Expert Insights on the AI Sector in the U.S. – Interview with Eva-Maria Olbers

Publish Date:  July 10th, 2023

[Editor’s note: The interview took place in early January 2023]

Eva-Maria Olbers is an Operating Partner at Point72 Ventures in San Francisco where she focuses on Artificial Intelligence and DeepTech investments. She works closely with AI startups in the US and globally, including in the Robotics, Natural Language Processing, and Voice AI space.

Originally from Germany, Eva is passionate about connecting the startup and VC ecosystems between Berlin and Silicon Valley. Prior, Eva worked at The Boston Consulting Group, the Machine Learning startup Knewton, and at the Microsoft Ventures Accelerator in London. She holds masters degrees from the London Business School and the Harvard Kennedy School of Government. 

While at Harvard, Eva co-founded STEMgem, an IoT company aiming to engage teenage girls in the STEM fields and winner of the Harvard President’s Innovation Challenge. Eva is a World Economic Forum Global Shaper and a selected invitee to the Annual Meeting in Davos. She is conversational in seven languages, including Spanish, French, and Mandarin Chinese. In her free time, Eva loves to mountaineer, ice climb, and backcountry ski.

We were thrilled to have Eva-Maria Olbers share her insights on the AI sector in the US and what Berlin companies should watch out for when venturing into the U.S. AI market. 

Did you always have a passion for AI? How did you get to where you are today, working as an Operating Partner at Point72 Ventures in San Francisco?

I have always been fascinated by the concept and the possibilities of Artificial Intelligence, but I really started to develop a passion for the space when I joined a Machine Learning startup in the education space in my early twenties. Here I learned how powerful AI/ML models can be specifically in the education sector, and how AI can improve and enrich existing applications and industries. 

After my experience of working in the AI startup sector, I was excited to switch to the investor side to monitor the emerging trends and work with inspiring founders around the world. I worked at the Microsoft Ventures Accelerator in London where I predominantly focused on the European and Israeli startup ecosystems, before pursuing a Masters degree at the Harvard Kennedy School, where I focused on AI policy and ethics. I decided to join Point72 Ventures and move to San Francisco to learn about current AI trends in the heart of Silicon Valley.

What problems do you deal with on a daily basis in your position (and how are they solved)?

In the AI and DeepTech investment space we look at industries and themes where we believe model-driven businesses can have a fundamental impact. We then assess which early-stage companies and potential founders are most interesting within that space and evaluate if they can be a good investment fit for us.

In my position specifically, I work closely with our portfolio companies post-investment. I work with our founders to support them in their growth strategy and help them overcome typical challenges that early-stage AI companies face, including setting clear goals and milestones, hiring key team members on both the technical and non-technical side, and developing a good company culture from the ground up.

What is one area of focus that has shifted in the AI scene over the years?

Historically, AI models have predominantly been used to analyze data patterns and to automate repetitive tasks. Based on my observations, there currently is a strong shift towards building creative and content generating applications on top of existing foundational AI models. In other words, developers and creators now use pre-existing models to create new use cases.

We see this, for instance, in the recent hype around “Generative AI”, which can loosely be defined as unsupervised learning models that are able to generate novel content (including text, images, and video) based on existing data. Increasingly, the creativity we see in AI comes from the interaction with systems, rather than from the systems themselves.

How would you summarize the main current trends in AI in a few sentences?

Next to Generative AI and Large Language Models (LLMs), I see a strong trend towards democratizing access to AI by moving to so-called “Low Code AI” or “No Code AI”. Low/No Code AI enables users who are not skilled data scientists or engineers to develop applications and test ideas quickly.

I have also observed an increased demand for explainable AI, since end users are increasingly asking for greater transparency around how AI models work and which data sources are being used. The necessary discussion on data bias removal and AI ethics has continued to grow in importance on both sides of the Atlantic.

What trend in AI are you most excited about?

Personally, I am very excited about how AI can be leveraged in systems and processes which humans are dependent on, but which have shown shortcomings when put under pressure, as for example during the COVID-19 pandemic. Many of the world’s supply chain processes as well as manufacturing systems were heavily disrupted, while, in parallel, a global workforce needed to reinvent itself and adapt to be fully or partially remote.

I am excited about the possibilities of AI to improve and automate critical processes and think that there is great potential in areas including supply chain automation, next-gen manufacturing, and Future of Work applications.

How important is Foreign Direct Investment & Innovation from overseas for the AI industry in your opinion?

I believe that a direct investment and innovation exchange between different ecosystems is critical in ensuring growth in the AI industry. Looking at the United States and Germany specifically, we see greater access to capital and venture funding in the US, as well as beneficial legal and policy incentives, making starting and funding a company comparatively easier for early-stage founders based in the US. 

Germany, by contrast, has fundamental strengths including sector specialization in manufacturing, automotives, and semiconductors. The country provides strong access to talent and research hubs centered around its universities, benefits from access to generous government funding, and its startups excel at being highly adaptable in their go-to-market strategies when expanding to other European countries. 

I also observe interesting differences in the type of innovation that comes out of the two ecosystems, in AI and beyond. The US is known for producing rapid innovation, while Germany is excellent at incremental innovation, where processes are improved – incrementally – over a long period of time. 

This is directly embedded into Germany’s education and labor system which encourages vocational training at a young age and benefits from employees often staying with a company for a long period of time, allowing them to perfect a certain mastery and/or process. 

I increasingly see US investors seeking these types of innovation in Germany, and vice versa, many German startups are still relying on expertise and capital investment from the US. The two ecosystems are complementary and unique in their own right, and an ongoing innovation and capital exchange is fundamental to maximize their potential.

Take the word “venture capital” for example. “Venture” implies something new and positive, a beginning, an exciting undertaking. The translation of this word in German is “Wagniskapital” which focuses on the potential hazard and risk element of startup financing. 

Is there anything in particular that companies and startups from Berlin could have in mind while looking at the AI industry in the U.S.?

Startups from Berlin should focus on the unique sector and innovation strengths they bring to the table and understand what they can contribute to the US-based AI industry. Further, they should be aware of some fundamental cultural differences, especially when raising capital in the US. Take the word “venture capital” for example. “Venture” implies something new and positive, a beginning, an exciting undertaking. 

The translation of this word in German is “Wagniskapital” which focuses on the potential hazard and risk element of startup financing. This subtle but important difference also shows in how American and German entrepreneurs typically pitch to investors. 

In my personal experience from hearing both American and German founders pitch to me on a daily basis, the former often have more assertive pitches (“this will work” vs. “this might work”), have more aspirational growth projections, and tend to ask for more money, while German entrepreneurs are often more conservative in their pitches and growth projections and ask for less funding at every stage of company growth. 

Finally, I have observed a cultural difference in how failure is being perceived. In Germany we often try to hide our failures, while in the US and in Silicon Valley specifically it is accepted and even celebrated to have started a company that eventually failed, as long as the founder can demonstrate that he/she has developed learnings and resilience from this experience. 

These are just a few of the subtle but important cultural differences that I would encourage Berlin startups to be aware of when looking at the AI and VC industry in the US.

Finally, what are the biggest obstacles that need to be overcome now to pave the way for the trends you have described? 

In order for the most promising AI trends to come to fruition, I believe we need to ensure that there is trust in the technology and that the best founders and startups can be supported effectively. I also think it’s vital that there is an adequate amount of transparency to allow for potential bias removal in the datasets being used.

Lastly, in order for AI technology to be as impactful as possible, I think we need to allow for a “human in the loop” element in order to ensure that there is a high degree of ongoing interaction between the technology and its users.