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Amazon launched Supply Chain Services (ASCS) to sell freight and delivery to P&G and 3M, fulfilling Ben Thompson's 2016 prediction that logistics would follow AWS's path from internal infrastructure to sellable platform.
What: Amazon announced ASCS, packaging air/ocean freight (including the 20+ planes it leased starting in 2016), trucking, and last-mile delivery for companies like P&G and 3M. Stratechery's Ben Thompson argues this validates Amazon's decade-long formula: massive capital investment, use Amazon as first customer, then sell to others. He connects this to AWS's $4 billion Anthropic investment and shift toward custom Trainium chips (launched 2019, now in third generation), which favor inference workloads over Nvidia's training-optimized GPU systems.
Why it matters: Companies rooted in the physical world (Amazon's logistics and data centers) can be neutral AI providers because their core business isn't threatened by AI, unlike Microsoft/Google/Meta who must prioritize internal AI workloads to defend digital products. Decade-long infrastructure bets create moats nearly impossible to replicate, and market shifts can retroactively vindicate architectural choices.
Deep dive
- Amazon announced Amazon Supply Chain Services (ASCS), consolidating air/ocean freight, trucking, and last-mile delivery for businesses like P&G and 3M, validating Ben Thompson's 2016 prediction that logistics would follow AWS's infrastructure-as-a-service playbook
- Amazon's repeating formula: convert marginal costs to capital costs through massive upfront investment, use Amazon as first customer to justify scale, then sell primitives to external businesses for leverage on capital costs
- AWS built structural cost advantages through Nitro (offloading server management to custom chips, increasing VM density) and Graviton (custom ARM processors initially deployed invisibly in PaaS products like RDS)
- SemiAnalysis warned in 2023 that AWS was ill-prepared for AI: custom networking instead of Nvidia/Broadcom solutions, inferior custom chips, and risk of Nvidia deprioritizing GPU allocations to AWS
- Those concerns were valid for training-dominated markets requiring thousands of horizontally-networked Nvidia GPUs, but the market shifted to inference (reasoning models, agentic workflows) which exponentially increased token generation and changed infrastructure requirements
- Inference workloads favor AWS: models fit in single servers vs. thousands of networked chips, expanded KV caches need dedicated memory servers compatible with AWS networking, heavy CPU dependency suits disaggregated resource abstraction
- Amazon's Trainium chips (first launched 2019 after acquiring Annapurna Labs in 2015) are now competitive with Trainium 3, positioning AWS for sustainable cost advantage by hiding custom silicon behind Bedrock API abstractions
- Amazon's $4 billion Anthropic investment provided compute capacity and made Anthropic available across all major clouds (unlike OpenAI's initial Azure exclusivity), creating an enterprise selling point for multi-cloud frontier model access
- AWS neutrality advantage: Amazon's physical-world core business (logistics, data centers) has no existential AI threat, allowing majority compute capacity for customers vs. Microsoft/Google/Meta who must prioritize internal AI workloads to defend digital businesses
- Microsoft demonstrated this tradeoff in early 2026 by missing Azure growth targets after devoting compute to internal workloads instead of cloud customers
- Amazon Leo satellite constellation follows the same decade-long infrastructure bet pattern: capital-intensive with Amazon as first customer (future drone delivery connectivity, following 2013 drone delivery announcements), external sales to companies like Apple
- Andy Jassy explicitly compared Leo economics to AWS: heavy early capital commitment for assets leveraged long-term, attractive free cash flow and ROIC in medium/long term
- Thompson argues AI vulnerability correlates with physical-world interaction: Amazon/Apple comfortable accessing models vs. building them due to physical foundations, while Aggregators (Google, Meta) must invest heavily in models to defend zero-switching-cost distribution
- ASCS announcement sent FedEx and UPS shares lower, signaling market recognition of Amazon as direct logistics competitor selling infrastructure previously internal-only
- The decade timeframe from 2016 prediction to 2026 announcement illustrates Amazon's willingness to make patient capital investments that compound over time as markets and technologies shift in their favor
Decoder
- Nitro: AWS system-on-chip that offloads server management (networking, storage, hypervisor) from main CPUs to dedicated hardware, increasing virtual machine density and reducing costs
- Graviton: Amazon's custom ARM processors for AWS, cheaper than Intel/AMD while delivering competitive performance, initially used invisibly in Platform-as-a-Service products
- Trainium: Amazon's AI accelerator chip line launched in 2019, now in third generation, designed to reduce Nvidia GPU dependency and provide cost advantages
- HGX: Nvidia's GPU rack system with integrated high-bandwidth networking optimized for AI training, contrasting with AWS's standalone rack approach
- Aggregators: Stratechery term for platforms controlling distribution by serving users at zero marginal/switching cost, forcing suppliers to compete for attention (Google search, Meta social) vs. linear businesses with physical or software distribution moats
- KV cache: Key-value cache storing attention states in transformer models, major memory bottleneck in long-context and reasoning workloads
Original article
Amazon consistently makes real-world investments at a massive scale that convert its marginal costs into capital costs, and then gains leverage on those capital costs by selling them to other businesses.
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