When Demis Hassabis, the CEO of Google DeepMind, says what he thinks AI will look like by 2026, it is worth paying attention. In a recent Axios interview, he laid out a clear theme for the next few years: the convergence of modalities into what he calls full “omnimodels.”
That idea runs through everything Google is building right now, from robots and image models to interactive video worlds and scientific agents. Taken together, it paints a picture of AI that is less like a single chatbot and more like a stack of coordinated systems that can see, listen, speak, move, remember, and plan.
This breakdown walks through that vision, anchored in Google’s current models and demos, and what they suggest about where 2026 is heading.
Demis Hassabis’ Vision: Full Omnimodels By 2026
In the Axios interview, Hassabis describes where Gemini is going over the next year as a push toward “convergence of modalities.” Gemini already takes images, video, text, and audio as input, and is starting to generate those same types of outputs. The next step is to fuse them into a single, coherent capability stack.
He is essentially talking about full omnimodels that can handle every major mode of perception and action in one system:
- Robotics
- Images
- Video
- Audio
- 3D
- Text
Hassabis points to the latest image model, Nano Banana Pro, as proof that multimodal training pays off. Because Gemini was multimodal from the start, they are seeing what he calls “cross-pollination” across modalities, where strengths in one area improve another. The image system does not just draw, it also seems to understand visuals well enough to create accurate infographics and structured diagrams.
A short clip of the Axios exchange is worth watching to hear how directly he frames this shift in AI capabilities. You can find it in this Axios interview clip on Hassabis’ AI 2026 vision.
If you zoom out, this omnimodel idea fits with the broader race around next‑generation models like Gemini 3 and GPT‑5.1. For a deeper comparison of those systems and their strategies, see this Gemini 3 Pro vs GPT 5.1 – battle of next‑gen AI models.
Why Google Is Positioned To Lead Multimodal AI
Most frontier AI labs can train large language models. Very few can train one model family to see, listen, read, generate images and video, and also run across phones, browsers, search, and robots.
Google has a few core advantages here:
- Gemini is multimodal by design, not bolted together from separate models.
- Distribution is built in, through Search, Android, YouTube, and Workspace.
- Specialized models sit on top of the same core stack, from Nano Banana Pro to Gemini Robotics 1.5.
Over the next sections we can walk the same ladder Hassabis describes, from robotics up through images, video, live guidance, world models, and finally agents. In practice, these are not separate silos. They are the six pieces of a single, full omnimodel stack coming together.
If you want a glimpse of where this stack already feels unified inside real products, the breakdown of an all‑in‑one AI breakthrough powered by Gemini 3 Pro multimodal model is a useful companion read.
Robotics: Gemini Robotics 1.5 Brings AI Into The Physical World
Google is not first in every robotics headline right now, but the direction is clear. Gemini Robotics 1.5 is their current robotics model family, and by 2026 it is likely to receive a sizable upgrade.
The key idea is simple: the same Gemini brain that runs your chatbot can also run a robot, and it can think step by step before it moves. In Google’s own words, these robots can now “solve longer multi‑step challenges” by perceiving the environment, planning, and then acting over several steps.
In demos, that looks like:
- Aloha robot sorting fruit by color
It sees mixed fruit on a table and follows instructions such as “Put the green fruit into the green plate” or “Put the banana into the yellow plate.” What matters is not the simplicity of the task, but the pattern: observe, reason, act, re‑observe, repeat. - Apollo humanoid doing laundry
A human tells the robot, “Put whites in white bins and the darks in the dark bin.” When the person swaps the bins mid‑task, Apollo notices the change and adjusts. You can see the robot’s reasoning trace as it updates its plan each time before moving. - Compost and recycling with local rules
In another demo, the Aloha robot is asked to sort objects into compost, recycling, and trash using San Francisco’s local waste rules. Gemini Robotics 1.5 calls out to the internet, pulls the city guidelines, then uses them to decide that the green bin is compost, the blue bin is recycling, and the black bin is trash.
Under the hood, a few things stand out:
- One model runs many robot bodies. Gemini Robotics 1.5 does not need separate fine‑tuning for each robot form factor.
- Tasks are broken into substeps. The model explicitly decomposes a broad instruction into smaller actions before executing.
- Agentic behavior is native. It can decide when to fetch external information, such as city regulations, then fold that into its plan.
Google describes this as a step towards bringing truly useful AI agents into the physical world, and that is not an exaggeration. It is still early, but this is the same playbook we are starting to see with more advanced humanoids and synthetic‑skin platforms that use strong multimodal models as their “brain,” as covered in more depth in Gemini 3 AI powering next‑gen synthetic‑skin robotics.
By 2026, an updated Gemini Robotics stack will likely combine better perception, longer planning horizons, and tighter links to world models. The trajectory is clear: fewer scripted behaviors, more flexible reasoning in real homes and workplaces.
Images And Video: Nano Banana Pro And V3 As Visual Workhorses
Nano Banana Pro: Image Generation That Thinks About Its Own Output
On the image side, Google’s standout model is Nano Banana Pro, the latest evolution of its image engine. It is not just a stylistic model for pretty pictures. Hassabis points to it as evidence that multimodal training can produce real visual understanding.
One important detail from people using it in practice: Nano Banana Pro behaves more like an agent than a simple renderer. When it generates an image, it does not stop at a single forward pass. It makes the image and then it adjusts it, almost like a designer doing a quick self‑review.
The loop looks roughly like this:
- Generate an initial image based on the prompt.
- Inspect the result to check layout, text, relationships between objects, and factual details.
- Make targeted adjustments, such as fixing mislabeled parts of an infographic or adjusting counts.
- Output a refined version that better matches the user’s intent.
That inspection step is what gives Nano Banana Pro such strong performance on accurate infographics and diagrams. It does not only draw a chart, it checks whether the chart still tells the right story.
V3 Video: From Image‑To‑Video To Rich Multimodal Sequences
On the video side, Google’s V3 model is still one of the strongest systems you can see in public demos. It handles image‑to‑video generation, turning a single still frame into a vivid short clip, and has become a reference point for quality and coherence.
People describe V3 with phrases like “pretty much the leader” when they compare side by side demos, and it is not hard to see why:
- High visual fidelity and smooth motion.
- Strong consistency in character and style across frames.
- Good alignment with the input image or text description.
V3 already looks strong in 2024. With another year or two of training and tighter fusion with Gemini’s language capabilities, it is reasonable to expect that by 2026 we will see:
- Video generation grounded in full conversations, not just single prompts.
- More control over camera movement, pacing, and narrative structure.
- Better integration with world models, so that characters and objects behave consistently across multiple clips.
In other words, image and video will not be side tools around a text chatbot. They will be first‑class parts of how AI systems think and communicate.
Audio And Live Help: Gemini Live As A Real‑Time Assistant
If there is one Gemini feature that feels underrated compared to its impact, it is Gemini Live. This is Google’s live voice and vision experience that lets you talk to Gemini in real time, show it things with your camera, and get step‑by‑step help.
A recent viral demo captured what this looks like in practice. The setup is simple: a person with a 2009 BMW 335i wants to do an oil change and uses Gemini Live as a guide.
The flow goes something like this:
- Gemini first checks that the person has the right oil type (5W40), the correct oil filter, and the necessary tools.
- It then walks through lifting the car and locating the oil drain plug, identifying it visually and confirming that it looks like a 17 mm plug.
- Gemini reminds the user to position the drain pan, let the oil fully drain, then wipe the area and install a new washer.
- It gives an exact torque spec of 18 ft‑lb for the drain plug and repeats the same figure later for the oil filter housing.
- It notices the old filter in the user’s hand, points out the two O‑rings that need to be replaced, and describes how to pry them off and fit the new ones.
- To finish, Gemini tells the user that the BMW N54 engine takes 6.9 quarts (6.5 liters), notes that this model has no dipstick, and explains how to use the electronic oil level sensor after running the engine.
The whole exchange feels like a calm expert standing next to you. There is back‑and‑forth, visual checks, torque numbers, and domain‑specific details that match the car.
Today, that already looks strong. By 2026, you can expect:
- Much lower latency, so the conversation feels even more natural.
- Better visual reasoning over complex scenes under the hood or in the home.
- Support for harder multi‑step tasks that blend planning, safety checks, and external reference material.
This kind of experience is one reason Google’s progress has triggered a strong response from rivals. If you want to see how seriously OpenAI is taking Gemini’s rise, the analysis in OpenAI ‘Code Red’ triggered by Gemini 3 adds helpful context.
World Models: Genie 3 Turns Prompts Into Live Worlds
Hassabis also highlights world models as one of the most important areas he is working on personally. The flagship example here is Genie 3, which he describes as “like an interactive video model” where you can walk around inside what the system generates.
The easiest way to think about Genie 3 is this: you type a text prompt, and instead of a static image or a single clip, you get a world that you can explore.
Google’s own description calls this out clearly: “These are not games or videos. They’re worlds.”
Key properties of Genie 3:
- Real‑time interactivity
The environment responds instantly when you move or act. You are not replaying a pre‑baked simulation. The model is generating each new frame live as you explore. - World memory
The system remembers what has already happened. If you paint on a wall, walk away, generate other parts of the world, then come back, your paint is still there. - Promptable events
You can add things to the world on the fly, like another character, a vehicle, or a surprise event. Genie folds that new prompt into the ongoing simulation. - Diverse settings
Prompts can describe real‑world physics experiments, historical scenes, fictional landscapes, or training spaces for robots. The world model adapts to each.
The obvious use cases are gaming and entertainment, where interactive AI‑generated levels and scenes open up a new category of experiences. The deeper opportunity is in training and simulation:
- Embodied research, where robots learn in rich virtual environments before touching real hardware.
- Disaster drills and emergency planning that would be too dangerous or expensive to stage in real life.
- Agriculture and manufacturing simulations that let teams test “what if” scenarios safely.
To support these kinds of worlds, models need stronger memory and consistency over long sequences. That connects directly to Google’s longer term work on architectures that fix the context and memory limits of classic transformers, such as the Titans family. If you are curious how Google is approaching long‑term memory in large models, the breakdown in Google Titans memory model explained is a useful deep dive.
The jump from Genie 2 to Genie 3 was already large. Genie 4 or 5, running on stronger multimodal backbones, could turn world models into a standard tool for robotics, science, and education by 2026.
Agent‑Based Systems: Google’s Quiet AI Workforce
Hassabis adds one more pillar to the 2026 picture: agent‑based systems. The idea is simple. Instead of one model responding to prompts, you have many specialized agents, each with tools and goals, that can work together on complex tasks.
He is upfront that these agents are not yet reliable enough to fully automate long workflows. But that is exactly where Google seems to be investing.
Scientific Agents: Co‑Scientist And Alpha Evolve
One of the more ambitious projects is Co‑Scientist, a multi‑agent system built with Gemini 2.0. It behaves like a virtual scientific collaborator that can:
- Generate and refine new, testable scientific hypotheses.
- Design experimental plans that match a researcher’s stated goal.
- Go beyond search and summarization to propose original directions.
The system is designed to mirror the core steps of the scientific method. It can read the literature, synthesize gaps, suggest what to test next, and outline how to test it.
On a related front, Alpha Evolve appears as a Gemini‑powered coding agent for scientific algorithmic discovery. Instead of writing app code, it explores algorithm space, looking for better methods or configurations. In practical terms, it plays the role of an AI scientist focused on algorithms and simulations.
Coding And Data Agents
Google DeepMind is also deploying AI agents against software security problems. One of these is a code agent that:
- Scans codebases, especially open‑source projects, for security vulnerabilities.
- Uses Gemini Deep Think models for reasoning about complex code paths.
- Applies tools like dynamic analysis to reproduce and fix issues.
On the data side, Google has built a data science agent that runs inside Google Colab and across Google’s data platforms. It can:
- Automate many steps of an end‑to‑end data workflow, from ingestion to modeling.
- Generate analyses and visualizations based on a high‑level goal.
- Keep work anchored inside tools data teams already use.
All of these are early examples of a larger pattern that people are starting to call the entire agentic system. You do not talk to a single AI anymore. You coordinate with a small, specialized staff that happens to be made of models.
By 2026, expect those agents to be:
- More reliable over longer tasks.
- More tightly coupled to world models and robotics.
- More integrated with workspaces that already use multimodal Gemini backbones, as described in the all‑in‑one AI breakthrough powered by Gemini 3 Pro multimodal model.
Google’s 2026 AI Roadmap: How It Fits Together
If you line up the threads from Hassabis’ interview and Google’s current demos, a clear 2026 roadmap emerges.
1. Full omnimodel stack comes online.
Robotics, images, video, audio, 3D, and text will not be separate product lines. They will be different faces of one Gemini‑style core, trained and served as a single family.
2. World models mature.
Genie 3 is the first public taste, but future versions are likely to become standard tools for robotics training, education, and simulation. Long‑term memory work, such as Titans, sits in the background to support this.
3. Agents get promoted from experiments to products.
Systems like Co‑Scientist, code agents, and data agents will move closer to daily workflows in science, software, and analytics.
4. Live multimodal assistance feels normal.
Gemini Live‑style experiences will expand from car repairs to home projects, small business operations, and more advanced technical tasks.
5. Competition accelerates the pace.
Google’s Gemini 3 already flipped expectations in the broader AI race, as unpacked in Gemini 3 launch and its impact on the AGI race. By 2026, multiple labs will likely ship their own takes on omnimodels and agents.
In short, AI in 2026 will feel less like chat plus plugins and more like a coordinated set of systems that can see, plan, and act across both digital and physical environments.
What I Learned Watching These Demos
Seeing all of these pieces together shifted how I think about AI in a few concrete ways.
First, multimodality is not a side feature anymore. When an image model like Nano Banana Pro can inspect and refine its own output, or when Genie 3 treats a text prompt as a live world, you start to feel that “text only” AI misses a big part of the picture. A real assistant in 2026 will read, watch, and listen in the same session.
Second, robots feel closer once you watch them think, not just move. A simple fruit‑sorting demo is easy to dismiss until you notice the reasoning trace behind each step. The moment a robot notices that you swapped its laundry bins and adjusts its plan, you stop seeing it as a remote‑controlled arm and start seeing it as a physical agent.
Third, live guidance is a bridge for real adoption. The BMW oil‑change session with Gemini Live looks almost ordinary, but it quietly solves a big problem: most people do not read repair manuals. A conversational guide that sees what you see and knows the difference between 18 ft‑lb and 80 ft‑lb removes friction that has blocked previous waves of smart assistants.
Finally, agents feel less abstract once you connect them to these demos. A scientific agent proposing new experiments, a coding agent hardening an open‑source project, and a data agent wiring up an analysis in Colab all look different on the surface. Underneath, they share the same pattern: a model that can plan, call tools, and check its own work before handing results back to a human.
All of that makes the 2026 timeline that Hassabis sketches feel less like speculation and more like a natural continuation of what is already on the table.
Final Thoughts: From Chatbots To Omnimodels
Hassabis’ 2026 outlook does not rely on distant breakthroughs. It is a straight line from the demos we can already watch today: Gemini Robotics 1.5 sorting fruit and laundry, Nano Banana Pro fixing its own images, V3 turning stills into rich clips, Genie 3 turning text into worlds, and agents acting like tireless collaborators.
As these pieces tighten around a single omnimodel core, AI starts to look less like a toy and more like shared infrastructure for work, science, and everyday life. The big question for the next two years is not whether this stack will arrive, but how quickly people, companies, and regulators can adapt to it.
If you follow this space closely, it is a good time to ask yourself: where do you want an AI that can see, listen, plan, and act sitting in your own workflows by 2026?
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