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 (32%)
• Sign in (23%)

Live Outage Map

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

City	Problem Type	Report Time
Conway	Website Down	2 hours ago
Holywood	Website Down	2 hours ago
Poussan	Website Down	3 hours ago
Paris	Sign in	6 hours ago
Westbrook	Website Down	22 hours ago
North Royalton	Errors	2 days 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:

• 
  Ridge Farmer (@RidgeFarmer) reported 10 minutes ago

  @SHERRYSRUN I figured out how to beat this for online (like Amazon - who I’ve caught scamming me on pricing) 1) clear the cache on your browser, reset your router to get a different ip address 2) crank up your vpn and select a different city 3) go to Amazon but don’t log in - shop in guest mode 4) fill your cart and try to check out - they will then force you to log into your account. The first time I did this they cancelled my order - claiming it was because they detected fraud. I validated my account but they wouldn’t honor the pricing. My mistake was that I chose a vpn server in the UK - limit your vpn server city to a domestic local.

• 
  Canadian Republic 🇨🇦 (@CanaRepublic) reported 10 minutes ago

  @coolestapricote @blind_via Put it this way, You install opencode, you configure it to use your local LLM's API endpoint, and then you start configuring your system. so let the first prompt be "configure my DNS to be cloudflare" . AI will take a look at how the system is configured, and then run the command and tell you when it's finished, it took it 21 seconds. I then I think my next one was "when i left the room for a few minutes, my monitor has turned off and locked the terminal, i don't want it to lock on it's own, i only want you to show the screensaver" It went through the system and found where that setting was, and then ran the command and told me it was fixed, but next time i left it, I came back and although the monitor had not blanked nor the screen come back on, the screensaver wasn't on. So I told it about the problem and it came back and said "ahh, looks like the screensave will never come on unless there is a value set for the monitor to turn off, and therefore I have set it to 277 days (or maybe it was hours?)" anyhow, that fixed the issue. Then I had it make me a custom screensaver by visiting my employers website researching the brand, and then creating a ASCII / ANSI art screensaver.. You can ask it to do things like "create a webapp for every link i have on my brave bookmarks and install them onto workspace 5, but have it clone the window settings from the one I already made on workspace 5 for amazon" and l've set it up with scripts to download to models based on keywords and size, every day at a certain time, unless there's less then 200 gig's free space. I'm planning on making a script logging and error notification system with a system tray icon, i've already planned it on one of my sessions but i've been busy at work this week.

• 
  Squall Loire (@squall_loire) reported 11 minutes ago

  @saga2K9 @TwitchSupport Indeed it's down to AWS, but they should still be able to handle this kind of surge in demand. They should at least be able to adapt to it in a reasonable timeframe, especially as Amazon owns both Twitch and AWS.

• 
  SoCal Populist (@LWPopulist90715) reported 15 minutes ago

  @Dan34017970 Real problem are the small companies that hire out the drivers for Amazon. (majority of the drivers are hired out and managed by 3rd party companies from what I understand) They look down on drivers who bring back undelivered packages. Drivers fear getting hours cut.

• 
  notjordy (@jordymob) reported 18 minutes ago

  @ecomrat Great info, we’re getting ripped by a bunch of chinese counterfeit listings on Amazon rn but we’re not currently on amazon. Would we still be able to take a TRO to amazon and get them taken down/collect their abandoned funds after judgement?

• 
  Dian (@Dian5) reported 19 minutes ago

  @AmazonHelp I’ve told you more than once that I’m having great difficulty having my package delivery instructions followed. I have been enjoying the services of Amazon Fresh l, who HAS been following my delivery instructions, for months without a problem. I was expecting a delivery of ordered items today, but instead, I find out, Amazon cannot even locate my address!!!!! What the heck is going on?!?!🤬

• 
  TheValueist (@TheValueist) reported 20 minutes ago

  $NVDA $MU $SNDK $LITE NVIDIA NVHBM AND NVLINK FUSION: TECHNICAL, STRATEGIC, AND INVESTMENT ANALYSIS (1/x) EXECUTIVE ASSESSMENT NVIDIA’s August 26, 2026 NVHBM announcement is strategically significant because it extends the company’s architectural control from GPUs, scale-up networking, scale-out networking, rack systems, and software into the memory subsystem of 3rd-party custom accelerators. NVHBM relocates an NVIDIA-designed memory controller from the XPU compute die into the HBM base die and uses a custom physical interface between the XPU and memory stack. NVIDIA claims that the resulting architecture delivers up to 30 percent more memory bandwidth, reduces HBM power consumption by up to 15 percent, and frees up to 25 percent more XPU compute-die area relative to standard HBM4E. Amazon’s Annapurna Labs is the 1st publicly identified collaborator, and Trainium4 is expected to support both NVLink Fusion and NVIDIA’s common rack-scale architecture. (NVIDIA Blog) The central investment implication is that NVIDIA is no longer attempting to preserve its economics solely by maximizing NVIDIA GPU unit share. The company is building an architecture under which hyperscalers can deploy internally designed accelerators while continuing to purchase or license critical NVIDIA technology. NVLink chiplets, NVLink switches, NVLink-C2C, networking adapters, rack architectures, system-management software, and now NVHBM can collectively allow NVIDIA to retain meaningful content and control even when the primary compute die is supplied by Amazon, Google, Meta, Microsoft, an AI laboratory, or a merchant custom-silicon provider. This is a direct strategic response to the risk that hyperscaler ASICs disintermediate NVIDIA. The announcement is both offensive and defensive. The defensive benefit is that custom silicon no longer necessarily represents a complete loss of NVIDIA content. The offensive benefit is that NVIDIA can make its architecture the preferred substrate for semi-custom AI systems, potentially expanding its addressable market from merchant GPUs into memory IP, chiplets, switches, networking, CPUs, racks, orchestration software, and design enablement. The countervailing risk is that NVLink Fusion and NVHBM may accelerate the development of custom XPUs by removing some of the hardest memory, networking, packaging, and rack-engineering tasks. NVIDIA is therefore accepting greater potential competition at the compute-die level in exchange for a stronger position at the platform level. The Amazon relationship demonstrates this co-opetitive structure. AWS plans to deploy 2 million additional NVIDIA GPUs during 2027 and 2028 while also integrating NVLink Fusion and NVHBM into future Trainium infrastructure. The simultaneous expansion of NVIDIA GPU capacity and Amazon custom silicon indicates that the relevant market is unlikely to resolve into a binary choice between merchant GPUs and internal ASICs. A heterogeneous architecture in which NVIDIA captures significant content from both categories is a more probable outcome. (NVIDIA Newsroom) The technical direction is credible. HBM4 and HBM4E substantially increase interface width, signaling rates, bandwidth, base-die complexity, power density, and interposer-routing requirements. Moving the memory controller and support logic into an advanced HBM base die can reduce XPU-side interface area, shorten or simplify some interconnect paths, improve memory scheduling, and create a more modular boundary between memory and compute. Similar custom-HBM architectures have independently been disclosed by Marvell and the major HBM suppliers, confirming that NVIDIA is participating in a broader structural transition rather than introducing an isolated concept. (Marvell Technology) The specific performance claims require substantial qualification. NVIDIA has not disclosed absolute NVHBM bandwidth, interface width, signaling rate, latency, capacity, stack height, controller architecture, error-correction model, benchmark methodology, manufacturing process, memory suppliers, pricing, yield, production timing, or customer-volume commitments. The claimed 30 percent end-to-end performance improvement is particularly under-supported because no workloads, model configurations, batch sizes, context lengths, system power limits, or competing designs are provided. The announcement should therefore be treated as a high-value architectural and ecosystem disclosure rather than a validated product-performance disclosure. The near-term earnings impact for NVIDIA is not quantifiable. No revenue model has been disclosed for the memory-controller IP, base die, qualification services, or associated NVLink Fusion content. NVHBM may eventually generate direct silicon revenue, IP royalties, development fees, or incremental networking and system sales, but the announcement does not identify which economic model will be used. The principal near-term equity significance is reduced perceived terminal-value risk from custom accelerators, rather than an immediate increase in fiscal 2027 or fiscal 2028 earnings estimates. The high-conviction equity read-through is strategically positive for NVIDIA, technologically positive but strategically mixed for Amazon, directionally positive for the custom-HBM category, positive but supplier-indeterminate for SK hynix, Samsung Electronics, and Micron Technology, positive for advanced logic base-die and packaging demand, mixed-to-positive for Marvell, mixed for Cadence, Synopsys, and Rambus, and modestly negative for competing open scale-up architectures from AMD and Broadcom. The announcement is not sufficient to support a material near-term financial estimate revision for any company. SOURCE QUALITY AND DISCLOSURE LIMITATIONS The primary source is NVIDIA technical marketing rather than a standards document, customer benchmark, silicon demonstration, or audited product specification. All headline metrics are expressed as “up to” outcomes. That wording permits each metric to represent a different configuration, workload, design objective, or simulation point. It is not established that 30 percent more bandwidth, 15 percent lower HBM power, 25 percent more compute-die area, and 30 percent more end-to-end performance can all be achieved simultaneously in the same production design. The detailed NVIDIA technical blog contains internally inconsistent area descriptions. The summary states that PHY and support area can decline by up to 67 percent and that up to 30 percent more main-die silicon can be made available. The comparison table states that up to 25 percent more compute-die area is available. The body states that narrower routing provides up to 80 percent more usable silicon across the layout and subsequently refers to a 30 percent increase in available main-die silicon. These figures may use different denominators, including PHY block area, package-layout utilization, routing corridors, or central compute-die expansion, but the denominators are not defined. The 25 percent area claim in the primary announcement is therefore more suitable as the central reference point than the more aggressive 30 percent and 80 percent figures. (NVIDIA Developer) The technical blog’s 30 percent end-to-end claim is presented as the result of “compounding” the bandwidth, area, and power benefits. No mathematical relationship or benchmark evidence is provided. Bandwidth, transistor area, and power headroom are not independently additive performance variables. Their interaction depends on workload arithmetic intensity, compute utilization, memory-access efficiency, thermal constraints, network congestion, software scheduling, and the actual use of reclaimed die area. The claim is technically possible for a specifically co-designed, memory-constrained accelerator, but it is not a generalizable estimate for training or inference performance. (NVIDIA Developer) ARCHITECTURAL CONTEXT HBM4 substantially increases the burden placed on the accelerator package. Current HBM4 implementations use a 2,048-bit interface. Micron states that its HBM4 operates above 11.0 Gbps and provides more than 2.8 TB/s per stack. Samsung’s disclosed HBM4E implementation operates at 14 Gbps, is designed to scale to 16 Gbps, and provides up to 3.6 TB/s per stack at the stated operating point. Rambus offers HBM4E controller IP supporting 16 Gbps and more than 4 TB/s per stack. Cadence has demonstrated standard HBM4 PHY and controller IP at 12.8 Gbps. These figures illustrate that “standard HBM4E” does not correspond to a unique bandwidth baseline; implementation speed can vary materially by memory device, controller, PHY, process node, and package. (Micron Technology) A conventional HBM implementation places a substantial portion of the memory-controller logic and physical interface on the accelerator die. Thousands of data, command, clock, and control connections must be routed across a silicon interposer between the XPU and each HBM stack. The resulting design consumes compute-die area, die perimeter, microbump resources, interposer-routing capacity, and power. Die perimeter can become as constrained as absolute die area because HBM, NVLink, PCIe, CXL, chiplet, power-delivery, and test interfaces all compete for limited edge connectivity. NVHBM moves the NVIDIA-designed memory controller into the HBM base die and connects the stack to the XPU through a custom, narrower PHY. The HBM base die becomes a more active subsystem that can aggregate the wide internal DRAM channels and expose a more compact interface to the compute die. This reduces the number or physical width of connections required at the XPU boundary and can reclaim both silicon area and package-layout space. NVIDIA states that the custom base die and memory controller will be validated with multiple memory providers. (NVIDIA Blog) The architecture can be understood as converting HBM from a largely standards-defined memory component into a semi-custom memory chiplet. The XPU no longer needs to implement the complete standard HBM controller and full-width interface in the conventional manner. NVIDIA instead defines more of the memory boundary, including the controller, base-die logic, and physical interface. This creates a stronger architectural abstraction between the DRAM stack and the accelerator. This abstraction can simplify custom-XPU development. A hyperscaler may be able to focus more engineering resources on matrix engines, vector units, scalar control, on-die SRAM, workload-specific dataflow, and compiler integration while relying on NVIDIA and qualified memory suppliers for the most difficult portions of the memory subsystem. The benefit can include lower nonrecurring engineering expense, shorter bring-up cycles, reduced interoperability risk, and less duplicated qualification work across HBM suppliers. NVHBM does not eliminate the need for advanced packaging. The DRAM dies must still be stacked using through-silicon vias, attached to an active base die, integrated next to the XPU, and connected through a high-density package or interposer. NVHBM changes the division of logic and the interface topology; it does not convert HBM into conventional board-mounted memory. NVHBM also should not be characterized as processing-in-memory based on the disclosed information. NVIDIA has not stated that tensor, vector, or general-purpose compute is placed in the memory stack. The disclosed base-die functions are principally the memory controller and custom PHY. Future base-die functions could theoretically include compression, prefetching, security, telemetry, address translation, reliability management, or near-memory operations, but none of these capabilities has been announced. No capacity advantage has been disclosed. Higher bandwidth can move model weights, activations, and KV-cache data more rapidly, but it does not increase the number of bytes in each stack. Long-context inference and very large models can be constrained by HBM capacity before they are constrained by bandwidth. NVIDIA’s statement that NVHBM can help support larger models should therefore be interpreted as an indirect throughput or utilization benefit, not evidence of increased memory capacity. BANDWIDTH CLAIM ASSESSMENT The claim of up to 30 percent more bandwidth per stack is plausible but insufficiently specified. A custom interface can improve effective bandwidth through higher signaling rates, fewer protocol inefficiencies, more efficient command scheduling, reduced electrical loading, lower routing congestion, more aggressive timing, or better coordination between the controller and DRAM. Moving the controller closer to the DRAM channels can also allow the external XPU link to operate on larger, more efficiently scheduled transactions rather than exposing the entire conventional HBM interface. The narrower-interface description does not mean that aggregate bandwidth must be lower. A narrower link can deliver higher aggregate throughput if each lane operates materially faster, if the protocol carries less overhead, or if the interface achieves better utilization. The trade-off is that higher-speed serial or semi-serial links generally impose more demanding signal-integrity, clocking, equalization, training, and error-management requirements than a low-speed, extremely wide parallel interface. Absolute bandwidth is required to evaluate the claim properly. Samsung’s disclosed 14 Gbps HBM4E produces approximately 3.6 TB/s per stack. A 30 percent uplift from that point would imply approximately 4.7 TB/s. A 30 percent uplift from a 16 Gbps, 4.1 TB/s implementation would imply approximately 5.3 TB/s. These calculations are illustrative rather than forecasts because NVIDIA has not stated its baseline or actual NVHBM transfer rate. (Samsung Global Newsroom) The absence of latency data is material. A packetized or serialized custom interface may introduce buffering, encoding, flow-control, clock-recovery, or serialization latency. Relocating the controller closer to the DRAM could offset some of that latency through improved scheduling and shorter command paths. The net result cannot be inferred from bandwidth alone. High-throughput inference may tolerate modest added latency if many requests are batched, while low-batch recommendation systems, sparse accesses, and latency-sensitive inference could be more exposed. Effective bandwidth is more relevant than peak bandwidth. HBM utilization can be reduced by bank conflicts, inefficient access granularity, irregular sparsity, address-mapping choices, refresh activity, error correction, synchronization, and software-level memory behavior. A 30 percent increase in physical interface bandwidth will not produce a 30 percent increase in useful workload bandwidth unless the controller, compiler, kernels, and data layout can exploit the additional capacity. The workload-speedup ceiling can be illustrated using an Amdahl-style framework. If 50 percent of execution time is limited by HBM bandwidth, a 30 percent bandwidth increase produces an approximate 13.0 percent total speedup. If 70 percent of execution time is memory-limited, the speedup is approximately 19.3 percent. If 90 percent is memory-limited, the speedup is approximately 26.1 percent. A full 30 percent speedup from bandwidth alone requires the workload to be almost entirely memory-bound and the bandwidth gain to translate nearly perfectly into useful throughput. The bandwidth benefit is therefore likely to be most valuable for autoregressive inference decode, where model weights and KV-cache data must be repeatedly read for each generated token and arithmetic intensity can be low. The benefit is less likely to be linear during compute-intensive training phases or large-batch prefill, where tensor-core throughput, collective communication, or software efficiency may dominate. NVIDIA specifically identifies model weights, KV-cache data, and activations as target data classes and emphasizes memory-bound inference. (NVIDIA Developer) AREA CLAIM ASSESSMENT The area advantage is strategically important because advanced accelerators are constrained by more than transistor density. The XPU must allocate area and die perimeter among matrix engines, vector units, scalar cores, SRAM, cache, networks-on-chip, memory controllers, HBM PHYs, NVLink or other scale-up interfaces, PCIe or CXL connectivity, security, management, test logic, and power-distribution structures. Removing a large conventional HBM interface from the compute die can create meaningful design flexibility. The reclaimed area does not necessarily need to be allocated to more tensor cores. For inference-oriented accelerators, additional SRAM or cache could be more valuable because it reduces HBM traffic and improves reuse. Extra area could also be allocated to sparsity engines, decompression logic, low-precision arithmetic, recommendation acceleration, larger register files, network interfaces, or reliability features. A design that combines more SRAM with higher HBM bandwidth could generate a greater performance improvement than a design that simply adds compute units. The 25 percent figure should not be interpreted as a costless 25 percent increase in compute. Expanding a compute die increases advanced-node wafer consumption, defect exposure, and manufacturing cost. If the original die is near the reticle limit, literal expansion may not be possible without changing the chiplet architecture. A designer may instead use the reclaimed package space to add another chiplet, enlarge an existing compute chiplet, increase HBM count, improve power delivery, or reduce the package footprint. The area benefit may also be more accurately characterized as a package-floorplan and die-perimeter benefit than as a pure transistor-area benefit. A conventional HBM interface requires a broad connection corridor between each stack and the XPU. Narrowing those corridors can permit the central compute die to occupy package space that was previously unavailable, even if the controller’s transistor area alone was much smaller than the claimed package-level gain. NVIDIA’s inconsistent 25 percent, 30 percent, and 80 percent descriptions create uncertainty about the exact magnitude. The 80 percent figure appears most likely to refer to usable package-layout or routing area rather than a literal 80 percent increase in compute silicon. The 30 percent figure appears to refer to potential central-die expansion. The 25 percent figure is used in the primary announcement and benefit table and is therefore the most defensible headline metric. (NVIDIA Blog) Independent industry disclosure provides some support for the general order of magnitude. Marvell’s custom-HBM architecture claims up to 25 percent more area for compute by replacing conventional HBM interfaces with higher-performance die-to-die connectivity and moving support logic into the memory base die. The similarity between the Marvell and NVIDIA area claims supports architectural plausibility, although neither company has provided an independent production benchmark. (Marvell Technology) POWER CLAIM ASSESSMENT The claimed 15 percent reduction applies to HBM power, not total XPU power, rack power, or data-center power. The distinction is material. If HBM represents 20 percent of an accelerator’s power consumption, a 15 percent reduction in HBM power reduces total accelerator power by approximately 3 percent, before considering any power consumed by additional compute placed in the reclaimed area. NVIDIA illustrates the power benefit by stating that a 1 GW facility populated with 2,000 W XPUs could support up to 15,000 additional XPUs. The implied arithmetic is revealing. An additional 15,000 XPUs at 2,000 W requires 30 MW. A theoretical 1 GW divided entirely among 2,000 W XPUs corresponds to 500,000 devices, so the implied saving is approximately 60 W per device. If 60 W represents a 15 percent reduction in HBM power, the example assumes baseline HBM consumption of approximately 400 W per XPU, or 20 percent of the stated 2,000 W device power. (NVIDIA Developer) The 15,000-device illustration does not represent a realistic full-facility capacity calculation. A data center must also power CPUs, networking, storage, memory outside the XPU, power-conversion equipment, cooling, pumps, control systems, and other infrastructure. Power-usage effectiveness and system overhead reduce the portion of grid power available to XPUs. The example should be interpreted as a normalized demonstration that a 3 percent device-level power saving can be material at hyperscale, not as an actual facility-design estimate. Lower HBM power can nevertheless create meaningful economic value. The benefit can appear as lower energy expense, reduced cooling requirements, lower junction temperature, improved reliability, higher sustained clocks, more compute within a fixed thermal-design-power envelope, or additional devices within a rack power limit. In power-constrained deployments, a modest percentage improvement can have greater economic value than the same percentage of component cost reduction. The custom base die introduces a potential thermal trade-off. Moving more active controller and PHY logic under the DRAM stack increases logic density in a thermally sensitive location. DRAM retention, timing margin, and reliability are temperature-dependent. NVIDIA and its memory partners must demonstrate that the base-die power savings and interface simplification exceed any additional thermal burden created by active controller logic, buffering, and higher-speed signaling. The NVIDIA and Marvell power claims are not directly comparable. Marvell claims up to 70 percent lower memory-interface power, while NVIDIA claims 15 percent lower total HBM power. Memory-interface power is only a subset of total HBM-stack power, which also includes DRAM-core activation, refresh, I/O, internal data movement, and base-die functions. Marvell’s larger percentage therefore does not necessarily imply a more efficient complete memory subsystem. (Marvell Technology) END-TO-END PERFORMANCE CLAIM ASSESSMENT The 30 percent end-to-end performance claim has the lowest evidentiary quality among the disclosed metrics. NVIDIA states that the result is obtained by compounding 30 percent more memory bandwidth, 25 percent more die area, and 15 percent lower HBM power. No production silicon, simulation configuration, model, precision format, batch size, context length, network topology, or thermal limit is specified. (NVIDIA Developer) The benefits cannot be added arithmetically. A 25 percent increase in compute area is useful only if that area is populated with productive logic, supplied with sufficient power, fed with data, and utilized by software. A 15 percent reduction in HBM power creates only a fraction of that amount in total-package power headroom. A 30 percent bandwidth increase matters only to the portion of the workload that is memory-bound. The same package may not achieve the maximum value of all 3 variables simultaneously. A well-designed inference XPU could plausibly approach a 30 percent throughput improvement. A design optimized for decode could allocate reclaimed area to SRAM, memory scheduling, low-precision engines, and additional compute; use the HBM power saving to sustain higher clocks; and exploit additional bandwidth at high batch sizes. The improvement could be much smaller for compute-bound prefill, dense training, communication-bound mixture-of-experts execution, or workloads limited by software and host-side bottlenecks. The correct interpretation is that NVHBM provides design resources that can support an aggregate performance gain of up to 30 percent in an optimized XPU. It does not establish that attaching NVHBM to an otherwise unchanged accelerator will increase performance by 30 percent.

• 
  🇬🇧 FiveStar 🇬🇧 (@FiveStrGaming12) reported 24 minutes ago

  @AmazonHelp amazon my account has wrongly been automatically blocked from speaking to you on live chat and the phone. I currently have an issue I really need resolved. Can this be looked at as a matter of urgency?

• 
  Amazon Help (@AmazonHelp) reported 25 minutes ago

  @INDIAN78091610 Kindly copy the link provided earlier > paste it on a 'web browser' > login to your Amazon account > connect with our Social Media team via chat. -Sindu

• 
  westghentwitch 😾 (@westghentwitch) reported 27 minutes ago

  @Kat03126 @amazon Yep, I ordered a litter box and when it came, it was much smaller than I thought it would be for my chubby fur babies I tried to return it, but instead, they just asked me to donate it which I did to the catnip café down the street

• 
  Kamsiob (@Kamsiob) reported 28 minutes ago

  @TaylorLorenz Let's say that we both agree that what Meta was doing was a horrible thing and that we both agree the road being taken now is essentially cover for mass surveillance. (Those are my assumptions. Let me know if I'm wrong in either one of those) What would you recommend as a way to solve this and deal with the problem? I'll put out my ideas first to be fair. I think that whether it's Meta, Google, Amazon, or any other monolithic mega-corporation, it should be broken up into smaller companies. And the people who were informed about the problems and still did nothing after discovering it should be held responsible and accountable with potential and meaningful jail time. A fine doesn't do anything.

• 
  Tushar | AI & Tech (@0xIamTush) reported 29 minutes ago

  Amazon is shutting down Mechanical Turk on September 30, after 21 years. Bezos called it "artificial artificial intelligence." Real humans, doing tiny tasks computers couldn't do yet.

• 
  IliasPlayz (@IliasPlayz1) reported 32 minutes ago

  @amazon Hi Amazon, I need help accessing my account. I'm still logged in on my phone, but I can't log in no my computer because it asks for a security passkey. I only had one on my phone, and removing it/chaning my password didn't resolve this issue. Can you help me regainaccess?

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  Shailendra Kamal (@Shailendra3412) reported 35 minutes ago

  @AmazonHelp The link opens normal sign in page of amazon

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  .IVTIR (@la_ritvi) reported 36 minutes ago

  @ODEONCinemas Hello I have a query that I need help with but can’t get in touch with you I have a code from Amazon prime membership which lets me get to recliner seats for £15 Monday to Thursday but it won’t work and I’ve checked with prime and the code isn’t an issue

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  chompie (@chompie1337) reported 36 minutes ago

  also, a VM can be sandboxed! It simply comes down to attack surface reduction. Look at how Amazon does it with Firecracker, security by design from first principles doesn’t stop being valid just bc ~AI~

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  Cdn Refugee in Alberta (@cdnrefugee) reported 37 minutes ago

  @JonHeron5 @amazonca Yes. I made an order once that could have been delivered in 1 box. Amazon sent it in 3 boxes...to the top of the Andes Mtns! Then, Ecuador decided to impose taxes that muddied things. Most of us used 'mules' (other people who we paid) to bring stuff in. It works, but it's slow.

• 
  TheValueist (@TheValueist) reported 37 minutes ago

  BUSINESS-PROCESS OUTSOURCING AND IT SERVICES AUTONOMOUS CUSTOMER SERVICE IS STRUCTURALLY NEGATIVE FOR CONTACT-CENTER OUTSOURCING (READ-THROUGH 10) AFFECTED COMPANIES: Concentrix Corporation (CNXC: US), TaskUs, Inc. (TASK: US), and Teleperformance SE (TEP: France). DIRECTION AND MAGNITUDE: NEGATIVE. MEDIUM NEAR-TERM TRADING IMPACT AND HIGH LONG-DURATION FUNDAMENTAL IMPACT. CALL EVIDENCE: Xero deployed Agentforce customer service to 100% of its customers after historically operating without a call center or real-time support. The agent handled more than 200,000 interactions and produced approximately 62% deflection while Xero maintained its gross-margin profile. Customers whose questions could not be resolved were routed to human agents. Xero also built an AI sales-development representative that increased email open rates 3x, generated more leads, and avoided the staffing expense required to perform the activity manually at scale. Salesforce’s internal help agent surpassed 5 million conversations with 64% resolved autonomously. Management also cited production voice agents at Amazon Blink and Berkshire Hathaway’s biBERK. TRANSMISSION MECHANISM: Contact-center outsourcing economics are commonly linked to agent headcount, staffed seats, interaction volume, and handling time. Autonomous resolution reduces the number of interactions requiring human labor. Even when a case is escalated, AI can collect information, summarize context, classify the issue, and recommend a response, reducing average handling time and staffing requirements. AI sales-development agents similarly reduce demand for outsourced lead qualification and outbound engagement. The threat is particularly significant because Salesforce can combine customer identity, transaction history, knowledge articles, permissions, and workflow execution in a single agent. This provides a stronger automation foundation than a standalone chatbot that lacks access to the customer record. Outcome-based pricing also creates a direct economic alternative to hourly or per-seat outsourcing contracts. NEAR-TERM TRADING CATALYST: Customer-service automation may pressure BPO sentiment before it materially affects reported revenue. Contract renewals, offshore hiring, seat growth, utilization, and management commentary regarding AI-related volume reductions will be important indicators. Customers may initially retain outsourcers while deploying AI, creating a transition period in which revenue remains stable but future headcount commitments decline. LONG-DURATION FUNDAMENTAL SHIFT: Routine customer-service labor is one of the most directly automatable enterprise functions because the work is high volume, text or voice based, data dependent, and governed by repeatable policies. BPO vendors must shift from selling labor capacity to selling AI implementation, orchestration, data preparation, quality control, and exception management. The transition can improve margins for successful providers, but it reduces the relevance of labor scale and threatens revenue models dependent on expanding agent headcount. The negative impact is moderated by 2 factors. AI can create support experiences that did not previously exist, as shown by Xero, increasing total interaction volume. Complex, regulated, emotional, or high-value cases will continue to require human intervention. These offsets should slow rather than eliminate the structural pressure on outsourced contact-center labor. AI-ASSISTED CONFIGURATION COMPRESSES SYSTEMS-INTEGRATION HOURS AND FAVORS HIGHER-VALUE TRANSFORMATION WORK (READ-THROUGH 11) AFFECTED COMPANIES: Cognizant Technology Solutions Corporation (CTSH: US), Infosys Limited (INFY: India), Wipro Limited (WIT: India), EPAM Systems, Inc. (EPAM: US), Globant S.A. (GLOB: Luxembourg), and Accenture plc (ACN: Ireland). DIRECTION AND MAGNITUDE: NEGATIVE FOR LABOR-INTENSIVE, TIME-AND-MATERIALS IMPLEMENTATION REVENUE; RELATIVELY POSITIVE FOR HIGH-VALUE STRATEGY, DATA, SECURITY, AND CHANGE-MANAGEMENT SERVICES. MIXED NEAR-TERM IMPACT AND MEDIUM-HIGH LONG-DURATION FUNDAMENTAL IMPACT. CALL EVIDENCE: Ohalo reported that 1 employee used Claude and Cursor to configure Salesforce in less than 1 month after the company had spent months attempting to customize a competing CRM. Marc Benioff stated that work previously requiring “a bunch of service folks” and potentially 6 months to 1 year could now be completed in approximately 1 month. Replit described non-engineers across sales, finance, human resources, and other functions as builders and created dedicated AI-engineering roles inside its go-to-market organization. Legora similarly distributed AI-workflow construction across operating functions rather than relying on a central IT ticket queue. TRANSMISSION MECHANISM: Generative coding and configuration tools reduce the labor required for data-model setup, interface creation, workflow design, reporting, testing, administration, and routine customization. This shortens implementation periods and reduces billable hours. Customers can perform more work internally, while software vendors can package prebuilt agents and workflows that require less partner customization. The effect differs by commercial model. Time-and-materials providers face direct revenue pressure because fewer hours are required. Fixed-price providers can initially benefit from higher delivery margins if productivity improves faster than pricing declines. Over time, competitive bidding should transfer part of the productivity gain to customers through lower project prices and shorter schedules. Offshore labor-arbitrage models face particular pressure because AI reduces the economic advantage of large pools of lower-cost technical labor. NEAR-TERM TRADING CATALYST: Near-term revenue can remain resilient or accelerate because lower implementation costs expand the number of projects and allow previously uneconomic AI deployments to proceed. Margins may improve as providers use AI internally. The negative read-through should first appear in headcount growth, utilization, entry-level hiring, project duration, and revenue per employee rather than in immediate demand collapse. LONG-DURATION FUNDAMENTAL SHIFT: Services demand should migrate from routine configuration toward enterprise architecture, data remediation, cybersecurity, governance, process redesign, and organizational change. Accenture is relatively better positioned because its value proposition includes senior transformation work, industry expertise, and change management, although it is not immune to lower implementation hours. Cognizant, Infosys, Wipro, EPAM, and Globant can also adapt, but the transition places pressure on labor-centric revenue growth and requires greater intellectual-property, platform, and outcome-based capabilities. SMB SOFTWARE AND CUSTOMER EXPERIENCE XERO DISCLOSED A DIRECT AI-DRIVEN SERVICE, SALES, AND GROSS-MARGIN BENEFIT; INTUIT IS THE CLOSEST PUBLIC ANALOGUE (READ-THROUGH 12) AFFECTED COMPANIES: Xero Limited (XRO: Australia) and Intuit Inc. (INTU: US). DIRECTION AND MAGNITUDE: POSITIVE. HIGH COMPANY-SPECIFIC READ-THROUGH FOR XERO, MEDIUM ANALOGUE READ-THROUGH FOR INTUIT, AND MEDIUM-HIGH LONG-DURATION FUNDAMENTAL IMPACT. CALL EVIDENCE: Xero rolled Agentforce customer service out to 100% of its customers and generated approximately 62% deflection across more than 200,000 interactions while maintaining its gross-margin profile. The deployment created real-time support that Xero had not previously offered because the company historically relied on digital self-service rather than call centers. Unresolved cases could be escalated to humans. Xero’s AI sales-development representative increased email open rates 3x, generated more leads than the company could previously pursue, reactivated dormant prospects, and avoided the staffing cost of performing the work manually. TRANSMISSION MECHANISM: Xero can materially expand customer-service availability without building a proportionately larger support organization. Instant answers can improve customer satisfaction and reduce friction for small businesses and accountants, while human specialists focus on complex cases. The AI SDR can increase the productivity of Xero’s installed lead base by nurturing inbound prospects and re-engaging inactive accounts. The combined effect should support customer acquisition, activation, retention, and gross-margin preservation. The read-through is particularly important because the benefit is not limited to labor substitution. Xero created a service that did not previously exist and expanded the number of leads it could economically address. This supports the view that AI can increase service supply and revenue capacity rather than only reduce cost. Intuit is the closest public analogue because it owns large quantities of proprietary small-business and consumer financial data, operates high-volume customer-service functions, and has substantial opportunities to automate support, onboarding, lead conversion, bookkeeping assistance, tax workflows, and financial recommendations. Intuit’s ability to integrate AI into QuickBooks, TurboTax, Credit Karma, and Mailchimp provides similar potential to increase customer value without proportionate service and sales headcount. NEAR-TERM TRADING CATALYST: Xero’s disclosures are direct operating evidence that AI can improve service capacity while protecting gross margin. Future Xero results should be monitored for changes in support expense, customer satisfaction, subscriber retention, conversion, and average revenue per subscriber. The current call did not quantify incremental revenue, churn reduction, or absolute cost savings, so the immediate earnings effect cannot be precisely modeled. LONG-DURATION FUNDAMENTAL SHIFT: Proprietary customer data and recurring workflow ownership allow SMB software vendors to deploy agents with better context and lower customer-acquisition friction than general-purpose AI providers. The benefit should accrue disproportionately to scaled platforms with trusted financial data, established distribution, and integrated transaction workflows. The strongest vendors can use AI to expand the economic service level offered to smaller customers who could not previously support human-intensive service. PORTFOLIO SYNTHESIS The most important non-consensus conclusion from the call is that AI is unlikely to affect all software vendors uniformly. The probable market structure is increasingly favorable to horizontal platforms that own authoritative data and deterministic execution, while becoming less favorable to vendors that primarily monetize a user interface, standardized workflow, or implementation labor. The highest-conviction positive read-through is for systems of record such as SAP, Oracle, and Workday. Salesforce’s retention, seat, contract-duration, and net new AOV disclosures indicate that enterprise customers are not broadly abandoning core application platforms. Customer testimony explains why: rapidly changing AI models still require stable customer, employee, transaction, permission, and audit layers. The user interface can become headless without eliminating the underlying platform. The highest-conviction negative software read-through is for point and vertical workflow products that can be recreated on top of an existing data platform. Replit’s internal CPQ, forecasting, revenue, and customer-health applications and Ohalo’s elimination of vertical software demonstrate that natural-language development can convert previously purchased applications into internally built workflows. This is not a broad negative call on every vertical SaaS vendor. Products with proprietary data, transaction networks, regulatory content, or deeply embedded industry processes remain more defensible. The most actionable company-specific competitive risks are ServiceNow and Veeva. Salesforce has established measurable customer traction in IT service management and life sciences, including named ServiceNow conversions and adoption across all 5 leading pharmaceutical companies. These initiatives are not yet large enough to alter near-term earnings materially, but they increase long-term competitive intensity and can affect valuation before revenue share shifts become visible. The strongest ecosystem beneficiary is Anthropic. Claudeforce provides enterprise distribution, governed data access, and a 15,000-person Salesforce sales force. The strategic value is substantial, but the financial impact remains impossible to size without model-pricing and revenue-sharing disclosure. The partnership is also non-exclusive, limiting confidence that Anthropic will retain the full economic value of the incremental usage. AI infrastructure receives a clear positive volume signal. Rapid Agentic Work Unit growth, repeat credit consumption, and large production deployments indicate rising inference, networking, storage, and data-processing requirements. The call does not establish which chip, cloud, or networking supplier captures the demand. Salesforce’s explicit view that customers consume AI through packaged software also suggests that infrastructure can experience strong unit growth without controlling the highest-margin customer relationship. The strongest negative read-through outside software is for contact-center outsourcing. Xero’s 62% deflection and Salesforce’s 64% autonomous-resolution rate demonstrate that AI can remove a large portion of routine service interactions from the human queue. The systems-integration impact is more nuanced: AI should expand total project activity and improve delivery margins in the near term, but it structurally compresses implementation hours and reduces the long-term value of labor scale. The most durable cross-sector investment theme is a transfer of value toward trusted data, identity, permissions, semantics, and business outcomes. Enterprise AI does not eliminate software architecture. It increases the importance of the layers that determine whether an agent has the correct context, may legally access the information, can execute the requested action, and produces an auditable economic result. Companies controlling those layers should capture a disproportionate share of enterprise AI value. Companies monetizing routine labor or replaceable interfaces face the greatest structural risk. SOURCE MATERIAL: Salesforce Q2 FY27 Earnings Call Transcript.

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  Rakesh Yadav (@RakeshY16552752) reported 37 minutes ago

  @AmazonHelp Link is not working when I open link it ask to login post login it shows webpage not available

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  Ashwin Viswanath (@AshwinV800) reported 39 minutes ago

  @HedgieMarkets Wouldn’t this mean great news for Amazon, Google, and Microsoft, and terrible news for Anthropic and OpenAI? The more the open source models are used, the more compute usage on these cloud platforms will be. They get more money, while the customer reduces their costs of training.

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  Carolyn Reilly (@casareilly) reported 39 minutes ago

  @naomirwolf12 AOC has a district in the midst of a terrible crime wave. They claim her staff won't even answer the phone. She was funded by Soros & trained by a Soros person, Alexandra Rojas. She doesn't do the job & sabotaged an Amazon facility coming to her District w 27K jobs.

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  Mel ╰⊹✿ (@button_melly) reported 40 minutes ago

  I ordered 2 glass candles from @Amazon and one arrived broken because both were packed in a paper bag with no padding. Glass. Website kept telling me to return the candle. Really? They want me to send them broken glass? Took 2 hours to resolve.

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  Scouse Clown Prince of Crime (@ScouseClown142) reported 42 minutes ago

  @CornbreadTTV @gamestop Amazon will have a similar issue I reckon, takes them ages get returns back on for sale, if at all

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  Kevin (@Kevincreates77) reported 53 minutes ago

  Thing 3: He broke down exactly what the $30.80 gap between the factory price and the Amazon price actually pays for. "People hear '440% markup' and assume it's all profit. It's not. The markup covers real costs. Understanding what those costs are is what separates 'this is a scam' from 'this is how retail works.'" "On a product I sell for $34 on Amazon with a factory cost of $3.20, here's roughly where the money goes." "Factory cost: $3.20. Shipping from China to Amazon's warehouse: roughly $0.80 to $1.50 per unit depending on volume and method. Amazon FBA fees, which include storage, picking, packing, and shipping to the customer: roughly $5 to $8 depending on size and weight. Amazon referral fee, the commission Amazon takes on every sale: roughly 15%, or $5.10. Professional product photography: roughly $0.50 to $1.00 per unit amortized across the order. Sponsored ad spend to drive traffic to the listing: roughly $3 to $5 per unit averaged across the campaign. Returns and damaged inventory write-offs: roughly $1 to $2 per unit. My margin, the actual profit I take home: roughly $8 to $12 per unit." "So the customer pays $34. The factory gets $3.20. Amazon gets roughly $10 to $13 between FBA fees and the referral commission. The ads, photography, and logistics eat $5 to $8. And I keep $8 to $12 for finding the product, creating the listing, managing the inventory, and taking the risk." "The product itself, the physical thing in the box, is 9.4% of the price you pay. The other 90.6% is the system between the factory and your doorstep."

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  Avieshek (@Avieshek) reported 55 minutes ago

  @morganlinton Let the memory and storage price calm down when the AI Bubble pops, a $399 base mac mini from Amazon is $1000+ after taxes.

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  Janet Feast (@feast9848) reported 55 minutes ago

  @cdnrefugee @amazonca Husband had a car issue this week. Shop quoted almost $400 for the part. Or, they said, order thru Amazon for about $40. It arrived in a couple of days.

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  Hago Community (@HAGOCommunity) reported 56 minutes ago

  How to Start a Smart Home Products Startup for the U.S. Market with Low Costs The smart home market is one of the fastest-growing markets due to American consumers’ increasing interest in home security, energy savings, and convenience. However, one of the biggest mistakes many young entrepreneurs make is trying to compete with large companies by offering many products and investing huge amounts of money. The right approach is to start with a small company that focuses on one product or a small group of products that solve a clear problem. The Core Idea of the Company Instead of saying: “I will sell smart devices.” Create a stronger mission: “We help American homeowners make their homes safer, more comfortable, and more energy-efficient through easy-to-use smart products.” Examples of Suitable Products to Start With 1. Smart Home Security Kit Products such as: Smart door sensors. Motion sensors. Small indoor security cameras. Smart video doorbells. 2. Energy-Saving Smart Products Examples: Smart plugs. Smart LED lights. Energy consumption monitoring devices. 3. Daily Comfort Products Examples: Smart cable organizers. Smart lighting for bedrooms or home offices. Home organization tools. Do not start with large appliances such as smart refrigerators or expensive devices. These require higher capital and more complicated after-sales service. Smart Startup Model (Lean Startup) Instead of buying 10,000 units at the beginning, start small and test the market. Phase One: Market Testing The goal is to discover whether customers are willing to buy your product. Estimated Costs: Creating a simple brand name and identity: $100 - $300 Logo and basic design: $50 - $200 Simple online store (such as Shopify): around $30 - $100 per month depending on the plan and features. Buying product samples: $300 - $1,000 Product photography and videos: $100 - $500 Test advertising on Facebook/TikTok: $500 - $1,500 Realistic Testing Budget: $1,500 - $3,500 Phase Two: Launching the Company After confirming that the product has demand: 1. Establishing the Company in the United States Many small business owners choose: LLC (Limited Liability Company) Costs vary depending on the state: Company registration: approximately $50 - $500 Registered Agent service: $50 - $300 per year Business bank account and accounting setup: depends on the service provider. 2. Product Sourcing There are two main options: Option One: Reselling Existing Products You buy products from suppliers and sell them through your store. Advantages: Lower cost. Faster launch. Disadvantages: High competition. Option Two: Private Label Manufacturing You create your own branded product by adding: Your company name on the product. Custom packaging. Special product features. However, this requires more capital. A suitable starting point: 50 to 200 units only to test customer response. Expected Monthly Costs Fixed Expenses: Online store: $30 - $100 Management and accounting software: $20 - $100 Email and online services: $10 - $50 Advertising: $500 - $3,000 depending on growth speed. Inventory Costs: Depends on the product. Example: If your supplier price is $15 per device: 100 units = $1,500 Shipping and customs = approximately $300 - $800 (depending on the supplier and country) Packaging = $100 - $300 How to Compete in the U.S. Market Do not compete only on price because there are huge companies like Amazon, Google, and other smart home brands. Compete through: 1. Solving a Specific Problem Example: “The easiest smart security system for elderly people and homeowners.” 2. Providing Better Service Offer: English product explanations. Installation videos. Fast customer support. 3. Building a Strong Brand Do not make customers feel that they bought an unknown product. Build trust through: Professional packaging. Clear warranty. Customer reviews. Reliable support. First-Year Growth Plan First 3 Months: Choose one product. Test three advertising campaigns. Get the first 50 customers. From Month 3 to Month 6: Improve the product. Collect customer reviews. Increase inventory. From Month 6 to Month 12: Launch complementary products. Build a community around the brand. Collaborate with small influencers. Realistic Starting Capital Very Small Startup: $2,000 - $5,000 (Testing the product + online store + advertising) More Professional Start: $10,000 - $25,000 (Larger inventory + branding + stronger marketing) Startup costs vary greatly depending on the business model. Online businesses started from home usually require much less capital than businesses that need large inventory, warehouses, or physical stores. The Most Important Advice for a Young Entrepreneur Starting from Zero Do not start a company that sells “all smart home products.” Start with a company that offers: One product that solves one specific problem. Example: “An American company providing easy smart security solutions for elderly people and homeowners.” Then expand after achieving success.

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  Priscila Marques (@Priscilagonca87) reported 58 minutes ago

  Amazon is shutting down Mechanical Turk on September 30. For 21 years, it was the internet’s tiny task counter: people labeling photos, checking data, and training AI for a few cents. @santisairi put it well: as AI gets better, the old booth closes. And the question that remains is the right one. who gets paid for teaching machines? Because the work doesn’t disappear. It just moves.

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  Gamesky (@OfficialGamesky) reported 58 minutes ago

  @dvdaltizer @derraleves It's already been a huge drop from what we used to make on Amazon once they broke down percentages based on category, now will YouTube be taking even more of a percentage? Probably.

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  sane girl (@kreamycrack) reported 60 minutes ago

  Please help with my current order issue @amazon the customer service I have received thus far has been abysmal