How to use AI assistants in spatial computing for immersive experiences

Introduction: Why combining spatial computing and AI assistants is crucial today

In 2026, spatial computing has gone beyond the boundaries of Augmented Reality (AR) and Virtual Reality (VR) to become a pervasive computing platform. At the same time, AI assistants have evolved from simple chatbots to contextual partners capable of understanding the surrounding 3D world, reading sensitive data (such as health records via ChatGPT) and generating GPU-optimized code in languages like TileLang. Merging these two technologies allows for immersive experiences that are both responsive and intelligent, from productivity apps to personal health tools.

1. Key concepts: What spatial computing and AI assistants mean in 2026

Spatial computing

• A 3D computational environment that maps positions, objects and gestures in real time.
• It relies on high'fidelity SLAM, neural'based meshes and low'latency rendering pipelines.
• Modern GPUs (A100, H100, Nvidia’s newest chips) and new languages like TileLang simplify the creation of high'performance kernels.

AI assistants

• Contextual models that integrate data from visual sensors, spatial audio and personal data sources (e.g., Apple Health, ChatGPT Health).
• Support prompt generation, code optimisation and natural'language interpretation for 3D workflows.
• The push for open'weight models (backed by an open letter from Meta, Microsoft, Nvidia, IBM and others) ensures greater transparency and customisation.

2. Practical example: Building a remote spatial collaboration AI assistant

Today a developer can build an assistant that helps place virtual objects in a shared space. Below is a step'by'step workflow with prompt engineering and real TileLang code.

Step 1 " Define intent with a prompt

You are an AI assistant specialised in spatial computing. I will give you a video feed from an AR camera and a set of 3D anchor points. Generate a prompt for a 3D vision model that extracts surface planes and returns a set of homogeneous transformations to position a unit cube on the floor.

Prompt optimised for open'weight models such as Llama'3.1 with visual adapters.

Step 2 " Execute the prompt via API

import requests
import json

payload = {
    "prompt": "Extract surface planes from these 3D anchor points and place a unit cube on the floor.",
    "context": {"modality": "depth_image", "anchors": anchor_list},
    "model": "llama-3.1-vision"
}
response = requests.post("https://api.ai-assistant.example/v1/generate", json=payload)
print(response.json()["transformations"])

Step 3 " Optimise the rendering kernel with TileLang

Once we have the transformations, we can compile them into a fast GPU kernel using TileLang.

# TileLang: TensorCore'optimised 3D rendering kernel
import tilang as tl

@tl.kernel
def render_cube(transforms: tl.Tensor(4,4, dtype=tl.float32)):
    for i in range(transforms.shape[0]):
        # Apply transformation using TensorCore GEMM
        tl.matmul(transforms[i], cube_vertices, out=transformed_vertices)
        tl.store(result_buffer, i, transformed_vertices)

if __name__ == "__main__":
    tl.run(render_cube, transforms)

This snippet shows how TileLang abstracts away the complexity of autotuning, producing near'optimal performance kernels for spatial rendering.

3. Real'world use cases in 2026

• Healthcare:ChatGPT Health connects Apple Health data and visualises 3D patient models directly in a doctor’s AR environment, guiding AI'assisted surgeries.
• Industrial prototyping:Engineers use AI assistants to generate spatial assembly instructions, while TileLang dynamically compiles GPU'optimised meshes for real'time visualisation.
• Remote collaboration:Platforms like Meta Spaces integrate AI assistants that label shared objects, suggest modifications and enforce accessibility controls based on users’ health records.

4. How to get started: Implementation checklist

1. Set up a spatial pipeline:Use Unity MARS, Apple ARKit or OpenXR with a low'latency video feed.
2. Pick an open'weight AI assistant:Models like Llama'3.1 with vision adapters, or newly released spatial'specialised models.
3. Implement prompt generation:Write prompts that bind visual input to 3D actions (e.g., "place a plane 1 m above the floor").
4. Optimise with TileLang:Write high'level kernels and let TileLang generate TensorCore'optimised GPU code.
5. Validate with health data:Integrate with ChatGPT Health for contextual feedback while respecting privacy guidelines.

5. Common pitfalls and how to avoid them

• Poor communication latency:Use low'latency WebRTC or dedicated protocols like Spatial Web Socket.
• Coordinate misalignment:Always normalise anchor spaces using a common global coordinate system (e.g., EPC or WebXR coordinates).
• Data privacy:Keep health data on'device wherever possible; use cloud APIs only for assisted AI processing.
• Model complexity:Balance larger open'weight models against device resource constraints.

Conclusion: The future of immersive experiences is intelligent and spatial

In 2026, the convergence of spatial computing and powerful AI assistants is redefining what’s possible in immersive applications. From healthcare to the factories of tomorrow, the ability to understand, manipulate and generate contextual 3D content unlocks new possibilities. Using prompt engineering, modern AI APIs and high'performance programming tools like TileLang, developers and creative teams can build experiences that are not just viewed, but actively guided by intelligence.

Final takeaway

• Try it now:Combine an AR feed with an open'weight vision model and generate a simple 3D object using a one'shot prompt.
• Explore TileLang:Start with its examples for 3D kernel generation to see autotuning in action.
• Follow the open letter:Keep an eye on developments in open'weight AI, which will underpin the next wave of spatial assistants.

Embrace these technologies today and you’ll be ready for the next generation of truly spatial digital interactions.