Some of our founders started with artificial intelligence and building new services, products and business models years ago.
As investors, tech builders, entrepreneurs and in top management.
That resulted in multi-billion dollar turnovers - not just unicorn status.
We have big visions and love tech.
And we have a passion for learning and thinking. Here are some results and thoughts based on the learnings:
"Financial engineering" must be as precise as other engineering:
Investments in Ventures and Tech needs to be high enough to reach critical milestones, but too much money can be even more destructive than not enough.
(More) Money can buy speed. But there are time-compression-diseconomies. In many areas of digital solutions the first mover will have a big chance to win the race to be the market leading platform. This applies especially to artificial intelligence (AI).
Data is the new oil - yes, but look closer:
Rockefeller earned his fortune at Standard Oil with access to oil (drilling), especially refinement and making a trustworthy product out of it (Standard Oil) and then later controlled transport channels to customers.
Oil is like data nothing. Only when you bring the right data together for a use case, you get value out of it. Also the analogy with transport channel in oil applies for AI.
It makes a lot of sense to look closer into the oil industry to learn for AI. Longterm stable data access is a key topic. And in some areas it needs even a co-development of AI algorithms together with new sensor systems to have access to data, which is precise enough. Sensors and IOT are still key topics for artificial intelligence.
Ecosystems are key - but only stable and exclusive ones!
Innovation needs often content with high production costs for it and a minimum of users or market coverage. For these situations you can see often discussions about open innovation. This is low cost and risk. But with this the innovation spreads fast and easy to all competitors: the innovation lose the USP and the margin in the whole market decreases.
And this is even worst case for the consumer, since they got so many solutions to choose and test and the companies do not earn enough to improve their products more.
Open innovation ecosystems with many programmers as co-producers have big relevance in some areas. But only, if new applications, content etc. can be produced with a very limited investment. Best examples are the smart phone apps.
But this strategy did already not work with smart watch apps. It didn't and will not work with augmented reality in many areas, many health-tech segments and not for automotive in-cabin innovation. There are many reasons for it and we analyzed them very carefully. But on the other side all this sectors need ecosystems.
Based on that analysis we developed a new concept for investment and building startups: Scalator X