Amazon Outage Map

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Amazon Issues Reports

Latest outage, problems and issue reports in social media:

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  pmanik (@pmanik94) reported 8 minutes ago

  @AmazonHelp I did not any resolution from you guys in this chat. I am not sure why customer will face problem if there is system glitch from amazon @amazonIN @PMOIndia

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  Mhykhael (@mhykhael) reported 9 minutes ago

  The interesting part about Anthropic’s compute expansion is that AI infrastructure is increasingly becoming an electricity problem. Anthropic has secured massive compute capacity across SpaceX, Amazon, Google and Microsoft, while acknowledging that frontier AI training will soon require gigawatts of power. The next AI advantage may not come from owning more GPUs. It may come from securing enough electricity to keep those GPUs running.

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  Loganpendragonmultiverse (@loganpendragonm) reported 17 minutes ago

  @SOOPLLC My advice after routing for many, many decades and not having a traditional publisher: there are two routes when it comes to publishing. You don't necessarily have to make them mutually exclusive but you either get published by a publisher in some scenario or you self-publish. There are different versions of each one. My recommendation is to decide upfront what you want to do, which route you want to pursue. Again you can do both but you've got to put time into whichever one you decide to do because if you're going to do traditional publishing then it requires a different approach for that. You need to: - draft your book - write your books - do a rough draft - write them - get it to a final draft - get better readers - get it in front of editors - get it in front of companies That means sending out manuscripts to many publishers and waiting forever. The alternative is self-publishing. I struggled with this for a long time. I didn't want to spend six years of my life writing and just dumping my manuscripts out to different publishers, hoping someone grabbed it. What I did is I just said, "You know what? I'm on a right for the passion of writing and I make money off of it. Don't get me wrong but that's not the reason why I do it." I don't make a whole lot off my writing right now and I may never find with that but I finally had to decide that my passion is writing, not publishing. What I started doing is I'll write the books. I believe in the snapshot theory so I don't ever try to make my work perfect but I make it as perfect as I can at that point in my development. I'll write a book, I'll get it edited, and I'll normally go through about three drafts over time. I'll take breaks from it. After the rough draft, when I feel like it's ready to publish, I put it on Amazon KDP, drop it into Select, and leave it. I'll move on to another book. I try not to obsess over it. Some of them grow, some of them don't. Some of them, months down the road, will finally get some traction and I'm not a big-name famous author by any means of the stretch. I have very few readers in fact but I've made some money off of it and I've got some growth. I've had quite a number of reads and some of my stuff takes off, some of it doesn't, but it's just the fact that I have a passion for writing. My recommendation for an aspiring author is to decide which route you want to take and gear up to pursue that route with vigor because if you get consistent and keep pushing, you never know what will happen.

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  Raye (@rayemarkets) reported 17 minutes ago

  Every time Damodaran uploads a video, I always watch it because he usually takes a concept that sounds simple on the surface and then breaks down the incentives and economics underneath it, and this discussion on scaling versus profitability is a good example. The common startup narrative is that companies should grow as quickly as possible, capture market share, and worry about profits later, but Damodaran's argument is that this approach only works when the structure of the business actually supports it. A large addressable market and fast revenue growth can tell us how big a company might become, but they tell us very little about how valuable that company will eventually be unless growth can translate into better unit economics, operating leverage, pricing power, and returns on invested capital. A company can therefore become much larger without becoming economically stronger, and in some cases scaling simply multiplies the weaknesses that were already embedded in the original business model. This is why the distinction between scalability and business quality is so important. Software businesses can often add customers at very low marginal cost, meaning revenue can grow much faster than the underlying cost base, while businesses involving manufacturing, logistics, physical infrastructure, or expensive customer acquisition may require significant incremental spending for every additional dollar of revenue. Even within technology, being asset-light does not automatically solve the problem because customer acquisition costs, incentives, cloud infrastructure, research spending, and competition can effectively become variable costs that rise alongside growth. Scale only creates meaningful operating leverage when the incremental economics improve as the company gets larger, and if costs continue rising roughly in line with revenue, the company may eventually discover that what looked like a temporary profitability problem was actually structural. Amazon is therefore an important example, but also a dangerous template for other startups to copy. Amazon could tolerate years of weak accounting profitability because its scale was gradually building infrastructure, distribution density, customer relationships, marketplace liquidity, and purchasing power that improved the economics of the business over time, so the losses were connected to assets and competitive advantages that eventually supported much greater profitability. The mistake is assuming that every company reporting losses while growing quickly is following the same path, because some businesses are simply using investor capital to subsidize prices, acquire customers, or enter markets without creating corresponding economic advantages. Both companies can initially show the same headline numbers of rapid revenue growth and negative earnings, but one may be accumulating future operating leverage while the other is accumulating obligations that require continuous external capital. Damodaran's "Field of Dreams" can become a "Field of Nightmares" precisely when investors assume profitability will automatically appear once sufficient scale has been reached. The venture capital structure makes this problem more interesting because the incentives of the investor and the economics of the underlying company are not necessarily aligned. Venture portfolios depend heavily on a relatively small number of very large winners, which means a venture capitalist may rationally prefer a founder to pursue a much larger and riskier outcome rather than build a smaller company producing steady profits. A company that could become a profitable business worth a few hundred million dollars may be economically attractive to its founder, employees, and customers, but it might barely move the returns of a multibillion-dollar venture fund, while turning that same company into a speculative attempt at a ten-billion-dollar outcome provides much more upside to the fund. Scaling therefore becomes partly a consequence of portfolio mathematics rather than purely a consequence of what is optimal for the company itself, which helps explain why startups are frequently encouraged to expand geographically, add products, increase hiring, and raise increasingly large funding rounds even before the economics of the original business have been fully proven. Damodaran's point about pricing versus valuation extends this incentive further. Private markets frequently anchor financing rounds around comparable transactions, revenue multiples, user growth, subscribers, or projected future revenue rather than the present value of sustainable future cash flows, so scale itself becomes an input into the next financing round. Once that happens, raising capital can create a self-reinforcing cycle where capital funds growth, growth supports a higher private-market price, the higher price enables another larger funding round, and that new capital funds even more growth. During favorable capital-market conditions this cycle can continue for years, making it difficult to distinguish between a genuinely improving business and a company whose growth is partly being manufactured by increasingly abundant financing. The real test only arrives when the marginal investor becomes less willing to finance losses and the company has to demonstrate that customers, margins, and cash generation can support the business without constant capital injections. The expansion of private capital has allowed this process to continue much further than it could several decades ago. Companies historically reached public markets relatively early because public equity was one of the few ways to obtain the capital required for large-scale expansion, whereas mutual funds, sovereign wealth funds, private equity firms, crossover investors, and very large venture funds can now provide billions of dollars while companies remain private. Damodaran describes this as the creation of a gray market between traditional venture capital and public equity, and one consequence is that startups can reach enormous revenue bases and valuations before facing the level of disclosure, governance scrutiny, and profitability expectations traditionally associated with public companies. His data also show how much this has changed the profile of companies reaching the public market, with companies generally arriving larger in revenue terms but substantially less likely to be profitable than companies going public several decades ago. There is also a governance dimension that becomes increasingly important as companies scale privately. A founder managing a small startup and a founder controlling an organization worth tens or hundreds of billions of dollars are effectively running very different institutions, yet rapid private-market scaling can allow the governance structure of the first company to survive into the second. Founder control, dual-class shares, fragmented investor bases, and competition among venture investors can weaken the normal mechanisms that challenge management decisions, while large valuations can reinforce the belief that the founder's strategy has already been validated. The danger is that valuation growth can substitute for operational accountability during the scaling phase, and by the time profitability, capital allocation, organizational complexity, or governance problems become visible, the company may already employ thousands of people and control significant amounts of capital. Another part of Damodaran's argument that I find important is that staying small should not automatically be interpreted as failure. Some businesses naturally have better economics when they remain concentrated around a specific customer base, product category, geography, or brand position, because expanding beyond that niche can weaken pricing power or require disproportionately higher capital and marketing spending. Ferrari is an obvious example of a company whose economics partly depend on scarcity, but the principle applies much more widely: maximizing revenue is not necessarily the same thing as maximizing enterprise value. A business generating high returns on capital within a limited market can be economically superior to a much larger competitor producing weak returns after enormous capital investment, which means the correct objective should ultimately be value creation rather than size itself. Personally, this is where I agree strongly with Damodaran, because I do not see profitability and growth as opposite objectives in the first place. A company should absolutely sacrifice near-term profits when it has opportunities to reinvest capital at attractive returns, especially when that spending strengthens distribution, technology, network effects, customer retention, infrastructure, or another durable competitive advantage, but there needs to be a credible economic mechanism connecting today's spending with tomorrow's cash generation. I care much less about whether a rapidly growing company currently reports a profit than about what happens to the economics of the next dollar of revenue, because improving contribution margins, lower acquisition costs, stronger retention, greater pricing power, and falling capital requirements provide evidence that scale is actually making the business better. This also makes the discussion extremely relevant to the current artificial intelligence cycle. Artificial intelligence companies are being pushed to scale models, computing infrastructure, data centers, users, enterprise distribution, and revenue extraordinarily quickly, while the capital required to support that expansion is also becoming enormous. Some of that spending could eventually create exceptional businesses if inference economics improve, utilization rises, customers become deeply embedded in the products, and artificial intelligence generates enough willingness to pay to produce strong margins, but scale alone cannot prove that outcome. If computing costs and capital requirements continue rising alongside usage, then very fast revenue growth could coexist with mediocre returns on capital, particularly when companies must continuously finance new generations of chips and infrastructure simply to remain technologically competitive. For me, the most important question in artificial intelligence therefore is gradually shifting from how fast these companies can grow to how much economic value remains after paying for the infrastructure required to generate that growth, because eventually the market has to separate companies that are using capital to build durable operating leverage from companies that simply need ever larger amounts of capital to keep the scaling story alive.

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  Onyx Novha (@ItzNovhaTV) reported 19 minutes ago

  Another problem back when i worked for amazon is i noticed amazon drivers shopping and getting food instead of delivering, this is also a reason you’re packages show up late, i can understand if drivers needed to stop and go to bathroom or take a lunch or dinner break that makes sense but call me old fashioned i do not believe amazon drivers should be doing there own personal shopping on shift, that is something they should do while there off shift or have days off.

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  Prescribed Slur ⋆˚✿˖° (@AestheticSlur) reported 21 minutes ago

  @brebvbi On the way via personal remote drone Amazon delivery, it drops me down like a claw machine

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  Steve Wise (@SteveWise_OT169) reported 23 minutes ago

  @Lord_Sugar Amazon is effective because it exists within a competitive environment. That is the real problem with the NHS. It needs to be broken up and made competitive.

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  Carlos Rivas MD (@CarlosRivasMD) reported 24 minutes ago

  @RikLesel @toobaffled @Peacelilly1961 I don’t recommend purchasing supplements on Amazon. Large rate of errors, counterfeits, scams, etc. No need to overcomplicate: Get levels tested, if deficient: replete. Retest later to monitor. Lithium is one trace mineral, there are various, all important to brain health and health in general.

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  Amazon Help (@AmazonHelp) reported 29 minutes ago

  @LexiTheLawNerd Hey there! Thanks for reaching out to us and sharing your feedback. While Alexa for Shopping cannot be removed entirely, the chat window can be dismissed or closed. If you're on the chat window in the Amazon Shopping app, you can dismiss the Alexa for shopping screen either by swiping down the chat window, by clicking on the Alexa icon in the bottom of your app, or clicking on the cross (x) in the top of the chat window. Hope this helps. -Shannon

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  Alon Michael (@SpiceP0dcast) reported 29 minutes ago

  "Amazon is a machine. The machine might be broken, and this anecdote is a signal. It's a squeak in the machine - and that's not how the machine is designed to work." Listening to this clip from @ShaanVP on @myfirstmilpod, I think this idea from @JeffBezos will be even more important in the age of agents doing the work.

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  David Murithi (@DaviMurithi) reported 30 minutes ago

  1 Customer research. Involves digging through reviews i.e. Trustpilot, Amazon to identify recurring patterns, avatars, benefits, objections and their frequencies. This is the most crucial step. You get this step wrong & you will flush all the ad budget down the ******* toilet

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  dgaf_dizzle (@HasanFarazDada) reported 33 minutes ago

  @AmazonHelp The problem has not been resolved, no one has called me back and i am being sent around in circles. I am shocked by how bad the customer service is and hope this reached a wider audience.

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  Lil’ Tim (@tinyTim420247) reported 34 minutes ago

  @applepay has been nothing but a headache due to a lost phone years ago. The problem is I need it to use my @Apple account, but I have never used it for anything else, and apple does not seem to protect my account from unfamiliar devices or locations, like Amazon and banks do.

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  Kolluru Akhil Teja (@akhilteja099) reported 35 minutes ago

  @ZeptoNow Placed order OIJJRHNRL21779 purchased 3 rakhi special amazon GC issued by pine labs and when we are trying to add it in amazon it is throwing validation error.Please help.Reference Id 6014854979439364,6014854979329348,6014854979407104 @AmazonHelp @PineLabs

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  Lupen (@0xLupenn) reported 38 minutes ago

  In 1956, a secretary invented something in her kitchen. She brought it to work in a small bottle. In 1975, she sold it to Gillette for $47,500,000. It was white paint. This is Jeff Bezos's lecture on innovation at Stanford. Her name was Betty Nesmith Graham. She was an executive assistant and a terrible typist. The new typewriters used film ribbons. You couldn't erase mistakes. So she went home, mixed white paint to match the paper, and started painting over her errors with a tiny brush at her desk. She called it Liquid Paper. Then the WD-40 story. Three people. Government contract to coat Atlas missiles in storage silos so they wouldn't rust. They failed 39 times. The name WD-40 stands for Water Displacement, 40th Attempt. They named it straight out of the lab notebook. The Atlas missile market turned out to be small. So they sold it in hardware stores instead. Then Bezos talks about Amazon. Barnes & Noble launches online. They have 30,000 employees and $3,000,000,000 in revenue. Amazon has 125 people and $60,000,000. Forrester Research publishes a headline: "Amazon.toast." Bezos calls an all-hands meeting. Tells his 125 employees to be terrified every morning. Not of Barnes & Noble. Of customers. Watch the moment he explains the question nobody ever asks him. Everyone asks what will change in 10 years. Nobody asks what will NOT change. Customers will always want low prices, fast delivery, and wide selection. So you build everything around that. It compounds for decades. One week before this lecture, Amazon launched Amazon Prime. $79 a year. Unlimited two-day shipping. Nobody thought it would work. A senior product manager who worked on Prime expansion: $210,000 base salary. 200,000,000 users. It started with a $79 idea announced to a Stanford classroom. Bookmark this and watch later - after this lecture, every "stupid idea" you have will feel like a small bottle of white paint.