Newly built AI start-ups lead to investments surpassing FinTech and marketing

Generative AI models may attract the most attention in areas such as marketing automation and financial technology. But a recent analysis by A/O PropTech reports that investments in AI startups are growing faster both in terms of volume and value of investments. These investments have had a significant increase in the work to prepare new sustainability mandates.

As the name suggests, A/O PropTech is a venture capital investment company specializing in the built environment. Currently in the built world, AI is still in its early days, due to the predominance of unstructured data that is more difficult to calibrate and adjust than other domains. An additional reminder is that A/O PropTech takes a broad view of the built world, including property management and insurance of physical assets that others might throw into financial services.

Climate technology is the fastest growing AI application, including climate risk, ESG reporting and energy management. The largest deals are focused on the most mature segments, including real estate transactions, property management and construction.

Getting beyond the AI ​​sink

Catriona Hyland, research analyst at A/O PropTech, said they commissioned the recent study to see how the recent hype in generative AI was affecting start-ups in the built world. For now, generative AI has not taken off in the sector due to the many challenges of understanding unstructured data. In the near term, generative AI may play a much more important role in expanding datasets to lay the foundation for other tools.

She said they were trying to tease out some of the problems with AI washing that plagued previous research on AI startups. A 2019 study by MMC found that 40% of startups allegedly using AI did not. Further analysis revealed that this was primarily due to third parties claiming the AI ​​properties that the firms never claimed or corrected. Her team developed a large language model to analyze data from Pitchbook and Crunchbase for the analysis. Hyland explained:

I think quite often AI is used as a sort of buzzword when you have a startup where AI is the core component of that business. But I think in the built world, and probably in most industries, it’s used very much as a tool rather than a focal point.

London leads deals

The study found that over the past ten years, AI-enabled Built World startups in Europe and North America have received $18.6 billion in venture funding, almost half of which was in the past two years ($8.6 billion). And in both 2020 and 2022, venture deals in AI-enabled Built World startups overtook FinTech AI funding, reaching over 600 deals globally in 2021 alone.

London also saw the highest number of deals, while the San Francisco Bay Area saw more capital. London also saw more deals than Paris, Berlin, Dublin and Tel Aviv combined. When asked why, Hyland explained:

When we looked at the breakdown of the types of deals that took place in London, many of them were quite early and focused much more on areas such as property transactions and the financial aspects of the built world. This makes sense when considering London’s position in Europe as a kind of financial hub.

Structuring the unstructured

In the short term, they expect more focus on using AI to make sense of structured data rather than generating new building designs. Innovations in computer vision are proving incredibly important for scanning construction sites, tracking progress and automating the processing of insurance claims. But other types of generative AI are still in the early stages.

Jess Clemans, investor at A/O PropTech, explained:

The innovation around the big language models and image generation models was hyped in the media quite an awful lot, but we haven’t seen much of that filter into real estate technology yet. I think we’re on the cusp of a lot of it being used, but it’s very early days of this. What we’ve seen is a lot of other innovative AI going into embedded technology, but we’re interested in seeing where this goes and trying to get a better picture of how we think these new innovations are going to show up in the next generation of technology .

What is challenging is that buildings fundamentally follow the laws of physics and have a lot of regulation and logic around how they are structured and how they must be built. You can’t leave it entirely up to a machine to decide the output. We have seen companies using this approach. And the result was often very illogical bathrooms connected to kitchens, corridors that lead nowhere or windows that were not connected to bedrooms.

What we have seen as a slightly more successful approach in this area is to mix generative AI models with readable rule systems that the AI ​​must adhere to. So I think we’re going to see a bit of an amalgamation of algorithmic approaches with generative AI approaches in the space.

A big challenge is that developers are still trying to figure out how to specify things like build processes and build codes in a way that AI can understand. Today, most construction data is stored in paper documents or PDF documents. It will take considerable work to catch up. In addition, building codes may vary between cities and countries. Technical and subjective aspects must be considered with respect to what the local authorities will approve.

Clemans explained:

There is no real structured rule system. This is in such infancy that every company we’ve seen do this has invented its own system for assigning rules to elements. And it is generally specific. So we’ve seen companies doing this for the design of electrical or plumbing systems. And they have had to attach it in a slightly different way to someone who does architectural floor plans or architectural detail design.

But then the last piece of the puzzle which is very interesting and intricate is basic human logic. For example, if you decide to place an electric handrail on the ceiling, you may not want to attach it directly to the edge of the wall because it is challenging to attach screws for a human hand to get in there. But a machine would never understand that. That’s just an example of some of the finer intricacies that aren’t part of building code standards that aren’t part of typical construction documents, but part of human logic that needs to be translated into this going forward.

My opinion

The generative aspects of AI are getting all the press this year. But better data translations and alignment can provide more value in the short term. Improving workflows and processes for building and operating physical infrastructure will require finding better ways to understand data captured for other reasons.

The recent crop of generative AI applications arose from Google’s attempt to build a better translator between French and English. These transformer models can also be important for translating documents, designs and 3D data capture into digital twins.

The UK was one of the innovators in building information modeling (BIM) technology to organize data about the built environment. It will be interesting to see how this head start plays out with the UK government’s ten-year plan to make the UK a global AI superpower.