At Innovation Endeavors, we have invested in the intersection of emerging technology and the physical economy for over a decade. In the construction industry specifically — the largest portion of the architecture, engineering, and construction (AEC) lifecycle — the macro market conditions continue to increase the urgency for innovation. Construction represents about 8% of global gross annual output and 8% of the global workforce, and demand is projected to grow by 70% globally by 2040 — climbing north of $22 trillion — expanding to almost the size of the U.S. annual GDP today. With that backdrop, the past decade has already proven that this trajectory will be impossible to actualize with traditional approaches. In the U.S., the average number of vacancies in construction labor doubled from 2017 to 2023, and it’s expected that 40% of the pre-2020 workforce will retire by 2030. Amid shrinking workforces in advanced economies, labor alone cannot solve flat productivity, much less industry growth; we will inevitably need technology to meet the moment. 

AI has the potential to bring unprecedented productivity gains to the construction industry and AEC more broadly. The opportunity is compelling not only because the industry has historically been slow to adopt digital technology — and therefore, the potential for improvement presents distinctly compelling white space for startups — but also because AEC runs on a uniquely vast amount of structured and unstructured data. The amount of documentation is staggering if we include the full end-to-end labor of a commercial construction project, from architecture and design to project completion. The full lifecycle includes architectural drawings, concept designs, blueprints, engineering plans, BIM models, CAD models, site plans, shop drawings, materials specifications, cost estimates, project schedules, and more. Construction spends <1% of revenues on digital technology, less than a third of what’s common in industries like automotive and aerospace, yet the documentation for a large project typically spans tens of millions of pages, which are often physically printed out. This treasure trove of data, which documents the details of multi-year projects employing thousands of workers, is a valuable training set for LLMs, agents, and automation. However, because most of this data is not public domain and generally isn’t labeled, more generalized model providers are unlikely to win the day or be the most performant and reliable in producing geometric, volumetric, and spatial representations.

So, what value levers, AI applications, and startups are most exciting, and how can we buck the historical trend in AEC to adopt new technology and increase productivity? Some of our recent investments in AI x AEC — like Trunk Tools and Parspec — are pointing the way. Trunk Tools is building the brain for the construction job site, ingesting millions of project documents to create a knowledge graph that will power agents for search, task-based incentives, and beyond. Parspec is automating large portions of the pre-construction workflow by creating an advanced configure-price-quote and product search platform for distributors, digitizing procurement across the industry. Both are already selling to many of the largest builders and distributors in the U.S.

Earlier this month, we hosted a salon-style dinner with leaders from North America's leading general contractors, architecture firms, wholesale distributors, and startups to share what’s top of mind. 

Three broad industry themes emerged from the conversation:

1. Requirements for the data needed to train models are quickly changing, creating excitement and uncertainty: Recent progress in AI research around fine-tuning and few-shot learning has been remarkable. Every month, we see significant improvements in the ability to produce accurate results based on small amounts of labeled examples. As noted earlier, these developments are particularly important in areas like AEC, where training data is scarce and typically under lock and key within architecture and construction firms. This specialized data need is a major reason why the largest model providers won’t naturally be the ones to solve the unique challenges of geometric, spatial, and volumetric representations, and therefore, the opportunity for startups, along with legacy players like Autodesk, is compelling. Leveraging relatively small numbers of high-quality designs, blueprints, and engineering documents — combined with the power of ever-improving pre-trained models — will be a major unlock for the industry. 
2. Predictability is the core value driver for digital technology in construction: The construction industry is notoriously bad at predicting costs, timelines, and requirements. This presents a massive challenge for builders, given that their entire margin is the delta between their bid on the project and the final cost to complete construction. The ability to win in the bidding room and execute on that budget over a multi-year project lifecycle is top of mind for key decision makers. Machine learning approaches, along with generative AI, can provide dramatic improvements here, particularly given that, for the most part, the industry has not seen the uplift promised from the last generation of building information modeling (BIM) and cost estimation software. Stitching together materials, labor, equipment, overhead, contingency, and regulatory data will require fundamental improvements in how builders log, label, store, and access vendor and historical data. 
3. More repeatability, modularity, and “kit-of-parts” approaches are key to bringing down costs: Automate the 80% and then tune the 20% — this is the path forward, according to many innovators in AEC. Historically, each project has been treated like a special flower, with minimal learning and work transferred from project to project. Companies like Katerra failed in large part due to being stretched too thin across too many non-uniform and custom projects. There will always be the Burj Khalifas of the world, but most buildings look like countless buildings before. Customization creates challenges not only because it’s inherently more complex but also because it contributes to the huge amount of tribal knowledge living in the heads of experienced AEC knowledge workers, which is not easily accessible in any central repository. Soon, many of these wise men and women will retire. The historic challenges of costly one-off designs, repeating mistakes, and limited automation will worsen. The good news is some companies are already efficiently reducing complexity and deploying infrastructure for repeatability. Aro Homes, a verticalized home builder incubated at Innovation Endeavors, is taking a product-first approach and allowing buyers to experience their future home before they buy. Home-buyers can customize to their desires, much like car-buyers can customize their Mercedes, such that no two vehicles are the same but still benefit from significant cost reduction and high-end design. In Aro’s case, customers get an Olsen Kundig-designed home that normally might be out of their price range.

With these trends in mind, here are some of the innovations we are excited to see in AEC:

1. Models that can produce geometric and spatial representations while understanding design intent: Imagine if you could record a conversation with a client describing their vision and immediately produce initial concept designs. Imagine if architects could extrude from 2D to 3D effortlessly (i.e., as described by Autodesk research) and auto-generate parametric logic, radically collapsing design cycles. Imagine if you could pull forward initial structural engineering reviews and bake them into the design stage, validating constructability and materiality much earlier in the process. These tremendous improvements are quickly becoming possible and will dramatically reduce costs and increase speed. 
2. New data representations, like entity component systems (ECS): Beyond the challenges of understanding geometric relationships and how to tokenize and normalize them, there is exciting potential for fundamentally new data and systems approaches in AEC. Historically, some of these techniques have been used mostly in video game development. ECS is a software architecture that follows composition over inheritance, potentially unlocking more modular, scalable, interoperable, collaborative, and simulated AEC workflows. This could solve a lot of today’s challenges that follow from siloed and heterogeneous systems and data types. 
3. Automating the shop drawing process: If construction has one master governing class of documents, it’s the shop drawings. Producing them is one of the most labor and cost-intensive parts of the lifecycle – when engineers, fabricators, contractors and suppliers translate vast amounts of documentation into detailed, technical instructions for the build process. With the right models and workflows, agents and automation could radically slash costs. 
4. Permitting automation: Permitting is one of the least glamorous but most painful steps in getting anything built. Local building codes, zoning laws, environmental regulations and safety standards create delays and friction, costing builders precious time and resources. Companies like PermitFlow and GreenLite are innovating here, and we expect to see new players going deeper into the value chain over time.
5. Tools to structure and classify the vast amounts of historical project data: Given the lack of centralized and accessible data noted above, the vision of a unified data model across materials, products, designs, fabrication, costs, and scheduling is tantalizing. If AEC is going to improve toward making original mistakes, builders and architects need to learn from everything they have ever done across the project lifecycle. Technology to make sense of the vast, multi-modal troves of historical data is rapidly improving and will help solve the lack of transferability across projects, as well as associated rework.
6. Collaboration tools that don’t unduly increase costs and complexity: Given the dozens of stakeholders in the lifecycle of a project across owners, architects, engineers, general contractors, subcontractors, fabricators, suppliers, and more, collaboration is a massive challenge in AEC. Tools like BIM have improved things, but builders note high implementation costs, steep learning curves, integration issues, and sometimes further delays. Agents can offer a new era of seamless collaboration as more of the workflow is automated and abstracted away. 

Historically, construction has been governed more by fear of loss than hope for gain. Founders building in the space must have a deep appreciation for the industry's relationship-based, risk-averse nature and recognize a fundamental asymmetry of risk in decision-making. Economic buyers across the value chain bear massive risks, from legal liability to hefty penalties for delays and, most critically, errors that cause rework. In the U.S., it’s estimated that rework alone costs the industry $50-100 billion per year. This wariness of new tech that might cause errors is not totally irrational, but it is precisely what’s holding back the solutions that could actually reduce rework in the first place.

Innovation will require high trust and easy-to-use tools. The industry has been pitched shiny objects, from drones to sensing and monitoring to project management apps, for decades. Few companies have been able to deliver scalable value, with incumbents like Autodesk and Procore continuing to dominate the market. Building excitement and adoption for new AI capabilities and aligning incentives within massive, slow-moving orgs will require technologists and industry veterans to join forces. Perhaps most critically, if AI can be understood as an augmenting force multiplier rather than a replacement risk, the AEC industry can become a leading beneficiary of the remarkable platform shift underway. If you’re working on bringing AI to AEC, we’d love to hear from you.