Big Tech Pivots to AI Infrastructure, Commercial Deployment and Drug Discovery

Big Tech companies moved this week to solidify their control over the artificial intelligence (AI) ecosystem, launching initiatives that range from massive data-center expansions to specialized tools for retail and drug discovery.

Meta’s Gigawatt-Scale AI Infrastructure Push

Meta is doubling down on the AI arms race with a sweeping initiative dubbed Meta Compute, designed to centralize and supercharge its data center and AI infrastructure build-out. CEO Mark Zuckerberg announced that Meta Compute will build tens of gigawatts of computing capacity this decade, with visions to scale to hundreds of gigawatts over time—energy levels comparable to those of small countries.

The effort places leadership for global data-center design, supply chain partnerships and strategic capacity planning under a unified organization co-led by Santosh Janardhan and  Daniel Gross, with guidance from newly appointed president Dina Powell McCormick, as reported by PYMNTS.

Meta’s push comes as the company seeks to catch up with AI heavyweights like Google, Microsoft and OpenAI. Last year’s Llama 4 model received a lukewarm market response, and Meta is now betting that owning larger swaths of compute, paired with reliable energy procurement, will yield strategic advantage.

Google Cloud’s Gemini Enterprise for Customer Experience

Google Cloud introduced a new suite of AI tools under the banner of Gemini Enterprise for Customer Experience (CX) to unify shopping and customer support on a single intelligent platform. Designed for retailers and service businesses, Gemini Enterprise for CX leverages the latest Gemini models to create agentic AI solutions, agents that can reason, execute complex tasks and drive customer conversions.

The offering includes prebuilt and configurable agents that businesses can deploy within days to handle tasks such as product discovery, personalized recommendations and autonomous post-purchase support. These agents operate across interfaces from chat assistants to voice and can pull in contextual information to deliver cohesive responses. A new Customer Experience Agent Studio helps enterprises build, test and scale these agents, while insight tools analyze customer interactions for trends and performance optimization.

Gemini Enterprise for CX also embraces open standards like the Universal Commerce Protocol (UCP), aimed at enabling seamless interoperability between AI agents and existing retail systems.

Amazon Expands Alexa+ With Browser-Based AI Access

Amazon is advancing its generative-AI assistant strategy with the introduction of Alexa.com, a browser-based interface for its Alexa+ assistant. Previously limited to Echo smart speakers and the Alexa mobile app, Alexa+ now runs in desktop browsers, allowing users anywhere to interact with the assistant.

Alexa+ marks Amazon’s broader transition from voice-only commands to agent-style capabilities that provide assistance and continuity across devices. Unlike traditional voice interactions, the web version retains conversational context and history, enabling a seamless experience when users move between devices. The rollout targets early-access users and reflects Amazon’s intent to compete more directly with chatbot-style AI offerings from Google Gemini and OpenAI’s ChatGPT.

Nvidia, Eli Lilly Launch AI Lab Focused on Drug Discovery

Nvidia and Eli Lilly on Monday (Jan. 12) announced a co-innovation lab to accelerate drug discovery using AI. This collaboration aims to apply Nvidia’s AI computing platforms and Lilly’s pharmeceutical expertise to reimagine how novel drug candidates are identified and optimized. The lab will explore techniques such as generative modeling, simulation-based design and predictive biology to reduce the time and cost associated with bringing new medicines to market.

By integrating deep learning with biomedical research workflows, the partnership seeks to overcome traditional bottlenecks in target validation, molecular design and preclinical testing.

Nvidia’s GPUs and AI frameworks can enable researchers to model complex biological systems at scale, while Lilly contributes domain knowledge and experimental capability.

Early efforts will likely focus on areas where AI can deliver clear predictive power, such as protein folding, molecular interaction prediction and virtual screening of compound libraries. If successful, these tools could dramatically shorten drug discovery cycles and unlock treatments for diseases that have long defied conventional approaches.