Amazon(AMZN.US) dispatches strategic signals for robot job cuts: a $200 billion full-scale investment in AI computing power, with self-developed AI chips becoming the core of cost reduction

Bloomberg News reports that Amazon (AMZN.US), a leader in e-commerce and cloud computing, is laying off employees in its strategically important robotics division. Some Wall Street analysts believe this move, combined with Amazon’s recent announcement to heavily invest in its self-developed AI chips—namely Trainium and Inferentia—AI ASIC clusters for developing and updating its own large AI models, signals a broader effort by the tech giant to cut costs and shift spending focus entirely toward AI computing infrastructure. Meanwhile, Amazon is increasingly relying on automation systems to support its fulfillment network.

According to sources cited by media outlets, the layoffs this week affected “certain robotics roles,” but the company is still actively hiring and investing in “multiple strategic areas.”

As these layoffs bring Amazon’s total job cuts since 2022 to 57,000, the company is ramping up large-scale investments in AI, data centers, and humanoid robots to maintain its competitive position in AI and the broader physical AI trend.

Amazon Launches AI Cost Revolution! Striving for Autonomous Training and Inference Control

This move does not mean Amazon is neglecting its robotics projects. Instead, it is shrinking some robotics roles with longer payback periods and reallocating more resources to AWS cloud computing, AI data centers, and its self-developed AI ASIC chips. Amazon aims for “model and chip co-design” to control training and inference costs internally, rather than being dependent on external GPU pricing structures.

Undoubtedly, with Anthropic—often called an “OpenAI rival”—spending hundreds of billions of dollars to acquire 1 million TPU chips, and Meta (Facebook’s parent company) considering spending billions in 2026 or 2027 to purchase Google’s TPU AI infrastructure—including the construction of massive AI data centers—along with Amazon’s announcement to develop large AI models using Trainium and Inferentia, it’s clear that cloud giants are initiating an “AI compute cost revolution” to expand AI ASIC adoption. This has raised concerns about Nvidia’s growth prospects.

On one hand, Amazon is reducing a relatively small number of roles in its robotics team; on the other, it is directing about $200 billion in capital expenditure in 2026 mainly toward AWS’s core cloud infrastructure and large AI workloads. AWS continues to develop self-made AI compute hardware like Trainium and Inferentia. Amazon’s operational network has deployed over 1 million robots and uses generative AI models like DeepFleet to improve robot scheduling efficiency.

In its latest earnings call, Amazon CEO Andy Jassy confirmed that the company plans to invest approximately $200 billion across all business units, primarily in Amazon Web Services (AWS), due to “our high compute demands and customer expectations for AWS to handle core workloads and massive AI tasks. The more capacity we install, the faster we can monetize it at scale.”

Jassy also described the robotics business as “a major project” for Amazon. With over 1 million robots in its fulfillment logistics network, automation will take on repetitive and hazardous tasks to significantly boost productivity and efficiency.

“We will continue optimizing inventory placement to shorten transportation distances, reduce handling times per package, and greatly improve package consolidation, while also launching cutting-edge robotics and automation technologies to enhance efficiency and customer experience,” Jassy said during the earnings call.

However, just weeks after Amazon abandoned development of its multi-arm robot product “Blue Jay,” the company decided to scale back its robotics division. This robot was initially expected to be widely deployed in Amazon’s same-day delivery warehouses.

AI Compute Infrastructure Takes Priority Over Everything

Amazon’s management is now shifting capital and talent from longer-term, complex robotics projects to the faster-to-monetize AI compute infrastructure layer. The layoffs in the robotics division, confirmed after the company’s large-scale layoffs in January, are part of this strategy. Meanwhile, Amazon has increased its 2026 capital expenditure target to $200 billion, mainly focusing on AWS and AI infrastructure. At the same time, Amazon has not abandoned its ambitions in warehouse automation: last year, it announced that its operational network had deployed 1 million robots and launched the generative AI model DeepFleet to coordinate robot fleets, claiming it can improve fleet efficiency by 10%. This suggests that the cuts are more about marginal returns on certain robotics projects rather than abandoning automation altogether.

In other words, Amazon’s current cost strategy resembles a typical technology stack overhaul: first building a universal AI platform and self-developed compute base, then leveraging this “cost-effective and scalable intelligence” to support robotics and fulfillment networks. This is not about “robots losing to AI,” but rather integrating robots into an AI platform strategy at the downstream application level.

From the perspective of the underlying relationship between robotics and AI data centers, Amazon seems to acknowledge a key reality: the future bottleneck is primarily the economics of compute power, followed by the form of terminal automation. Robots remain important, but they are increasingly seen as downstream execution layers within Amazon’s ecosystem. The real determinants of scaling speed, unit costs, and iteration efficiency are whether upstream can train/deploy models at lower costs and reuse these capabilities across AWS customers, Nova, Alexa, Rufus, and warehouse scheduling and robot control.

Amazon’s stock rose nearly 4% on Wednesday, closing at its best single-day performance since November, driven by a rebound in tech stocks amid rising risk appetite, strong US service sector growth—the fastest since mid-2022—and easing inflation pressures. Additionally, better-than-expected ADP employment data boosted confidence, temporarily overshadowing macroeconomic concerns from Middle East geopolitical tensions. The three major US stock indices all rose, US Treasuries and the dollar declined, and risk assets like cryptocurrencies surged accordingly.