Amazon status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of Amazon reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by Amazon users through our website.

• Website Down (45%)
• Errors (31%)
• Sign in (24%)

Live Outage Map

The most recent Amazon outage reports came from the following cities:

City	Problem Type	Report Time
Paris	Website Down	4 hours ago
Guadalajara	Errors	18 hours ago
New York City	Website Down	19 hours ago
Pozza di Fassa	Website Down	24 hours ago
Bristol	Website Down	24 hours ago
Paris	Website Down	1 day ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

Amazon Issues Reports

Latest outage, problems and issue reports in social media:

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  Kerry Myers 🛡️⚔️🪽🇺🇸♥️☘️🏝️ (@7kerry757) reported 5 minutes ago

  I realize I am not much of a fan of books that go back and forth in time by chapters. The "here and now" then chapter 3, ""9years ago...". Then it tells about a particular night, a date or something similar. The conversation, what they ate. Usually rom-coms It suppose to give background but I feel it just fills up a book. Everytime I turn the page and it's switching back to the past I put the book down and do something else. X, Sudoku, whatever Looking forward to Alias Paine by Betty J Ownsbey. A friend ordered it for me on Amazon

• 
  AE Groyper (@sneed683317) reported 6 minutes ago

  @ProxyJuice Companies are actively trying to expand "AI" into stuff like Amazon search bars and customer service help lines. For that the number of requests is growing exponentially so it's not speculation. The issue is nobody wants data centers and nobory wants AI Amazon search either.

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  Lindsay ♿️🏳️‍🌈🏳️‍⚧️ (@YesAnastasia13) reported 18 minutes ago

  @JesseVanasse It's because they updated the app. I had that problem on my tablet because I was using the old version. I tried to update it but tge new version is not yet in the Amazon fire store. Annoying.

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  Kerry Myers 🛡️⚔️🪽🇺🇸♥️☘️🏝️ (@7kerry757) reported 18 minutes ago

  I realize I am not much of a fan of books that go back and forth in time by chapters. The "here and now" then chapter 3, ""9 years ago...". Then it tells about a particular night, a date or something similar. The conversation, what they ate. Usually rom-coms It suppose to give background but I feel it just fills up a book. Everytime I turn the page and it's switching back to the past I put the book down and do something else. X, Sudoku, whatever Looking forward to Alias Paine by Betty J Ownsbey. A friend ordered it for me on Amazon

• 
  nandini choudhury (@nandinichoudhur) reported 23 minutes ago

  @AmazonHelp The issue is that the specialist team member Ms Sakshi yesterday spoke to me. When I said I would raise this on social media, she asked, “Is this a threat?” I said yes—because after repeatedly calling for a ₹300 item, I have no option left.

• 
  Pranat Dwivedi (@Pranat_Dwivedi_) reported 24 minutes ago

  🏆 The Outstanding Customer Harassment Award goes to @Lenskart_com! 🎉Ordered glasses, received them perfectly DAMAGED. And the customer support is so legendary that when I faced an Amazon Pay issue, their grand solution was: Sir, make a new account.Peak problem-solving! 👏 (1/4)

• 
  akshay.ulgekar (@akshayulgekar) reported 28 minutes ago

  @UberIN_Support being chasing since months, messaged and provided details but no response. Amazon pay issue. Can you fix this for once? #uber This could be my 10 th tweet for the same issue

• 
  Aravan (@aravintharavan) reported 30 minutes ago

  @AmazonHelp I m purchased prime but not working y????

• 
  Graeme Pennell (@Directandhated) reported 30 minutes ago

  @veritasium I would watch it, but the have more blocked non sign in access from Amazon fire tablets & I'm not making another account.

• 
  Vishal Sharma (@VS2609) reported 32 minutes ago

  @AmazonHelp Share the link once again or arrange a call back. It is not that difficult. The links doesn’t solve the problem.

• 
  Cecil (@cecil_21m) reported 32 minutes ago

  @JayCampbell333 There seems to be an issue with buying the kindle format on Amazon. The buy button is not available?

• 
  Dapsy𓃵 (@symplyDAPO) reported 34 minutes ago

  Ryan Hurst’s injury just forced Amazon MGM and Sony to make a brutal decision. After filming four episodes, Hurst tore his bicep during a stunt on the Vancouver set. The injury required surgery and could keep him out for 6–12 months. Rather than shut down production for nearly a year, the studios decided to recast his role.

• 
  もり (@LowKarmaPerson) reported 35 minutes ago

  @Safoualo @LilithLovett What rich company lets anybody unionize? ******* Walmart and Amazon will shut down any location trying to unionize if they can - you think Rockstar is different? Oh, the employees shouldn't do anything about it then?

• 
  Leigh Prideaux (@leighsovlyfe) reported 35 minutes ago

  The first week of my book asks you to change absolutely nothing. Just write down what you intended, what happened, and what was going on at the time. Seven days. Sixty seconds a day. Most people skip it and build on a story instead of facts. Free on Amazon until Sunday.

• 
  Qwerty (@talkqwertytome_) reported 41 minutes ago

  I am so embarrassed of the absolute neurodivergent path I have gone down today. Cleaned the yard until my bins are almost full, moved my old fridge outside, made a meal plan spreadsheet, built three storage trolleys, shopped for an Amazon Echo on FB , and done NO ******* WORK

• 
  Sanjeev Gosar (@SanjeevGosar) reported 42 minutes ago

  @HDFCBank_Cares @HDFC_Bank I have written mail on 18 Aug 2026 regarding my issue, yut no one replied on it. refund from Amazon for product return reflacted with lesser amount. Amazon customer care is saying short payment is from bank side they will pay partial refund.

• 
  Tomorrow’s James (@james__art) reported 45 minutes ago

  @wholemars A designated Amazon Dropbox could solve this problem. The drone could detect a Dropbox.

• 
  sanjeev kumar (@sanjee5) reported 47 minutes ago

  @AmazonHelp Not working this link Msg given try after sometime

• 
  @DextrousNinjaReturn (@DextrousNinjaNu) reported 50 minutes ago

  @amazonIN This is not merely a routine delivery issue Sr Citizen urgently requires the diapers, & every additional delay is causing genuine hardship Pl treat this as an URGENT ESCALATION & resolve it immediately #Amazon should not mark an order as delivered when he has not received it

• 
  Professor X⚕️ (@pepple_miracle) reported 52 minutes ago

  One of the most underrated ways I study my competitors on Amazon and this strategy can X10 your sales: I read their reviews. Not just the 5-star reviews. I pay serious attention to the 3-star and 1-star reviews. Why? Because buyers will literally tell you what the book is missing. They’ll tell you what they loved. What disappointed them. What they expected but didn’t get. What they wish was included. This gives me a blueprint. I can see what the market already likes, identify gaps competitors are leaving behind, and build my book around solving those problems better. Your competitors’ reviews are free market research. Don’t just study what they are selling. Study what their customers are saying.

• 
  TheValueist (@TheValueist) reported 53 minutes ago

  HYPERSCALER CLOUD EVIDENCE Combined Q2 revenue across AWS, Microsoft Intelligent Cloud, and Google Cloud reached approximately $106.3 billion, increasing approximately 43 percent year over year and 15 percent sequentially. The absolute level and acceleration strongly support the conclusion that newly installed cloud capacity is finding paying demand. (Citadel Securities) The combined figure is directionally powerful but not an apples-to-apples measure of AI infrastructure revenue. AWS is a cloud segment with substantial infrastructure and platform exposure. Microsoft Intelligent Cloud includes Azure as well as server products, cloud services, and enterprise services. Google Cloud includes Google Cloud Platform, Workspace, and, beginning in Q2, sales of TPU systems. The $106.3 billion total therefore overstates pure AI compute revenue and should be treated as a broad cloud-demand indicator. AWS generated Q2 revenue of $42.2 billion, increasing 37 percent year over year, with operating income of $16.6 billion. The implied segment operating margin was approximately 39.3 percent. Management stated that AWS’s AI business had exceeded a $25 billion annual revenue run rate and was growing at more than 100 percent, while its internally designed chip business had also exceeded a $25 billion annual run rate. These disclosures provide strong evidence of both aggregate AI demand and customer willingness to adopt custom silicon as an alternative to merchant accelerators. (Amazon) Microsoft Intelligent Cloud generated $39.3 billion of revenue, increasing 32 percent, while Azure and other cloud services grew 43 percent. Microsoft Cloud revenue reached $59.3 billion, Azure annual revenue surpassed $100 billion, Microsoft 365 Copilot exceeded 30 million paid seats, and commercial remaining performance obligations reached $678 billion. The data indicate powerful demand across infrastructure and application layers, although the very large backlog includes long-duration commitments and may not convert uniformly into near-term revenue or cash flow. (Microsoft) Google Cloud generated $24.8 billion of revenue, increasing 82 percent year over year, with operating income of $8.8 billion and an operating margin of approximately 35.5 percent. Backlog reached $514 billion, increasing approximately $50 billion sequentially. Management stated that cloud supply remained constrained and that token usage through Google’s APIs increased from approximately 16 billion tokens per minute in the prior quarter to 22 billion, representing growth of approximately 37.5 percent sequentially. TPU-system sales contributed to Q2 revenue, although management indicated that underlying cloud growth also accelerated materially excluding those sales. (SEC) These results are difficult to reconcile with an immediate AI-demand collapse. Cloud platforms are reporting strong revenue growth, expanding backlogs, high utilization, accelerating token volumes, and continued supply constraints. The evidence is particularly important because cloud revenue represents paid demand rather than model benchmarks, user registrations, or noncommercial experimentation. However, cloud revenue does not prove satisfactory incremental returns on AI capital. Revenue growth can coexist with deteriorating free cash flow when capital expenditure grows faster than monetization. Amazon’s trailing free cash flow turned negative as property and equipment purchases increased sharply, Alphabet raised 2026 capital-spending guidance to $195 billion-$205 billion, and Meta projected 2026 capital expenditure of $130 billion-$145 billion. The near-term income statements benefit from accounting depreciation schedules that can lag the economic obsolescence of AI hardware. (Amazon) Cloud-segment margins are encouraging but should not be attributed entirely to AI. Mature storage, database, networking, productivity, security, and conventional compute services can subsidize lower-margin accelerator workloads. AI-specific gross margins can be burdened by expensive accelerators, power, networking, cooling, reserved capacity, and underutilization during deployment. Greater disclosure of AI revenue, depreciation, utilization, and incremental margins will be required before cloud growth can be translated into confident capital-return estimates. The most favorable hyperscaler attribute is vertical integration. Full-stack platforms can capture economics across custom silicon, cloud infrastructure, model APIs, developer tools, enterprise applications, advertising, commerce, and productivity software. Falling token prices can pressure model API margins while simultaneously increasing cloud consumption, improving application engagement, and reducing internal operating costs. This ability to monetize demand across several layers makes full-stack hyperscalers structurally better positioned than businesses dependent on a single source of AI rent. The rise of custom silicon is a critical 2nd-order implication. AWS Trainium, Google TPU, and other internally designed accelerators can lower hyperscaler cost per token, reduce dependence on merchant GPU supply, and segment workloads according to performance requirements. Aggregate AI compute demand can remain extremely strong while the merchant accelerator share of incremental workloads declines. The elastic-demand thesis is therefore more bullish for the total compute ecosystem than it is automatically bullish for any 1 accelerator vendor. EARNINGS AND MACRO EVIDENCE Underlying Q2 S&P 500 earnings increased approximately 33 percent after excluding mark-to-market gains at Amazon and Alphabet. Including those gains, aggregate growth was approximately 52 percent. Approximately 85 percent of reporting companies exceeded earnings expectations, 7 of 11 sectors produced double-digit growth, and AI-infrastructure companies contributed approximately 1/3 of aggregate earnings growth. The results indicate that current economic and corporate profit conditions are stronger and broader than a narrow AI-capital-expenditure boom alone. (Reuters) Broad earnings strength reduces the near-term vulnerability of AI spending to an abrupt cyclical downturn. Healthy cash generation outside the infrastructure complex allows enterprises to fund experimentation, software adoption, and workflow redesign. It also improves hyperscaler customers’ willingness to commit to cloud contracts and reduces the risk that AI budgets are the 1st expenditure category eliminated during a slowdown. The earnings data nevertheless introduce a higher comparison base. Sustaining 30 percent-plus aggregate earnings growth becomes progressively more difficult as prior-period results strengthen. Equity returns depend on the gap between realized earnings and embedded expectations, not on growth in isolation. Strong results can therefore support fundamentals while increasing the risk that subsequent deceleration disappoints elevated consensus forecasts. The note also observes that a cross-asset principal component associated with global growth stood approximately 0.84 standard deviations above its historical mean, corresponding to approximately the 65th percentile of the previous 5 years. This suggests that broad macro markets were reflecting above-average growth expectations without reaching the extreme conditions normally associated with generalized euphoria. (Citadel Securities) The cross-asset factor should not be used as evidence that AI equities are inexpensive or that TMT expectations are restrained. A global growth factor derived from rates, currencies, commodities, and broad equities can remain moderate while a concentrated group of AI-linked securities prices in aggressive long-duration outcomes. The factor is more useful for assessing the macro backdrop than for evaluating semiconductor, hyperscaler, or software valuation risk. A successful elastic-demand regime may also have ambiguous implications for discount rates. Higher AI productivity can improve potential growth and corporate margins, but an extended capital-expenditure cycle can increase demand for power, construction, electrical equipment, networking, memory, and skilled labor. Stronger real growth and capital demand can keep real interest rates higher than would otherwise prevail. Higher earnings can consequently be partly offset by higher discount rates, particularly for long-duration equities. The macro implication is therefore not uniformly risk-on. An AI investment boom can support nominal growth, industrial activity, electricity demand, and corporate profits while pressuring free cash flow and keeping capital costs elevated. The most attractive exposures are likely to be businesses with near-term revenue conversion and pricing power rather than distant terminal-value narratives. METHODOLOGICAL LIMITATIONS TEMPORAL MISMATCH The token-price decline and spending acceleration do not cover precisely the same interval. The approximately 40 percent price decline is measured from the end of June through mid-August, while the 49 percent spending increase compares July with June. Spending recorded in July cannot have been caused by price reductions occurring in August. Enterprise contracts, annual subscriptions, cloud commitments, and internal deployment plans can also be established months before payment data appear. The temporal mismatch does not invalidate the observation that prices are falling while activity remains strong. It does prevent a clean claim that the measured July spending acceleration was a direct response to the full 40 percent price decline. A valid elasticity study would require synchronized price and quantity observations for the same customer, model, workload, and period. PRODUCT-BASKET MISMATCH The token-price index measures usage-weighted API token prices. The corporate-spending dataset includes software subscriptions, coding products, API charges, model-hosting services, and GPU-cloud expenditures. A company can increase spending because it purchases more seats or rents accelerators even while its average token price is unchanged. Conversely, a token-price decline can reduce API spending without affecting subscription expense. The current evidence therefore demonstrates ecosystem activity rather than a single-product demand curve. The distinction is essential for value attribution because different products carry different margins, capital intensity, and competitive dynamics. ENDOGENOUS PRICE MIX The token index falls when customers move toward cheaper models. That substitution is itself an expression of demand elasticity. Treating the resulting lower index as an independent cause of higher demand can overstate causal confidence. A stronger design would isolate explicit provider price reductions for unchanged models and compare subsequent usage among matched customer cohorts. INCOMPLETE QUALITY ADJUSTMENT Price per token is not price per unit of useful intelligence. Improvements in reasoning accuracy, latency, tool use, context handling, coding performance, and reliability can change the number of tokens required to complete a task. The same nominal price can represent dramatically different economic value across model generations. A quality-adjusted series should measure cost per accepted task, cost per resolved customer interaction, cost per successfully completed coding objective, or cost per unit of incremental revenue or labor time saved. Current market data remain too immature to provide those measures consistently. SAMPLE-SELECTION BIAS The spending dataset disproportionately represents high-growth and technology-forward firms. Those companies are likely to adopt AI earlier, tolerate greater experimentation, and possess the engineering resources needed to integrate models. The results are valuable for identifying leading-edge behavior but are unlikely to represent the median company across the entire economy. (Ramp) The top 1 percent cohort is also likely to contain AI-native businesses for which inference is a direct production input. Rapid spending growth in that cohort validates demand for compute but does not necessarily validate productivity adoption across traditional enterprises. The distinction matters for long-run market size because AI-native demand can scale rapidly but may remain concentrated and exposed to venture financing, customer acquisition economics, and model commoditization. SPENDING IS NOT RETURN ON INVESTMENT Higher spending can signal successful scaling, but it can also signal inefficiency. AI workloads can generate unexpected token consumption through long contexts, repeated agent loops, retries, hallucination correction, and redundant model calls. Payments data cannot distinguish productive usage from waste. Durable enterprise adoption requires measurable benefits in revenue, gross margin, labor productivity, cycle time, quality, customer retention, or risk reduction. Spending growth without outcome disclosure should be viewed as a leading activity indicator rather than proof of economic value. ECOSYSTEM REVENUE CAN BE DOUBLE COUNTED The same end-customer dollar can appear as application revenue, model API revenue, and cloud infrastructure revenue as it passes through the stack. Adding revenue across layers can overstate end-market economic value. Cloud acceleration is a valid demand signal, but it cannot be added directly to model and application revenue to estimate an unduplicated AI market. FORWARD-CURVE DEPTH IS LIMITED Compute forward curves are informative but shallow. Prices reflect a market with limited liquidity, heterogeneous contract terms, regional differences, service-quality differences, and rapidly changing hardware. Current backwardation should be viewed as an indicator of expected depreciation and supply growth rather than a precise institutional forecast. UTILIZATION IS NOT DIRECTLY OBSERVED Firm accelerator prices imply strong marginal demand but do not reveal fleet-wide utilization. A small pool of scarce, immediately available capacity can trade at high prices while larger committed fleets experience variable utilization. More complete analysis would require active GPU hours, cluster utilization, queue lengths, power consumption, and customer concentration across providers. SUPPLY CONSTRAINTS CAN MASK DEMAND ELASTICITY When capacity is constrained, revenue and prices can rise even if latent demand is weakening because customers compete for limited supply. Conversely, demand can appear modest because supply is unavailable. Current hyperscaler commentary indicates persistent constraints, making observed revenue a lower bound on potential demand but also limiting the ability to estimate how demand behaves once supply becomes abundant. VALUE CAPTURE ACROSS THE AI STACK MERCHANT ACCELERATORS The evidence is favorable for near-term accelerator shipments and utilization. Falling token prices are not currently reducing the appetite for compute, while new-generation rental markets remain firm and hyperscaler supply remains constrained. Reasoning models, agents, video generation, multimodal inference, synthetic data, and test-time compute can all increase accelerator intensity. The long-run implication is more conditional. Elastic demand supports total accelerator quantity, but vendor economics depend on market share, average selling price, product cadence, software lock-in, networking attach, and customer returns. Custom silicon can capture a growing share of stable, high-volume inference workloads. Smaller models, quantization, sparsity, caching, speculative decoding, and improved utilization can lower compute required per token. Merchant accelerator revenue grows only if workload expansion exceeds both efficiency gains and share loss. The most durable merchant-platform advantage is not raw chip performance alone. It is the combination of software compatibility, developer tooling, networking, systems integration, rapid deployment, reliability, and access to a broad customer ecosystem. A platform that reduces implementation risk can preserve economic value even as raw compute commoditizes. CUSTOM SILICON Custom accelerators are among the clearest relative beneficiaries of elastic AI demand. Larger workload volumes improve the economics of fixed design investment, enable optimization for internal software stacks, and reduce the need to pay merchant-platform margins. Hyperscalers can allocate frontier training and highly variable workloads to general-purpose GPUs while moving mature inference workloads to internal chips. The growth of custom silicon does not necessarily reduce total semiconductor demand. It redistributes value toward foundry capacity, advanced packaging, high-bandwidth memory, networking, intellectual property, and hyperscaler-specific design ecosystems. Aggregate compute consumption can rise while the mix becomes more heterogeneous. The principal risk is software fragmentation and underutilization. Custom architectures create the greatest advantage when workloads are sufficiently large, predictable, and vertically integrated. Merchant accelerators retain an advantage for rapidly evolving models, external customers, and workloads requiring broad software compatibility. HIGH-BANDWIDTH MEMORY, NETWORKING, PACKAGING, AND SYSTEMS These layers offer relatively architecture-agnostic exposure to rising compute intensity. Large-scale AI systems require high-bandwidth memory, advanced packaging, optical and electrical connectivity, switches, interconnects, power delivery, and thermal management regardless of whether the accelerator is supplied by a merchant vendor or designed internally. Value capture remains cyclical. Capacity additions can convert scarcity into oversupply, standards can change, and customers can redesign systems to reduce bottleneck intensity. However, the diversity of accelerator architectures may increase rather than decrease demand for integration, networking, and memory expertise. Networking demand may be particularly durable as inference becomes more distributed and agentic workloads require communication across models, tools, databases, and regions. Training remains highly scale-up intensive, while inference increasingly adds scale-out complexity. The infrastructure burden therefore moves rather than disappears as model efficiency improves. HYPERSCALERS Hyperscalers are structurally advantaged because they control distribution, capital, infrastructure, customer relationships, proprietary data, and several monetization layers. Elastic demand increases cloud usage, improves the economics of custom silicon, and supports application adoption. Model commoditization can be beneficial because lower model costs expand usage while hyperscalers retain the customer relationship. The major risk is capital intensity. Hyperscalers are committing extraordinary amounts of capital before the ultimate mix of training, inference, custom silicon, and application revenue is known. Supply constraints currently protect utilization, but future capacity additions can reduce pricing and expose differences in deployment efficiency. Depreciation represents a material accounting and economic risk. If hardware becomes obsolete more rapidly than its accounting life, reported operating margins can overstate steady-state economics. Conversely, firm H100 rental prices suggest that older systems currently retain more economic value than feared. The direction of residual values over the next several hardware generations will be a critical determinant of capital returns. The preferred hyperscaler exposure is characterized by visible cloud acceleration, high utilization, internal-silicon capability, durable enterprise distribution, and monetization outside infrastructure. Platforms relying primarily on infrastructure resale without application or advertising economics carry greater sensitivity to rental-price compression. MODEL LABORATORIES The evidence is mixed to negative for long-duration model-layer rents. Aggregate token consumption is likely to grow, but usage-weighted prices are falling rapidly, open-weight models are improving, customers are multihoming, and sophisticated users are routing workloads based on price and quality. Frontier capability can command a premium for high-value tasks, but the premium may narrow rapidly as competing models converge. Falling token prices combined with firm GPU prices create a potential gross-margin squeeze. Model providers must offset the lower revenue per token through improved inference efficiency, batching, quantization, caching, hardware procurement, proprietary silicon, or higher utilization. Providers without superior infrastructure economics or differentiated distribution are particularly exposed. The model layer can still create substantial value through consumer distribution, proprietary data, developer ecosystems, enterprise trust, agent platforms, and control of high-value reasoning workloads. The risk is not disappearance of model revenue. The risk is that competition transfers most of the productivity surplus to customers and adjacent platforms. Open models create a further asymmetry. They can expand the overall market by lowering costs, increasing customization, and enabling private deployment. At the same time, they create a reference price that limits proprietary-model rents. The industry can grow rapidly while model-provider returns remain below the level implied by headline demand growth. GPU CLOUDS AND NEOCLOUDS Near-term utilization conditions are favorable for specialized GPU-cloud providers. Hyperscaler constraints, firm rental prices, and customer demand for immediate capacity support revenue growth. Specialized providers can also differentiate through cluster design, bare-metal performance, rapid deployment, and flexible commercial terms. The long-duration risk profile is materially higher than for hyperscalers. Neocloud economics depend on financing costs, customer concentration, power contracts, accelerator residual values, utilization, counterparty quality, and access to the next hardware generation. Backwardated rental curves and rapid hardware turnover make asset-liability matching difficult. A provider financing accelerators over several years while selling short-duration capacity assumes substantial residual-value and refinancing risk. Contracted revenue provides protection only if customers remain solvent and committed through technology transitions. High headline growth can coexist with weak equity value if most cash flow accrues to lenders, hardware vendors, or concentrated customers. The strongest operators will possess long-duration power, diversified customers, disciplined financing, high utilization, and software or managed-service differentiation. Pure commodity rental exposure should command a lower valuation because elastic demand does not prevent price competition once supply catches up. APPLICATION SOFTWARE Application software is likely to be the most bifurcated layer. Incumbents with proprietary workflow data, distribution, embedded permissions, compliance infrastructure, and control over system-of-record actions can capture substantial value from cheaper intelligence. Lower model costs improve gross margins and allow AI functionality to be bundled into existing products. Generic wrappers face the opposite economics. If underlying models become cheaper and more interchangeable, applications lacking proprietary data, workflow integration, or distribution can experience rapid feature replication and price compression. Lower technical barriers may expand the number of competitors faster than the end market. Seat-based software models also face disruption. AI agents can reduce the number of human users required to complete a workflow, creating pressure on per-seat revenue even as customer productivity improves. Vendors will attempt to migrate toward usage, agent, transaction, or outcome-based pricing. The transition can improve long-run value capture but reduce near-term revenue visibility and complicate customer budgeting. The key application metric is not AI feature adoption. It is incremental revenue or retention net of model cost, cannibalization, implementation expense, and seat compression. Vendors that demonstrate paid conversion and workflow expansion should outperform vendors reporting only usage statistics.

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  Amazon Help (@AmazonHelp) reported 53 minutes ago

  @PankajN7 Sorry to learn about the issue you've faced with the product. Amazon being a marketplace we can assist you with a return/replacement only within the return window. If it's post the return window, kindly contact the manufacturer to avail the warranty or charged services and followup with them for further assistance on this. Please don’t provide your order/account details as we consider them to be personal information. Our page is visible to public. -Sankita

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  Priyanshu Saxena (@PriyanshuS09) reported 55 minutes ago

  Its not just damaged products, @amazonIN is degrading day by day in India! Plethora of delivery executive issues which I have been reporting since so many times, but nothing from Amazon. And now, damaged products! Moreover, return/replace has become a pain too! Useless ****!

• 
  AWS Support (@AWSSupport) reported 55 minutes ago

  ✅ Amazon Connect AP-NORTHEAST-1 Region Issue Resolved Between 4:58 PM and 6:34 PM PDT, we experienced increased delays affecting real-time metrics for Amazon Connect in the AP-NORTHEAST-1 Region, resulting in missing or stale data. During this time, customers may have experienced missing data within analytics reports, and may have observed issues if accessing real-time metrics within contact Flows, such as checking agent staffing. We identified the root cause to be an issue with the subsystem responsible for metric event delivery. We started applying the mitigations at 6:13 PM and mitigated the issue by 6:34 PM. The issue has been resolved, and the service is operating normally.

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  keyur karia (@keyurkaria) reported 57 minutes ago

  @AmazonHelp @amazon @amazonIN Dear All even after waiting as suggested by your team. Still same issue. No refund. It still shows failed. Still take me all around other than contacting chat person. Can you anyhow ensure this is refunded today ( as it’s beyond tat committed by Amazon) can we close this today?

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  Truth hurts (@unknown_indian) reported 58 minutes ago

  Why not try building an international link like link Amazon with Nile with narmada with kavery... Problem solved for eternity.

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  BrianKnowsTrumpGotScrewed (@BrianCantWin) reported 59 minutes ago

  @japan_nobunaga buy it on Amazon, problem solved

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  TiSia, The new international internet model; (@Gla22s) reported 59 minutes ago

  @SecKennedy @FBIDirectorKash Great, but wait a minute, What about Amazon ? during the pandemic Amazon collected the most Ip addresses And basically was the only one that made money while countless other companies had to shout down, Some have not gotten back on their feet until this day. most citizens have become more isolated and have lost countless friends. Many family as my little family - fell apart during the pandemic also as a single mama, I can testify real social difficulties and weak immune system for baby’s that born during the pandemic. Not to munition a big hole in my resume. Oh, and I also have asked Amazon for a special report indicating the number of employee deaths during the pandemic - in order to understand the necessity of isolation and our long stay at home - and received no response - and yet I see the White House advertise Amazon on a daily basis, and wonder, Why ?

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  Vishnu Gaikwad (@Vishnugaik123) reported 60 minutes ago

  @AmazonHelp @AmazonHelp Thank you. I will follow up with your specialist team. However, my issue is still unresolved. The TV delivered has 1GB RAM, while the Amazon listing showed 2GB RAM + Google TV. This is a specification mismatch, not a physical damage issue. I have already submitted all technical evidence and also raised a complaint with NCH (Docket No. 9987730). Please arrange a return pickup and full refund. Kindly resolve this through your specialist team instead of repeatedly referring to the Open Box OTP. Please DM me for the required details.

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  Er.Rishikesh Sharma (@Rishike25023913) reported 1 hour ago

  @AmazonHelp No happy my problem not solve