The Artificial Intelligence (AI) revolution was ignited more than half a century ago. In the last decade, AI has grown from an academic scientific field to start being a practical part of our everyday lives. The most common AI business strategies we see are built around data. We believe that proprietary data is currently the most strategic moat for AI companies, but in the coming years, it will become less of a unique asset, making proprietary data differentiation less sustainable. Therefore, we expect a shift in focus, from data-based AI strategies, to knowledge-based AI strategies.
The big data advancement, facilitated by the deployment of numerous sensors, internet connectivity and hardware and software improvement in computational power, communication abilities and digital storage, have enabled AI to scale from small academic research projects to large enterprise production applications. Essentially, big data required sophisticated AI models to analyze and derive knowledge and insights, while the AI models needed the critical mass of big data for training and optimization. Hence, at present, data is often perceived as a sufficient strategic moat for AI startups. As venture capital investors, we see this phenomenon routinely. In recent years, we have seen many startups that place data acquisition at the heart of their business strategy. An increasing number of such companies emphasize the unique data sets they have acquired and their long-term strategy for acquiring additional proprietary data – as a sustainable barrier of entry. Moreover, as AI tools and AI-as-a-service platforms have commoditized the development of AI models, and public data has become ubiquitous, the perceived need to build and defend a data moat has become palpable.
In today’s technology ecosystem, the markets have increasingly rewarded companies with leading AI programs and control over proprietary data – as a substantial and sustainable competitive advantage. Companies such as Google and Netflix have developed and curated massive and authoritative datasets over a long period of time, while many other companies struggled in vain to match their success. An example is the massive disruption of rival media service providers and production companies, which were outmaneuvered by Netflix’ sophisticated data strategy.
Nevertheless, due to expected advancements in the ability and willingness to exchange data, we believe that within a decade, proprietary data moats will be less sustainable. While data will still fuel the AI value engine, AI business strategies will be increasingly focused on knowledge.
Moving Up the AI Value Pyramid, towards the Knowledge Layer
The AI value pyramid is based on data and driven by knowledge. While today “we are drowning in information but starved for knowledge”, we expect moving up the AI value pyramid, towards the knowledge layer. Indeed, we have begun to see advances that will foster and accelerate this trend by creation of data exchanges. We expect that data exchange will be facilitated by a combination of increased feasibility and a willingness to share commoditized data in return for valuable knowledge. In summary, data will become more plentiful, available, reliable and standardized and inexpensive – the perfect definition of an ideal commodity. Using data as a sustainable barrier of entry will be more difficult in the future.
The increased feasibility to share data will be accelerated by the proliferation of data sources through the Internet of Things (IoT). In addition, there are new techniques, protocols and standards for pooling, sharing and exchanging data. Looking ahead, the increased ability to share data will become truly significant when there is incentive and a growing inclination to do so. As AI undermines and disrupts legacy competitive barriers to entry, many organizations relentlessly attempt to collect their own proprietary data and monetize it. Alas, this data acquisition and utilization is neither easy, nor fruitful and therefore creates strategic dissonance. This is because, while AI is increasingly indispensable for most organizations, it’s not part of their legacy skills or core expertise. In addition, the chronic and enduring shortage of engineers, developers, product leads and managers trained in AI sharpens this dissonance and leads to a solution preference for data sharing with the goal of knowledge exchange.
An example of the combination of ability and willingness creating through the exchange of data for knowledge generation is the new proposal by the European Union, to create “a single market for data,” in order to empower people, business and organizations to make better decisions based in insights from non-personal data in order to compete with the current tech giants.
Another factor contributing to data moats becoming less sustainable is the invention of novel data solutions which enable using smaller sets of data for training models. Synthetic data solutions (for example, with Generative Adversarial Networks) and other minimization techniques, like data augmentation, might allow companies to create disruptive AI products, without huge amounts of data.