Google's 2026 AI Masterplan Revealed

The 2026 Prophecy from Google's AI Chief

Axios handed Demis Hassabis a simple question: what changes in AI will we feel a year from now? He answered with a roadmap that stretches far beyond the usual model-parameter flex, sketching a world where Google’s Gemini stops being a chat box and starts behaving like infrastructure for daily life.

At the Axios AI+ Summit, Hassabis repeated a tight timeline: the next 12 months belong to multimodal convergence. Gemini already ingests text, images, video, and audio; he says the real jump comes as those modalities stop being bolt‑ons and start cross‑pollinating, letting language models reason directly over visuals, sound, and motion in one fused system.

Hassabis pointed to Google’s latest image system — the video calls it “Nano Banana Pro” — as proof of concept. The model doesn’t just paint pretty pictures; it builds accurate infographics, parses complex scenes, and iterates on its own outputs, behaving less like a filter and more like a visual analyst wired into a language model.

That same philosophy drives Gemini’s broader positioning. Hassabis sells Gemini as a “universal assistant”, not a single app or website, but a layer that runs on phones, laptops, cars, and eventually glasses, answering questions, watching what you’re doing, and manipulating documents, spreadsheets, and code across your Google account.

In Hassabis’s near‑term framing, you delegate an entire task—plan a trip, draft a contract, debug a codebase—and a Gemini‑powered agent gets “close” to finishing it end‑to‑end. He argues current agents fail because they juggle tools and APIs loosely; a tightly integrated multimodal Gemini could watch, listen, read, and act in one continuous loop.

The YouTube video that sparked this “2026 masterplan” narrative takes that 12‑month Axios forecast and stretches it into a full omnimodel horizon. By 2026, it claims, Gemini will span six modalities in a single stack: - Text - Images - Video - Audio - 3D - Robotics

That is a more aggressive timeline than Hassabis stated on stage. His public bet centers on the next year of multimodal fusion and assistant‑like behavior, while creators extrapolate a 2026 endpoint where Gemini stops being a product family and starts looking like a single, world‑modeling brain for Google’s entire ecosystem.

Decoding the 'Full Omnimodel' Stack

Omnimodel is Google’s new buzzword for a single AI stack that spans six modalities at once: text, images, video, audio, 3D, and robotics. Instead of separate specialist models stitched together with brittle APIs, Hassabis describes a converged system where one foundation model family, Gemini, natively speaks all these languages of the world.

Today’s “multimodal” systems mostly bolt vision onto language or add audio I/O on top of text. A full omnimodel goes further, sharing one representation space so the same internal neurons reason about a sentence, a video frame, a room layout, or a robot’s sensor stream.

That unified core lets capabilities bleed across boundaries. Stronger visual understanding from models like Google’s latest image system (the video calls it “Nano Banana Pro”) feeds back into better language grounding, which then sharpens step‑by‑step planning and tool use.

In an omnimodel stack, each modality actively trains the others. Google’s vision looks roughly like: - Text: Gemini’s reasoning, coding, and planning backbone - Images/video: perception via models in the Veo/V3 line and interactive video systems like Genie - Audio: Gemini Live’s low‑latency conversation and real‑time guidance - 3D: world models that infer geometry and affordances from video - Robotics: Gemini Robotics 1.5 controlling arms, mobile bases, and humanoids with the same brain

Unified training lets the model map “put the green fruit on the green plate” to pixels, depth, and motor commands without hand‑engineered bridges. A repair tutorial watched as video becomes a 3D scene the robot can navigate, narrated in natural language, with audio cues aligning to physical actions.

That’s the leap beyond current multimodal chatbots that mostly stay trapped in the browser. An omnimodel can watch your environment through a camera, reason about it using the same stack that writes code and summaries, then act on it via a robot or phone‑level agents.

For Google, this is the strategic path to general‑purpose AI: one model family that can read, watch, listen, simulate, and manipulate the real world. Whoever ships a reliable omnimodel first does not just win search; they own the interface to both digital and physical reality.

Gemini Robotics: From Sorting Fruit to Humanoid Helpers

Gemini Robotics 1.5 is Google’s bid to turn large language models into physical workers, not just chatty copilots. In Google’s demo, an Aloha robot arm uses Gemini to visually parse a table of fruit, reason through color-matching rules step-by-step, and then execute a multi-step sorting task with verbal explanations for each move. The system doesn’t just run a hard-coded script; it “thinks aloud,” exposing an internal chain of reasoning between perception and action.

Another demo pushes the same model into an Apollo humanoid that sorts laundry. A human suddenly swaps the bins mid-task, and Apollo updates its plan on the fly, showing Gemini’s ability to re-ground its understanding of the scene and adapt. Gemini Robotics 1.5 also taps the web: the Aloha arm uses San Francisco waste guidelines it just pulled from the internet to classify trash, recycling, and compost.

The real breakthrough hides under the theatrics: a single model controlling wildly different robot bodies without per-robot fine-tuning. Google claims Gemini Robotics 1.5 runs across all its platforms—Aloha arms, mobile bases, humanoids—using the same weights and the same high-level action interface. That hints at a genuine “omnimodel” for embodiment, where one brain generalizes across form factors, tasks, and environments.

Hardware remains Google’s weak spot. Boston Dynamics, Figure, Tesla, and Agility Robotics ship or test physical platforms at larger scales, while Google mostly shows lab-bound prototypes. Even Apollo, built by Apptronik, underscores that Google leads on AI control stacks, not on actuators, batteries, or ruggedized supply chains.

By 2026, a plausible Gemini Robotics 2.x starts to look less like a demo reel and more like a platform. Expect: - Reliable manipulation of cluttered household scenes, not just staged tables - Multi-hour, multi-room workflows such as “clean the kitchen and load the dishwasher” - Industrial pick-and-pack, kitting, and basic inspection in real warehouses

Google’s own AGI timelines and public comments, including Demis Hassabis on the future of AI – Google DeepMind (Fortune Global Forum fireside), suggest rapid gains in planning and multimodal reasoning over the next 2–5 years. If those advances land inside robots, Gemini Robotics 2.x could turn today’s fruit-sorting party tricks into quietly competent household and factory labor.

Beyond Sora: Google's Play for Video and Image Supremacy

Forget chatbots. For Demis Hassabis, the real shockwave in the next 12–24 months arrives on screen: video and images that don’t just look real, but actually understand what they’re showing. Google’s Veo (often called “V3” in demos) sits at the center of that push, quietly becoming one of the most capable generative video systems in the field.

Veo generates high‑fidelity clips from text or a single image, with consistent characters, coherent camera motion, and physically plausible scenes. In internal and partner demos, it has already matched or beaten early OpenAI Sora clips on temporal coherence and prompt adherence, even if Google has rolled it out more cautiously.

Hassabis argues that Veo’s real edge will not be cinematic tricks but reasoning. Because Gemini is natively multimodal, Veo can, in principle, ingest: - A script or outline - Reference images or storyboards - Constraints about continuity and style

and then produce video that respects narrative logic rather than just surface style. That is the gap between “cool demo” and “usable tool” for film, advertising, and simulation.

On the image side, Google’s latest model – jokingly labeled “Nano Banana Pro” on stage – hints at where this is going. Instead of a single forward pass from prompt to pixels, it behaves more like an agent: generate, inspect its own output, detect errors, then regenerate with corrections.

Ask for a complex infographic and Nano Banana Pro can lay out axes, legends, and labels that actually match the underlying data. It can, for example, render a bar chart of smartphone market share, realize a label overlaps a bar, move it, and adjust colors for accessibility – all without a human in the loop.

Hassabis believes the real unlock comes when these visual systems fuse tightly with large language models. A future Gemini could read a 20‑page report, fact‑check the numbers, design an infographic, and then spin it into a 30‑second explainer video, all while maintaining internal consistency.

Strategically, that matters more than photorealism. For Google, winning this race means models that generate visuals that are not just high‑resolution, but accurate, context‑aware, and grounded enough that users and regulators can actually trust them.

Your AI Co-Pilot Just Got Real: Gemini Live

Gemini Live finally makes the “AI co‑pilot” pitch feel concrete. In the viral oil change clip, a user points their phone at an engine bay, talks naturally, and gets step‑by‑step guidance on what to unscrew, what to drain, and what not to touch. No pausing to type queries, no YouTube scrubbing, just a persistent, conversational assistant riding shotgun.

Under the hood, Gemini Live fuses three hard problems into a single experience. First is low‑latency speech‑to‑speech, where the model listens, reasons, and responds in near real time instead of the 2–5 second lag typical of cloud assistants. Second is real‑time visual reasoning: the system parses the live camera feed, tracks objects like oil caps and filters, and updates instructions as the frame changes.

The third pillar is access to Google’s gigantic knowledge graph and web index. Gemini Live does not just see a bolt; it maps that bolt to repair manuals, forum posts, and safety guidance, then condenses that into a single actionable step. That synthesis makes it feel less like voice search and more like a dedicated expert quietly watching over your shoulder.

As a result, Gemini Live is the clearest move yet toward the “universal assistant” Hassabis keeps teasing. Instead of confining AI to documents and code, it starts to handle messy, real‑world workflows: car maintenance, home repairs, cooking, even basic diagnostics on consumer electronics. The oil change demo works as a proxy for any task where you would normally juggle a how‑to video, a PDF, and a Reddit thread.

By 2026, expect this stack to look very different under the surface. Latency will likely drop under 300 ms end‑to‑end, making speech exchanges feel effectively instantaneous and enabling more natural barge‑in and interruption. Visual understanding should extend from static parts to dynamic systems, from spotting a leak to modeling how fluid should move through an engine or appliance.

Deeper reasoning will matter even more than speed. A 2026 Gemini Live could decompose multi‑hour jobs into sub‑tasks, track progress over days, and adapt plans when tools, parts, or environments change. At that point, “co‑pilot” stops being a metaphor and starts sounding like an accurate job description.

Building New Realities with Genie 3 World Models

World models flip generative AI from passive content into playable reality. Instead of spitting out a fixed 10‑second clip, a world model learns the underlying dynamics of an environment—how objects move, collide, and respond—so users or agents can step inside and interact in real time. Think less “AI video filter,” more “AI‑generated level in a game engine” that updates as you poke it.

Genie 3, Google DeepMind’s latest world model line, pushes this idea hard. From a single text prompt—“rain‑slick cyberpunk alley,” “Martian canyon at dusk,” “flooded subway station”—Genie 3 can synthesize an explorable 2D or pseudo‑3D world with coherent physics and navigation. Instead of pre‑baked camera paths, you get a controllable avatar, continuous movement, and objects that behave consistently across frames.

Crucially, Genie 3 does not reset every time you press a button. The system maintains world memory, tracking object states, positions, and previous interactions, so knocking over a crate or opening a door persists as you keep exploring. On top of that, Google layers “promptable events”: you can inject new instructions mid‑simulation—“trigger an earthquake,” “start a power outage,” “spawn a rescue drone”—and the world updates on the fly while remaining physically and visually consistent.

Gaming is the obvious first stop. Genie‑style models could auto‑generate playable levels, side quests, or entire micro‑worlds tailored to a player’s skill or narrative choices. Designers could sketch a vibe in text, then iterate on a living prototype instead of hand‑crafting every tile and collision box.

The deeper play sits outside entertainment. Roboticists need billions of safe trial‑and‑error interactions before trusting a robot around humans. World models like Genie 3 can create synthetic training grounds where virtual agents learn to grasp, navigate, and recover from edge cases long before touching a real warehouse or hospital. Disaster planners could spin up controllable simulations of wildfires, chemical spills, or urban floods and repeatedly stress‑test evacuation plans.

Hassabis has argued that teaching AI common sense and physics requires this kind of grounded simulation, not just more web text. World models give Gemini‑class systems a sandbox to learn cause and effect, object permanence, and constraints like friction or gravity. That same philosophy runs through Google’s broader multimodal push, detailed in Introducing Gemini: Google’s most capable multimodal AI model, where text, vision, and action fuse into a single stack ready to inhabit both virtual and physical worlds.

The Dawn of Truly Reliable AI Agents

Reliable AI agents remain the missing piece in Google’s 2026 masterplan. Demis Hassabis told Axios that today’s systems still fail too often on long, multi-step jobs to trust them with true “set it and forget it” delegation. They hallucinate tools, drop subtasks, or stall when APIs change.

Hassabis also drew a near-term line in the sand: within about 12 months, he expects agents that are “close” to reliably accepting and executing complex end-to-end tasks. That means going from “help me write this email” to “plan and book my entire trip, handle changes, and keep me updated” with minimal oversight. Reliability, not raw IQ, becomes the gating factor.

Google already runs controlled experiments with agentic systems inside research. Hassabis has described a “co-scientist” that can: - Generate hypotheses from literature - Design and run simulations or lab workflows - Interpret results and propose follow-up experiments

Those same patterns show up in Gemini’s emerging tool-use stack. Gemini can already call Calendar, Gmail, Docs, and external APIs, chain actions, and revise plans when constraints change. Early internal agents handle things like multi-step customer support workflows or ad campaign optimization, but Google keeps them behind guardrails because failure still carries real-world cost.

To cross Hassabis’s reliability threshold, agents need three things: stronger reasoning, robust tool orchestration, and continuous feedback from the environment. Google is attacking each layer with the omnimodel push. A useful agent cannot just read text; it must see, listen, and act.

Tie Gemini Robotics 1.5, Veo, Nano Banana Pro, and Genie 3 together and you get a blueprint for that agent. A future Gemini instance could watch a factory floor via video, interpret spoken instructions from workers, consult CAD models in 3D, and dispatch robots to reconfigure a line. The same backbone could live in a browser, quietly negotiating your subscriptions while also guiding a humanoid robot to fix a leaky sink.

Google’s bet: once a single model reliably spans text, images, video, audio, 3D, and robotics, “AI agents” stop being a UX layer and start becoming infrastructure.

Google's Unfair Advantage: Compute, Data, and Brains

Google’s AI bet starts in its data centers, not its demos. Where rivals rent GPUs from cloud providers, Google runs on a vertically integrated stack built around its custom TPU v5p and next‑gen TPU v6 Trillium accelerators. That control lets DeepMind and the Gemini team tune everything from compiler to cooling loop, squeezing more training runs out of every megawatt.

TPU v5p targets large‑scale training with pod configurations that scale to tens of thousands of chips, while v6 Trillium pushes performance‑per‑watt even further for frontier multimodal models. Google claims v6 Trillium delivers major efficiency gains versus v5e, which already underpinned Gemini’s earlier generations. Owning the silicon roadmap reduces exposure to Nvidia’s supply chain crunch and gives Google predictable unit economics for multi‑billion‑parameter experiments.

Hardware alone does not win the race; Google also owns the world’s most valuable multimodal training corpus. YouTube’s billions of videos, tightly coupled with audio, comments, and engagement data, form an unmatched substrate for video and audio models like Veo and Gemini’s perceptual stack. Google Images and decades of web‑scale crawling add labeled photos, diagrams, and screenshots in virtually every domain.

That data depth matters specifically for the “omnimodel” vision Demis Hassabis talks about. Training a single model to reason across text, images, video, audio, 3D, and robotics requires synchronized signals across modalities: frames aligned with transcripts, actions aligned with outcomes, scenes aligned with language. YouTube alone gives Google petabytes of exactly that kind of paired data, at global scale and in dozens of languages.

Then there is Google DeepMind’s research bench, arguably the strongest in the field. AlphaFold did not just predict protein structures; it reset expectations for what deep learning can do in scientific domains, with more than 200 million predicted structures released to the community. Earlier work like AlphaGo, AlphaZero, and MuZero established a culture of long‑horizon bets that combine theory, systems engineering, and massive compute.

That culture now flows directly into Gemini, Genie world models, and the new wave of agentic systems. DeepMind’s researchers do not just fine‑tune models; they invent new architectures, training schemes, and evaluation methods, then push them into production‑scale stacks. Few competitors can match that pipeline from fundamental idea to global deployment.

Combine those three pillars—custom compute, proprietary data, and elite research talent—and Google has more than a head start. It has a structural moat that compounds over time, as every new model both consumes and generates data that further trains the next generation.

Is AGI on the Horizon? What Hassabis Really Thinks

AGI, for Demis Hassabis, sits just beyond the 2026 hype cycle. While he sounds confident about near-term “full omnimodels” and robust agents, his horizon for Artificial General Intelligence stays at roughly 5–10 years out, not two or three.

He defines AGI as more than today’s flashy demos. Systems must show true invention, sustained creativity, and deeper abstract reasoning, not just remix training data or chain-of-thought prompts. Current Gemini models still fall short on reliably generating novel scientific hypotheses or engineering designs without heavy human scaffolding.

Hassabis argues that getting there demands two ingredients in parallel. First, an aggressive continuation of the scaling playbook: bigger models, richer multimodal data, and denser integration across text, code, images, video, audio, 3D, and robotics. He explicitly ties this to Google’s TPU roadmap and the ability to train frontier models at lower marginal cost.

Second, he insists scaling alone will not unlock AGI. He expects “one or two major scientific breakthroughs”—new architectures, learning algorithms, or representations that let models build and manipulate causal world models, not just statistical correlations. Work like DeepMind’s Genie 3 and research described in The future of AI – Google DeepMind sketches the direction, but he treats it as early-stage.

Hassabis’s optimism comes with a blunt risk register. He repeatedly flags cyber-terror scenarios, where powerful models automate vulnerability discovery, spear-phishing, and deepfake-driven social engineering at scale. He also worries about agentic deviation—autonomous systems pursuing misaligned subgoals once given long-horizon tasks and tool access.

That mix of ambition and caution shapes Google’s public posture. Hassabis frames safety work—red-teaming, evals, alignment research, and policy engagement—as a prerequisite for pushing toward AGI, not an optional brake. For him, the race is not just to build general intelligence, but to keep it controllable when it finally arrives.

What Google's AI Vision Means for You in 2026

Welcome to a 2026 where Gemini quietly sits behind almost everything you do with a screen, a camera, or a motor. Hassabis’s “full omnimodel” stack means one brain spans text, images, video, audio, 3D, and robotics, so your assistant no longer feels like a collection of apps—it feels like a single, persistent system that remembers, reasons, and acts.

Day-to-day work shifts from “using tools” to “assigning outcomes.” A reliable agent takes a vague brief—“plan and book a 3-day client offsite under $15,000, prioritize trains over flights, keep everyone’s kids’ schedules in mind”—and executes across Gmail, Docs, Sheets, Slack, and your calendar, asking for clarification only when constraints collide.

On your phone and laptop, Gemini follows you as a universal layer, not a chatbot tab. Start drafting a strategy deck on your desktop, refine slide layouts by voice on your commute, then have Gemini auto-generate a narrated video version for stakeholders who never open slides, all from the same underlying project state.

Glasses or lightweight wearables turn Gemini Live into a real-time coach. Point your gaze at a car engine, a server rack, or a medical device and get step-by-step overlays, safety checks, and live error correction, powered by fused vision-language models and latency measured in tens of milliseconds instead of seconds.

Creative industries feel the shock first. Interactive world models like Genie 3 let a single creator sketch a game mechanic in text, generate a playable 3D scene, iterate by talking to the world (“make gravity lower, add two enemies, change the art style to cel-shaded”), and publish to the web without touching a traditional engine.

Video production turns into prompt engineering plus direction. A filmmaker roughs out a storyboard, feeds in reference footage, and uses Veo-class models to generate scenes that editors then cut, grade, and composite, turning what used to be a 30-person VFX pipeline into a hybrid of human taste and machine-rendered dailies.

None of this happens by magic. Google’s vertically integrated stack—TPU v5p and v6 Trillium hardware, petabyte-scale data, and DeepMind’s research bench—gives its roadmap unusual credibility, even if timelines slip. Hassabis’s 2026 vision reads less like sci-fi and more like a product plan for AI woven directly into both your browser tabs and your dishwasher.

Frequently Asked Questions

What is Google's 'omnimodel' concept?

An 'omnimodel' refers to a single, unified AI system or model family that seamlessly handles multiple data types (modalities), including text, images, video, audio, 3D environments, and robotics control. The goal is to create a truly universal AI.

What did Demis Hassabis predict for AI by 2026?

He predicts significant progress in multimodal convergence, where language models fully merge with image and video capabilities. He also expects AI agents to become reliable enough to handle complex, multi-step tasks autonomously.

What are Google's 'world models' like Genie 3?

Genie 3 is an interactive video model that allows users to generate and explore virtual worlds using text prompts. It maintains memory and consistency, enabling real-time interaction, and is a key step toward training more capable embodied agents.

How is Google's Gemini being used in robotics?

Gemini Robotics 1.5 powers physical robots to perceive their environment, think step-by-step to solve problems, and execute complex tasks. The same model can be used across different robot forms without fine-tuning, enabling more versatile and capable machines.