Historian Niall Ferguson and technologist John-Clark Levin look to the history of oil exploration to assess the likely future of obtaining data for training AI models. While many have noted that data is a “new oil,” the authors extend the analogy to consider how market prices and production costs will interact to determine when previously unexploited sources of data are likely to be tapped. Turning to world affairs, they emphasize:
“Much like the geopolitical scramble for oil, competition for top-quality data is also likely to affect superpower politics. Countries’ domestic privacy laws affect the availability of fresh training data for their tech ecosystems. The European Union’s 2016 General Data Protection Regulation leaves Europe’s nascent AI sector with an uphill climb to international competitiveness, while China’s expansive surveillance state allows Chinese firms to access larger and richer datasets than can be mined in America. Given the military and economic imperatives to stay ahead of Chinese AI labs, Western firms may thus be forced to look overseas for sources of data unavailable at home.”
What do you see as some additional likely future sources of data for training and enhancing AI models?
As a race for new sources of useful data plays out, do you think countries may increase “national security” related restrictions on data exports? Do you think this is likely to play a significant role in geopolitics in the years ahead?
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Historian Niall Ferguson and technologist John-Clark Levin look to the history of oil exploration to assess the likely future of obtaining data for training AI models. While many have noted that data is a “new oil,” the authors extend the analogy to consider how market prices and production costs will interact to determine when previously unexploited sources of data are likely to be tapped. Turning to world affairs, they emphasize:
“Much like the geopolitical scramble for oil, competition for top-quality data is also likely to affect superpower politics. Countries’ domestic privacy laws affect the availability of fresh training data for their tech ecosystems. The European Union’s 2016 General Data Protection Regulation leaves Europe’s nascent AI sector with an uphill climb to international competitiveness, while China’s expansive surveillance state allows Chinese firms to access larger and richer datasets than can be mined in America. Given the military and economic imperatives to stay ahead of Chinese AI labs, Western firms may thus be forced to look overseas for sources of data unavailable at home.”
What do you see as some additional likely future sources of data for training and enhancing AI models?
As a race for new sources of useful data plays out, do you think countries may increase “national security” related restrictions on data exports? Do you think this is likely to play a significant role in geopolitics in the years ahead?