Beyond Models and Compute: Big Tech’s AI Moves

Models, compute and infrastructure dominate AI headlines, but the deeper transformation is unfolding quietly behind them. This week’s tech roundup surfaces the incremental systems work, platform upgrades and engineering shifts that show how AI is weaving itself into the core of global production, communication and decision-making.

Meta Shares New Insights on GEM, Its ‘Central Brain’ for Ads

In a Monday (Nov. 10) press release, Meta shared new details about GEM, short for Generative Ads Model, which it calls the “central brain” of its global advertising network. The system, already deployed across Meta’s platforms, combines reinforcement learning and multimodal generation to design, test and optimize ad creatives in real time. GEM continuously retrains on billions of impressions, adjusting campaign targeting and creative strategy with little human input.

Meta said the model has improved conversion rates by up to 5% on Instagram and 3% on Facebook since its rollout, highlighting how generative artificial intelligence (AI) is reshaping advertising performance.

The company described GEM as a foundational step toward a self-learning marketing ecosystem capable of adapting to user intent in real time. By automating decision-making at scale, Meta is reducing the delay between consumer behavior and campaign response, creating what it calls “a feedback system that learns as quickly as the market moves.”

The update also signals how Meta is rethinking the economics of ad optimization by embedding AI deeper into its revenue engine. Executives said the technology is part of a broader plan to increase efficiency across Meta’s multibillion-dollar ad infrastructure, enabling campaigns to scale with fewer resources and higher precision.

Salesforce, Nvidia and IBM Deepen Enterprise and Industrial AI

Salesforce announced plans this past week to acquire Spindle.AI,  a Silicon Valley startup that builds neuro-symbolic agent systems for business modeling and return on investment (ROI) forecasting.

The acquisition will strengthen Salesforce’s Agentforce 360 platform by adding “agent observability,” which allows self-learning analytics tools to explain their reasoning and refine their methods. For industries that depend on transparency and compliance, the deal advances Salesforce’s goal of making enterprise AI both autonomous and auditable.

Read more: Salesforce Collaborates on AI Projects With OpenAI and Stripe

Nvidia expanded its European presence with the launch of an industrial AI cloud in Germany to serve manufacturers, logistics companies and robotics firms. The platform provides localized compute capacity for predictive maintenance, quality control and automation, helping industrial users deploy AI closer to production lines while meeting European data-sovereignty standards.

The new hub will also allow regional partners to train and fine-tune models on proprietary data without relying on hyperscale infrastructure located abroad. Nvidia said the initiative is designed to accelerate Europe’s transition toward smart manufacturing and autonomous logistics, aligning with regulatory goals for digital resilience. By embedding compute closer to industrial activity, the company aims to reduce latency, improve energy efficiency and create a blueprint for sector-specific AI infrastructure across the continent.

IBM entered the week’s AI headlines with its partnership with Agassi Sports Entertainment. Together, the companies launched an AI-powered racquet-sports analytics platform that uses match footage, player biometrics and rally data to generate real-time performance insights.

Built on IBM’s watsonx.ai foundation models, the system applies AI’s predictive power to human performance, offering coaches and players data-driven strategies mid-match.

Tesla Targets Hardware Independence With Mega AI Fab

Reuters reported that Elon Musk plans to build a Tesla mega AI chip fabrication plant and is in talks with Intel about a possible collaboration. The facility would allow Tesla to produce its own high-performance chips for self-driving vehicles, humanoid robots and the Dojo supercomputer, reducing its reliance on third-party suppliers.

The project reflects Tesla’s effort to control its entire AI value chain, from the data generated by its vehicles to the hardware that trains its models. By developing its own chips, Tesla could improve the efficiency and scalability of its compute infrastructure while gaining greater resilience against global semiconductor supply constraints.

The move also signals a broader industry shift toward vertical integration, as AI-driven companies seek to balance performance and supply stability by bringing compute design in-house. Such control could position Tesla to optimize chip architecture specifically for its autonomous systems, potentially accelerating development cycles and lowering long-term costs.