German AI Startup Landscape (2020)
The third annual AI Startup Landscape

Why we are building a start-up landscape

Andrew Ng, one of the most renowned AI researchers, says: ‘AI is the new electricity.’ We can only imagine the impact AI will have on our daily lives in ten years' time. As a general-purpose technology, AI has unlimited applications. Many applications have the potential to augment human capabilities, significantly improve the quality of products and services, make processes more efficient and environmentally friendly, and ultimately free us from many tedious tasks.

Currently, the US and China are leading the way in AI adoption. So it's no surprise that most of the world's top 100 AI start-ups come from the US or China (see CB Insights AI 100). Although Germany is one of the world's strongest industrial nations and has a particularly strong network of SMEs, it is underrepresented among the top AI start-ups.

In 2018, not a single German AI start-up was included in the list. In 2019, only one Berlin-based start-up (Twenty Billion Neurons) made it onto the AI 100 list. This is alarming, because start-ups reflect a country's innovative strength. Germany must do much more to avoid falling behind. As Alan Kay said, ‘The best way to predict the future is to invent it.’ That's why appliedAI – together with our more than 40 partners from academia, government and industry – has set out to create an ecosystem in which AI start-ups can thrive and help shape the future of AI for the benefit of society.

We support German AI start-ups by helping them identify pilot customers, access data, find talent and select hardware resources.

Together with our contributors, the technology companies NVIDIA and Google, as well as eight leading venture capital firms (Digital+ Partners, Earlybird Capital, eCAPITAL, High-Tech Gründerfonds, HV Holtzbrinck Ventures, Lakestar, Speedinvest and Unternehmertum Venture Capital Partners), we screened more than 1,000 start-ups to find the best AI start-ups in Germany. The 247 AI start-ups that stood out are shown in the following landscape.

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Download the high-resolution overview of the AI Startup Landscape 2022 here: 

Methodology

The AI start-ups included in the landscape are private companies founded after 2009 that have their headquarters or central development activities in Germany. They focus on machine learning (ML) or make significant use of ML. The selection process can be summarised as follows:

• The start-ups are compiled from various public (e.g. Crunchbase, LinkedIn) and private (VC network) sources to create a comprehensive long list.
• The start-ups are evaluated based on data, talent, AI methods, scalability and overall quality, and then clustered (see logic for clustering).
• The start-ups are first evaluated by our AI engineers and strategists (‘valid’, ‘rising’, “longlist” and ‘rejected’) to create a shortlist.
• The shortlist is independently evaluated and assessed by our contributors (jury consisting of Digital+ Partners, Earlybird Capital, eCAPITAL, Google, High-Tech Gründerfonds, HV Holtzbrinck Ventures, Lakestar, NVIDIA, Speedinvest and Unternehmertum Venture Capital Partners). The feedback is synthesised and the final result is visualised.

Clustering-Logik

The logic for clustering is based on Shivon Zilis' Landscape for Machine Intelligence. It is developed from the perspective of companies that want to use AI in their business:

• Enterprise Functions: Increase the productivity of existing tasks – support your employees with ready-to-use, AI-enabled tools that make their daily work easier and thus increase productivity.
• Enterprise Intelligence: Utilise new data sources – gain new insights that were previously too difficult or too expensive to obtain using conventional methods.
• AI Technology Stack: Build products with ML – give developers the tools they need to create and use machine learning software and gain a competitive advantage.
• Industries: Use AI-first products – leverage and collaborate with start-ups that offer industry-specific products and services using machine learning.

Locations

Facts and figures

As in previous editions, Berlin and Munich continue to dominate the German AI start-up landscape. The two cities are home to up to two-thirds of German AI start-ups. Berlin leads the landscape as the German city with the largest number of start-ups.

Funding

Facts and figures

Since 2010, the AI start-ups shown have raised a total of €2.2 billion in funding. The average funding received per AI start-up rose by 24% in 2020. While Berlin continues to lead in terms of the number of AI start-ups, Munich ranks first with average funding of €27 million per company, ahead of Berlin with €9 million.

Sector

Facts and figures

Around 40% of the selected start-ups operate across multiple sectors. Among companies with a sector focus, we are seeing a dominance and continuous growth of AI start-ups in the following key German industries: manufacturing, transport and mobility, and healthcare. In the manufacturing sector in particular, we are seeing strong growth with eight new start-ups in the 2020 landscape compared to 2019.

Business Functions

Facts and figures

When it comes to process expertise, German start-ups tend to be more active in areas such as marketing and customer service and less so in strategically sensitive areas such as IT and security. This may be due to the fact that customers (large companies) avoid working with start-ups on strategically sensitive issues and instead prefer large, well-known international companies.

Rights & permission to use

We consider it our duty to pass on this information. Feel free to use this landscape as part of a presentation, lecture or project. This is permitted and encouraged, as long as you always use the visual representation and refer to us accordingly. If you change anything in our landscape, you must mark this as your own modification. The content of this landscape is published under the CC-BY 4.0 licence.