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

How quickly Norwegian (and soon Danish) founders leave the country when the government sets a tax on unrealized gains.

Let’s make this clear:

Any developed government that thinks citizens should pay unrealized capital gains tax on illiquid assets is simply anti-entrepreneur and anti-business.

#founders

Sweden is not only becoming the AI application capital of Europe, but also Europe's new capital of capital.

But why does Sweden have so many unicorns? And why does it feel like we have more momentum than ever during the AI platform shift?

I’ve been saying for years that it’s a mix of factors that have compounded over decades.

Early role models who went for the global maximum (e.g. Niklas Zennström, Daniel Ek). Strong founder factories producing highly technical talent (e.g. Spotify, King, Klarna, iZettle). A new wave of AI-native founder factories (Depict AI). Strong technical institutions like KTH. Early grassroots communities for builders like Stockholm AI. Strong UX and product taste.

Add liquidity events that created a strong angel culture combined with a campus-like density of talent, founders and capital (almost everyone is a 10-minute walk from each other). That’s when things start compounding fast.

And as a final catalyst: a new generation - what we call the YC generation - showing what’s possible and becoming role models themselves (e.g. Anton at Lovable, Max at Legora).

But there’s another piece that doesn’t get talked about enough: tax policy.

Luis Garicano (Professor of Public Policy at LSE, PhD in Economics from UChicago and ex-Member of the European Parliament) wrote a good post about this (link in comments).

Tax policy reflects what a society values. If you value production and creation, you reward it. If you value redistribution more, you extract and redistribute.

Sweden had a solid first wave of tech companies (Skype, Spotify, King, iZettle, Klarna). What really mattered is what happened next. Alumni from those companies went on to start a huge number of new ventures.

One reason is a 2003 tax reform that allows corporate capital gains from selling shares in unlisted companies to be deferred as long as the money is reinvested into other unlisted companies. Since Swedish founders and early employees often hold their shares through a company, this created a strong incentive to recycle capital after an exit.

Sweden didn’t create angel investors through subsidies. It created the conditions for them to emerge naturally: exits, reinvestment incentives, and a culture where starting again is normal.

Sweden also scrapped the wealth tax (2007) and inheritance and gift taxes (2005). The ISK account (introduced in 2012) made equity investing simpler and more accessible. By 2023, Sweden’s market cap was 169% of GDP - more than double the EU average.

And importantly: startups need exits. 

Sweden has deep, multi-layered public markets. Between 2016 and 2023, Sweden had 823 IPOs - the most in the EU - with nearly 90% on SME growth markets.

None of this happened overnight. It compounded.

And now we’re seeing the result combined with a generational AI opportunity.

#artificialintelligence

What if HF0 Residency and the Thiel Fellowship had a baby?

We’ll never know.

What we do know is this:

The Nordics and Baltics have a long history of producing technical outliers - and billion-dollar outcomes.

Now the AI platform shift is triggering a fundamental mindset reset across the region.

More ambitious.
More technical.
More fearless.
Younger than ever.

Enter Frontier Residency.

This is extreme signal-to-noise.

10 young technical outliers from the New Nordics. 5 frontier AI companies.

This is about catalyzing the next generation.

Letting them see where the global maximum is early.

And creating clear second- and third-order effects across the ecosystem.

More to come.

Change will never be this slow again.

#artificialintelligence

Given everything that’s happened over the past few months, it feels more evident than ever that we chose the right slogan for Inception Fund.

We’re still early.

PS. Here's a great blog post by Kenneth on the importance of technical founders in this day and age: https://lnkd.in/dpfmjRDv

"If anything, the bar for technical leadership is higher now. It's no longer enough to be a great engineer who can write clean code and lead humans.

You need someone who understands the current AI landscape, who can evaluate when to use agents versus when to write code by hand, who can make the right build-versus-buy decisions, and most importantly: who can help you stay ahead of the curve, in a world where the options are compounding exponentially."

#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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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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