Author: David Bernal Raspall

Apple seems to have decided to make an interesting shift in its Apple Silicon chip strategy. According to Mark Gurman, of Bloomberg, the company will unveil the M6 chip late this year, but without Pro or Max versions, and will quickly move on to focus on the M7. This change, apparently, is aimed at the evolution of Macs when artificial intelligence and graphics performance are the main focus.

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Apple M6: a technical leap for entry-level Macs

The M6 will be Apple’s first chip manufactured on a 2-nanometer process, with memory bandwidth of up to 200 GB/s and a redesigned 12-core GPU, optimized for AI workloads. It will feature an improved memory architecture, a more powerful Neural Engine, and improvements in video encoding and decoding.

According to reports, the first models to receive it will be the entry-level 14-inch MacBook Pro and, later, some Mac mini and iMacs.

Apple M7: the future of Macs and artificial intelligence

The M7 will arrive, as far as we know, in the first half of 2027, with the Pro and Max variants toward the end of the year. Standard models will offer 240 GB/s of bandwidth and will be especially optimized for AI and graphics, laying the groundwork for increasingly demanding applications.

So, the MacBook Ultra, which would be the company’s first touchscreen laptop, could take a little longer to arrive than we expect right now. While rumors had placed it at the end of 2026, the change in the chip schedule could move its arrival to mid-2027 or even late 2027. In any case, one thing is clear: the wait will be more than worth it.

OpenAI has had to change its launch strategy for ChatGPT 5.6, and the reason is directly related to the new guidelines from the Trump administration. As reported by The Information, while the other models were introduced with immediate access for everyone, this time the company has opted for a limited-access phase only for a select group of partners.

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A private access phase supervised by the government

According to The Information, during a recent internal meeting, Sam Altman explained that the government will approve access on a case-by-case basis during an initial period. This oversight is being coordinated with the National Cybersecurity Office and the Office of Science and Technology, which have shown particular interest in evaluating the model before its full release. The intention is to ensure that the capabilities of ChatGPT 5.6 are used safely and without posing risks to the country’s technological infrastructure.

The goal is to avoid potential security-related issues, since models like this can detect and exploit vulnerabilities at a speed no human could match.

When can we expect the general launch?

OpenAI has confirmed that if this controlled phase goes smoothly, the model will be opened to the public in a matter of weeks. The question, of course, is who will be able to use this update, especially after Anthropic’s withdrawal of its most powerful models.

Meanwhile, we know that OpenAI reportedly already has the next version of ChatGPT ready and that, sooner rather than later, we should be able to use it. We’ll see how the timetable unfolds.

OpenAI is updating ChatGPT 5.5 Instant, the model most people use. The rollout has already started for paid users, and free users should get it over the next few days, according to the company. OpenAI says the goal is pretty simple: make ChatGPT smoother to use, easier to talk to, and more helpful for people who rely on it every day. The update focuses on three things: understanding, adaptability, and response speed.

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A more natural, dynamic conversation

OpenAI says the new ChatGPT 5.5 Instant is better at figuring out what you mean and adjusting to the context on its own. In practice, that should make replies feel less stiff and less padded out for no reason. Answers are also supposed to land at a more natural pace, with the important information coming through more clearly. OpenAI has also tuned the model so local recommendations, shopping suggestions, and replies that have to handle more complicated constraints are easier to follow and more consistent.

The company also spent time on something less technical but easy to notice: making conversations feel lighter and more enjoyable, while keeping the accuracy people expect from ChatGPT 5.5 Instant. So rather than throwing endless lists at you or drifting into long explanations, the model is meant to stay clearer, tighter, and closer to what you’re actually asking for.

With this update, ChatGPT 5.5 Instant feels a little closer to everyday use, the kind where people look something up quickly or work through a question as they go. It’s a real step toward making artificial intelligence feel easier to use, more comfortable, and less intimidating.

This fall, Apple says it’s bringing a new version of Siri to iOS 27. The update changes how you use your iPhone through Siri AI. Apple says the assistant now runs on Apple Intelligence, the company’s AI system, so conversations should feel more natural, Siri can pull information from your apps, and it can handle tasks with more context than before.

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So the obvious question is: when iOS 27 arrives this fall, will Siri AI work on your iPhone? Here’s what Apple says.

Devices compatible with Siri AI

Here’s the part that matters most: Siri AI only works on iPhones that support Apple Intelligence, according to Apple. Right now, that means you’ll need at least an iPhone 15 Pro or a newer model to use these features**.

Apple has also said that newer phones, including the iPhone 17 Pro and iPhone Air, use their extra processing power for quicker responses, even when you’re offline. Those models, along with future iPhones, are also the only ones that will let you customize Siri’s voice and fine-tune its tone and speaking pace so it sounds exactly the way you want, again according to Apple.

Aside from those two differences, every iPhone that supports Apple Intelligence will get Siri AI over the next few months, Apple says. It’s a big upgrade.

Apple says Siri AI uses a mix of on-device processing and its Private Cloud Compute system, which should keep performance solid across all supported iPhones. With it, Apple says you’ll be able to ask Siri to write text , summarize documents, use information from what’s on screen, and take action inside your apps. That’s a real shift in how the iPhone works day to day.

Apple says some of its products are going to get more expensive in the coming months. The reason is simple enough: memory and storage have gotten absurdly expensive, as Softonic recently reported, and that extra cost is starting to show up in the price of the devices people use every day. Tim Cook put it plainly in an interview with The Wall Street Journal: these price hikes aren’t avoidable.

Pressure from the memory chip market

The Wall Street Journal reports that demand for chips tied to artificial intelligence has surged, creating a shortage that has sent memory and storage prices through the roof. Apple has spent months trying to absorb those higher costs, but it has now reached the point where it can’t keep doing that.

One clear example came just a few weeks ago with Apple’s Mac mini. Its entry price effectively rose from $599 to $799 after Apple dropped the base model. Some forecasts now suggest the iPhone 18 Pro may need a $270 price increase to maintain the same profit margin. Those same projections suggest the iPad and Mac could be headed the same way.

Cook also told The Wall Street Journal that Apple will use part of its cash reserves to lock in enough memory supply and keep Apple devices available. In the meantime, buyers should expect price increases that have already become common across other brands and components, but hadn’t really hit Apple until now. The big question at this point is how steep those increases will be.

Since the arrival of the first generative AI, in architecture we have lived with a fairly clear separation between two families of tools. On the one hand, what we call Generative AI has helped us explore different atmospheres, typologies, materials and design languages at very high speed and with an enormous capacity for iteration. On the other hand, Generative Design has focused on optimization, design constraints, regulations and performance. Between the first image and the underlying structure there was such a huge gap that sometimes the use of AI in design was simply unviable.

“The stroke has intention, but AI lacks it.” This is a phrase I repeat many times when I talk about what it means to design something. I do not claim it as my own, but I do share the line of reasoning it leads us to. For years we have lived with a generative AI that served us, at best, to inspire what would later become the first sketches. Now the story is changing.

Has AI finally understood architectural intention? Can it glimpse the ultimate intention that sometimes the architect is not even fully aware of when drawing a line? No, clearly not. But it has learned to understand that, as we are taught at university from the very first classes, “be careful with the lines you draw, because in real life they become walls.” AI has taken a huge step forward in understanding what lies behind a design and, with that, its capabilities have changed radically.

If until now we had Generative AI tools clearly separated from Generative Design tools, in 2026 that separation is starting to become much more blurred. Autodesk already describes Neural CAD as a new category of foundation models capable of reasoning directly over CAD objects, and along that same line it places Neural CAD for buildings in its Forma app, with the ability to translate between a conceptual model of masses and layouts and the final design of a room or space.

It may not seem like much, but the evolution we are seeing changes a great deal for architects, computational designers, BIM managers and consultants. When we work in the early phases, we want speed, and when the project matures, we look for traceability, spatial coherence, compliance and performance. Autodesk is aiming precisely at that combination when it speaks about tools designed to accelerate the process while maintaining creative control and gaining real-time analysis capabilities, while also reminding us that BIM and IFC describe identity, properties, relationships, processes and systems, that is, much more than a geometric shape. Here, with this information, AI takes on a new meaning. This is where digital architecture gains a completely new depth.

Generative AI and Generative Design start from different logics

The basic difference between Generative AI and Generative Design is still something to keep in mind, because each one comes from a different way of producing results, and that is something we must be very clear about.

The former works through patterns and probability, through our own language and through large volumes of data. The latter works with objectives, constraints, parameters and the constant evaluation of alternatives according to the results we are seeking.

Autodesk sums it up very well by distinguishing AI that expands creative exploration from the Generative Design we find, for example, in Revit, which generates alternatives to our design based on goals, inputs and constraints in order to shape our decisions. One imagines possibilities; the other makes those possibilities possible.

With that difference clear, when we are in the concept phase, Generative AI offers us enormous power to open up the project’s visual field. It helps us explore a façade, the density of space, the materiality of the atmosphere, scale, or even the atmospheric experience itself almost as quickly as we can formulate an intention. The best part is that this is precisely where we do not want to apply too many constraints.

We have a tool that greatly accelerates the studio’s internal conversation, radically changes the quality of references and presentations, and turns a verbal intuition into something visible in just a few minutes. Its lack of constraints or, more precisely, its ability to propose impossible solutions is actually extremely useful here. When we as architects have spent years thinking in a certain way, AI comes along and asks us the big question: what would happen if…?

The key is that we need to understand this ideation for what it is. When what we want is for that idea to become architecture, that is when form needs rules, spatial relationships, hierarchies, layers of information, families, thermal properties, orientation, thicknesses, openings, systems, regulations and, above all, physics. That is where Generative Design has a very clear advantage, because its working framework already starts from those constraints and criteria. Architecture begins to gain substance when data enters the picture.

The case studies are, truth be told, genuinely interesting. In OMA’s case, the team used generative design to study the envelope and the stands of Feyenoord Stadium in depth and managed to add no fewer than 600 seats while maintaining the C-values they were looking for. Meanwhile, the MG AEC team focused on daylight, the window-to-wall ratio and solar potential, linking the Generative Design system in Revit with Dynamo to evaluate building performance from, so to speak, the very first sketches. When the project criteria are clear, the generative design machinery iterates with simply astonishing depth and speed.

Neural CAD takes the conversation from the prompt environment directly into the model

This is where Neural CAD comes in, probably the most interesting concept of the moment if we work in architecture, BIM or computation in the construction field. Autodesk presents it as a new class of generative technology based on neural networks, and describes Neural CAD for Geometry as a system capable of creating precise, editable CAD geometry from a text prompt and the spatial constraints already present in the design or expressed as part of the prompt.

Do we need to say it again? At the beginning we were talking about an AI that can generate ideas, ideas that in the vast majority of cases are completely disconnected from what is possible to build. Then we moved on to an AI that understands physics and structure in order to propose a solution that, so to speak, speaks the language of architects. Now we take another step forward and arrive at an AI that, taking into account the existing context or the one we present to it, can give us a valid design ready to be incorporated and edited as part of the evolution of our work in CAD.

The difference is enormous, the evolution is simply astonishing, and the doors that Neural CAD opens are practically infinite. We are talking about editable geometry, directed and with technical intent.

Neural CAD for Buildings is, according to Autodesk, the foundation model for AEC. We find it within Forma, and it allows us to move with enormous speed from a conceptual massing model to the different layouts and systems of a well-detailed building. The key here lies in the word “translate,” because the system speaks to us of continuity between decision scales that until now lived separately outside the architect’s mind: volume, floor plan, spatial organization, system. The AI we see today, however, is beginning to understand architecture as the related whole that at university we learn to see.

Shall we dare to take one step further? When we bring this into the language of BIM, the results move up a level. IFC is, as we already know, a standardized digital description of a building or environment. Its schema encodes identity and semantics, as well as attributes such as material, colour or thermal properties, and also relationships such as location and connections. Until now we have been used to the fact that, when we say “window” within a BIM environment, we are talking about an element with meaning, properties and links, not a simple void cut out of an image or the cut of a line. When this is the context Neural CAD works with, its capabilities go far beyond simply drawing lines on a screen.

Here we are at a point where the boundary between text-to-image and text-to-BIM is beginning to shift. Autodesk Research has already presented work such as TileGPT, where the system completes designs by adding orientations, windows and interior walls until it arrives at a valid proposal that can be transferred to our BIM software for later editing and analysis. And at Autodesk University, the KLH Engineers group has shown how machine learning is capable of generating walls in Revit with the correct thicknesses from simple AutoCAD plans and of placing door and window families using the logic of its algorithms. The translation from an abstract scheme to a genuinely usable model is already here.

This is where many firms are going to start seeing how AI becomes truly useful in the field of architecture. Getting the scheme or the inspirational image needed for marketing, competitions or visual exploration requires one set of tools and a team with very specific capabilities, but that is not what a practice mainly does. Meanwhile, the day-to-day reality of a project calls for something completely different: continuity with Revit, continuity with IFC, continuity with analysis, coordination and oversight.

In that field, Autodesk presented Forma Building Design in September 2025 as a cloud solution for schematic design that combines different modelling tools, generative AI and real-time analysis. It also includes connections to Forma Site Design and to Revit, making the transfer of information between the sketch and BIM extremely efficient. This is where Neural CAD makes sense. This is where, in day-to-day work, we are faced with a tool capable of greatly lightening our workload.

So much so that it actually changes our role as users of these tools quite significantly. With classic visual Generative AI, our work usually consists of writing prompts, choosing the results and guiding the aesthetic direction. With Neural CAD and the connected workflows we have just seen, our task begins to look more like defining objectives, selecting systems, evaluating variants and guiding the translation between formats or information systems. We are still designing, of course, but we increasingly design through decisions, rules and hierarchies.

Autodesk Forma, Revit and the architect’s new role

The architect’s role changes with these tools. The degree of change depends on our usual way of working and on the type of projects under our responsibility, but the change is real. Where it is perhaps most noticeable is in Autodesk Forma.

Autodesk presents Forma Site Design as an environment with real-time AI analysis of noise, wind, embodied carbon, daylight potential, microclimate and solar energy. Forma allows us to carry out predictive analysis for wind and noise in real time, with the aim of supporting different design decisions from the earliest stages. This approach goes much deeper than it may seem. It means that analysis stops being a later checkpoint and becomes an ongoing conversation about geometry.

Until very recently, when we talked about AI and generative design in architecture, we thought of lighter and more stylized forms, of optimizing certain structural components, or of managing to create components using less material. In this field AI is still a great help, of course, but now it is beginning to carry more weight in reasoning at the level of the complete system.

We are looking at models capable of operating directly at building systems level, reasoning about HVAC, lighting and more. We are looking at AI-assisted workflows that can design a wall assembly with sustainable materials, translating our assembly sketches into representations that capture components and relationships, and then assessing the material options and aligning them with project goals and market options.

Here AI acts as support for decisions on constructability, environmental performance and technical selection, precisely in an area where, as architects, we have to retain control over the criteria.

All of this leads us to a fundamental question: authorship. In a classic workflow, drawing a line was equivalent to fixing a decision. In these new environments, our role often consists of framing the challenge, selecting the variables, accepting or rejecting the suggested solutions, and guiding the system in a specific direction. Of course we have creative control, and everything AI produces will be editable as well as aligned with our intention, but authorship shifts from the isolated stroke to the direction of the system as a whole.

The ethical dimension appears right here. Because the more the level of autonomy of these tools rises, the more important transparency, data traceability, intellectual property protection and responsibility for the built result become. In this context, Autodesk articulates its Trusted AI Practices around responsibility, transparency, reliability and security, and that is the foundation we rely on when we work with its tools.

From here on, the future that is beginning to take shape for architecture is simply fascinating. It looks far less like a replacement of the architect and much more like an expansion of our capabilities. AI automates repetitive tasks, offers us predictive insights and places us in a supervisory position when a more manual intervention is not required.

We are on the verge of working with Co-Pilot systems that review compliance, performance and coordination while we design. A logical journey from the first text-to-image we started from, leading to text-to-BIM, our second stop, and far beyond. Logical, yes, but no less surprising for that, and a banner of a paradigm shift in how architecture is practised.

All things considered, Generative AI and Generative Design stop being rivals and begin to function as evolutionary parts of the same ecosystem. The former remains excellent for opening up possibilities, language and formal direction. The latter continues to provide structure, performance and verification. And drawing on both, Neural CAD is creating a completely new territory in which an idea can gain semantics, systems and analysis from the earliest stages. For those of us who work in architecture, this means that we can devote much more time to the more artistic aspects of architecture, knowing that the ecosystem will accompany us from the first sketches and make the rest of the process easier.

With Zootopia 2, Walt Disney Animation Studios has taken large-scale animation to a whole new level. Blending creativity with technology, we are looking at a film that is simply astonishing. A film overflowing with detail, references and humour, and one that shows it in its script, its dialogue, its sets and in every character that inhabits them. When a film consists of more than 2 055 shots, 178 unique characters, and requires more than 700 professionals working in coordination over several years, the final result is as much a product of artistic talent as it is of the quality of the software that, when it does its job well, fades into the background while supporting the entire process.

Some films expand a story and others expand an entire universe. In the case of Zootopia 2, the city, the characters, the settings and the volume of information all grow exponentially and at the same time, meaning that the scale of production changes completely. This is where Autodesk Maya and Flow Production Tracking become essential parts of the film’s very production language.

When we go to the cinema to watch Zootopia 2, the first thing we see is the expressiveness of the characters, the richness of the sets and the natural way in which every space comes to life, but behind all of that there is another universe that is just as important: the invisible organisation that keeps the entire feature moving. Let’s talk about how Disney Animation used Maya to build worlds and Flow Production Tracking to turn an immense production into a fluid, connected and highly precise process capable of taking what a film can be to another level.

The biggest, densest and most vibrant city to date

From its conception, Zootopia 2 was designed to go well beyond the original film. The sheer scale of the project makes that more than clear: 41 sequences, 2 055 shots, a total running time of 97 minutes and 30 seconds, and a film that, using stereo 3D, reaches 288 710 rendered frames. Astonishing numbers, but let’s put them into context.

If we think about what it means to build an entire virtual city practically from scratch, we immediately understand why the simple geometry of this production required a different approach. In animation, every building, every market stall, every background object and every decorative element is created from zero, passes through several hands and, by the end, without even counting revisions and improvements, has to fit precisely into a huge ecosystem. In Zootopia 2, that process is repeated for each of the more than 8 000 modelled elements.

The clearest example of that growth can be found in Marsh Market, an environment whose scale, density, detail and performance demand an especially careful approach. As the production leads explain in the video from a few paragraphs above, they found themselves facing a setting that required volume, visual richness and great agility to iterate at top speed in order to keep up with the pace of the creative teams striving to refine a scene until they found exactly the right tone. This is where Maya comes into play.

Autodesk Maya: the tool that brings an entire universe to life

Autodesk Maya’s greatest strength here has a lot to do with something that sometimes goes unnoticed when we talk about software: intuitive work. For a team that has to model, block scenes, set up cameras, prepare layouts and connect departments, working in a well-structured tool makes an enormous difference. According to the production team itself, Maya offers exactly that, a versatile foundation for building complex environments naturally, something essential when the project is growing in every direction at once.

That versatility is key when we talk about Maya as a kind of connective tool between departments. In Walt Disney Animation Studios films, environment modelling, layout, animation, simulation and staging constantly feed into one another. Every adjustment, tweak or change has an impact on several areas, every shot carries information into the next, and every department needs to be able to access the decisions that have already been made so that its work can be efficient.

Lastly, there is something especially interesting about Maya that deserves to be highlighted: the ability to customise the toolset itself. Faced with a film that brings together such different, and rather unusual, species, scales and proportions, software that opens the door to extensions and bespoke developments is crucial. The people behind this production explain how the team relied on that extensibility to create several specialised utilities, including a scale-auditing tool for Gary De’Snake, allowing them to adapt the pipeline to certain specific needs.

All in all, the visual grandeur of Zootopia 2 begins long before the final lighting or the definitive render. It begins in the way spaces are built, proportions are tested, the elements that appear on screen are organised and the decisions are connected across every creative area. That is why talking about production means talking about Maya, because we are going far beyond modelling or animation: we are talking about a creative environment that sustains the growth of the entire film.

A pipeline that grows in every dimension and says goodbye to linearity

We have talked about sets and animation and we have seen where Autodesk Maya fits into that part of the equation, but that is only half the story, in every sense. The production process, the organisation and the joint evolution of every department pass through Maya in terms of results, but in terms of planning and tracking they pass through Flow Production Tracking.

For years, the most widespread image of an animation pipeline looked very much like a straight line: one department delivers, the next receives, and the project moves forward in more or less sequential stretches. In a production on the scale of Zootopia 2, and in many Walt Disney Animation Studios productions, that logical progression gives way to a far more interwoven dynamic, because creative changes occur throughout the film’s development and call for a different kind of evolving structure.

When excellence is the goal, the process itself feeds back into the basic structure of the film, which is why, at the studio that brought Mickey Mouse to life, the script continues to undergo changes and adjustments until shortly before production ends.

From the development team to the post-production team, every department participates in building the overall picture. In different layers and in different ways, each one touches the film’s entire content. These changes, whether they come from editorial, story, visual development, asset creation, layout, animation, technical direction, lighting or production, have to be recorded, tracked and, as far as possible, predicted and planned.

The pipeline cannot be linear. A rigid relay-chain would not produce the result we see in Zootopia 2 or in other productions from the studio, simply because it is impossible to plan the entire production from the outset. What is needed is a shared data environment where changes can spread quickly and where several departments enter the conversation earlier. The result is a project that moves forward with greater flexibility and with a much more precise reading of the film’s real status while the creative areas continue developing the story.

Yvett Merino sums it up very well when she explains that the team keeps working on the story while it is creating it, with editorial decisions directly affecting tasks that are already underway at the studio. That constant flow requires tools capable of absorbing adjustments, redistributing information and keeping hundreds of artists aligned.

Why? Because every single person working on the production has to be able to do so with a connected view of the project and of the supervisors’ and directors’ notes. When layout, modelling, production and direction share common ground, iterations move forward with greater quality, clarity and a much fuller understanding of the objective.

The idea of “higher quality, faster iterations” that appears in Disney Animation’s process is the key to understanding how a film like the one we are discussing gets made. Here, speed is very visibly in the service of quality, and it does so thanks to a working infrastructure that enables exchange and dialogue between all the areas involved. We have to talk about Flow Production Tracking.

Flow Production Tracking: the film’s invisible operating system

If Maya is essential for building the worlds of Zootopia 2, Flow Production Tracking is the foundation that allows them to move, be organised and stay coordinated. With more than 2 000 shots, thousands of elements and around 700 artists involved, the production needs a system capable of translating the feature’s overall vision into specific daily tasks. That is where Flow Production Tracking stands out, as a kind of operating system for the entire project.

In a film of this scale, every artist has to know clearly what they are supposed to do today, which version of the scene or shot is current, what dependencies are being carried forward and how their work fits into the rest of the sequence. At the same time, supervisors, producers and directors need to be able to observe the film from another level: which sequences are progressing as planned, where pressure is building up, whether technical or creative, which teams need support and which adjustments are ultimately going to reshape the schedule. The magic of a good production-tracking system lies in bringing those two scales together within a single platform.

That explains why Autodesk Flow Production Tracking is so important. Its role goes far beyond marking tasks as completed or listing assets. In a production this alive, it acts as the nerve centre connecting departments, reflects the latest changes, organises priorities and maintains an up-to-date picture of the film’s status. When the story evolves, production needs that change to land as quickly as possible, immediately if it can, across the entire workflow.

There is one especially interesting aspect of Flow Production Tracking when it comes to visibility: the ability to detect bottlenecks before they can slow the process down. Blockages rarely appear in isolation, because they tend to spread, affect several areas and disrupt the pace of a whole sequence. Having real-time reporting makes it possible to identify those potential tensions in advance, reorganise resources and make decisions more efficiently. Data, colder by nature, becomes the core of the team’s creative momentum, because each day of production moves an enormous amount of interdependent work.

The data-guided creativity approach we see in Autodesk’s Flow Production Tracking fits especially well at the studio behind major hits such as Moana, Encanto and Frozen because it respects the artistic nature of the film. It does not replace the artists’ intuition or the directors’ eye; rather, it creates the context in which those qualities can operate more effectively. When the production team knows precisely where the film stands, what it still needs and what impact each adjustment will have, the creative team can focus more clearly on what matters.

The product is far more than the sum of its parts

If Zootopia 2 is far more than the sum of the individual contributions of everyone involved, then on the technical side the connection between Flow Production Tracking and Maya is crucial. While Maya supports the construction of the content, Flow Production Tracking supports the circulation of that content throughout the studio. One creates, the other synchronises; one allows the visible world to be iterated, the other organises the invisible film that makes every improvement and change possible.

From this perspective, we understand much better why a current blockbuster like Zootopia 2 looks far more like a network than a straight line. Every shot belongs to a sequence, every sequence belongs to a narrative, every asset touches multiple departments, and every creative change, whether in technical animation or lighting, has a translation in the very context of the story. With Autodesk’s tools, we have both the creative tools and the structural tools needed to ensure that this networked production remains readable and manageable, even at moments of maximum pressure. Order drives creativity, and we can see the results from the very first sequences.

A large part of a blockbuster’s success is decided in that invisible layer. The audience will remember a chase, a glance, a setting or the charisma of a character, and that is exactly what should happen. At the same time, behind those moments live thousands of decisions involving tracking, versioning, scheduling, validation and coordination that made it possible for the shot to exist as a faithful reflection of the directors’ intent.

That is why it is so interesting to look at this film from the point of view of the software that supports it. Autodesk Maya provides the flexibility, power and extensibility needed to build a richer, more populated and also more demanding universe. Flow Production Tracking provides the common foundation that allows hundreds of artists to work with an aligned vision, detect potential risks in advance and keep the “production monster” moving forward even in the middle of major creative changes. The achievement lies in how both tools support human work and allow imagination to grow from order and with clarity.

The film’s success rests on charming characters, extraordinarily rich staging and the excellence of Walt Disney Animation Studios, of course. It also rests, however, on something less visible but already decisive from the very first films Walt himself worked on: a technical capability that pushes the limits of storytelling. A technical ecosystem, in this case, capable of sustaining 2 055 shots, 178 unique characters, more than 8 000 modelled elements and 700 artists working like a single organism, like the very city that gives the film its name. That is where Maya and Flow Production Tracking truly take centre stage, and where we understand that, in animation, software is part of the art.