competitive. Yeah, it's not cheap, but it is a typical price of a restaurant quality pizza. Soft Bank invested $375 million in 2018 and valued Zoom at $2.25 billion based on visions of becoming the Amazon of pizzas. They saw Zoom as part of their strategic portfolio alongside food delivery companies like Uber. Now, Zoom had other investors as well, but the largest sum came from SoftBank. For Soft Bank, the investment became a complete loss because Zoom shut down in 2023.

They tried to make a pivot by 2020. They abandoned the pizza delivery service as a whole and pivoted to sustainable packaging manufacturing using the very robots that they already paid for. Investor pressure, especially from Soft Bank, led them to scale prematurely and make an aggressive pivot into unrelated packaging and logistics industry, which kept burning cash without any sight of product market fit. End result, Zoom's failure demonstrates how fundamental physics can defeat all kinds of sophisticated AI and robots.

Now, let's come back to Product Management 101 for a second and realize that they created a business around a problem that didn't really exist. When doing research for this video, I found this awesome, very niche YouTube channel that talks about how to start and operate pizza businesses and they explain this very problem as people who specialize in pizza business. The thing is, pizza delivery service as we know it works fine. It's not ideal, but it's good enough. And there isn't enough evidence for a service gap that Zoom set out to fix. Zoom's focus on technology over basic product quality created an unscalable business model. They went into aggressive expansion without validating their business model and product in initial markets. I'm frankly extremely surprised how this problem did not come up in initial testing. But if the initial testing never happened, then that would answer my question.

And lastly, Quibby, the 1.75 billion short form streaming disaster. Quibby was a short form video streaming platform in early 2020s. They raised approximately $1.75 billion from investors before launching in April 2020 and shutting down in December 2020. The company was alive for 6 months. Quibby is probably the fastest and the most expensive content platform failure. And the core theme here is that they failed to understand how people consume mobile content. This is definitely the fastest failure given the funding of all the companies that we covered today.

Let's understand why. Quibby raised funds through multiple rounds and multiple investors. They were backed by top tier Hollywood studios. NBC, Sony, Warner Brothers, Lionsgate, MGM, tech firms like Alibaba, and investors like Goldman Sachs and JP Morgan. The company spent lavishly on content production. Budgets reached up to $100,000 per minute for original scripted shows. They produced a wide range of original short form content with a heavy focus on high value production and big Hollywood talent. Given that they were on the market for six months, they've got quite a few shows under their belt. Quibby invested heavily in content, announcing plans to spend over $1.1 billion on original programming during its first year of operation. prestigious filmmakers and stars signed on, and many shows had massive production budgets, very much comparable to high-end cable TV and even Netflix originals.

Advertising spending reached $63 million in just 6 months of operations. But despite all of that, Quibby's revenue from subscriptions and ads was $7 million, and advertiser payments started being deferred due to low viewership. So, let's talk about what went wrong. The biggest theme with Quibby is that they really misunderstood the market. Quibby's executives believe that people wanted high quality short form content for commuting, but they launched in 2020 when commuting disappeared. The service charged from $5 to 7 for content that users could get free on Tik Tok or YouTube.

Now, another problem is the production costs. Despite spending over $1 billion on content from Hollywood talent, Quibby failed to produce any breakout hits. Now, as someone who got into content creation fairly recently, I genuinely think that there is no point in making large investments into production until you start feeling the content market fit, so to say. And to be clear, I'm not comparing myself to a media company with massive Hollywood investors. They're in a polar opposite scale. But nevertheless, this is a content business and I treat my own channel from a perspective of a product manager. This is my product. The point where we knew that it makes sense for us to start investing in better visuals was when we hit several consecutive breakout videos.

When our channel had 500 subscribers back in April, our video started reaching 50 to 80,000 views, which was a massive number for our channel size at the time and given our subscriber count. and we hit those views when videos were filmed on my iPhone 13 with one $80 softbox. I deliberately kept everything extremely low budget because I wanted to see if my content could get breakout through the value of the research and the content that I produce. So, investing billions in production when you have absolutely no idea whether your content is going to be watched makes no sense.

They also had a lot of technical limitations. The app's inability to share content on social media prevented organic growth and word of mouth marketing. And that is the bread and butter of modern content platforms. Users could not take screenshots. They could not create clips. They could not share anything with friends. End result, within 6 months since inception, Quibby announced it shut down. They admitted to have failed to achieve sustainable subscriber numbers for profitability. The company returned some remaining investor funds as goodwill and laid off nearly 250 employees. Roku acquired Quibby's content library in early 2021 for hund00 million and rebranded it as Roku Originals.

Building a scalable AI business is very hard. It's just as hard for larger corps as it is for startups. The difference is in the scale, but it's equally as hard. Don't overestimate the model and underestimate basic product quality. Price the invisible labor. Account for basic physics. Test before you invest and benchmark against the boring product management metrics that always work. Ship the smallest thing that pays for itself and let AI amplify an already solid product. If the unit economics don't beat the baseline, it's not a pivot and it's not a business. You're building a demo and demo doesn't pay the bills. If this video was helpful, let us know what you think in the comments. Till next time. Bye.