Amazon Sells Out AI Data Centers to 2027 in $220 Billion CapEx Push
Erik Torenberg, Ben Horowitz, Andy Jassy
Chief Executive Officer Andy Jassy says pervasive enterprise adoption of AI inference will expand AWS into a $1 trillion annual business despite leaving unmet demand on the table through 2028.
AI-assisted summary of Amazon CEO Andy Jassy on Why Amazon Is Investing $220 Billion in AI — original source. Check the original for full context.
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Key ideas
- Amazon projects $220 billion in capital expenditures for 2026, yet its data center capacity remains fully booked through 2026 and largely sold out through 2027. (0:00)
- AWS has surpassed a $169 billion annualized run rate with 36.7% year-over-year growth, leading leadership to upgrade long-term targets toward a $1 trillion annual run rate. (3:45)
- AWS's custom Graviton CPUs and Trainium AI accelerators deliver 30% to 40% better price-performance than standard x86 and dominant GPUs, lowering the ROI threshold needed for enterprise inference migration. (33:00)
- Andy Jassy predicts a permanent reduction in corporate headcount for coding, support, and research, requiring enterprises to rewrite nearly all codebases to support autonomous agent maintenance. (37:35)
Detailed notes
## Background Published on RSS and web platforms on October 23, 2026, by hosts Erik Torenberg and a16z co-founder Ben Horowitz, this episode features Amazon CEO Andy Jassy discussing Amazon's projected $220 billion capital expenditure in 2026. According to the show description, Jassy explores why AI infrastructure demand outpaces supply, the path toward AWS becoming a $1 trillion annual run-rate business, the impact of lower inference costs, and how generative AI is fundamentally reshaping Amazon's organizational structure and legacy products like Alexa.
## TL;DR Amazon CEO Andy Jassy explains why Amazon is committing roughly $220 billion in CapEx for 2026, noting that data center capacity is completely sold out for 2026, largely committed through 2028, and still unable to satisfy runaway customer demand. He details how proprietary silicon developed by Annapurna Labs, including Graviton and Trainium, significantly undercuts standard market costs to make pervasive enterprise inference economically viable. Operationally, generative AI and autonomous agents are drastically shrinking engineering team sizes at Amazon while necessitating a complete architectural rewrite of legacy software and products like Alexa. Jassy also highlights the pressing cybersecurity arms race sparked by agentic coding and explains how Amazon uses decentralized, single-threaded leadership to manage concurrent ambitions across AI, robotics, and satellite communications.
## Atmosphere & style The conversation carries the pragmatic, deeply grounded atmosphere of a high-level operational debrief between veteran tech leaders rather than a speculative tech hype session. Ben Horowitz and Andy Jassy share an easy, collegial rapport, swapping war stories and dissecting corporate governance, capital cycles, and engineering inertia with mutual candor. Jassy speaks with calm, calculated conviction, framing eye-popping figures—like spending $600 million a day on CapEx—not as vanity metrics, but as sober, mathematically calculated responses to historic infrastructure shifts. The narrative arc moves steadily from massive macro-level grid and datacenter economics into the intimate human and organizational challenges of tearing down legacy code and embracing autonomous AI agents.
## Bulletin - `00:00` Amazon projects $220 billion in capital expenditures for 2026, yet its data center capacity remains fully booked through 2026 and largely sold out through 2027. - `03:45` AWS has surpassed a $169 billion annualized run rate with 36.7% year-over-year growth, leading leadership to upgrade long-term targets toward a $1 trillion annual run rate. - `33:00` AWS's custom Graviton CPUs and Trainium AI accelerators deliver 30% to 40% better price-performance than standard x86 and dominant GPUs, lowering the ROI threshold needed for enterprise inference migration. - `37:35` Andy Jassy predicts a permanent reduction in corporate headcount for coding, support, and research, requiring enterprises to rewrite nearly all codebases to support autonomous agent maintenance.
## Key takeaways - `00:00` Amazon expects to deploy roughly $220 billion in CapEx in 2026 while still leaving meaningful AI demand unserved due to capacity bottlenecks. - `00:00` Data center capacity is completely committed for 2026, largely committed for 2027, and substantially allocated into 2028. - `10:00` AWS's dedicated AI business crossed a $25 billion annualized run rate in three years, scaling hundreds of times faster than early cloud infrastructure. - `13:40` The CapEx model splits into 30-year physical data center assets requiring two years of lead time and server hardware refreshed every 5 to 6 years with cash breakeven within 3 years. - `15:24` Domestic data center construction represents a vital national security imperative to ensure the United States, rather than China, sets the standards and norms for AI. - `21:00` The AI market exhibits a barbell structure with research labs on one side and cost-avoidance automation on the other, while pervasive enterprise inference will dominate the middle over the next 5 to 7 years. - `23:45` AWS preserves dedicated capacity for Amazon Bedrock and core cloud workloads, refusing to deprioritize legacy customers for short-term frontier lab premiums. - `28:00` Amazon's acquisition of Annapurna Labs produced the Nitro System, Graviton CPUs, and Trainium accelerators, establishing structural cost advantages across AWS infrastructure. - `35:00` Strategic investments in Anthropic and OpenAI secure multi-gigawatt compute commitments on proprietary Trainium hardware while expanding Bedrock's foundation model offerings. - `37:35` Agentic development tools have dramatically accelerated cycle times, enabling six engineers to rebuild Bedrock's core inference engine in 76 days. - `40:00` Organizations must rewrite existing code to allow autonomous agents to discover, patch, and deploy code within predefined guardrails to avoid falling behind AI-native competitors. - `42:08` AI creates an immediate cybersecurity emergency as automated model exploits outpace manual patching cycles, necessitating autonomous remediation systems like Amazon's Continuum. - `46:00` Amazon is executing a ground-up architectural overhaul of Alexa, abandoning its original conversational engine in favor of large language models despite substantial internal resistance. - `53:15` While generative tools automate reading, data aggregation, and synthesis, human critical reasoning and nuanced judgment remain indispensable competitive differentiators. - `56:55` Amazon manages massive concurrent bets across AI, robotics, and Project Kuiper's satellite broadband constellation through decentralized, single-threaded leadership teams.
## Detailed walkthrough ### `00:00` Unprecedented CapEx and Selling Out Capacity Years in Advance
Even as Amazon projects an eye-popping $220 billion in capital expenditures for 2026, demand for artificial intelligence infrastructure remains so overwhelming that the company is still unable to satisfy all of it. Cloud providers and frontier AI labs are locked in a relentless race for power, cooling, and compute silicon, creating a dynamic where the biggest players are willing to pay virtually whatever it takes to secure physical capacity. Financial markets have voiced skepticism over whether the global economy can even supply enough capital to bankroll this buildout, but the reality on the ground is that infrastructure is spoken for years into the future.
Host Erik Torenberg frames the discussion alongside Andreessen Horowitz co-founder Ben Horowitz, pointing out that this massive infrastructure push is poised to transform Amazon Web Services into a potential $1 trillion annual run-rate powerhouse. Beyond cloud hosting, the conversation centers on the economics of inference, the structural redesign of Amazon’s internal engineering teams, and how emerging technologies like robotics, autonomous driving, cybersecurity, and low-Earth orbit satellites fit into the company's broader operational playbook.
As Andy Jassy explains the current supply squeeze: “Even spending $220 billion this year, we're leaving a meaningful amount of demand on the table. We always leave a certain amount for on demand, but 26 is obviously sold out. 27 is largely sold out. A good chunk of 28 is sold out.”
- Projected CapEx spend for Amazon reaches roughly $220 billion in 2026. - Data center capacity for 2026 is completely sold out, 2027 is largely sold out, and substantial portions of 2028 are already committed. - Core economic sectors—including modern equity markets, institutional banking, and healthcare systems—are already deeply intertwined with data center capacity.
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`01:15` Five Years as CEO: Doubling the Fulfillment Network and Regionalizing the US
Reflecting on his transition to CEO in 2021, Andy Jassy remarks that while five years have passed, the sheer pace of change makes it feel like an entire decade. Across that timeframe, Amazon’s overall top-line revenue expanded by about 80%, its high-margin advertising arm grew roughly two-and-a-half times, and AWS nearly tripled in size. However, stepping into the top job during the tail end of the COVID-19 pandemic immediately presented a high-stakes operational crisis. To keep pace with surging consumer demand while physical brick-and-mortar stores were shut down, Amazon had essentially doubled the physical footprint of its fulfillment network—which had taken 25 years to build—in just 18 months, while simultaneously erecting a last-mile delivery network rivaling the scale of UPS in only 20 months.
Building physical supply chains at that breakneck speed inevitably introduced widespread inefficiencies, misallocated inventory, and elevated cost-to-serve metrics. Observers initially lamented Jassy’s timing, wondering why anyone would want to step into the CEO seat amid such daunting operational headwinds. Yet Jassy views that trial by fire as an invaluable catalyst that forced the executive team to question long-held assumptions. The breakthrough insight was abandoning the company's historical single, flat national fulfillment network in the United States and replacing it with eight regionalized networks.
As Andy Jassy observed regarding that tumultuous transition: “And I think in retrospect, it was really a great time to start and a real blessing because it allowed us to consider a bunch of things that we probably wouldn't have considered for many years.”
```mermaid flowchart TD accTitle: US Fulfillment Network Evolution accDescr: Diagram contrasting Amazon's original flat national network with the regionalized model adopted to cut shipping distances and costs.
subgraph Legacy["Legacy Flat National Model"] A["Single National Pool"] -->|"Cross-country transit"| B["Longer Delivery Times"] A -->|"High freight expenses"| C["Elevated Cost to Serve"] end
subgraph Regional["Regionalized Network Model"] D["8 Distinct Regional Hubs"] -->|"Localized inventory"| E["Shorter Travel Distances"] E --> F["Lower Transportation Costs"] E --> G["Faster Same-Day & Next-Day Delivery"] end ```
- Amazon's top-line revenue grew roughly 80% between 2021 and 2026. - AWS nearly tripled in size over the same five-year window. - Amazon Advertising expanded to roughly 2.5 times its 2021 scale. - The company doubled a fulfillment infrastructure built over 25 years in an 18-month sprint. - Amazon constructed a nationwide last-mile parcel carrier equivalent to UPS in roughly 20 months. - Regionalizing the national logistics footprint placed inventory closer to end-users, lowering transit distances and unit shipping costs while accelerating delivery speeds.
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`03:45` Generative AI as an Inflection Point and AWS's Path to $1 Trillion
While Amazon had spent well over a decade deploying applied machine learning across its product recommendation engines, search systems, and warehouse automation, Jassy highlights 2023 as the true watershed moment where generative AI fundamentally separated itself from previous waves of artificial intelligence. Prior to this inflection, AWS had already built a formidable lead in enterprise cloud infrastructure, with executive leadership projecting the business could steadily reach a few hundred billion dollars in annual revenue over a ten-year horizon.
With generative AI reshaping modern software, Jassy argues that every consumer and business interaction will inevitably be reinvented around inference workloads. Rather than settling for a multi-hundred-billion-dollar business, Amazon now views AWS as possessing a viable trajectory toward a $1 trillion annual run-rate enterprise. This growth engine is complemented by an array of newer bets that have graduated from internal experiments into substantial businesses, including a profitable Prime Video streaming platform, the Amazon low-Earth orbit (LEO) satellite communications project, autonomous robotaxi operations at Zoox, primary healthcare services, and advanced warehouse robotics.
Summarizing the scope of the shift, Andy Jassy underscores the momentum of the cloud infrastructure division: “We actually think the AWS business, not only is it growing really rapidly, I mean, last quarter was 36.7% year over year on $169 billion annual revenue run rate business. Just the opportunities today are really striking.”
- AWS annualized run rate reached $169 billion, expanding at a 36.7% year-over-year rate. - Management has upgraded long-term expectations for AWS from several hundred billion dollars toward a $1 trillion annual run rate. - Beyond core cloud services, Amazon’s diversified pipeline includes Prime Video, low-Earth orbit satellite infrastructure, autonomous ride-hailing through Zoox, healthcare initiatives, and warehouse robotics. - The overarching corporate umbrella continues to center on building tools and services that systematically reduce friction in daily customer life.
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`05:40` Balancing Competing Leadership Principles and Navigating the "Truth Culture"
Ben Horowitz presses Jassy on the apparent paradox between Amazon's historical devotion to frugality, process discipline, and operational excellence, and the sheer operational "sloppiness" that inevitably surfaced when scaling a massive physical logistics operation during the pandemic. In response, Jassy notes that outside observers frequently misunderstand Amazon’s 16 Leadership Principles by assuming they operate as a harmonious checklist. In practice, the principles are deliberately designed to live in natural tension with one another, forcing leaders to make uncomfortable, contextual trade-offs rather than searching for neat, risk-free solutions.
During the lockdown phase of the pandemic, Amazon’s commitment to "Customer Obsession" and "Bias for Action" directly collided with "Relentlessly High Standards" and "Frugality." With independent third-party merchants needing a livelihood and locked-down households relying on essential household deliveries, the executive team made a conscious bet to prioritize rapid capacity expansion over upfront unit cost efficiency, accepting that they would have to clean up the operational debt later. Jassy emphasizes that surviving such bets requires what he describes as an internal "truth culture"—an operating environment where teams refuse to paper over failures, pull unvarnished operational metrics directly into executive reviews, and confront broken topologies head-on. Once the physical capacity was stabilized, engineering and operations leadership tackled previously deprioritized architectural overhauls, cementing structural cost improvements that outlasted the crisis.
Describing how Amazon's leadership philosophy functions under real-world pressure, Andy Jassy explains: “We have this set of leadership principles that a lot of people know about. We have 16 leadership principles. And a lot of these leadership principles, they're supposed to really, and they do govern the way we think about operating as a team and culturally. But there are several that are in tension with one another. And sometimes people are confused about that, but the reality is it just reflects the way the real world works. Nothing is really perfectly clean. You're always making trade-offs.”
- Amazon's management framework relies on 16 codified Leadership Principles intended to guide hard trade-offs rather than provide formulaic answers. - Core leadership principles often sit in direct tension: "Bias for Action" can challenge "Frugality" and "Insist on the Highest Standards." - Executive leadership deliberately chose to absorb operational friction and elevated costs in order to prioritize continuous delivery for consumers and marketplace sellers during the COVID-19 pandemic. - Amazon's "truth culture" mandates exposing operational defects directly via unvarnished metrics rather than masking performance issues behind narrative justifications. - Structural initiatives—such as re-architecting fulfillment topologies—shifted from middle-tier operational backlogs into top-tier executive priorities once foundational capacity was installed. - Horowitz draws an analogy between this high-stakes logistics expansion and the early days of launching AWS, where massive capital was committed under a "build it and they will come" philosophy, only now executed on an exponentially larger global stage.
`10:00` The Capital Buildout: From AWS Inception to the $220 Billion AI Wave
The conversation turns to the sheer velocity and magnitude of Amazon’s infrastructure spending, which has left equity and debt markets visibly nervous about whether there is even enough global capital available to finance such unprecedented construction. Erik and Ben press Andy Jassy on how Amazon determines "how much is too much" when expanding data center and chip capacity, especially amid fierce supply chain contention for chips, power, and liquid cooling. Jassy readily concedes that the decision-making is extraordinarily complex, noting, “And I wish there was an equation that would make it simple. It would save us a lot of time as a team.”
Reflecting on the inception of Amazon Web Services in the early 2000s, Jassy recalls that AWS was originally born out of an internal operational bottleneck rather than an external market forecast. Amazon’s retail and product engineering teams were moving far too slowly because individual teams spent 70 to 80 percent of their time building undifferentiated infrastructure from scratch, leaving only 20 percent of their time for differentiating customer-facing features. To solve this, Amazon decided to build a standardized suite of low-cost, highly flexible primitives—compute, storage, databases, messaging, and content delivery. Internal demand was so intense before launch that the team knew they had struck a nerve, even if nobody anticipated how explosive external adoption would become.
The current generative AI surge, however, eclipses that first cloud wave by orders of magnitude. While prior AI cycles generated solid business for AWS, the current demand is entirely unmatched in historical tech transitions. Jassy illustrates the contrast: after its first three years, AWS was considered a runaway success operating at roughly a $60 million annualized run rate; by comparison, AWS's AI business alone surpassed a $25 billion run rate within its first three years. To satisfy that demand, Amazon is spending roughly $220 billion in capital expenditures in 2026. Jassy chuckles recalling Amazon leadership breakfasts 15 years ago where executives were stunned that telecom giants spent $50 billion on CapEx, whereas today Amazon spends quadruple that and is still constrained, emphasizing, “And even spending $220 billion in CapEx this year, we're leaving a meaningful amount of demand on the table.”
- AWS was conceived after Amazon realized internal product teams spent 70% to 80% of their engineering time on undifferentiated infrastructure. - In AWS's first three years, the entire business grew to an annualized run rate of approximately $60 million. - In the first three years of the generative AI era, AWS’s dedicated AI business crossed an annualized run rate of over $25 billion—growing several hundred times faster than early cloud. - Amazon anticipates spending approximately $220 billion in CapEx in 2026 while still failing to fulfill total incoming customer demand.
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`13:40` The CapEx Cash Cycle: Data Centers, Servers, and Headwinds
```mermaid flowchart TD accTitle: AWS Two-Tier Capital Expenditure Cycle accDescr: Shows the distinction between long-cycle data center investments and short-cycle server deployments.
subgraph Facilities["Tier 1: Facilities & Core Shells"] DC["Data Center Buildout<br/>• Lead time: ~2 years in advance<br/>• Frequency: One-time investment<br/>• Asset life: ~30 years"] end
subgraph Hardware["Tier 2: Servers & Networking"] Srv["Server & Gear Procurement<br/>• Lead time: Few months in advance<br/>• Breakeven: ~3 years<br/>• Asset life: 5-6 years"] Deals["Customer Commitments<br/>• Typically 5-year contracts"] end
subgraph CashImpact["Financial Impact"] Headwind["Initial Years: Heavy Free Cash Flow Headwinds"] Return["Later Years: Monetization & High Cash Flow Returns"] end
DC --> Srv Deals -.->|"Demand Visibility"| Srv DC --> Headwind Srv --> Headwind Headwind --> Return ```
Jassy explains that Amazon’s ability to commit hundreds of billions of dollars stems from deep operational visibility across two distinct capital expenditure buckets: physical data center shells and modular server/networking hardware. Constructing a data center requires capital outlays roughly two years before any servers can be racked and monetized. However, that shell is a one-time capital commitment with an operational lifespan of approximately 30 years. In contrast, servers and networking gear require only a few months of procurement lead time, allowing AWS to gauge real-time enterprise demand before spending money on the actual compute silicon. If demand fails to materialize, the servers simply are not purchased.
The financial model relies on predictable payback periods paired with long-term customer contracts. Server and networking assets break even within about three years and have functional operating lifespans of five to six years, which aligns cleanly with the standard five-year contractual commitments AWS signs with enterprise customers. However, because Amazon is constructing so many massive data center footprints concurrently, free cash flow absorbs intense near-term pressure. Jassy makes no effort to downplay the temporary accounting drag: “And so when you are building as fast as we're building with so many data centers simultaneously, you're going to spend a lot of capital and your free cash flow is going to have headwinds in those years until you get to those later stages, a few years down the road where all those data centers are online and they're all being monetized and you're making back your money.”
- **Data center structures:** Committed ~2 years in advance; useful asset lifespan of ~30 years. - **Server and networking gear:** Ordered only months ahead based on visible demand; useful asset lifespan of 5 to 6 years. - **Unit economics:** Hardware reaches cash breakeven in approximately 3 years; matched against typical 5-year enterprise customer contracts. - **Free cash flow trajectory:** Simultaneous multi-region data center expansion causes substantial front-loaded cash flow headwinds before online assets flip to high-margin cash generation.
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`15:24` The Politics of Data Centers, National Security, and Debunking Myths
The discussion shifts to the emergence of data centers as a lightning-rod political issue, especially in the United States, where local opposition and political skepticism have outpaced almost anywhere else in the world. Jassy frames the backlash as fundamentally contradictory to the expectations of modern society. Every pillar of contemporary life—financial markets, healthcare, mobile banking, digital entertainment, retail, and transportation—relies fundamentally on data centers, leading him to joke about the cognitive dissonance of those who oppose infrastructure while glued to their smartphones: “No, everybody is, I'm anti-me. I don't want to use my phone anymore.”
Beyond supporting standard digital conveniences, Jassy argues that the ongoing AI infrastructure buildout is the most consequential technological transition of our era, exceeding the collective impact of the internet, cloud computing, and mobile revolutions. He compares the required scale of construction to the nationwide mobilization behind the Interstate Highway System in the 1950s. From Jassy's perspective, domestic infrastructure investment is a critical matter of national security and geopolitical competitiveness: “The country, I think the world in general, but I say the U.S. specifically, cannot afford not to build data centers. Or we will find that another country like China will establish all the leadership and all the norms there.”
Addressing local environmental critiques, Jassy takes aim at what he characterizes as pervasive political mistruths and opportunism that target data centers as convenient scapegoats. On water usage, he points out that agricultural industries like almond farming consume 200 times more water than all data centers combined—using more water in two days than data centers consume in an entire year. On air quality, he notes that backup generators are legally required to register their theoretical emissions as if operating 24 hours a day at 100% capacity, even though they typically run for only 10 hours a year during maintenance testing. Finally, regarding power bills, Jassy stresses that the underlying issue is decades of chronic underinvestment in the American electric grid, and AI is simply the catalyst finally forcing necessary modernization. In states where data centers have scaled and grid investments were finalized—such as Indiana, Georgia, and Mississippi—energy rates have flattened or declined, while local communities benefit from immense property and corporate tax revenues paid by hyperscalers.
- Jassy likens the current AI data center buildout to the 1950s expansion of national highways and bridges. - Almond agriculture consumes 200 times more water than all data centers combined, consuming more water in 48 hours than data centers use over an entire year. - Backup generators operate only ~10 hours per year for routine testing, despite environmental filings requiring disclosures calculated as if they operate 24/7 at maximum load. - In states that welcomed massive data center investment, such as Indiana, Georgia, and Mississippi, power rates have stabilized or dropped after utility grid capacity expanded to absorb the load.
`20:00` Community Partnerships and Infrastructure Investments
Andy Jassy defends the massive physical footprint of Amazon’s data centers by emphasizing the direct economic windfalls delivered to local municipalities. Pointing to concrete examples, he notes that in places like St. Joseph County, Indiana, Amazon has contributed roughly $3 billion in taxes, providing local governments with vital funding to allocate toward schools, public services, and infrastructure that residents care about. Beyond tax receipts, Amazon established community-facing programs such as "Built Together," committing an additional $1 billion over the next five years specifically for localities hosting its data center clusters.
Jassy argues that constructing this digital backbone requires a genuine public-private partnership, drawing a direct historical analogy to national civil projects. The rapid deployment of data centers, power substations, and fiber networks mirrors past federal investments that defined American economic dominance. Addressing the broader societal necessity of these investments, Jassy stresses:
> “Like, imagine if we hadn't built out highways and bridges in what the economic prosperity and the capabilities of the country would be. And we have to kind of keep it in perspective, and we have to be partners in the communities in which we built.”
- **Local Tax Impact:** Roughly $3 billion in tax revenue paid in St. Joseph County, Indiana alone. - **Community Commitments:** $1 billion pledged over the next five years via Amazon’s Built Together initiative. - **Civic Analogy:** Compares modern data center construction to the historic buildout of national highway systems and bridges.
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`21:00` The Barbell Structure of AI Demand
Reflecting on Amazon’s projected $220 billion capital expenditure—amounting to roughly $600 million every single day—Jassy breaks down the sheer magnitude of market demand. The business velocity of AI infrastructure has outstripped AWS's own historic launch, expanding several hundred times faster in its initial three-year window than AWS did in its first three years. However, when examining where the compute is actually flowing, Jassy describes an asymmetric "barbell" dynamic across the industry.
```mermaid flowchart LR accTitle: The Barbell Model of AI Demand accDescr: Diagram showing AI Labs on the left, Production Workloads in the middle, and Cost-Avoidance Enterprises on the right.
subgraph Left["Barbell End: High Compute"] A["Frontier AI Labs & Runaway Apps<br/>(Gobs of compute, training, ChatGPT, Claude)"] end
subgraph Center["The Untapped Center (Future Majority)"] B["Core Enterprise Production Workloads<br/>(Gradual shift to pervasive inference over 5-7 years)"] end
subgraph Right["Barbell End: Narrow Utility"] C["Enterprise Productivity & Cost Avoidance<br/>(Customer service bots, fraud detection, process automation)"] end
Left <--> Center <--> Right ```
On one side of the barbell sit frontier AI research labs burning through unprecedented volumes of compute, accompanied by a handful of breakaway consumer and developer applications such as ChatGPT and Claude Code. On the opposite end sit traditional enterprises deploying AI tactically for productivity gains and cost avoidance—primarily automating back-office processes, fraud prevention, and call center customer service. Sitting in the middle is the massive reservoir of enterprise production workloads running on traditional cloud infrastructure. While a subset uses Amazon Bedrock or raw EC2 inference today, the vast majority have yet to integrate foundation models pervasively. Jassy anticipates that this middle category will ultimately dwarf all other segments combined:
> “But if you just zoom out five to seven years, I think that quite a few, if not most, of the enterprise applications that we use and that exist today are going to be using inference in a pervasive way.”
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`23:45` Navigating Scarcity: The Core Cloud vs. Frontier Lab Dilemma
Ben Horowitz raises the pressing bottleneck facing the technology sector: an impending, compounding shortage of power, data center space, cooling infrastructure, and memory components like High Bandwidth Memory (HBM) and DRAM. Horowitz asks whether AWS faces internal economic pressure to deprioritize legacy customers who have paid steady baseline rates for two decades in favor of well-capitalized AI labs willing to pay exorbitant premiums for every available megawatt and GPU cluster.
Jassy acknowledges that capacity constraints will extend well past 2027 rather than easing up. AWS's committed infrastructure is effectively sold out for 2026, largely sold out for 2027, and has already seen substantial portions of 2028 capacity spoken for. Despite these supply pressures and the constant internal trade-offs—such as allocating semiconductor production lines between HBM for AI accelerators and standard DRAM for general-purpose servers—Jassy firmly rejects the idea of price-gouging or evicting long-standing enterprise customers:
> “They're running all their core applications. We're not gonna squeeze our customers and we're gonna make sure that they can scale.”
Jassy compares their current strategy to Amazon's stance during the COVID-19 pandemic. While rival cloud providers sought to extract maximum revenue every fiscal quarter, AWS proactively helped enterprise clients optimize workloads and reduce cloud bills, accepting short-term revenue hits to preserve long-term trust. Amazon takes the same stance regarding its core cloud: even if capital could technically be spent at an even higher rate than $220 billion without satisfying aggregate market appetite, AWS refuses to compromise reliability for its foundational customer base.
- **Infrastructure Supply Runway:** 2026 compute capacity is sold out; 2027 is largely sold out; significant allocations of 2028 capacity are already contracted. - **On-Demand Buffer:** AWS intentionally reserves dedicated capacity pools for on-demand compute spikes and its Amazon Bedrock inference services. - **Component Competition:** Severe manufacturing allocation trade-offs exist between AI accelerator memory (HBM) and general cloud memory (DRAM).
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`28:00` The 2015 Annapurna Acquisition and Custom Silicon Roots
Horowitz asks about the strategic rationale behind Amazon’s 2015 acquisition of Israel-based Annapurna Labs, executed long before generative AI transformed semiconductor demand. Jassy explains that the purchase was never originally conceived as a play for standalone AI processors, but instead grew out of the operational scaling limits AWS encountered in its standard server fleet.
At hyper-scale, AWS discovered that too much of an EC2 server’s valuable CPU capacity was being wasted on administrative overhead—specifically virtualization hypervisors, networking pipelines, storage management, and hardware security. To deliver better price-performance to customers, Amazon partnered with Annapurna prior to the acquisition to develop an offload Network Interface Card (NIC) designed to handle those platform tasks separately, freeing nearly 100% of host server compute exclusively for customer applications. This collaboration evolved into the AWS Nitro System and laid the architectural foundation for Amazon's proprietary custom chip designs, including its Graviton CPUs and Trainium and Inferentia AI chips. Regarding the long-term payoff of that team, Jassy reflects:
> “And it's one of the very best acquisitions we've done at Amazon in the history of the company, if not the best.”
`30:00` Annapurna Labs, Custom Silicon, and the Economics of Inference
Andy Jassy details the origins of Amazon’s custom silicon strategy, beginning with the acquisition of Annapurna Labs. Originally, Annapurna's technology was brought in to power core compute capabilities, specifically the AWS Nitro System, which ended up being deployed across every single EC2 server Amazon operates. Because that team proved to be exceptionally skilled at chip design, Amazon realized they could build other silicon as well. At the time, Intel held nearly 100% of the CPU market. While Amazon had—and continues to have—a close partnership with Intel, a dominant partner with near-total market share naturally focuses on driving overall revenue and features to capture more share rather than aggressive price-performance. Jassy notes this is classic "innovator’s dilemma" territory. To solve this for customers demanding cheaper, better price-performance to migrate on-premises workloads to the cloud, Amazon tasked Annapurna with building its own CPU: the Graviton chip.
```mermaid flowchart LR accTitle: Evolution of AWS Custom Silicon accDescr: Diagram tracing Annapurna Labs acquisition to Nitro, Graviton CPU, and Trainium AI accelerators. subgraph ComputeFoundation["Infrastructure Base"] A["Annapurna Acquisition"] --> B["Nitro System\n(Every EC2 Server)"] end subgraph CPUs["CPU Disruption"] B --> C["Intel Monopoly\n(High margins, lagging price-performance)"] C --> D["Graviton CPU\n(40% better price-performance)"] end subgraph Accelerators["AI Accelerators"] D --> E["GPU Dominance\n(High cost, supply constraints)"] E --> F["Trainium / Inferentia\n(30-40% better price-performance)"] end ```
Today, Graviton delivers roughly 40% better price-performance than comparable x86 processors, and 99% of AWS's top 1,000 EC2 customers run on it pervasively. Jassy explains that Amazon recognized this exact same movie unfolding in artificial intelligence, where a single dominant vendor (NVIDIA) held sway. While Amazon maintains a deep partnership with NVIDIA, Jassy recognized that enterprise adoption required dramatic cost reductions. Amazon spun out a dedicated group within Annapurna to create the Trainium AI accelerator, delivering 30% to 40% superior price-performance relative to comparable alternative chips. Jassy emphasizes that lowering the cost of inference is the ultimate gatekeeper for enterprise AI adoption:
> “Because if you're asking enterprises to rewrite their applications that use CPU today with AI accelerators, if the difference in those costs is very significant, the amount of incremental benefit they have to get from having the AI accelerators on there is a much higher bar to want to rewrite those apps.”
Ben Horowitz underscores how punishing these costs can be in practice, joking about his own venture firm’s bills: an a16z employee ran up a $2 million inference tab over a short period doing good work, making Horowitz realize firsthand that current inference is far from cheap.
- Annapurna silicon is running inside every AWS server in production today. - Graviton delivers roughly 40% better price-performance over traditional x86 architecture and is used by 99% of AWS’s top 1,000 EC2 customers. - Trainium offers a 30% to 40% price-performance advantage over rival AI accelerators. - Lowering inference costs reduces the high ROI threshold required for enterprises to migrate existing CPU workloads over to AI accelerators.
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`35:00` Strategic Bets on Anthropic and OpenAI
Ben Horowitz brings up Amazon’s massive multi-billion-dollar investments into leading foundation model developers Anthropic and OpenAI, pointing out that Wall Street initially grew nervous about circular financing dynamics that evoked memories of the dot-com boom. Jassy defends the investments, stating that both companies possess extraordinary teams and have achieved top-line revenue velocity unseen in modern business history. Amazon’s strategy rests on deep infrastructure and product alignment rather than passive financial speculation:
> “And the rate at which they've grown their businesses, at least in the top line, I don't know if we've ever seen anything like it.”
With Anthropic, the relationship is long-standing. AWS customers run Anthropic extensively via Amazon Bedrock, and the Anthropic team has co-developed and trained models directly on Trainium silicon, sharing engineering feedback that has elevated both teams.
On the OpenAI front, Jassy explains that countless AWS customers were clamoring to run OpenAI models within AWS environments but had been blocked by prior exclusivity agreements. Following months of negotiations, Amazon established a broad partnership allowing OpenAI to run on Bedrock, co-developed a specialized stateful runtime environment that Jassy considers essential for future autonomous agents, and secured a multi-year, multi-gigawatt compute commitment from OpenAI on Trainium chips. Jassy emphasizes that while not every AI company will survive, both Anthropic and OpenAI are poised to be long-term category leaders.
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`37:35` Reimagining Organizational Design in the Agentic Era
The conversation shifts to the internal impact of generative AI on Amazon's corporate structure. Horowitz highlights a striking metric: the Amazon Bedrock team managed to completely rebuild their core inference engine using only six engineers in 76 days—a monumental engineering effort that previously would have required roughly 40 engineers working for an entire year. Horowitz asks how this shift disrupts Amazon’s iconic "two-pizza team" philosophy, noting that tech organizations spent decades hyperspecializing roles (designers, PMs, front-end, back-end), whereas AI tools are turning every worker back into a multi-disciplinary Renaissance person.
Jassy agrees enthusiastically, noting that the Bedrock inference engine milestone was shared across the company to break institutional inertia and demonstrate what modern engineering velocity looks like. He contrasts today’s hyper-fragmented corporate structures with Amazon’s early days: when Jassy joined the company 29 years earlier as an assistant product manager, he personally oversaw five distinct products. Over time, as tech companies grow, organizational bloat sets in until a single product manager might own only a minuscule slice of a product, such as pricing rules. Jassy argues that AI completely invalidates the assumption that hyper-specialization is required to build large-scale systems.
To illustrate, Jassy shares an anecdote from the consumer division that took place just the prior week. On a Thursday, leadership aligned around an agentic consumer prototype idea; a lean squad of four to five people picked it up and had an 80% shippable, fully functional prototype running by Sunday night. Jassy predicts that this capability heralds a fundamental reduction in the headcount required for core corporate functions:
> “I think number one, I think that we're going to have fewer humans doing the work that they're doing today in the future than we have today, just if you look at the amount of coding that's being done agentically, you look at the amount of customer service and business intelligence and research.”
`40:00` Smaller Cross-Functional Teams and the Agent-Native Imperative
Andy Jassy reflects on how enterprise staffing and team structures are going to fundamentally shift as artificial intelligence permeates the corporate world. Looking ahead at how everyday workflows—from building spreadsheets to staffing legal departments—will be handled, he argues that the historical playbook of throwing headcount at scaling businesses is coming to an end. While human labor will not disappear entirely, routine tasks will no longer require the massive volumes of people previously necessary. Instead, new categories of employment will emerge around fields like AI, robotics, and space development, while legacy roles will contract. Jassy envisions future corporate teams being substantially smaller, staffed by generalists who can operate cross-functionally rather than owning narrow functional slivers, precisely because underlying AI models can already carry teams 80% of the way across multiple operational disciplines.
Because modern AI tools establish such an advanced baseline right out of the gate, product teams can invent with far greater speed and breadth. Jassy observes that early-stage startups are capitalizing on this advantage immediately by launching as agent-native or AI-native organizations from day one. In contrast, incumbents with extensive capabilities and entrenched customer bases face a perilous transition: moving too slowly makes them acutely vulnerable to nimble newcomers. To keep up, established enterprises will have to undertake massive technical overhauls to support automated workflows. As Jassy predicts: “I predict that companies will have to rewrite almost all of their code to be agentically capable.” Organizations will need to empower autonomous agents to update production codebases while simultaneously engineering rigorous boundaries and constraints around what those agents are permitted to alter.
- **Cross-functional leverage:** Foundational AI models can complete roughly 80% of routine functional work, allowing small, interdisciplinary teams to start projects at an advanced stage and bypass repetitive low-level setup. - **Incumbent vulnerability:** While startups are architected as agent-native from inception, older enterprises that drag their feet on adapting will find their historical moats undermined by faster competitors. - **Codebase overhaul:** Maintaining parity will require enterprises to re-engineer existing software architectures so agents can autonomously inspect, modify, and deploy code within predefined guardrails.
`42:08` The Immediate Threat: Cybersecurity and Autonomous Remediation
Turning to the broader debate over AI risk, Jassy contrasts the popular fixation on apocalyptic doom scenarios with practical, immediate hazards. While acknowledging that theoretical threats—such as an evil botnet hijacking critical infrastructure or robots imbued with malicious intent—remain technically possible, he expresses confidence that industry and public-private partnerships will resolve them over time. The genuine, clear, and present danger demanding urgent attention today is cybersecurity. Frontier AI models are systematically identifying code vulnerabilities that human engineers previously missed. Consequently, the conventional security posture—where humans uncover flaws, categorize tickets, write patches, and slowly navigate complex release dependencies over weeks or months—is obsolete. When attackers leverage AI to exploit flaws instantly, defensive systems must detect, patch, and deploy remediations in minutes.
Ben Horowitz strongly concurs, noting that the software industry historically did a poor job of building robust security into everything from massive enterprise systems down to embedded edge devices, making an industry-wide day of reckoning inevitable. Jassy highlights that virtually every AWS partner and customer of scale is consumed by this challenge. To address it, Amazon deployed an internal automated system called Continuum to surface vulnerabilities and generate patches, backed by AWS professional services. Horowitz points out that organizational silos have long crippled software security: hyper-specialized, esoteric security engineers often avoid collaborating with everyday software teams, creating a structural bottleneck. Automated model-driven penetration testing finally makes it feasible to test an entire attack surface continuously, surfacing vulnerabilities that previously would only have been detected by pure luck.
“And so if you don't actually have your applications rewritten to be able to be updated by agents, you're going to have no chance in that new world,” Jassy emphasizes regarding the sheer speed required to defend modern infrastructure.
```mermaid flowchart TD accTitle: The AI Security Cycle Transition accDescr: Comparison between legacy human-dependent patching cycles taking weeks or months versus modern agentic AI patching executing in minutes.
subgraph Legacy["Legacy Vulnerability Management (Weeks to Months)"] L1["Human / Fluke Discovery"] --> L2["Manual Triage & Ticket"] L2 --> L3["Manual Patch Engineering"] L3 --> L4["Complex Dependency Testing & Staged Rollout"] end
subgraph Agentic["Agentic Security Remediation (Minutes)"] A1["Automated Full-Surface Pen-Testing"] --> A2["Autonomous Vulnerability Identification (e.g., Continuum)"] A2 --> A3["Agentic Patch Generation"] A3 --> A4["Instant Autonomous Deployment & Hotpatch"] end ```
- **Speed of deployment:** Defense cycles must compress from weeks or months down to minutes to counteract adversaries leveraging automated exploitation models. - **Continuum:** AWS developed Continuum to autonomously scan software surfaces, isolate security flaws, and generate remediation patches for deployment. - **Democratizing pen-testing:** Automated agentic models eliminate reliance on scarce security personnel by comprehensively scanning entire enterprise software footprints.
`46:00` Reimagining Legacy Products: The Alexa Overhaul
Horowitz outlines three distinct enterprise approaches to the AI revolution: using models for crude administrative cost-cutting (such as trimming legal or accounting staff, which he dismisses as an uninspired venture strategy), creating entirely novel zero-to-one products, and taking a massively successful incumbent product to rebuild it from the ground up on new foundational platforms. He highlights Amazon's total reinvention of Alexa as an example of the third path, asking Jassy how leadership recognizes when an existing platform must be scrapped and rebuilt. Jassy agrees with the framework, noting that streamlining headcount is only valuable if it frees up talented builders to focus on novel, uninvented experiences. When AI handles the baseline 80% of development toil, it energizes engineers to dedicate their mental cycles to ambitious, original ideas.
However, Jassy warns that incumbent companies run an existential risk if they treat generative AI merely as an engine for incremental tweaks to their flagship products. Leaders must push teams to imagine how their applications would look if designed from scratch today. In the case of Alexa, although the assistant's original brain architecture was revolutionary when launched, the step-function advances of large language models rendered that legacy structure obsolete. Jassy explains that while rebuilding Alexa's core brain was an obvious strategic necessity, executing it internally was emotionally fraught. Engineering teams had earned "hard-earned scars" building the original system, leading them to resist change by pointing to historical constraints and past failed experiments. Overcoming that cultural inertia is essential, not just for Alexa, but across Amazon's entire consumer ecosystem, including Prime Video, Retail, Fire TV, and Amazon Music.
As Jassy warns established businesses: “Because I think every single one of these experiences we know, even the ones that seem the most difficult to imagine changing, are going to be completely reinvented.”
- **The three AI postures:** (1) Pure back-office cost reduction, (2) zero-to-one inventions the world has never seen, and (3) complete architectural overhauls of established, scaled category leaders. - **Overcoming internal inertia:** Engineering teams naturally cling to past systems they sacrificed to build, often resisting architectural rewrites with institutional refrains like "that's not how we've done it" or "we've tried that and it didn't work." - **Broad ecosystem reimagination:** Beyond voice assistants, Amazon is applying ground-up agentic and generative AI redesigns to its flagship retail operations, Prime Video entertainment stack, and Fire TV platforms.
`50:00` The Leadership Challenge of Reinventing Existing Businesses
Andy Jassy emphasizes that while it is not always obvious where new technology offers a superior solution on day one, established companies must proactively reinvent their core offerings. Amazon currently has dedicated teams building completely reimagined versions of its existing businesses. Rather than expecting perfection from the get-go, the goal is to iterate rapidly, unearthing compelling ideas and novel customer experiences along the way. Jassy frames the key strategic race ahead: will companies launch brand-new, standalone applications, or will these advancements integrate into legacy workflows quickly enough to transform the existing customer experience bit by bit over the next two to three years? Regardless of the path chosen, Jassy stresses that leaders must embrace this evolution:
> “But I do think every one of these experiences has to be different in the next few years if you want to be a successful leader.”
Ben Horowitz observes that this presents a notorious organizational and psychological trap for leaders. Recalling his early career as an engineer, Horowitz recounts an argument where his manager pulled him aside to deliver a foundational lesson:
> “Everything is up for discussion until the first line of code is written, and then it's religious.”
When an organization has spent a decade building institutional knowledge, architecture, and customer loyalty around an existing product, deciding to walk away from that foundation creates immense internal resistance. Jassy agrees, comparing the AI shift to past architectural overhauls where veterans who knew the existing stack inside and out were reluctant to adopt new paradigms:
> “To think about how do I completely start over is it's intellectually and emotionally hard to do.”
```mermaid flowchart TD accTitle: The Product Reinvention Dilemma accDescr: A flowchart depicting the strategic paths for modernizing legacy customer experiences using AI.
subgraph Legacy["Existing Business Core"] A["Legacy Architecture & Codebase"] -->|"Institutional Attachment"| B["Incremental Feature Improvements"] end
subgraph Fork["The Strategic Choice"] B --> C{"Reinvention Path"} C -->|"Option 1"| D["Brand-New Standalone Experience<br/>(Clean-slate application)"] C -->|"Option 2"| E["In-Place Progressive Modernization<br/>(Iterative feature injection)"] end
subgraph Destination["End State"] D --> F["Next-Gen Customer Experience"] E --> F end ```
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`53:15` Advice for the Next Generation: Why AI Cannot Replace Critical Reasoning
Erik Torenberg points out that Andreessen Horowitz incubated the Horowitz & Jason Academy to help young people navigate this technological transformation, and asks Jassy what guidance he would give his 18-year-old self in 2026. Reflecting on his own children (aged 25 and 22), Jassy notes that when generative AI began taking off, he realized immediately that attempting to ban AI in schools or workplaces was futile: the train has already left the station, and academic institutions should embrace rather than fight the technology. At the same time, Jassy warns against over-reliance on generative outputs. At Amazon, which maintains a deep narrative-writing culture, low-effort AI writing stands out instantly because the nuance and structural cohesion are lacking. His core recommendation to young people is to lean in aggressively, experiment outside their comfort zones, and make the technology a central instrument of their daily workflow.
However, Jassy draws a sharp boundary between automated preparation and original analysis. Looking back at his time in business school, students were assigned three complex business cases each day, requiring them to comb through background text, annotate dense financial exhibits, identify strategic dilemmas, and outline an action plan. AI will easily handle the grunt work of reading, summarizing, and gathering data, democratizing that starting baseline for everyone. What AI will rarely accomplish on its own is higher-order synthesis and novel thinking:
> “I still think critical reasoning and thinking is going to be very important in everything we do.”
- **Embracing AI literacy:** Young builders should actively test AI tools on problems outside their comfort zones rather than trying to wish the disruption away. - **The limit of LLM synthesis:** While models accelerate information synthesis and initial draft preparation, human judgment, invention, and experimentation remain essential for deciding how strategic puzzle pieces fit together into a viable business.
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`56:55` Juggling AI, Robotics, and Space: How Single-Threaded Leadership Scales Ambition
Torenberg questions how Amazon manages an expansive footprint that spans cloud infrastructure, low Earth orbit satellite networks, autonomous driving, robotics, and healthcare—a staggering evolution from an online bookstore. Jassy explains that society is living through three of the most significant simultaneous technological transformations in human history: artificial intelligence, robotics, and space technology. In space, for example, Amazon is developing its low Earth orbit (LEO) satellite constellation to deliver broadband access to the estimated 500 million households worldwide that currently lack reliable connectivity.
Addressing whether Amazon risks being spread too thin across these capital-intensive bets, Jassy explains that organizational structure makes the difference. Rather than tasking the same managerial layer with juggling multiple distinct product lines, Amazon organizes into decentralized, "single-threaded" teams led by dedicated builders:
> “And so because we don't try and have the same group of people manage multiple businesses and we organize in this decentralized, single-threaded way, we're not.”
When Amazon identifies a large, underserved problem space where it possesses a differentiated point of view and can deploy high-conviction leadership teams, it is willing to invest heavily while continuing to grow its core businesses:
> “It just comes down to how much you're willing to invest.”
- **The Three Meta-Trends:** AI, robotics, and low Earth orbit space networks represent the defining technological shifts of the era. - **Targeting Global Infrastructure Gaps:** Amazon's LEO constellation targets 500 million unserved and underserved broadband households. - **Single-Threaded Ownership:** Breadth succeeds without loss of operational focus because individual business units operate with dedicated teams rather than shared executive bandwidth.
## Notable quotes > `50:00` Everything is up for discussion until the first line of code is written, and then it's religious. — Ben Horowitz
> `40:00` I predict that companies will have to rewrite almost all of their code to be agentically capable. — Andy Jassy
> `00:00` Even spending $220 billion this year, we're leaving a meaningful amount of demand on the table. — Andy Jassy
> `15:24` The country, I think the world in general, but I say the U.S. specifically, cannot afford not to build data centers. Or we will find that another country like China will establish all the leadership and all the norms there. — Andy Jassy
## Mentioned Amazon, Andy Jassy, Ben Horowitz, Erik Torenberg, AWS, UPS, Prime Video, Zoox, Alexa, ChatGPT, Claude Code, Amazon Bedrock, EC2, Annapurna Labs, AWS Nitro System, Graviton, Trainium, Inferentia, Intel, NVIDIA, Anthropic, OpenAI, Continuum, Fire TV, Amazon Music, Project Kuiper
## Video description Amazon expects to spend roughly $220 billion in CapEx in 2026, yet CEO Andy Jassy says the company is still leaving meaningful AI demand on the table.** He believes the buildout could ultimately help make AWS a $1 trillion annual run-rate business.
Andy joins a16z co-founder Ben Horowitz and Erik Torenberg to discuss why AI demand is growing so quickly, how Amazon decides how much infrastructure to build, and why lower inference costs could unlock broader enterprise adoption.
They also discuss how AI is reshaping Amazon itself, from smaller teams building products faster to reimagining Alexa, and why Andy sees cybersecurity, human judgment, and critical thinking as increasingly important in the AI era.
[Source](https://mgln.ai/e/1344/afp-848985-injected.calisto.simplecastaudio.com/3f86df7b-51c6-4101-88a2-550dba782de8/episodes/e1185d26-c41a-40d4-9210-eca5adab915a/audio/128/default.mp3?aid=rss_feed&awCollectionId=3f86df7b-51c6-4101-88a2-550dba782de8&awEpisodeId=e1185d26-c41a-40d4-9210-eca5adab915a&feed=JGE3yC0V)