Google is taking its AI-powered search experience a step further by infusing “Personal Intelligence” into AI Mode, allowing the conversational tool to draw directly from users’ Gmail inboxes and Google Photos libraries for responses that feel custom-built to their lives.

The update, announced Thursday, January 22, 2026, extends a feature first introduced last week in the standalone Gemini app, marking another push by the company to leverage its vast ecosystem for deeper personalization in an increasingly competitive AI landscape. AI Mode, Google’s advanced conversational interface for handling complex, multi-part queries in Search, now gains the ability to reference personal context like travel bookings in emails or visual memories in photos—without users needing to manually input details.

“With Personal Intelligence, recommendations don’t just match your interests — they fit seamlessly into your life,” wrote Robby Stein, VP of Product for Google Search, in the company’s official blog post. “You don’t have to constantly explain your preferences or existing plans, it selects recommendations just for you, right from the start.”

The rollout begins with an opt-in process for Google AI Pro and AI Ultra subscribers in the U.S. using English, accessible via Google Labs—the experimental hub for cutting-edge Search features. Users can enable connections to Gmail and Google Photos independently, and the settings can be toggled off or fully disconnected at any time. The feature is limited to personal Google accounts, excluding Workspace, enterprise, or education users for now.

Practical examples highlight the potential. Planning a family vacation? AI Mode might scan a hotel confirmation in Gmail and cross-reference family photos from past trips to suggest an itinerary that includes kid-friendly spots or nostalgic favorites—like an old-timey ice cream parlor inspired by recurring “ice cream selfies” in your library.

Shopping for a new coat ahead of a trip? It could factor in preferred brands from purchase history, detect the destination and weather from flight details in email, and recommend windproof, versatile options that align with your style.

Other use cases Google showcased include crafting personalized anniversary scavenger hunts with hints drawn from shared memories or generating bedroom decor ideas tailored to a child’s interests inferred from photos. Stein emphasized that the tool aims to provide a “personalized starting point” rather than generic lists, acting like a proactive assistant already familiar with your routines.

Google stresses privacy safeguards: AI Mode does not train its underlying models directly on users’ Gmail inboxes or Photos libraries. Instead, learning occurs from specific prompts and the model’s responses, with data processed securely and under user control. The company acknowledges potential imperfections—such as misinterpreting context or making inaccurate connections—and encourages feedback via thumbs-down ratings or explicit corrections to refine performance over time. This expansion builds on Personal Intelligence’s debut in the Gemini app on January 14, 2026, which initially connected Gmail, Google Photos, YouTube watch history, and Search activity for tailored responses.

The Search integration focuses on Gmail and Photos to keep things targeted while hinting at future additions. Stein positioned it as a natural evolution of Google’s advantage: a deeply integrated ecosystem that rivals like OpenAI or Anthropic struggle to match without similar data moats. Analysts see the move as strategic amid intensifying AI competition. By making search feel more intuitive and anticipatory, Google aims to boost engagement and retention in AI Mode, which launched in 2025 as a multimodal, reasoning-heavy alternative to traditional results.

Early adopters in Gemini have praised the “set-it-and-forget-it” convenience, though privacy advocates have raised concerns about deeper data access—even opt-in—potentially normalizing broader surveillance-like capabilities in everyday tools. As the rollout continues over the coming days and weeks, Google plans to expand access beyond paid subscribers and potentially to more regions and languages.

Alphabet’s Waymo has opened its robotaxi service to paying riders in Miami, marking another decisive step in what is shaping up to be a critical year for the company’s U.S. expansion and its bid to entrench itself as the dominant player in autonomous ride-hailing.

The launch makes Miami the sixth U.S. market where Waymo operates a fully driverless, revenue-generating service, extending a lead that rivals have so far struggled to close. While competitors such as Tesla and Amazon-owned Zoox continue to test, pilot, or promise future rollouts, Waymo is steadily converting years of experimentation into operational scale.

To start, the service will cover a roughly 60-square-mile zone spanning Miami’s Design District, Wynwood, Brickell, and Coral Gables, areas that combine dense urban traffic, nightlife, tourism, and affluent residential pockets. The geography matters because these neighborhoods offer high ride demand, complex driving environments, and a steady stream of visitors, allowing the company to showcase its technology under real-world conditions while tapping into a lucrative customer base.

Waymo said it began testing its autonomous vehicles in Miami in early 2025, using the city’s mix of aggressive driving patterns, heavy rain, and unpredictable congestion as part of its training ground. The company plans to extend service to Miami International Airport, a move that could significantly boost ride volumes and visibility, though it has not provided a timeline.

According to Waymo, nearly 10,000 Miami residents have already signed up to try the service, with access being rolled out gradually. Riders can hail robotaxis using Waymo’s app, mirroring the experience the company has refined in other cities.

Behind the scenes, Waymo is relying on mobility firm Moove to handle fleet operations in Miami, including charging, cleaning, and vehicle maintenance. The partnership reflects Waymo’s push to separate the autonomous driving stack from the logistical grind of running a large vehicle fleet, a challenge that has weighed heavily on many mobility startups.

The Miami debut also comes at a delicate moment for the company. Waymo has faced public scrutiny over the behavior of its vehicles, particularly after a series of incidents in San Francisco last month, where robotaxis contributed to gridlock during severe storms and widespread power outages. Those episodes reignited concerns about how autonomous systems respond to edge cases such as extreme weather and infrastructure failures.

Waymo said it has since refined its systems to better handle such scenarios, and the Miami rollout will serve as another test of those improvements, especially given South Florida’s intense rainstorms and hurricane season.

By the end of 2025, Waymo had commercial robotaxi operations in Austin, Atlanta, Los Angeles, Phoenix, and the San Francisco Bay Area. Miami now anchors the company’s push into the Southeast and sets the stage for a much broader rollout planned for 2026.

The company has said it intends to expand to a long list of U.S. cities, including Dallas, Denver, Detroit, Houston, Las Vegas, Orlando, San Antonio, San Diego, Washington, and Nashville. It is also testing vehicles in New York, Tokyo, and London, and has indicated it will launch its first overseas commercial service this year, signaling growing confidence in its technology and regulatory playbook.

Waymo’s scale is no longer theoretical. In December, the company said it had crossed 450,000 paid rides per week and served a total of 14 million trips in 2025. Those figures, while still small compared to traditional ride-hailing giants, underscore how far Waymo has moved beyond pilot programs.

Investors are paying attention. Waymo is reportedly in talks to raise $15 billion in new funding, a round that would underscore both the capital intensity of autonomous driving and the belief that Waymo is best positioned to turn autonomy into a sustainable business.

Competition remains fierce, though unevenly distributed. In Asia, Waymo faces strong rivals such as Baidu-owned Apollo Go and WeRide, which have advanced rapidly with support from local governments and dense urban deployments. In North America, Tesla, Zoox, and startups like May Mobility and Nuro are still racing to match Waymo’s combination of technical maturity, regulatory approvals, and real-world usage.

The Miami launch highlights a broader strategic reality: as rivals lag or focus on future promises, Waymo is using 2026 to lock in riders, normalize driverless transport, and build brand familiarity city by city. That early foothold could prove difficult to dislodge, even as competition eventually heats up.

Miami is not just another dot on the map for Waymo. It is part of a calculated effort to turn years of costly development into durable market presence, before the rest of the autonomous driving field fully arrives.

In the shadow of its blockbuster language models, OpenAI is quietly charting a course toward physical intelligence, reviving its robotics program with a secretive lab that could bridge the gap between digital AI and tangible, human-like machines, according to people with knowledge of the matter who spoke to Business Insider.

This resurgence, emerging after a five-year hiatus, positions the company at the forefront of embodied AI—a field where software brains meet hardware bodies to navigate and manipulate the real world. Insiders describe the effort as a foundational step toward artificial general intelligence (AGI) that operates beyond screens, potentially transforming everything from household chores to industrial labor.

The initiative represents a strategic pivot for OpenAI, which disbanded its original robotics team in 2020 to concentrate on generative AI breakthroughs like ChatGPT. That early project, unveiled in 2019, featured a robotic hand trained via reinforcement learning to solve a Rubik’s Cube one-handed—a feat that demonstrated dexterity but highlighted data and compute limitations.

“We chose to refocus the team on other projects,” a spokesperson said at the time, citing challenges in scaling physical AI.

Fast-forward to 2025, and the company has reassembled a dedicated robotics unit, hiring over a dozen engineers specializing in humanoid systems and filing trademarks for “user-programmable humanoid robots” with communication and learning capabilities.

Launched in February 2025, the San Francisco lab—co-located with the finance team—has expanded rapidly, now employing around 100 data collectors working in three shifts across dozens of workstations. The core work involves teleoperating Franka robotic arms using affordable, 3D-printed GELLO controllers, a technology inspired by a 2023 UC Berkeley study on scalable teleoperation.

These controllers mimic the robot’s kinematics, allowing human operators to demonstrate tasks with precision while cameras capture both sides for training data. Progress has accelerated: Initial exercises involved simple actions like placing a rubber duck in a cup, evolving to household duties such as toasting bread or folding laundry.

Performance metrics emphasize “good hours” of functional data, with recent months seeing doubled collection rates amid calls for greater efficiency. A humanoid robot prototype, likened to an “iRobot-like” design, is on display but largely inactive, underscoring the lab’s arm-focused strategy over full-body integration—for now.

Plans include a second facility in Richmond, California, with job postings for robotics operators already circulating. This data-centric approach addresses a perennial robotics bottleneck: acquiring vast, high-quality datasets for training.

“Everyone is fighting for a way to develop large data sets,” said Jonathan Aitken, a robotics expert at the University of Sheffield.

GELLO’s low-cost design offers advantages over motion-capture suits or VR systems used by rivals, enabling more direct human-to-robot motion translation. One Berkeley researcher from the GELLO study joined OpenAI in August 2024 to contribute to “Building the Robot Brain.”

OpenAI’s hardware ambitions extend beyond the lab. Last week, the company issued a Request for Proposals (RFP) seeking U.S.-based manufacturers for consumer devices, robotics components like motors and actuators, and cloud infrastructure—aiming to foster domestic supply chains amid geopolitical tensions.

The RFP, open through June 2026, aligns with broader efforts to scale production, though timelines and budgets remain undisclosed.

The revival draws on OpenAI’s investments in external ventures. Partnerships include 1X Technologies (backed since 2023), which develops home-focused humanoids like EVE and NEO, with preorders open for 2026 shipments. A 2024 collaboration with Figure AI—to integrate AI models into humanoids—ended in February 2025 as Figure advanced in-house capabilities, including pilots at BMW plants.

OpenAI also supports Physical Intelligence, focusing on versatile manipulation software. CEO Sam Altman’s vision frames this as inevitable: Last year, he predicted the “humanoid robots moment” was approaching, emphasizing AI’s need for physical embodiment to achieve AGI.

Internal discussions, per reports, explore humanoid development as a path to “AG-level intelligence in dynamic, real-world settings.” Job listings seek experts in sensor suites, actuators, and large-scale manufacturing, hinting at ambitions beyond research.

Yet OpenAI faces stiff competition in a booming sector. Tesla’s Optimus, with 50-actuator hands and 2026 production targets, leads in dexterity demos.  Figure’s 02 model, backed by $700 million from Microsoft and Nvidia, plans 5,000 units in 2025, scaling to tens of thousands by 2026. Chinese firms like Unitree (R1 humanoid) and EngineAI showcase acrobatic prototypes, while Agility’s Digit operates in warehouses.

However, market projections are staggering: Morgan Stanley forecasts 1 billion humanoids by 2050, generating a $5 trillion market, with 302 million in China alone, while Bank of America anticipates 10 million annual shipments by 2035.

But there are challenges: Oregon State’s Alan Fern notes that scaling arm data to full humanoids is “something that hasn’t been proven out yet.” Safety, ethics, and job displacement loom large, with experts warning of workforce disruptions.

OpenAI’s integration of its language models with physical hardware—potentially enabling robots to interpret commands and learn from interactions— is expected to blur the line between virtual and real. With pilots in homes and factories accelerating, 2026 could mark the dawn of widespread embodied AI, driven by OpenAI’s methodical resurgence.