At the Rise of AI Summit 2020, Vanessa Cann, Managing Director of the German AI Association (KI Bundesverband), presents findings from a study run with the German Startups Association, Telekom hubraum and the Institute for Applied Artificial Intelligence in Stuttgart. She argues Germany has strong preconditions, ranking second in AI research behind China and fourth in industrial GDP, but still fails to translate research into AI products, with only two German companies appearing on a widely used list of the top 100 global AI startups. She points to a large venture capital gap, a scarcity of AI startups compared with Israel, and weak connections between startups clustered in a few cities and the small and mid-sized companies that make up most of the economy. She proposes four steps toward AI leadership: clear and proportional regulation, more education on AI and entrepreneurship, direct state investment in young companies, and incentives for investors and established firms, then details her association's regulatory recommendations before taking audience questions on incentives, liability and why the well-known funding problem persists.
Key takeaways
- Cann says only two German companies appear on a widely used list of the top 100 global AI startups.
- She cites a 36.3 percent gap between the 57.5 percent of German AI startups wanting VC funding and the 21.1 percent that get it.
- Israel, about the size of Hesse, has roughly 1,300 AI startups versus Germany's 300, with 30 times higher per-capita AI investment.
- She proposes regulating AI through existing sector rules and measuring it against human performance rather than 100 percent accuracy.
- She says SMEs, 90 percent of the economy, rarely meet AI startups because startups cluster in a few cities near VCs and tech universities.
Detailed notes
Strong assets, a missing product layer
Cann says Germany ranks second in AI research, just behind China and ahead of the US, and fourth in industrial performance measured by GDP, calling both genuine assets for its AI ecosystem. Despite this, she says Germany is not world class at building AI products, noting that ecosystem maps of the top 100 global AI startups tracked by CB Insights list only two German companies. She attributes part of the gap to how research spin-offs are funded: startups founded out of research institutions often have to share revenue or give up significant equity to the institution, which she says creates a weak foundation for raising later-stage venture capital.
The funding gap with Israel
Cann contrasts Germany, with around 300 AI startups, to Israel, a country about the size of the German state of Hesse with roughly 1,300 AI startups. She says German AI startups do receive more funding than the average German startup across all sectors, but 57.5 percent of them want venture capital funding while only 21.1 percent obtain it, a 36.3 percent gap concentrated in later-stage capital; per-capita investment in AI companies is around 30 times higher in Israel than in Germany. As a result, she says German AI companies tend to stay small and under-invest in marketing and investor visibility, unlike companies in Israel and the US that use funding to scale and internationalize.
Startups and SMEs rarely meet
Cann says three of four German AI startups already cooperate with established companies on a B2B basis, treating German industry as an asset, but that small and mid-sized companies, which make up 90 percent of the economy, rarely meet startups because startups cluster where venture capital and technical universities are, mainly Berlin, Munich, Hamburg and Karlsruhe. She says only about one in seven companies is currently working on AI products, though she notes improvement: in 2018, 87 percent of established companies said AI was not relevant to them, while now 22 percent are planning an AI project and 6 percent are already using AI.
Four steps toward AI leadership
Cann outlines four priorities: clear and proportional regulation; more education on AI and entrepreneurship, from schools through universities to SMEs and corporates, so that research talent also learns to commercialize; direct state investment in young companies, noting the federal government has earmarked five billion euros for AI that young companies have so far barely accessed; and tax and other incentives that make it more attractive for investors and established companies to work with AI startups.
A blueprint for proportional regulation
Cann details the association's regulatory recommendations: proportionality, since risk depends on the use case rather than the algorithm, so high-risk applications should be regulated while low-risk ones keep a light bureaucratic touch; measuring AI against human performance rather than an unattainable 100 percent accuracy standard; regulating AI through updates to existing sector rules, such as in mobility or health, rather than writing new AI-specific law; treating a single general AI regulation as impractical, since AI is hard to define legally and not all software is labeled as containing it; and ensuring regulation does not add extra bureaucratic burden on SMEs and startups, which she calls the main drivers of innovation.
Q&A highlights
Challenged on why SMEs need extra incentives rather than being drawn in by promising research, Cann says many SMEs still do not understand AI and need education and peer learning from other SMEs, not only from startups. Asked whether regulation will deter founders, she says clarity matters more than the amount of regulation, since current law predates the digital transformation. Asked about liability when AI itself acts, she calls it an unresolved regulatory issue needing clarity. Asked about the main bottleneck for scaling startups, she says it is partly cultural: investors want proven numbers and a finished product rather than backing a vision, unlike in the US, Israel or China, and says the state must also fund young companies directly. Asked whether founders should consider leaving Germany, she says startups already look abroad for VC funding and that Germany must help companies scale and exit domestically rather than losing them. Asked why the well-known funding problem remains unsolved, she says there is a gap between agreeing on the problem and acting on it, and points to the promised five-billion-euro federal investment as a step toward closing it.
Quotes
It's not that the talents are missing, it's about an entrepreneurial mindset that we have to get going.
Vanessa Cann
I think we have to invest more in visions than in numbers.
Vanessa Cann
Questions answered in this talk
How does Germany's AI startup ecosystem compare to Israel's?
According to Vanessa Cann at Rise of AI 2020, Israel, though about the size of the German state of Hesse, has around 1,300 AI startups compared with roughly 300 in Germany, with per-capita AI investment about 30 times higher.
Why do German AI startups struggle to raise venture capital?
According to Vanessa Cann at Rise of AI 2020, 57.5 percent of German AI startups want VC funding but only 21.1 percent get it, a gap of 36.3 percent that is especially pronounced for later-stage capital.
How does the German AI Association want AI to be regulated?
According to Vanessa Cann at Rise of AI 2020, the association wants proportional, risk-based regulation built on existing sector rules rather than a single general AI law, with AI measured against human performance and no extra bureaucratic burden placed on SMEs and startups.