Amazon Unleashes Nova 2, Igniting an Enterprise AI Arms Race

*Source: https://zdnet.co.kr/view/?no=20251204170410#_enliple

● Amazon Unveils Game-Changing Nova 2 Series, Corporate AI Revolution

Amazon Unveils ‘Nova 2’ Series and Enterprise Services — Rebalancing Model Performance, Cost, and Customization

Why you should read this article: A comparison of Nova 2 models by performance and use, analysis of the meaning of ‘Open Training’ in the enterprise-customized model (Nova Forge), the practical applicability of high-reliability AI agent (Nova Act), and a comprehensive overview of AWS and Amazon’s strategic intentions and its global AI and economic implications.This article covers the following core topics:

Features of the Nova 2 lineup (Light, Pro, Omni, Sonic) and differentiators compared to competing models.
The impact of Nova Forge’s ‘checkpoint approach (Open Training)’ on corporate AI competition.
Nova Act’s reliability in UI automation and real-world application cases.
An overview of Amazon (AWS)’s business strategy, cloud competition, changes in the large language model (LLM) ecosystem, and economic impacts.
Five key insights not widely covered by other media (from legal, data, cost, vendor lock-in, and global regulation perspectives).

Key Summary — News-Type Core Facts

Amazon (AWS) unveiled the ‘Nova 2’ series and two enterprise services (Nova Forge, Nova Act) at AWS re:Invent 2025.Nova 2 is subcategorized into four models (Light, Pro, Omni, Sonic) aiming to balance speed, cost, and accuracy.Amazon claims competitive or superior performance on benchmarks compared to models like Claude, GPT, and Gemini.Nova Forge supports an ‘Open Training’ approach, allowing enterprises to access Nova checkpoints (pre, midway training) to create custom frontier-grade models.Nova Act is a service for training and deploying browser-based UI automation agents, offering a 90% reliability figure and applicability to CRM updates, web functionality testing, and more.Some Nova 2 models incorporate web grounding and code execution capabilities to ensure responses based on the latest facts and dynamic task execution.Corporate clients (Cisco, Siemens, Trellix, Booking.com, Reddit, Sony, etc.) are emerging with early adoption cases.

Model-Specific Detailed Analysis — Features, Applications, Competitiveness

1) Nova 2 Light

Purpose: A fast and cost-efficient inference model optimized for everyday workloads.
Input: Capable of processing text, images, and videos.
Competitiveness: Claimed to have equal or superior performance in majority benchmarks compared to Claude Haiku 4.5, GPT-5 Mini, and Gemini 2.5 Flash.
Practical Uses: FAQs, chatbots, lightweight content creation, consumer interaction automation.

2) Nova 2 Pro

Purpose: Focused on tasks requiring the highest accuracy (such as agent coding and long-term planning).
Features: Emphasis on high-precision language understanding and reasoning.
Practical Uses: Automated decision support, complex engineering and financial analysis, code generation and validation.

3) Nova 2 Omni

Purpose: Promoted as the industry’s first integrated multimodal (text, image, video, audio input, text, and image generation) model.
Differentiators: Capable of simultaneously analyzing hundreds of pages of documents, long videos, and entire product catalogs.
Economic Significance: Reduces the total cost of ownership (TCO) by eliminating the need for connecting multiple specialized models.
Practical Uses: Enterprise-wide knowledge search, multimedia content summarization and indexing, integrated customer support analysis.

4) Nova 2 Sonic

Purpose: Human-level real-time interactive AI (speech-to-speech).
Competitiveness: Claimed cost-performance and quality advantage over GPT Real-Time and Gemini 2.5 Flash.
Practical Uses: Natural voice conversations in call centers, real-time interpretation, virtual assistants.

Nova Forge (Custom Enterprise Model) — The Meaning and Impact of ‘Open Training’

Key Features

Enterprises gain access to Nova checkpoints (pre, midway) for additional training with their own data.
Allows blended training with Amazon-curated datasets, resulting in customized models like ‘Novella.’

Strategic Significance (Key Points)

Shifts away from the traditional closed frontier model strategy, transferring ‘frontier capabilities to enterprises.’
Although aiming to ease vendor lock-in, there is a high possibility of leading enterprises to greater dependency within the AWS ecosystem (data+training+deployment integration).
Enterprises with their own frontier models can more easily establish differentiated business models (content filtering, specialized search, product recommendations, etc.).

Regulatory, Legal, and Data Risks

Access to checkpoints and blended training can raise legal issues related to data use, copyright, and privacy.
Impact of national data sovereignty and regulations (e.g., EU AI Act) and the rising demand for internal corporate data governance.

Nova Act (High-Reliability AI Agent) — Advancements in Automation Viability

Key Facts

Recorded a 90% reliability rate in browser-based UI automation tasks.
Learns web UI interactions through simulation-based reinforcement learning.
Real-World Applications: CRM system updates, website functionality testing, bulk UI task automation.

Practical Significance

Automating repetitive web tasks that require human involvement reduces labor costs and errors.
However, 90% reliability means 10% failure — supervision required for high-risk tasks (financial transfers, legal document dispatches, etc.).

Strategic Intent of AWS and Amazon and Market and Economic Impacts

Enhancing Cloud Revenue and Customer Dependence

The Nova series, along with Forge and Act, is designed to integrate data-model-deployment within AWS services (Bedrock, SageMaker, etc.).
This potentially increases AWS’s short-term sales and long-term customer dependency on the cloud.

Competitive Landscape Shift

Amazon moves beyond being a mere cloud provider to becoming a frontier model provider in direct competition with OpenAI and Google.
Price competition and performance competition among models and services are expected to intensify.

Labor Market and Productivity Impacts

The automation of UI and agent-based task automation accelerates the transition from repetitive tasks to high-value-added tasks.
While boosting productivity, it could increase the risk of structural unemployment in certain occupations.

Global and Geopolitical Implications

The possession of frontier models by major cloud providers reinforces strategic technological dependencies between countries.
Regulation strengthening regarding data sovereignty and security could accentuate corporate risk management needs.

Key Insights from the Announcement That Are Not Widely Covered by Other Media (Most Important Content)

1) ‘Open Training’ is more than mere technology provision — it signifies a strategic power shift.

When enterprises access frontier checkpoints, Amazon sells technological superiority but secures a new form of platform dominance through data and deployment infrastructure.

2) The integration capability of the Omni model replaces multiple specialized models, reducing total infrastructure costs.

As companies reduce complexity by operating fewer models, the deployment and spread speed of AI projects accelerate significantly (cost savings → spread acceleration).

3) Checkpoint access triggers both model independence possibilities and the transfer of legal and ethical responsibility.

As companies create their models, accountability for incorrect predictions and content becomes more complicated.

4) The practical reliability (90%) of Nova Act sets a baseline for automation adoption.

While 90% is a commercially viable figure, companies must design how to compensate for the 10% failure (human-in-the-loop, function failsafe).

5) Amazon’s strategy is to redesign its cloud and AI investment recovery model.

The intention to diversify revenue sources is clear, not just collecting model usage fees but by structuring integrated costs that cover data, training, and deployment.

Recommended Action Guidelines for Companies and Investors

Review Data Governance: Before utilizing checkpoints, conduct a preemptive review with legal and compliance teams on risks related to data usage rights, privacy, and copyrights.
Assess Multimodal Strategy: If there is a demand for multimedia or large document processing, evaluate the potential for TCO reduction based on Omni integration.
Manage Automation Risks: When adopting Nova Act, designing processes for failure coverage (monitoring, rollback, human intervention) is essential.
Diversify Vendor Risks: Although the convenience of integrating with AWS is high, consider a multi-cloud and backup strategy to manage long-term dependency.
Prepare for Regulatory Responses: Monitor regulatory trends in the EU, the US, and Asia and prepare distribution strategies in line with data sovereignty policies.

< Summary >AWS’s Nova 2 series (Light, Pro, Omni, Sonic), along with Nova Forge and Nova Act, are strategic products aimed not only at technical performance but at restructuring the enterprise AI ecosystem.Nova Forge’s ‘Open Training’ offers a path for companies to use frontier checkpoints to create proprietary models while also entailing vendor dependency and legal risks.The Omni model, by integrating multimodal aspects, is likely to accelerate AI adoption by reducing costs and complexities.Nova Act’s 90% reliability makes real-world automation implementation feasible, yet requires a system for handling failures.Companies should proactively manage data governance, regulations, and vendor risks while optimizing AI investment efficiency.

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