During the second quarter, which saw Tesla deliver an archive number of vehicles but fall short of Wall Street’s earnings estimates, the German firm scooped up 350,258 Tesla stocks, doubling its holdings to a complete of 772 nearly,972 shares. Interestingly, Commerzbank scooped up its new Tesla shares in the next quarter, a time when the electric car machine’s stock hit over two-year lows.

Following an initial quarter that dropped significantly below targets, Tesla shares were bombarded by one negative forecast after another. 10 for TSLA stock at one point. Yet, despite all this, as well as the insistence of Tesla critics that demand for the company’s vehicles is deceased, Commerzbank opted to almost double its investment in the electric car maker. In a way, the German banking giant likely has a unique perspective on the premium auto industry, being in a country that hosts a few of the world’s most iconic premium brands such as BMW, Mercedes-Benz, and Audi.

As such, its significant additional investments in Tesla, especially at the same time when the company’s shares were beleaguered for the most part, bodes well for the Silicon Valley-based carmaker. Tesla seems to be attracting some votes of confidence from notable investors as of late. Apart, from Commerzbank, former Tesla carry and longtime Shark Tank Judge Kevin O’Leary lately exposed on CNBC that he had purchased TSLA stock for himself, on account of Tesla’s remarkable capability to catch the attention of the best skill in the field.

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The market for data labeling, and image labeling specifically, has an especially low barrier to admittance for developing countries where English is not the first vocabulary since classifying images doesn’t require English literacy. This offers hope that we now have ways to drive a more internationally inclusive machine learning overall economy. But dedicated attention is necessary. Governments should research where in the machine-learning value chain they may have a comparative advantage.

Development agencies can form tools to aid such analysis, and they can invest in strategies to help slow starters better position themselves to participate in the device-learning value chain. Researchers can study which stage of the device-learning value chain creates the most value in order to determine whether any given country’s best opportunities for investment are in data collection, data storage, or another stage.

Such research must account for country-specific factors and the magnitude and comparative shelf life of the worthiness created at each stage. For instance, the profitability of operating an HDC directly depends upon local energy prices and data localization laws, among other things. Likewise, a hardware component might become obsolete after three years, while a well-trained data scientist may yield value for decades, possibly becoming more valuable over time with the benefit of experience. Unpacking the profitability of each stage for each country will not be easy, but it can help countries improve their competitive advantage in the age AI.

Finally, it’s important to notice that the physical concentration of machine learning value among the fast movers and even moderate movers will have first- and second-order effects on the distribution of prosperity and power within and between countries. Concentrating talent and wealth in certain countries will exacerbate economic inequality between countries likely.

A similar physical concentration of talent and wealth using cities could also impact land and housing prices, causing demographic shifts. Taken collectively, these first- and second-order influences suggest increasing inequality between and within countries-a different tendency than what took place during the last quarter century, when globalized commercial production increased inequality within countries but decreased inequality across countries. Policymakers must prepare now for the geopolitical outcomes of countries’ varied capabilities and investments in the machine learning value string. Charlotte Stanton is the inaugural director of the Silicon Valley office of the Carnegie Endowment for International Peace as well as a fellow in Carnegie’s Technology and International Affairs Program.

Vivien Lung is a senior policy analyst at Google’s Trust and Safety department. Previously, she was a research helper at Stanford’s Center for Security and International Cooperation and a consultant at Deloitte’s Global Transfer Pricing practice. Nancy (Hanzhuo) Zhang is an analyst for Cornerstone Research. Previously, she worked well for the World Bank or investment company’s Development Impact and Evaluation Unit, the Economist Group, and the Bill and Melinda Gates Foundation. Minori It is a diplomat at the Ministry of Foreign Affairs of Japan.

Steve Weber is a professor of political science and information at the University of California, Berkeley, and the faculty director for the Berkeley Center for Long-Term Cybersecurity. He is experienced in international relations and international political economy with expertise in international and nationwide security, the impact of technology, and the overall political economy of knowledge-intensive sectors software and pharmaceuticals particularly.