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Amazon reportedly in final talks to acquire AI infrastructure firm Decart

Wall Street Journal reports Amazon may close a deal worth up to $7 billion for Decart.ai, following Anthropic's exit.

Illustration: A sleek black server rack sits in a cool, minimalist data hall, its front panels displaying only soft, abstract blue lights.
AI-generated illustration: TechSec Observer

Key points

  • Amazon is in advanced discussions to acquire Decart.ai for up to $7 billion.
  • The startup’s DOS software optimises AI models for specific processors in weeks.
  • Anthropic previously pursued a $6 billion deal but withdrew after due diligence.

Amazon.com Inc. is reportedly engaged in advanced discussions regarding the potential acquisition of artificial intelligence developer Decart.ai Inc. According to the Wall Street Journal, the deal could be valued at up to $7 billion. Sources familiar with the talks suggest an agreement could be finalised in the coming days, provided discussions do not collapse and no competing bidder emerges.

Root cause

Decart has created software that simplifies running AI models across different processors. This capability addresses a critical bottleneck as organisations struggle to secure computing infrastructure for AI applications. The platform, called DOS, allows developers to optimise neural networks for specific chips, compressing a workflow that traditionally took months into just a couple of weeks. This flexibility enables companies to shift workloads if chip availability changes.

Attack path

The competitive landscape for Decart remains active. Bloomberg reported last month that Anthropic PBC was in talks to buy Decart for $6 billion. However, Anthropic ultimately pulled out of the deal after conducting due diligence. It remains unclear what specific findings dissuaded Anthropic from pursuing the acquisition. Another suitor could still emerge and outbid Amazon, though current reports indicate Amazon is the primary contender.

Affected versions

Decart was founded in 2023 by Israeli software engineers Dean and Orian Leitersdorf and Moshe Shalev. The company most recently raised $300 million at a $4 billion valuation in May, bringing its total funding to over $400 million. Major investors include Radical Ventures, Nvidia Ventures, Adobe Ventures, Sequoia Capital and Benchmark. Decart also develops world models like Lucy and Oasis for simulating three-dimensional environments.

Mitigation

The acquisition would integrate Decart’s technology into Amazon Web Services. AWS offers its own custom AI processors, including Trainium3, for training models. Decart’s DOS platform could enhance the utility of these chips by allowing faster optimisation and easier migration of workloads. This integration aims to streamline the process for developers using AWS infrastructure for AI training and inference tasks.

Background: Neural networks

Neural networks are mathematical models that find patterns in data. Small businesses use them via third-party services to automate tasks. Security requires understanding how inputs are stored, how models are queried, and where data privacy boundaries fail during daily operations.

Read the full guide: Neural networks for small businesses: practical security risks

What to do and how to stay safe: Amazon

  • Monitor official announcements from Amazon and Decart for confirmation of the deal.
  • Review current AI infrastructure dependencies for potential integration changes.
  • Assess how processor-specific optimisations impact your deployment timelines.
  • Watch for updates on AWS Trainium3 compatibility with new tools.

General security guidance from the TechSec Observer newsroom. It is not confirmed advice from the organisations named in this story.

Frequently asked questions

How much is Amazon reportedly paying for Decart?

The Wall Street Journal reports the deal could be worth up to $7 billion.

Why did Anthropic drop out of the bidding?

Anthropic withdrew after conducting due diligence, though the specific reasons are unclear.

What does Decart’s DOS software do?

It optimises neural networks for specific processors, reducing workflow time from months to weeks.

Sources

  1. SiliconANGLE

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