According to an October 11 report from *The Wall Street Journal*, Amazon is in talks to acquire AI startup Decart for approximately $7 billion (roughly 47 billion Chinese yuan).

Amazon is gearing up for another big‑ticket spending spree, this time setting its sights on an AI firm founded merely three years ago.

According to an October 11 report from *The Wall Street Journal*, Amazon is in talks to acquire AI startup Decart for approximately $7 billion (roughly 47 billion Chinese yuan).

Upon the news breaking, many observers remarked how valuations for Silicon Valley AI companies have grown increasingly exuberant.

What merits greater attention, however, is that this firm previously drew interest from heavy‑hitters including NVIDIA and Anthropic. Why would a startup only a few years old spark bidding wars among tech giants?

Beneath this potential CNY 47‑billion deal lie ongoing shifts unfolding across North America’s tech job market.

01 What Exactly Is Amazon Buying for CNY 47 Billion?

Founded in 2023, Decart first made waves across the tech industry with Oasis, its AI tool for real‑time game‑scene generation. It later released Lucy, capable of real‑time generation and transition of video scenes.

For a giant such as Amazon, the flashy consumer‑facing AI applications are unlikely to be the main draw. What truly matters is Decart’s formidable computing‑optimization capabilities that drive down compute costs.

Core pain point: Today’s AI models keep growing larger, causing inference and operating costs for major tech firms to surge exponentially.

Decart’s competitive edge: It enables AI models to run faster, cheaper and more efficiently across diverse chips.

Decart has already engaged in deep‑running collaboration with Amazon AWS, testing real‑time video models on Trainium chips. Early tests show throughput improvements of up to 4‑fold in certain scenarios, alongside a 2‑fold gain in cost efficiency.

This technology carries self‑evident value for Amazon, which pours tens of billions of US dollars annually into building AI infrastructure.

What is more, Amazon controls core AI assets including AWS, its Trainium chips and Bedrock. Rather than remaining constrained by NVIDIA’s GPU and software ecosystem, it can acquire critical technology and top‑tier talent in one fell swoop.

(Note: As of October 14, the acquisition remains in advanced‑stage negotiations and no official announcement has finalized the transaction.)

02 North‑American Tech‑Job Trends Are Truly Shifting

Placing this acquisition within Silicon Valley’s broader AI‑competitive landscape reveals a clear signal:

The battleground for big‑tech players is shifting from “who can train more powerful large models” toward “who can make AI run faster, more reliably and at lower cost”.

This offers highly actionable guidance for international students majoring in CS, EE, ECE, Data Science and AI preparing for fall or spring recruitment cycles.

In the past, when students thought about AI‑related jobs, most rushed to compete for MLE (Machine Learning Engineer) and AI Research Scientist roles, or filled their resumes with LLM demos built merely by calling APIs.

The reality is that large‑model research positions carry extremely high entry barriers, nearly exclusively reserved for PhD holders. Meanwhile, demo‑focused frontend‑level projects tend to look identical across candidates.

Roles focused on low‑level engineering are what underpin real‑world AI commercialization — and these are exactly where major companies face severe talent shortages.

💡 Key low‑level AI tracks worth prioritizing:

‑ AI Infrastructure / Cloud Infrastructure

‑ ML Systems / ML Compiler

‑ Distributed Systems Engineer

‑ GPU / AI Performance Engineer

‑ Software Engineer – AI/ML

‑ Model Inference / Optimization Engineer

From NVIDIA and Amazon through to OpenAI, Silicon Valley giants are pouring tens of billions into AI infrastructure.

Where capital flows, job opportunities will follow.

The next round of AI‑talent competition will test more than just the ability to use large models. It will reward engineers who can deploy models reliably and bring down operating costs. These robust technical capabilities constitute ever‑green high‑value skills sought by engineering teams.