Data & AI/ML Entrepreneur | Early Stage Investor

Over a decade of experience within AI/ML and Data Infrastructure as a founder, operator and investor.Passionate about bringing new and paradigm-shifting technologies to market. This passion has led me to a over decade-long entrepreneurial career focused on emerging software industries and global markets - from AI &amp; Data Infrastructure startups and venture capital funds to Fortune 500 technology leaders such as Google. Raised +$50M in early-stage funding from top VCs as a founder and operator. A big believer in people, data, design and telling a story. Passionate about building new products with the right GTM, messaging and growing teams that do so.Deep understanding of the venture capital, private equity, SaaS, cloud, data and AI ecosystems with solid global relationships at all levels.Frequent keynote speaker on topics around data, AI, startups and venture capital. Considered as one of the top AI &amp; data thought leaders in the Nordics.

Oliver Molander

The Anthropic team wrote all of Cowork (their newly launched non-technical version of Claude Code) using Claude Code 👇 

And they built it in 1.5 weeks (confirmed by Felix Rieseberg).

Holy sh*t.

Things are changing fast.

At the same time, I see founders struggling to make a simple update to their website in 1.5 weeks.

Anthropic launched an entirely new product in that time.

And as Felix noted, they’re releasing Cowork early. It has rough edges. But figuring out what to build is increasingly the hardest part of software engineering - and they believe getting feedback early and hearing what users actually need is how you build something truly good.

And this is a company valued at close to $0.5 trillion, with thousands of employees.

Founders, you have no excuse anymore.

Don’t fall into the anti-AI hype.

There’s a saying that the future is already here - it’s just not evenly distributed.

I still see plenty of developers insisting that AI coding tools are overhyped and error-prone.

But I’m also starting to see the opposite:

People who’ve fully embraced them and now find they’re so good that it’s triggering a real crisis of confidence.

If you haven’t seriously tested AI coding tools in the last year, try them again. 

Don’t assume anything.

You’ll be surprised.

#artificialintelligence

I see too many brilliant technical founders in Europe getting stuck in what I call intellectual procrastination.

They debate in their own minds what to do, weighing different outcomes - instead of just doing.

This is why high-agency founders succeed. They start doing, fail, learn, and repeat.

As Andrej Karpathy puts it:

Agency > Intelligence

Karpathy has said he intuitively had this wrong for decades, shaped by a pervasive cultural veneration of intelligence - media narratives, entertainment, obsession with IQ, etc.

Agency is significantly more powerful and significantly more scarce.

Are you hiring for agency?

Are we educating for agency?

Are you acting as if you had 10× agency?

The best thing about high agency is that it compounds.

This is something organizations like YC are especially good at: pushing founders out into action.

There’s a saying in Finnish, “Liike on lääke”, which roughly translates to “Motion is the cure.”

The best founders get anxious when there’s no motion - when nothing is happening or being tested and validated.

For too long, European cities like Stockholm - despite an incredible concentration of talent - have been held back by societal expectations: first get a master’s degree, then gain experience at a big company, and only then maybe think about building something. 

But the truth is, you don’t need permission - you can just do things.

The tides are turning fast, though.

Now is the time of the high agency builder.

#artificialintelligence

One of the biggest myths right now that needs to be busted is that Claude Code is only for coding.

It can be used for so much more.

As my friend Antoine Moyroud recently said, we need to stop treating AI like a chatbot.

The gap between power users of AI and casual chat users is going to widen very fast. Start experimenting with Claude Code now.

Non-technical people shouldn’t be afraid of the CLI.

You can already leverage Claude Code for almost any kind of knowledge work:

Sales, marketing, growth, fundraising, law, taxes - you can become surprisingly competent quickly.

It’s hard to overstate how big the gap will be between people who understand how massive this shift is and those who don’t.

As Greg Isenberg put it:

You’re just a few .md files away from outperforming 99% of people on this planet.

2026 is the year Claude Code grows beyond coding.

#artificialintelligence

Probably not a biggie, but the creator of Node.js just admitted that the era of humans writing code is over.

#artificialintelligence

Here's a shout-out to all young technical operators from the Nordics working at the frontier in leading AI companies - you embody the future.

Gabriel Petersson 
Erik Kaunismäki 
Jonatan von Martens 
emil fagerholm 
Emil Ahlbäck 
Minette Kaunismäki 
Christian Ryan 
Matheus Mendes 
Eric Zakariasson
Jonathan Granskog

...and many more that I've missed.

The future is in the youth.

#artificialintelligence

Ford Motor Company

Statistical Programmer

VMware

Developing and testing VMware Cloud Foundation (VCF).  Specialties were test automation and user interface development.  Wrote user interface for cluster renaming and revised the user interface for Edge cluster expansion. Co-wrote VCF common services test suite, wrote cluster rename test suite and did extensive work on the Edge cluster test suite.

Member of Technical Staff

Self-Employed

Programming and statistical work for Salford Systems (2017-2018):
Focus on model deployment via the Translate facility.  Also active in development of the PMML standard.  Linux system administration and software development.  SPM technical support.

Work for Choice Analytics (2019):
Research on machine learning algorithms.  Developed a Jupyter kernel for SPM.  Developed a system to store information on SPM models in a PostgreSQL database.  Wrote the Exact Data Partitioner (xpartition), which randomly partitions a table into training, test, and holdout partitions (or any number of cross-validation folds), balanced on one or more fields.  Wrote the Small Command Generator (scmdgen), which generates SPM code using a template and field values.

Contract Statistical programmer and software consultant

Salford Systems

Managed all aspects of data preparation for subsequent predictive modeling in dozens of projects, including competitions that our team won. Data wrangled arrived in many different forms and encodings and was transformed using SQL, SAS, Linux tools (awk, sed, Korn Shell) and Perl.

Managed and prepared bank transactional data, click stream data, brick-and-mortar daily sales data of over 100K products in a network of 165 mega-stores 

Predictive Modeling

Responsibilities included developing first round predictive models using single decision trees, gradient boosting machines, random forests, and various statistical summary tools.  This state of modeling focused on elimination of unsuitable predictors, recursive feature elimination, first round hyper-parameter optimization. Interaction with chief modeler to refine models. Principle frameworks: R, SAS, Salford Systems SPM.

Winner: KDDCup 2000, (1st place) Duke/Teradata Churn Modeling Competition 2002 (first place)

Model Deployment/ Java, C++, PMML, and SAS.

Responsible for the rendition of complex predictive models and ensembles of models into Java, C++, PMML. Author of the translation facility inside of Salford SPM for rendition of Random Forests, Gradient Boosting, and other learning machines.

Salford's representative to DMG (developers of the PMML and PFA standards) since 2000. Authored a number of the features in PMML to support tree-based model ensembles.

R-Package Construction

Author of Salford’s in-house R package allowing an R-user to run Salford learning machines. The package manages all communication, transfer of data, and conversion of predictive modeling results into R objects.

Script Writing/ Data Science Automation 

Developed an extensive library of Perl and shell scripts and SAS macros for automation of the post-processing of predictive modeling plain text outputs for the creation of custom reports.  

Software development

Ported SPM to Linux, MacOS X, AIX, HP/UX, and Solaris.

Programmer/Data Analyst

Advanced Development Group

Developed two computer training courses for the US Army using the Quest authoring system.

Programmer

I am a statistical programmer and analyst with many years of experience.  I have worked on numerous data science projects, and have a thorough knowledge of all stages of the process including data examination and preparation; model development, report generation, and model deployment.

My specialties include data preparation, model deployment, and automated code generation ("programs that write programs").  I am an ace SAS programmer skilled in the data step, macro processor, and base procedures.  I am the world's leading authority on use of the non-GUI version of Salford Predictive Modeler (SPM) and am a skilled Perl, Python,  and shell scripter.  I believe strongly in taking the time to write clean, elegant code as it tends to save time in the long run.

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Oliver Molander

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Oliver Molander's LinkedIn Performance

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