Having worked with tech startups who have a strong academic/ research background and at startups that even had specific research teams at a very early stage, I started reflecting on the difference between research culture and startup culture. There’s no idea to beat around the bush: they’re very different. Based on my experience, there are more differences between the two than common denominators for a harmonious coexistence.

There are some startups, such as Deepmind and OpenAI, that have had research as an absolute core part of their organization from the getgo and have become successful in their own ways (e.g. getting acquired by Google or pivoting towards a more commercial framework by offering massive AI models such as GPT-3 behind paid APIs). However, trying to emulate these is not a good idea if you’re a startup pitching to investors. …


There’s a megatrend underpinning the advent of the data age: the rise of the data engineer, the fastest-growing job in tech right now. We believe that data engineers are the unsung heroes and the change agents in a decade-long process that will revolutionize data.

How did we get here?

We decided to gather some of the most significant moments from the past two decades that shaped the rise of the data engineer in an infographic illustrating the 2000–2020 timeline.

You can access the high-resolution version of the timeline here.


In our ML & Data trends post from February we discussed whether one believes MLOps has crossed the chasm or not, the rise of MLOps (i.e. DevOps for ML) signals an industry shift from PoC’s (how to build models) to operations (how to run models). Even though this shift is something that we’re extremely excited about, there’s a recurrent bottleneck that keeps haunting us year after year: data quality.

In a survey by O’Reilly from 2019, 26% of the respondents with a mature machine learning practice stated poor data quality as the nr 1 bottleneck holding them back from further AI/ML adoption.


2020 brought a digitalization explosion across the world. Microsoft estimates that the first two months of the pandemic (March & April) drove two years’ worth of digitalization. Throughout the rest of the year, the pandemic accelerated a wake-up call to the markets, which had been a long time coming: every successful modern company will need to be not only a software company, but also a data company.

The accelerated digitalization and our ever-increasing appetite for and generation of data fuelled a lot of development in the Data + ML landscape in 2020. As companies have started to reap the benefits of the last few years’ predictive analytics and ML initiatives, they clearly show a healthy appetite for more in 2021. “Can we process more data, faster and cheaper? How do we deploy more ML models in production? Should we do more in real-time?” … the list goes on. We’ve experienced an amazing evolution in the data infrastructure space during the past few years. Data-driven organizations have moved…

Oliver Molander

Early-stage tech investor. Preaching about the realities and possibilities of ML and Data. Former Google. Current Validio, J12 Ventures and Mumintroll.

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