AU 2018 General Session

[MUSIC PLAYS]

PRESENTER: Please welcome to the stage Autodesk CEO Andrew Anagnost.

ANDREW ANAGNOST: Good morning, welcome to AU. [CHEERS] Special thank you to all of you that smoked me this morning in the 5K run. That was awesome having you lap me several times. It really is a pleasure to welcome all of you here, especially when I'm surrounded by so many of the wonderful things all of you make-- things of really ever increasing complexity, things with greater and greater fidelity.

Automation is changing the very things we're capable of making and how we make them. The things we make perform better, last longer, and are more meaningful. Automation is not only changing what we work on, but it's also changing how we work. And as automation gives us the opportunity create more meaningful things it also gives us the opportunity to create more meaningful work.

Automation is introducing new ecosystems, new jobs, and whole new ways of working. Now, last year I talked about automation not as a threat but as an opportunity for all of us to make more things, make them better, and with less negative impact on the world. I talked a lot about more demand being inevitable-- it's coming-- but I also talked about the need for more with less negative impact being a reality that none of us can avoid.

But today we're going to focus on the opportunity of better. The opportunity to make things better and do better work. Remember last year I talked about how the ATM changed the work of the bank teller-- how automating the repetitive task of cash handling didn't take the jobs away, but it did change their work. The work shifted to solving problems, selling new products, and forging relationships.

As the tellers discovered, having a subset of what they did automated didn't make the other things they did redundant, it made them much more important. It made their work more creative, more stimulating. It made their jobs better.

But how about an industry where the work is almost entirely repetitive? You know, I'm not talking about making cars, I'm talking about making food. In 1900, 40% of the United States worked in farming. 40%. Today, only 2% of us do-- just 2%. 100 years ago the value chain for food started at the farm and it ended at the market, but today that value chain extends way beyond just growing and selling. It extends into cooking shows, plant-based meats, and gluten free everything. Everything. Vegan, too.

Now, not all of these things are to everyone's taste, but we have more access to food than ever before. Automation has enabled us to use farmland more effectively and with better precision. And in place of repetitive jobs there's a whole new ecosystem of jobs-- better jobs.

The industries around us are changing, too. The transportation industry is a great example-- the business of moving people. Today there are 4 million people in the US whose jobs involve driving. And we have to face it, there are going to be fewer people working as drivers just as there's fewer people employed making cars.

But there are going to be more opportunities in the transportation ecosystem than ever before. Mobility advisors, fleet operators, in-vehicle experienced designers all working on the challenge of moving more people more efficiently than ever before. The ecosystem is only going to get better, and with it so will the jobs.

Just look at your ecosystems and how the jobs in it have changed. How many sustainability coordinators existed a decade ago? How many cloud architects, user experience architects, data scientists, drone operators, big data analysts, or even BIM managers? Think about all those new roles.

Just look at your ecosystems and how the jobs have changed. Yeah, we have fewer people stuck behind safety glass at banks, fewer farmers toiling in the fields, and soon we're probably going to have fewer truck drivers falling asleep behind the wheel, but we'll also have better ecosystems with better jobs. Automating our repetitive tasks doesn't make the other tasks we do redundant. It makes them more important. It increases the importance of our expertise and our creativity.

Removing these repetitive tasks means we have less friction and more time to focus on what adds value. 100 years ago, the family farm was not going to feed the world. But now today we have a real opportunity to feed the world, and we've even found a place in that value chain for the family farmer again. We call them organic farmers.

We've made more kinds of food accessible to everyone, and automation made it possible. Maybe next we can make housing accessible to everyone. There are plenty of design challenges left to solve, so we shouldn't spend a lot of time being concerned about our jobs going away. What we should concern ourselves with is how our jobs are changing and what skills we'll need to thrive in the future.

So just how will our jobs change? All of the people here, what does the opportunity of better look like? Now, for some clues I want to take you on a little journey to the worst best city in the world, Los Angeles.

Now, I can say that. I grew up there. I know its best and its worst side, and I wish with everything you've heard in the news I really am thinking a lot about my old hometown. But that doesn't change the fact that Los Angeles is a very diverse and dynamic place.

It has more of everything-- more people, more celebrities, more beaches, more culture-- some people might not call it culture-- more theme parks. And one thing people don't know is that Los Angeles County has more manufacturing jobs in it than any other county in the US-- any other county in the US. But more comes at a real cost, which is something that anyone that lives in LA knows all about.

Because alongside LA's population diversity you'll find population density. Alongside LA's culture you'll find incredible congestion. LA Roads are the third worst in the country, and its commuters spend an extra two weeks a year stuck in rush hour traffic-- two weeks a year.

So what's going to happen 10 years from now when LA hosts the Olympics? Los Angeles is a very different place from when they were the host city before in 1984. The population of LA will be twice as big in 2028 as it was in 1984-- twice as big. On top of that, two million more people will descend on Los Angeles from all over the world, and they're going to be demanding comfortable housing, good food, and a great experience.

LA has a decade to plan for this-- 10 years of planning for 16 days of competition. Now, I think all of you that build things, you know, that's plenty of time, right? Plenty of time to plan for big changes like that. But like so many host cities before, the legacy of the games will live on long before they're over, and it can either be a positive or a negative one. But imagine if we could capture all of that and ensure it was an uncompromisingly better experience, satisfying not only the short term needs of the Olympics, but also the long term needs of LA's citizens. Could automation present Los Angeles with the opportunity to do more with its Olympic ambitions, do it better, and with less negative impact on the city and its citizens?

Take the design of the athletes' village, for example. After most Olympics, these developments are generally turned into housing, usually low income housing. But I'm sure all of you can imagine that the short term needs of world class athletes and the long term needs of low income families often compete. Reconciling these two can be a big challenge, and failing to meet these design challenges is why cities are often left with better outcomes after the Olympics. But imagine if automation could help us reconcile competing goals.

Now, today all of you would approach a design challenge like this in the typical way of the waterfall process-- designing through cycles of work and rework, redrawing what has already been drawn, re-detailing what has already been detailed, recreating what already exists, and dealing with the inevitable problems that cascade from all the rework and redos, leaving us a lot less time to use our creativity to solve real problems and less time to address all the requirements that could have been. Leaving the promise of the athletes' village exactly that-- a promise.

But what if automation could change the way we work? How might automation free us from repetitive tasks and give us more time to be creative, to be collaborative? To not only work with our clients, but with all the other disciplines involved in every aspect of the project? Whether you're an architect, an engineer, or a contractor, imagine being able to build something before it's actually built.

Tomorrow, automation is going to allow us to explore dozens of design options really early in the design process and share the ones that best meet the requirements with all of the disciplines-- the disciplines most of us consider downstream today. These tools are going to give all of us real time feedback on the constructability, the cost, the reliability, and the risk of our designs. Automation is going to facilitate ideation and collaboration between all these disciplines.

So how might this work? How might this address the challenges of a project's changing requirements-- the changes that can be headaches for engineers or contractors just simply trying to lock down their plans? What if the building's footprint needed to change because of changes in equipment availability? What if the ground floor needed the change because the owner decides to change it from residential to commercial? In these situations, compromises are typically made and key requirements go completely unmet.

What if we could work on everything in the model before anything is built? We could deliver a better outcome on time and on spec. And when the material costs change during the construction-- and they always do-- everyone knows about the impact. Even the owner gets real time insights about what is happening. Automation will capture every change and communicate it to everyone instantly, sending the right information to the right people at the right time without reentering the data, without risking human error in the process of communicating.

Beyond that, the final model will actually represent what was built. The final model is better, the building is better, and the work is better. When automation means we can spend less time on non-creative and non-productive tasks, we'll all have more time to spend understanding how buildings and teams come together. That's when we design an athlete's village that can be adapted to become low income housing, student housing, better housing.

Now, if automation can help with the challenges of housing people at the Olympics, what about other aspects of the experience? People are going to come to LA not just to experience the Olympics, but to experience other aspects of LA like its great food. Now, supply of food isn't much of an issue in LA in the summer, but demand is a very different matter. It shifts from event to event, neighborhood to neighborhood.

What if automation could help LA reimagine the humble food truck? What if a fleet of reconfigurable kiosks could help the city adapt to shifting demand during the Olympics and well beyond the Olympics? Remember the way we approach these things today is this waterfall process, this process of iteration. This iteration would happen in distinct phases. It would cut across distinct silos.

Designers' ideas would get passed along to the engineer for validation, and eventually someone on the manufacturing side gets a design thrown to them that they have to figure out how to build. But imagine if all the disciplines could work everything out in the model together. How might that change the way they work? Now one thing's for sure, the requirements of this design aren't going to change. It still needs to be something that can be towed anywhere, that can power itself, and unfold itself.

But rather than working on one potential solution, automation could help the designer explore a set of solutions with everyone, all of which meets the needs of the requirements not just for how the kiosk looks but for how it works. And maybe the designer could even exceed the original requirements by ensuring the kiosk could provide power not just for the cooks, but for the entire community. And rather than passing the idea to the mechanical engineer to figure exactly how it would work, how the mechanisms would function, what materials would be used, and how much the structure would weigh, automation would allow the engineer to work in tandem with the designer to explore multiple options for ensuring each solution is feasible, viable, towable.

And if automation could help the engineer explore the full manufacturing spectrum, they can make decisions about what parts are best made how and where-- what parts are best printed, milled, or molded. And if the engineer could understand how to get the best of mass production and mass customization the kiosks wouldn't have to be made all in one run in a monolithic factory. Instead, they could be made on demand in a network of micro factories all capable of making a variety of things.

So not only would the food supply be local, so would the supply of manufacturing expertise. The city could tap into LA's existing manufacturing infrastructure and its talent. But how might this help address the inevitable change, like when an engineer imposes a new requirement, a bigger motor, which means a bigger solar panel, and stronger mechanism, and also ultimately a bigger budget.

No longer will these problems cascade upstream and downstream. When everyone has access to the information they need, we remove the constraints-- the artificial constraints-- that we all have imposed on each other. And when these disappear, just-in-time manufacturing becomes truly just in time.

And if these disciplines can work out everything in the model, just imagine what that could mean for the city and its citizens. When all these digital assets are combined together, anyone can look across a digital twin of the entire city-- a digital twin that aggregates data in real time. You can always find where your food is when you need it. Athletes can always get to where they need to go, and the city can look out for hazards, congestion, and stop them before they ever happen. Automation makes the whole experience better for everyone.

That's what the opportunity of better is all about-- better knowledge, better insights, better outcomes. Now, with all these things you're probably wondering, is this even possible? Is anybody looking at this today, is anyone trying to make their work better? Of course, the answer is yes.

Take for example Atkins. Atkins is a well-known multinational consulting company, and they're seeking to achieve a lot better results, do better work by collaborating more effectively with their clients. They're working on automations that will completely change the way they work. And to tell you about it, I'd like to invite Annette Chapman and Mark Durand onstage to share their story with you. Please welcome Atkins.

[APPLAUSE]

ANNETTE CHAPMAN: So imagining how automation might change the way we work and the way a city like Los Angeles can prepare for the 2028 Olympics is an interesting exercise not just because of our work on the London 2012 Olympics but because at Atkins we're already developing a new way of working together. In the not so distant past we faced lots of challenges at the early stage of the design process from managing multiple stakeholders and complex design changes to working globally and working within teams with varying levels of technical skills. And project, after project, after project, we saw three fundamental things challenging our design process from time, cost, and decision making, and these obstacles were just getting in the way of how we collaborate, hindering not only what we design but how we design.

And we're not alone. We found we spent way too much time having to redo design work. Now, usually these changes are linked to the gap between the client desires and the outputs that they receive. And often, this is because of miscommunication or last minute changes as well as where projects are increasing in complexity and project duration.

We also experienced difficulty controlling our costs. And as much as we tried to plan for the unexpected expenses, usually these are things that we just can't control. Major design changes means you have to redo everything from drawings, to calculations, to quantity extraction, to coordination and project schedules. And as you all know, this can result in large construction overrun.

And then what about project approvals? These can take longer than anticipated. And again, staff demobilization becomes really expensive. Why should we wait until the final product is presented when we can embed the client as part of our team and improve the frequency of the quality of our decisions? We needed a way to deliver products differently, and in an environment where we could control communication and we could collaborate better, allowing us to make well-informed and real time decisions.

So we needed better collaboration, and we needed less time spent on unnecessary work, and we needed more effective control of our costs. If we could get our clients engaged early in the design process we could reduce our project changes, and we'd increase our early collaboration. And critical to that feeling was ensuring that we could collaborate with them whenever and wherever.

Now, we knew that if we had a real time visibility into how we are delivering our projects, we can minimize project delays and we can control the cost of design and construction. So we have developed an app that takes full advantage of both cloud computing and the Autodesk Forge platform to improve the design workflow. This allows for better collaboration, faster iteration, and quicker decision making. I'd like to welcome my colleague Mark to the stage now to show you the technology we've been working on to further advance collaboration across our projects while ultimately improving the experience and outcome for our clients. Come on out, Mark.

[APPLAUSE]

MARK DURAND: Thank you, Annette. So I'm here to introduce you to our new app, codename caterpillar. It's only a codename, but it was inspired by the butterfly effect concept-- how changes can ripple in other projects. Caterpillar evolves the napkin sketch concept by letting us doodle ideas and then fits them to other solutions. It transforms sketches in real time to [INAUDIBLE] study linked to analytics. Anybody who knows how to handle a pen can use the app.

We can already start to see our technology will arbitrate decision making in the future, and we can imagine how our tech will improve everybody's work. On the client side, the app provides better insight into project progress and key performance indicator. For the design team, the app reduced the effort required to better focus on your design. And for the contractor, the app provides access to the information needed to efficiently facilitate the project delivery.

In fact, just as Andrew looked forward to what the LA Olympic could look like 10 years from now, I can look back on our experience working on the London Olympics in 2012. Now that we have this kind of technology, we can easily reflect on how delivery would have been improved, making an even better product with less time iterating and more time innovating.

On the London Olympic during the early design stage, a huge amount of time was spent on data collection-- data on land availability, land price, land occupation, land phasing, land accessibility, pedestrian movement, and much more. We're talking about thousands of Excel spreadsheets, drawings, and PDF where contents needed to be extracted. All was generally managed individually, and there was no easy way to reconcile it all together. If we'd had Caterpillar, we would have had a single source of truth. Caterpillar surfaces different data sources which are connected to the app as microservices. It allows the design team to connect and interact directly with the data all in one centralized location.

As you can imagine, working on a project like the Olympic games is quite the undertaking. One of the challenging parts is the large amount of time between conception and delivery. You might think having this much time makes it easier but a collective vision of the future is never easy to predict. On top of that, this is one of the project where there is no flexibility on timing as being late is not an option.

With so many stakeholders involved, decisions can take time-- time to communicate, but also time to edit and change. Due to time constraint, leap of faith are taken at 60% decision maturity. By facilitating faster design iteration and simplifying the impact of changes, caterpillar speeds up the whole process. For example, a critical equation we receive when preparing the London Olympic was is the stadium in the right location? Not a small change, don't you think?

Just by selecting the stadium and moving it around, we can now see in real time the impact on cost, land, and any other data set we plug into. This would have allowed us to make more informed and quicker decision. We would no longer need to estimate the impact, we would simulate it.

Finally, project approval. This is always a tedious topic, and this was especially true for something of this scale. For the London Olympic, reporting and communication was really time consuming. With so many activities happening in parallel, information was a topic of its own.

Caterpillar gives us access to information we need through a centralized dashboard. So whether you're from the client side, the design team, or the contractor, a project and its progress can be viewed from anywhere on any device. We can query any specific zone, pull part of the project, and run analytics. Having access to real time data means we can drill down right to the root of the data source. We've developed a way to eliminate the time we spend on reporting. Instead, all our stakeholders have access to live information.

Caterpillar is still in an early development stage. We are currently testing with a global master team. The early feedback are really, really encouraging. There is no denying that moving from a linear workflow to a data-centric one allows more focus on design rather than documenting and reporting. We are both thrilled by how far we've come just under a year and inspired from what's to come.

ANNETTE CHAPMAN: So thank you, Mark, that was really great to see. This work on Caterpillar has not only improved tools of Atkins, but it's helped shift our mindset around how we deliver on projects, and how we work together, and how we see the possibilities for the future of how we design. We can now do more through rapid design iteration, and we can spend less time having to redo our work. And we're seeing better outcomes for our clients and our teams.

But what if the scale of design could go beyond that? What if we could open the design process further for collaboration? And what if at the next Olympics design could include the host city and its citizens helping to shape buildings that live on beyond the games?

Now, that's what we're really thinking about. And because of this work has emerged a new mindset. And now we think that anything is possible when we reimagine the way we work. And thank you.

[APPLAUSE]

ANDREW ANAGNOST: Thank you, Mark. Thank you, Annette. So that's an excellent example of a customer building their own tools to use automation to make the work of their teams better. Now, next I'd like to invite our Chief Technology Officer Scott Borduin on stage to talk about some of the automation technologies we're building to help you design, make, and work better. Please welcome Scott Borduin.

[APPLAUSE]

SCOTT BORDUIN: So what you heard from Andrew this morning was an expansive and aspirational vision of a better future. What I'm here to tell you is that future is arriving much faster than we all might think. The products and technologies that we're building and the creativity of our customers in applying those technologies are transforming the way that our industries work.

Let's start by reflecting on that great story by Annette and Mark from Atkins. What we learned from that story in short is the value that data flow and automation can bring to you and the rest of our customers. You've been asking for meaningful ways to move data since AutoCAD first appeared. I know because I've been at Autodesk for 25 years, and for 25 years I've heard your pain and felt your pain.

Over that period of time, our tools got more and more sophisticated and so did the work that you all do with our tools. But at the same time, it's become increasingly difficult to move the ever increasing amounts of data that are created by those tools. One thing you all have in common is the need to move data and move data through your unique processes and workflows. One size doesn't fit all. So if you're going to be able to innovate more with less busywork, we need better ways to move data and automate your processes.

So when we embarked on the journey to the cloud, one of the first challenges we knew we had was how to move data from desktop applications into the cloud. We knew that just rebuilding applications like Revit or something in the cloud was only just going to change the location of the app. But we did know that the cloud was part of the solution to the decades-old problem of moving data. It had the potential to serve as a universal data pipeline and enable you to move data in a meaningful way through your processes.

Another thing we knew was that this was never going to be an out-of-the-box solution. Just as all the things that you design and make are different, then so too are your workflows and your processes by which you make those things. We couldn't build one monolithic product to help you do everything that you all do. We knew we had to put the tools in your hands to define your own workflows, your own data flows. We needed a solution that would allow data to flow through our tools, our competitors' tools, any other important tools in your ecosystem.

Today, we're getting to the point where the cloud platform that we've been building is becoming highly customizable, highly flexible, and highly automatable. And you saw from Atkins the kind of automation and the kind of value that is starting to emerge from what you all are building on our platform using Forge.

We're using Forge ourselves to deliver value to you directly like this dashboard that shows your product usage so that you can make more informed decisions-- decisions based on data and not just instinct-- so that you can spend less time managing your costs, more time doing productive work. And you'll see in tomorrow's industry keynotes the productivity and value that Forge is delivering to customers like Lira and Paric from Lira's work around structural optimization and VR exploration to Paric's work on BIM coordination and field integration. These automation efforts eliminate non-creative, non-productive tasks, giving you and your colleagues more time to focus on your expertise and your creativity and more time to focus on what adds value.

And what all of these stories show is that data is a positive sum game. More data equals more knowledge. More knowledge equals more insights, more insights equals better outcomes, better projects, better products, better ideas.

So today I want to tell you a couple of stories showing how automation and data can lead to better outcomes. To show you how far we've come with the products that you use today, what we're working on for tomorrow, what progress we've made with automation tools like generative design, and then ultimately just how far data and automation can take us.

Our first story takes us a long way to the harsh and desolate environment of an ice covered moon where scientists hope to discover what lies beneath that icy crust. To take us on this journey I want to welcome on stage one of our scientists, Dr. Erin Bradner.

[APPLAUSE]

ERIN BRADNER: This is one of the moons of Jupiter, and NASA's Jet Propulsion Laboratory is thinking about how they one day could land there some 365 million miles away. Our tools are helping them design a concept for a lander that might one day give humanity its best chance at finding life beyond Earth. We're helping their designers use generative design to create optimized geometry. They've already reduced the weight of the main structure of the lander by over 1/3. And we're working with their engineers to validate how the design performs, using the cloud to handle simulations that used to require dedicated computing.

But since JPL has access to this infusion today, we're now working together on a much bigger problem. Because a rate-limiting step for JPL today isn't geometry optimization and it isn't access to computing. It's the same thing that's a problem for all of us. It's the challenge of how they can better explore all possible design solutions.

For most companies, the answer is a random walk through a field of solutions one design meeting at a time. Imagine an early design meeting flags the need to focus on light weighting. So your designers go and solve for that. But this presents a problem with manufacturer ability, so your engineers are tasked with solving for that. And then cost becomes an issue, and schedule, and so on.

So just how do you look at all your design solutions? That's what our tools are helping JPL figure out. This concept lander is for a proposed future mission. But wait, this is a rendering. Do you want to see it? Do you want to see the lander?

[CHEERS]

Let's bring it out.

[APPLAUSE]

This concept lander is for a proposed future mission. Now, the proposed mission is not just about landing. The goal of the lander is not to land. Its task may be to land, but its goal is to explore. Any weight they can save on the lander could instead be spent on more science instruments. And less weight means less rocket fuel, so light weighting is a big deal, as is manufacturability.

So we're helping JPL explore and choose design strategies that cover not just additive manufacturing but machining and casting, too, all from a single problem definition. The problem definition produces a variety of potential design solutions, each optimized to help them determine manufacturability.

Now let's start with additive. As amazing as 3D printing is, when it comes to space travel there are still challenges around material qualification. But we can still help JPL print molds for sand casting, and that's what they've done here. We know the world is not ready to retool around additive, so we're helping explore a different path with JPL-- one that leads to dozens of manufacturing options that JPL already has access to and that are optimized for materials qualified today.

The lander's legs are optimized to be milled, and we've written new algorithms to encompass three axis and five axis milling. Taking generative design beyond the realm of additive and into the realm of machining brings it into the realm of the entirely practical for JPL and for many of you. Because three and five axis manufacturing constraints can already be found in Fusion today, now we're turning our attention to the rest of the manufacturing spectrum from milling to molding, from carbon fiber to casting.

Casting is what we're working on next, and JPL is helping us make progress with this, as you can see. Our work on solutions where customers can explore manufacturability is allowing JPL to avoid spending valuable cycles pursuing non-manufacturable output. Instead, their engineers are able to pursue design options that they decide are ready to make. This allows them to focus on their design goals rather than getting hung up on one design solution at a time.

Ultimately, we want JPL and all of you to be able to explore the full design space and have a clear line of sight from your part to your production, including pricing. And if JPL can use generative design to help them model all types of parts and manufacture them in all types of ways, can you imagine using generative design across a system of parts? We can, and we're working with JPL to optimize across entire assemblies like the one in this lander concept. So if this approach can work for JPL's space exploration projects, just imagine what it could do for your projects and how you explore the space around your ideas. Thank you.

[APPLAUSE]

SCOTT BORDUIN: Thank you Erin and thank you, JPL. I mean, what a great story, huh? But I know what some of you are thinking. You're thinking, of course the rocket scientists at JPL are going to use things like generative design, but is it really all that relevant to me and to the work that I do every day in my company? Well if that's what you're thinking, tomorrow morning in the design and manufacturing keynote you'll hear about Claudius Peters, a German manufacturer who's also using general design in Fusion to innovate on the design of industrial machinery. It's an equally great story that you really don't want to miss.

Our next starring brings us back down from space and back down to Earth with a bump. Why? Because despite Japan's declining population, its urban population is still growing. Yet in a country where buildings are still so rooted in craft it is drawings and not data that still predominate. We might all see the need for increased productivity, but in Japan this need is paramount.

So we're working with one of Japan's biggest construction companies to help them explore how productive data and automation can make them and where that can take them. And at the same time, we're working on figuring out which industry we're going to take generative design into next. Daiwa House have been taking an industrialized approach to the craft of housing for decades. Today housing is still one of their core businesses, but they have used automation to extend their industrialized approach to condominiums, and care homes, resorts and retail parks, hotels, and hospitals, and to extend their business well beyond Japan.

They're already using the data from BIM to drive automation further into their factories and into fabrication, but we're helping Daiwa House take their data further-- much further. We're helping them explore how automation can better balance their business needs and Japan's human needs. Because despite the need for more urban housing, the short term boom in construction driven by the 2020 Tokyo Olympics means that pockets of her undeveloped urban land are very hard to come by.

Reconciling this inevitable long term need for more housing with the reality of less available land is something that Daiwa House have been thinking about for a long time, and they've hit upon an opportunity. We're helping them explore how generative can help them identify the best small developments to build on small pockets of urban land. Daiwa House themselves don't care whether they can buy the land. Instead, what they've been doing is approaching the landowners with speculative proposals for development. But the proposals are only speculative in the financial sense of the word because generative design enables them to model proposals to fit local needs, from local demographics to local regulations, and to reconcile local needs with human needs from site access to sight lines and more.

And of course, revenue and ROI are also part of the model. Daiwa House needs to ensure their business needs are met, too. So our technology has helped them build a system that allows their sales team to assess plots and build proposals that make sense for them and the landowners, and this system allows them to do this quickly because for every proposal that gets accepted many do not.

So Daiwa House wants to ensure that the ones that do get accepted don't change dramatically when they move from the sales team to the design team. So using BIM as a data platform helps their sales team move efficiently from proposals to pre-construction. And because they're generating lots of proposals, they're generating lots of data-- data that machine learning can use to generate insights about what works across all of the proposals that they've developed.

So as the platform learns from them, it learns what designs work best for them. And better insight into their design process can bring better outcomes. Daiwa House is building better housing, delivering more economic value with less upfront investment, less risk.

Automation helps them reconcile the desirable with the viable and the sustainable with the profitable. Reconciling goals that seem to conflict is something that generative design is particularly good at. So if Daiwa House can use generative design to do this for their housing business, can you imagine what it could do for their bigger projects-- their hospitals, nursing homes, senior housing? The places that are designed around very real human needs. We can imagine that, and we're working to bring generative design technologies from our customers' world of manufacturing into their world of the built environment.

So as you can see from all of these stories this morning, we've come a long way with our automation tools like generative design, and we still have quite a journey ahead. But it's really you, our customers, that help take our tools down some really interesting paths. The epic story of our work on this lander concept may seem like science fiction, but it's by working with customers like JPL that we move our science from fiction to fact.

We're excited to be helping JPL with its own efforts to evolve space exploration, but what we're equally excited about is helping JPL improve its ability to focus on upfront ideation. Optimization and ideation across whole systems of parts is the next thing we'll be moving from fiction to fact. Optimization and ideation featured in the story of our work with Daiwa House, too. They balance optimization to build things better with ideation to build better things. And we can learn a lot from their approach as we think about how designing and building are converging, and where generative design can bring value to the built environment.

As we saw with Atkins, in this new era of automation and data flow Autodesk cannot be an island unto ourselves. Which is why we partner on research with customers like JPL and Daiwa House. It's also why we partner with other software companies within your ecosystems. We know that you all have to move data between systems from multiple companies, and we're embracing that fully.

Last year we talked about our partnership with Esri and how that would help you move BIM and GIS data together. And you'll hear about how this is bringing value to Mott McDonald in tomorrow's AEC keynote. But the first thing we're going to talk about tomorrow morning is a brand new exciting partnership with Unity Technologies to help connect VR and AR to your data in the tools that you use today. So you definitely don't want to miss that.

In an era where even the nature of change is changing, it's not always the application that creates the value, but instead the collective behavior of everyone that is plugged into it. We've just seen some very powerful stories about the rapidly emerging future of data and automation. As you absorb that future, you, our customers, should want more than just a place to store your data, more than just tools to create drawings or models. You should expect systems that will grow more intelligent over time, that will waste less human and physical resources, and will enable you to make a better business and a better world.

That's the future which is emerging, and that's the future we're accelerating for you now. Change and emergence are also big themes of our next speaker. She's a noted business leader and author. Please welcome to the stage Beth Comstock.

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BETH COMSTOCK: Good morning, so great to be here with all of you. So over the course of a 30 year career in business I may change my job. I've learned about change from working across industries ranging from media, to manufacturing, energy, health, transportation. I made it my job to get out there and look for the points of connection-- the points where the rules get rewritten, where the future emerges.

And throughout my time at these different industries and leading some of the companies I've led, one of the biggest revelations that I've made is that the nature of change is changing. I say welcome to the emergent era. Well, what's driving this? First, information is everywhere and it's getting there a lot faster. You know that.

Consider this. The world will never be slower than it is right now. A little moment to celebrate maybe, or panic. 50 years ago the life expectancy of a Fortune 500 company was 75 years. Today it's 15. That's pretty sobering.

And I also want to tell you it's not your imagination. If you feel you're walking faster, the reality is you are. It's been documented in cities we're walking 10% faster than we did a decade ago. The world is also hyperconnected. It's changing our business, but it's also-- and importantly, I think, to think about-- it's changing our behaviors. We've seen the impact of nearly 5 billion people who've been connected via the internet, and now we're in the very early stages of a connected machine world where more machines come online and more connected things start to emerge.

Well, all of that adds up to a lot more complexity. We've seen the same drivers of change as we always have-- technology, geopolitics, of course human behavior. Now all of this is exaggerated by our instantaneous digital nervous system. It's a way to think about it, it's our nervous system. It's effectively reorganizing people, money, information, things.

As the data flow connects to objects, we're going to see the same kind of disruption from the digital world now to the physical world. So despite the rise of digital, the physical world isn't going to go away. It's just not. It still matters. The very things we make-- both what we make and how we make them-- those are going to change.

The way we work with the things that we make and the way we work with each other-- that, too, is going to change. Only by combining digital capabilities and physical world expertise will we be able to ride that wave of change to the future. And I think this is what people often miss. We talk a lot about digital transformation, but it's not just about the digital. It's about wholesale transformation.

For one thing, when you start to look at digital you start collecting new data. The first thing that starts to emerge from all the noise is new insight. I like to think about Waze. Waze is an app that started out by giving its users an efficient route home in exchange for real time traffic information. But armed with data from 50 million users, Waze is now helping cities operate how they manage their traffic lights. So in effect, Waze went from a traffic app to now infrastructure management service.