Big Tech Momentum Holds at Year End With Meta Buying Manus

Big Tech is closing the year with continued momentum as companies push artificial intelligence deeper into products, infrastructure and monetization strategies.

Meta’s acquisition of Manus adds millions of paying users and signals a sharper focus on subscription-based consumer AI, while Google, Amazon and Microsoft are rolling out new models and platforms across edge computing, smart homes and climate data.

Meta Acquires Manus to Accelerate Consumer AI Monetization

Meta acquired Manus, an AI startup that operates a consumer-facing assistant with millions of paying users, for more than $2 billion, The Wall Street Journal reported Tuesday (Dec. 30). The acquisition adds an established subscription-based AI product to Meta’s portfolio as the company faces growing pressure to show clearer returns on its AI investments.

Manus offers a productivity-focused AI assistant designed for task execution rather than open-ended conversation. The product has gained traction with users willing to pay recurring fees for features such as structured task handling, document interaction and workflow support. That paid user base differentiates Manus from many consumer AI tools that rely primarily on free tiers or advertising.

The deal gives Meta a scaled, revenue-generating AI product with direct consumer payments. Although Meta has invested in AI infrastructure and promoted open-source models through its Llama family, monetization has remained indirect, tied largely to advertising and engagement across Facebook, Instagram and WhatsApp.

By acquiring Manus, Meta gains technology and distribution, along with immediate exposure to subscription revenue and insight into consumer willingness to pay for AI-powered assistance. The transaction also shortens the timeline for rolling out premium AI offerings without having to build a paid user base from scratch.

Google Pushes AI to the Edge With FunctionGemma

Google expanded its AI model lineup with FunctionGemma, a compact, edge-optimized model designed to translate natural language instructions into structured function calls on mobile and edge devices.

Built on the Gemma 3 270-million-parameter base, FunctionGemma enables localized, low-latency automation without continuous cloud connectivity. The model can interpret voice or text commands to control device features, initiate actions or route tasks to larger cloud models when needed.

The release reflects a broader shift toward hybrid AI architectures that combine on-device intelligence with cloud-based systems. Rather than relying solely on centralized models, FunctionGemma emphasizes responsiveness, privacy and cost-efficient inference at the edge, dynamics that increasingly shape mobile, wearable and IoT platforms.

Amazon Brings Conversational AI to the Front Door

Amazon introduced Alexa+ Greetings, an AI-powered feature that allows Alexa to interact directly with visitors through compatible Ring video doorbells.

Instead of simply recording motion or sending alerts, Alexa can now converse with visitors, provide delivery instructions, decline solicitations or take messages when homeowners are unavailable. The system combines Ring’s video analysis capabilities with Alexa’s natural language models to generate context-aware responses.

Users can customize interactions through the Ring app, specifying how Alexa should respond in different scenarios. The feature is rolling out in early access to select Ring models in the United States and Canada, reflecting Amazon’s push to make smart home devices more proactive rather than purely reactive.

Microsoft and UN Climate Change Launch AI-Powered Data Hub

Microsoft partnered with UN Climate Change and regional advisory partners to launch the Climate Data Hub, a centralized platform designed to improve access to national climate data.

The initiative supports implementation of the Paris Agreement’s Enhanced Transparency Framework, under which countries report emissions, mitigation actions, adaptation efforts and financial support. While more than 190 countries submit climate reports, differences in format and structure have made comparison and analysis difficult.

The Climate Data Hub aims to address this fragmentation by unifying official climate data into a standardized system. Using AI-powered tools, the platform allows users to explore national reports through natural language queries, returning contextual answers drawn from verified submissions. The goal is to make climate data easier to analyze, compare and apply in policymaking and research.

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