Meta to Deploy Homegrown Chips, Uber to Offer Zoox Rides

2026-03-11 · Source: Bloomberg Tech · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

This Bloomberg Tech segment covers several key developments in the technology and AI sectors. Oracle reported strong third-quarter sales, with a full-year outlook indicating sustained high demand for AI computing, leading to an 11-12% stock increase. Meta plans to deploy four new generations of its in-house AI chips, MTIA (Meta Training and Inference Accelerator), by the end of 2027 to power its expanding AI workloads, with MTIA 300 already in production and MTIA 400 moving towards deployment. Uber and Amazon's Zoox announced a partnership to launch robotaxi services in Las Vegas this summer, expanding Uber's platform offerings. Additionally, Databricks introduced "Genie code," an autonomous AI assistant for data professionals, and acquired Quotient for quality measurement. China has reportedly restricted state-owned enterprises and banks from using OpenClaw AI apps due to security concerns, while Google is investing in AI animation studio Animage to combat "AI Slop" videos on YouTube.

Key takeaway

For CTOs and VP of Engineering evaluating AI infrastructure strategies, recognize that the "buy vs. build" decision for AI compute is evolving. Your teams should consider a hybrid approach, securing large-scale GPU capacity from vendors like NVIDIA while simultaneously investing in custom silicon for proprietary workloads to optimize for specific performance and cost efficiencies. Prioritize robust quality measurement and monitoring for any AI agents deployed to ensure reliability and prevent "AI Slop" in production environments.

Key insights

AI demand drives significant investment in computing infrastructure, custom silicon, and autonomous systems across multiple industries.

Principles

• Diversify compute acquisition for AI workloads.
• AI innovation requires continuous talent acquisition.
• Quality measurement is critical for AI agent deployment.

Method

Meta's MTIA program accelerates chip design cycles to outpace traditional chip development, aiming for improved performance, cost, and power efficiency for internal AI workloads.

In practice

• Explore custom silicon for unique AI workloads.
• Utilize AI agents to automate data science tasks.
• Implement quality monitoring for AI code in production.

Topics

• AI Infrastructure
• Custom AI Chips
• AI Agents
• Autonomous Vehicles
• AI Investment

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Investor, Business Analyst, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Bloomberg Tech.