Q1: How are we adapting our investment strategy for global geopolitics?
Venture cases have global disruptive potential to it
Europe: covering smart charging, example AMPECO
DD: scenario analysis
Q1 - 1.) Global disruptive Potential
I believe most successful venture cases have a global disruptive potential to it
To get to an outlier that drives returns and ultimately moves the needle for us as an investor, true global scale is required
With that, we are almost always taking a global view
=> So not a lot has changed for us as a CVC unit
Q1 - 2.) My Europe example
This means for the long term. Of course, the path there can differ.
To give you an example, I spend several years as an investor in Europe, covering smart charging
We invested in a company called AMPECO, providing EV charge point management software
The product is globally applicable, and the company had a global footprint since we invested
But I believe resource allocation is important:
EV charging infrastructure in Nordics, Western Europe is very mature, having enough growth avenues
Other regions are catching up quickly
=> what’s the right time and scale to access those?
Q1 - 3.) Due diligence change
What has changed during due diligence:
we spend more time on running different scenarios for local impacts
We use scenario analyses: on things such as roll out timelines and local capacity planning
Not something new to us, but just more emphasis to it.
Q2: Where do you see real AI breakthroughs happening in this industry?
Breakthroughs will be coming from Foundational Models for the Physical world and not those for Language
Production world or driving experience
Q2 - 2.) Physical Foundational Models
Big problem: a) Data need b) Low procurement maturity
Data: real world production data, vehicle data required
Q2. 3.) Robotics and Customer Experience
Production world:
Robotics and Digital twin technology
Increase efficiency, shorten time to market, cut costs and get to more sustainable production
Vehicle data:
Understanding conditions around it; enhance safety based on that
Or be context aware for in-car interactions; adjust comfort settings or help make decisions based on driver behavior
Q3: Do you think there is still such a thing as defensibility in AI- or is speed to distribution the only moat that matters?
Defensibility will be coming from domain specific data, vertical applications and specific solutions
Engineering Workflow example
Supply Chain example
Past cycles
Q3 - 2.) Engineering workflows
Looking into an automotive company: tool chain is very complex (several different teams and engineering and design tools)
Access to the right data? Integration to Design and Engineering Tool Chain ? Access to existing Know-how?
=> Ultimately about automating and streamlining processes (With engineers focusing on value-driven tasks)
Q3 - 3.) AI driven engineering and supply chain mgmt
Supply chain management
Automotive industry has complex supplier networks
Predicting demand and spotting disruptions
Q3 - 5.) Past cycles
First value was accruing in the infrastructure layer
Then application ecosystem grew over time
=>so it inverts over time
Q4 - Balance between long-term vision and short-term justification? Have your investment filters changed?
Our job is to look for technologies that are guiding the industry and will be a key disruption in 5-10 years
We invest at the venture stage
business units look into business case
Q4 - 1.) Looking for technologies that will be guiding the industry
Look into technologies that will be a key disruption in 5-10 years
We are less reactive to the current ups and downs as we are looking more into the future
Q4 - 2.) We invest in the venture stage
We invest in Series A / B round
We are a bit removed
anticipate future trends into what public markets demand
Developments trickle down in private markets
What I mean by it:
Public market volatility first
Impacts private markets, first Pre-IPO then growth stage
Also important for us to see what happens in growth stage => become investors in our PortCos
Hence, also impacts venture stage for us
So what has changed: moving away from "growth at all costs" towards a focus on efficient growth
More capital efficient business models are favored
Q4 -3.) Business units and business case
=> Ultimately looking for best of both worlds: needs to meet our financial targets and strategic narrative
Developments on corporate level play a role in startup investment strategy
BU looking for business impact
Impact = value created either in a) lowered cost, b) increased revenue
Organization has always been very ROI riven:
We look at it from a perspective of: a) how fast can the change be and b) how disruptive is its potential
Add 1) What advice do you have for startups who are just starting out in the current market and economic conditions?
Be modest on your runway and cashflow projections:
times of 18 months to your next rounds are over – will take much longer and milestones higher
Be clear about your vision, why it’s disruptive and don’t worry about current market conditions
Add 2) What technologies are you most excited for and why?
Not a single technological disruption but rather an interplay and orchestra of different technologies:
Change the way we buy and finance a car; make the workflows much more efficient; give you more data; make better decisions; more tailored to you
Add 3) What should founders do to demonstrate their ability to serve a paint point?
Engage with customers early on
Run PoCs; sign LOIs and MOUs
If the pain point is there; corporates will engage in those conversations since the pain point is really there
Add 4.) GenAI copilots. Is there enterprise value here or just another UI layer on top of ChatGPT?
Less important if it’s just layer
Importance: does it create real business impact? If yes, there is real enterprise value here
Question then is if the business model is defensible but that’s another topic
Ultimate goal: make different organizational processes more efficient
Last changed18 days ago