This is Part 2 of our podcast on China and the AI race. In this second part of our podcast, we continued discussing with Olaf J. Groth (Global Strategist, Professor, Author, and CEO) on China’s progress on AI and its implications for global affairs. This part of our discussion focused on access to global data, the emergence of a global data economy, and the tools that could be employed to build such an economy.
The following are key points from our discussion with Olaf:
Olaf called out three parts to the cognitive triad in the AI context: (1) compute power (chips), (2) AI models, and (3) Data. One needs to harness parts of these three pillars to develop AI applications. Hence, data is a critical part of the AI equation. Data is becoming the central point of Geo politics and Geo-economics.
In the future, we expect to see more discernment from countries regarding where their citizens’ data is going and how it’s being consumed. Data will become the key object of negotiations around the world, and we saw this instantiated when Elon Musk went to China and requested that the data gathered from Tesla cars in China be moved to the United States. A world in which one country or region will have complete technological sovereignty over all three pillars (Chips, AI Models, and Data) will not be efficient. A certain amount of data must inevitably cross borders to be processed by the best tools and chips and then bring the best solutions across the borders again.
These macro trends in data will lead to agency-assured data in the future, and as a result, the global data economy will grow. This new data paradigm will enable individuals to contribute data and feel secure and trusted that the data will be used for the right purposes and according to one’s preferences.
Several tools will be developed along the way to help build that vision of the global data economy. One of the interesting approaches Olaf shared was watermarking an individual data footprint, disaggregating that footprint, and then reassembling it into data clusters that meet specific market demands. Those data clusters can then be put into the markets for appropriate bids. These clusters will be licensed to be given away and will be trust assured. This approach will lead to a richer experience in creating and allocating value in the data economy.