Why Investors Should Focus More On The Infrastructure Supporting The AI Revolution
AI has been heralded as the catalyst for a new industrial revolution. While the potential for massive impact is very real, venture investors looking to capitalize on growth ought to spend more time considering the enabling infrastructure.
Although applications are myriad and diverse, from drug discovery to driverless cars, practical adoption in the enterprise has been lackluster. Only 1 in 20 business leaders would describe their companies as “implementing AI widely across the organization.”
An infrastructure-first approach to investing has the potential to yield greater venture returns with a lower risk profile. Looking at the smartphone market, for example, it’s unlikely that an investor in 2005 could have accurately projected that today Google, an internet search engine, would have a mobile business that is 5x larger than Nokia’s. That said, making broad investments in major chip manufacturers would have accurately identified Qualcomm as being a provider whose parts have supported the rise in mobile technology.
Innovations in AI are exciting, but it’s less difficult to identify and bet on, the technologies supporting AI rather than predicting who will provide the voice assistant of the future. The starting point for identifying these investment opportunities is the deconstruction of the AI workflow—extracting each step in the process, from aggregation to deployment and seeking efficiency, scale and access.
The process of building and deploying AI-tools can be bifurcated into two steps: training and inference. Training is the process by which a framework for deep-learning is applied to a dataset. That data needs to be relevant, large enough, and well-labeled to ensure that the system is being trained appropriately. Also, the machine learning models being created need to be validated to avoid overfitting to the training data and to maintain a level of generalizability. The inference portion is the application of this model and the ongoing monitoring to identify its efficacy.
Within the aforementioned stages of development, we can envision a more comprehensive development lifecycle. Those stages are as follows: data acquisition, data preparation, training, inference, and implementation. For this evaluation, the three most interesting stages are acquisition, preparation, and implementation, as they’ve arguably garnered the least amount of investor attention.
The training process is dependent on data that is appropriate for the defined business objective. Where do businesses that are developing internal models acquire this data? For some, this data is internal customer data. This is particularly relevant for large consumer companies that have been collecting data for some time. Using historical customer data is, generally, an inexpensive proposition but can come with its issues concerning data cleanliness and completeness.
What do companies without historical datasets do to train their models? They either lean on publicly available datasets or they purchase data directly. Providers like Narrative are emerging that are primarily focused on selling clean, well-labeled datasets explicitly for machine learning use cases. As of now, the market remains relatively fragmented and difficult for organizations to get the data they need.
