The digital divide: why Germany lags behind

Germans know how to build. They build things exceptionally well. Cars, physical infrastructure, industrial robotics, homes, appliances…all serve as testaments to stellar German engineering. To my surprise, though, this mastery in engineering often falls short in digital infrastructure and technology sectors that we expect to power the future. Put simply: Germany lags behind.

the problem isn’t siloed to Germany alone. It is endemic to Europe as a whole. According to the discussion paper, Tackling Europe’s gap in digital and AI, by Mckinsey Global Institute (MGI), “Europe’s average digital gap with the world’s leaders is now being compounded by an emerging gap in artificial intelligence… Without faster and more comprehensive engagement in AI, that gap could widen…”

At face value this is somewhat of an anomaly. A nation that revolutionized the audio and music industries with MP3s; home to the world’s third largest enterprise software company, SAP; and deploy far more robotics in industrial manufacturing than China and the United States combined, now lags behind in key technology sectors, but pronouncedly so in digital and artificial intelligence (AI). We could dissect and examine the technical grounds to explain this phenomenon; we could even formulate reasoned solutions supported by strong data. But data absent of context doesn’t always paint an accurate picture. Until we gain a contextual understanding of the dynamics at play, we may never find the pulse of the matter.

Context matters.

In Germany (Deutschland), more often than not, everything works like a well-oiled machine. Things are reliable, and they are orderly. Germans are also incredibly disciplined. To see this in action, one only needs to observe how they dutifully sort their rubbish—paper goes in one bin, plastic goes in another, all else in another. On the subway (U Bahn) and suburban trains (S Bahn) tickets are almost never checked, yet everyone still purchases one. (I know they do because the two times I’ve witnessed random checks, in a full train of commuters, no one cheated. They all had tickets.). Even German politics is significantly more civilized and orderly than the chaos, bad dealings and dysfunction that typically define the global political landscape. Recently, Annegret Kramp-Karrenbauer, heir apparent to Ms. Angela Merkel, made a series of poorly thought through statements about a YouTuber. A national scandal ensued, and some even called for her resignation. You see, Germans, it appears, are neither disruptive by nature nor receptive to disruption. Sure, this hasn’t always been the case historically; but in today’s Deutschland, order, discipline, restraint, decorum and structure reign supreme. However, this might actually be part of the problem. This lack of rebellious and disruptive streak may very well be a contributing factor to why Germany lags behind the United States and China in disruptive technologies.

Digital innovation is largely driven by disruption.

Unlike Germans, Americans, by nature, are more open to disruptive ideas and efforts. To an extent, this also holds true for Chinese industry, despite being largely government controlled. It is partly due to this disruptive core why significantly more innovation, particularly in the digital space, has provenance in the United States and China. Let’s be clear, though: the problem isn’t siloed to Germany alone. It is endemic to Europe as a whole. According to the discussion paper, Tackling Europe’s gap in digital and AI, by Mckinsey Global Institute (MGI), “Europe’s average digital gap with the world’s leaders is now being compounded by an emerging gap in artificial intelligence… Without faster and more comprehensive engagement in AI, that gap could widen…”

China and the United States dominate big data, not because they possess more technical expertise, but because they operate in markets that are more conducive to digital and AI innovation.

European culture and laws

Another piece to this contextual puzzle lies in the fundamental differences, culturally and legally, in how Germans (and Europeans as a whole) engage with technology. China and the United States dominate big data, not because they possess more technical expertise, but because they operate in markets that are more conducive to digital and AI innovation. At its core, big data is intrinsically what powers AI. The US and China view data, including personal data, as a marketable commodity to be gathered, stored, sold, shared, parsed and manipulated at will. The lack of regulations in these markets is what largely allows these innovations to thrive, often unchecked. Europeans, on the other hand, treat data differently. Europe has considerably higher legal barriers in the big data space. With higher restrictions, digital innovation vis-à-vis big data and AI becomes increasingly more difficult. Stricter data protection rules will continue to impede many attempts to keep up with markets that aren’t guided by the same restraints.

The mighty unions.

Now, at the risk of coming across as anti-unionist, it is important to note that I am neither advocating for nor against them. However, one needs to examine the indirect effects they have on advanced AI innovations and the prospect of job automations that follow.

Workers skilled in antiquated positions made redundant by AI automation won’t necessarily be ideal candidates for retraining. Pockets of job insecurity and redundancies are therefore inevitable.

The most obvious (and discussed) considerations for AI will be the long-term implications for jobs. However, a uniform scenario won’t play out for all labor markets. In Germany, for instance, automation, thus far, has been well received. Of course, there are many factors at play, but it is largely due to conditions that may not be possible elsewhere. For example: As a powerhouse manufacturing hub, Germany is both a large-scale consumer and a large-scale supplier of industrial automation. It builds it’s own robots and supplies many other markets too. As a result, job loss due to automation is significantly lower in Germany that elsewhere.

The data is also quite optimistic. AI innovations are likely to create (perhaps even as many as they eliminate) a considerable number of entirely new jobs. These jobs will be wholly dependent on new skill-sets in order to engine the new digital economy. However, there is no certainty or indication that this will hold true across the board. Workers skilled in antiquated positions made redundant by AI automation won’t necessarily be ideal candidates for retraining, nor is it clear that they will have enough transferrable skills to pivot to other industries. Pockets of job insecurity and redundancies are therefore inevitable.

This then leads to an important and final—yet surprisingly overlooked—caveat to consider: Unlike China and the United States, labor unions in Europe exert significant influence and wield considerable power. In highly unionized markets like Germany or France, for example, the idea of “job reallocation” may not as easily take root as it does/will in largely non-union China and increasingly de-unionized United States. AI will certainly drive new business models and an entirely new talent pool of technical skills. However, this idea will be a harder sell to union-centric Europeans whose first instinct is to protect existing jobs at all costs. The question then becomes: Is there and will there be enough buy-in from European labor-unions, or will there be overwhelming pushback? If the latter—and there is a significant chance that it might be—then we go right back to square one. How does Germany (and Europe as a whole) bridge the digital and AI gap if Europe must operate under different conditions?