When artificial intelligence is mentioned, most people imagine a chatbot or an image generator. However, the biggest AI battle in the second half of the decade is taking place elsewhere: in the real world, in factories, hotels, and hospitals, where robots are finally supposed to start functioning as true assistants. This is where MicroAGI plays its part.

Who is MicroAGI and What Makes Them Different

MicroAGI is a startup founded in 2025, headquartered in Munich. It has a research center in Zurich, and offices in London and New York. Although the company is young, it operates on one of the most challenging fronts of AI: in the field of so-called embodied AI, or incarnated artificial intelligence — systems that must perceive and react to the physical world, not just process text.

The key product is the Atlas platform. It allows specialized AI models to be trained directly on specific customer operations — in other words, a robot doesn't just learn general movements, but learns how to work precisely in a given warehouse, kitchen, or production line. Atlas can process video and multimodal data, i.e., information from multiple senses at once (camera, sensors, sound).

Among the first customers are humanoid robot manufacturers Unitree and UBTECH — both are among the leading global players in the field. Unitree produces robots from quadruped dogs (Go2, B2 series) to humanoid models (G1, H1), while UBTECH is known for a range of industrial and service humanoids.

Why Google Cloud and Why Now

The partnership with Google Cloud announced on July 22, 2026, is not just a marketing move. MicroAGI needs enormous computational power — training models for physical robots is orders of magnitude more demanding than training language models, as it works with vast volumes of video data from real environments.

The infrastructure they will use includes:

• NVIDIA Blackwell GPU — specifically RTX PRO 6000 Server Edition and GB300 NVL72 rack-scale systems. Blackwell is NVIDIA's latest generation of GPUs, available since 2025, which brings a significant leap in performance for both AI inference and training.
• Google Cloud G4 VMs and A4X Max instances — cloud servers optimized for AI workloads.
• Gemini Enterprise Agent Platform — a Google-developed platform for working with multimodal data, which will enable robots to better understand complex visual scenes.

Bercan Kilic, co-founder of MicroAGI, summarized it this way: „Building the next generation of embodied AI requires intensive computational capacities, a complex stack of AI technologies, and deep engineering expertise." Google Cloud is set to provide exactly this.

What is Embodied AI and Why is it Harder than ChatGPT

Language models like GPT or Claude work with text — input comes in, output goes out. A robot in a factory, however, must perceive (what do I see?), plan (how do I grasp it?), and act (move in real-time) — all at once, in a constantly changing environment. A small change in lighting or an unexpected obstacle is enough for the entire system to fail.

Precisely for this reason, Xiaomi used over 100,000 hours of motion data when training its Xiaomi-Robotics-1 robot — and found that more data brings greater improvement than a larger model. Training data is the most valuable commodity in robotics.

MicroAGI builds on a similar principle: instead of one universal robot, the Atlas platform offers task-specific models — that is, models specialized for a particular task. A robot in a hotel learns different things than a robot in a car repair shop. This approach is more practical and realistically deployable today.

Competition: Who Else is Fighting for Physical AI

MicroAGI is not alone. The embodied AI market in 2026 is full of strong players:

• Figure AI (USA) — humanoid robot Figure 02 collaborates with BMW and OpenAI
• 1X Technologies (Norway/USA) — received investment from Andreessen Horowitz, developing the EVE robot for industry
• Agility Robotics (USA, Amazon subsidiary) — Digit robot deployed in Amazon warehouses
• Boston Dynamics — Atlas and Spot, most famous, but commercially the most expensive so far
• Physical Intelligence (π, USA) — pure software approach, similar to MicroAGI, trains models for various robotic platforms

MicroAGI distinguishes itself from the competition with its European roots and a bet on a software-first model: the company does not produce its own robots, but supplies the AI brain for robots from other manufacturers. This is an approach that can bring scalability faster than if it also had to focus on hardware.

European and Czech Perspective

MicroAGI is based in Munich — and that is a relevant detail for both the Czech Republic and the EU. Europe has long lagged behind the USA and China in cutting-edge AI research, but it has a strong industrial base in robotics. Germany, the Czech Republic, and Slovakia have developed automotive and manufacturing industries, which are natural target customers for these technologies.

The EU AI Act, which came into force in 2024 and is gradually taking effect, will have a direct impact on the deployment of autonomous robots in Europe. Robots in industrial environments and robots interacting with humans will be classified as high-risk systems and will be subject to strict safety and transparency requirements. MicroAGI, with its European headquarters and focus on the enterprise sector, will have to reflect these requirements — which could be an advantage over American or Chinese competitors who are still getting used to EU regulation.

It is not yet confirmed whether MicroAGI plans direct expansion into the Czech or Slovak markets, nor whether their interfaces are available in Czech. Given the focus on B2B enterprise customers, the Czech language in the interface is less critical — what will be important is whether they find customers among Central European manufacturers.

What This Means for the Future of Work

The deployment of AI robots in hospitality and industry — two sectors explicitly mentioned by MicroAGI — opens up an unavoidable discussion: what will happen to jobs? Optimists argue that robots will take over repetitive and physically demanding tasks, while people will focus on more creative and social work. Realists point out that the transition will be painful, especially in countries with a high proportion of manual workers.

In the Czech Republic, where industrial production is a strong component of the economy, this question will be particularly pertinent. The automation of robots with advanced AI capabilities to perform complex physical tasks is qualitatively different from earlier industrial automation — and will require a systemic response in education and social policy.

Conclusion: Quietly, but Quickly

MicroAGI is not a company that all media write about. It hasn't released a model that beats GPT on benchmarks, nor has it reported billions in funding. But it is doing something that is perhaps more important in 2026: building the infrastructure that will enable physical robots to truly work in the real world. With Google Cloud and NVIDIA Blackwell as technology partners, and with clients like Unitree and UBTECH, it has a solid foundation for this.

Embodied AI is the next big leap after large language models. And this time, it's happening in Munich, not just Silicon Valley.