Journal

It has been one year since Apple Vision Pro launched, and the architecture community has had enough time to separate hype from utility. We have been using it in our studio since month two, and the verdict is nuanced.

What works brilliantly: client walkthroughs. Placing clients inside an unbuilt space at 1:1 scale remains magical. We have seen a measurable drop in change orders, roughly 40% fewer on projects where we used Vision Pro for design review versus traditional screen-based presentations. Clients simply understand space better when they stand inside it.

What also works: collaborative markup. Multiple team members in different cities can stand in the same virtual room, point at walls, and discuss proportions. We use this weekly between our Ankara and Istanbul teams.

What does not work yet: design iteration. The Vision Pro is a consumption device, not a creation device. You cannot meaningfully sketch, model, or parametrically adjust anything while wearing it. The workflow is still Rhino/Grasshopper on a screen, then export to Vision Pro for review, then back to the screen for changes. That round-trip friction is real.

The hardware itself is heavy for long sessions, call it 45 minutes maximum before fatigue. And at $3,500, outfitting an entire team is a significant investment. Meta Quest 3 at $500 covers 80% of the use cases at 15% of the cost.

Our recommendation for studios considering spatial computing: start with Meta Quest 3 for internal design review. Reserve Apple Vision Pro for high-stakes client presentations where the visual fidelity justifies the cost. And wait for the next hardware generation before going all-in.

The AI floor plan generation landscape has changed dramatically since ArchiGAN first demonstrated that neural networks could produce plausible residential layouts. Three years later, the technology is both more capable and more honestly understood.

The current state of the art uses diffusion models, the same architecture behind Stable Diffusion, adapted for architectural plan generation. These models can produce floor plans that satisfy basic spatial constraints: rooms connect through doors, corridors provide circulation, and wet areas cluster near plumbing risers. The visual quality is impressive, but the architectural quality remains questionable.

The fundamental challenge is that a floor plan is not an image. It is a topological graph with geometric embedding, structural constraints, MEP routing requirements, fire egress compliance, and accessibility standards. Current AI models excel at the visual pattern but struggle with the engineering reality. A generated plan might look plausible but place a load-bearing wall where no column exists below, or route a corridor that violates fire egress width requirements.

The most promising approaches combine AI generation with rule-based validation. Systems like the one we built for Archly.ai generate candidate layouts using AI, then filter them through a constraint satisfaction engine that checks structural feasibility, code compliance, and spatial quality metrics. Only layouts that pass all checks reach the architect. This hybrid approach produces fewer options but dramatically higher quality ones.

For practicing architects, the practical advice is: use AI floor plan tools for early-stage exploration, not for production drawings. They are excellent at expanding the design possibility space, showing configurations you might not have considered. But every AI-generated plan needs professional review for buildability, code compliance, and spatial quality. The tool is a starting point, never a final answer.

OpenAI released o3-mini in January 2026, and the architecture community should be paying attention. Not because it generates floor plans, it does not. Because it reasons through complex multi-constraint problems in ways that directly map to architectural analysis.

We tested o3-mini on three real problems from our studio practice. First, a zoning compliance check: given Istanbul's imar yonetmeligi for a commercial zone, can a 7-story mixed-use building with 60% ground coverage satisfy parking, setback, and FAR requirements simultaneously? o3-mini worked through the calculation correctly, identifying the FAR conflict that our junior architect missed during initial feasibility.

Second, we tested structural intuition: given a 12-meter clear span with residential loading, what beam depth is reasonable for steel, glulam, and reinforced concrete? o3-mini provided ranges that matched our structural engineer's preliminary sizing within 10%.

Third, specification writing: generate a technical specification for curtain wall insulated glass units meeting Istanbul's climate zone requirements. The output was surprisingly detailed, correctly referencing U-value targets and appropriate glass coatings.

The pattern is clear: reasoning models excel at tasks that require combining domain knowledge with logical deduction. They struggle with spatial reasoning, geometric manipulation, and aesthetic judgment. The creative work remains human territory.

Midjourney V7 dropped last month, and architectural Twitter erupted with renders that look indistinguishable from professional visualization. We ran a controlled comparison.

Three scenes from recent projects, each rendered in V-Ray, Enscape, and Midjourney V7 (using detailed prompts describing the actual design). Five architects and three clients evaluated the results blind.

Image quality: Midjourney V7 wins for atmosphere and mood. Materials have a photographic quality that V-Ray achieves only with careful HDRI mapping and material tweaking. Clients consistently preferred the Midjourney images for "feeling" and "wow factor."

Accuracy: Midjourney loses decisively. Every generated image contained spatial inaccuracies: wrong proportions, impossible structural elements, inconsistent shadow directions. These are obvious to trained eyes but invisible to clients.

Our conclusion: Midjourney V7 is exceptional for early-stage concept visualization and competition boards. For client presentations and construction documentation, traditional rendering remains essential because accuracy is non-negotiable.

The hybrid workflow we have adopted: use Midjourney for concept exploration during schematic design, Enscape for real-time iteration during development, and V-Ray for final presentation renders.