Inception Fund

Still in pre-training. Backing New Nordic made technical brilliance at the earliest inception stages.

www.inceptionfund.vc

In pre-training...

Zenith Ventures

Founder and operator turned VC. Backing obsessed Nordic B2B software and vertical AI founders from the earliest stages. 

No-nonsense, hands-on and fighting for founders.

General Partner

Stealth AI Startup

Pre-seed Investor

Natively

Build and launch native mobile apps with AI.

www.natively.dev

Lovable

Building the last piece of software.

Careers: https://lovable.dev/careers

www.lovable.dev

Angel Investor

Vantel (YC W25)

AI Software for commercial insurance brokers (YC W25).

www.vantel.com

Pre-seed Investor 

Tandem Health

Helping clinicians focus on providing care instead of on administrative tasks through AI and automation.

Careers: https://jobs.ashbyhq.com/tandem-health 

www.tandemhealth.ai

Tzafon

An applied AI research & development firm looking to push the boundaries of what machines are capable of through the intersection of AI & software engineering.

www.tzafon.ai

Lightbringer

Navigate and protect your IP and patent journey with AI.

Careers: https://www.lightbringer.com/apply

www.lightbringer.com

Seed Investor

Pruna AI

The AI optimization engine for ML teams seeking to simplify scalable inference.

Careers: https://careers.pruna.ai/

www.pruna.ai

Data & AI/ML Entrepreneur | Early Stage Investor

Over a decade of experience within Big Tech, B2B software, Data and AI 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 ML & 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 & data thought leaders in the Nordics.

Oliver Molander

I don't think people truly understand how early we're in enterprise adoption of AI agents.

McKinsey just released their 2025 AI report. 

Some nuggets:

1️⃣ 90% of companies “use AI,” but 67% are still stuck in pilot mode. Corporate AI theater is alive and well.

2️⃣ 62% of orgs are experimenting with AI agents, 23% are scaling AI agents. Most are in tech and healthcare.

3️⃣ The impact gap is massive. 64% say AI helps innovation, but only 39% see real EBIT gains. 

4️⃣ The high performers (top 6%) think bigger. They rebuild workflows, set growth goals, and invest real budgets not just PoCs.

5️⃣ Leaders who own AI personally are 3x more likely to scale it. Makes sense.

6️⃣ The winners use AI to transform how work gets done, not just speed it up.

7️⃣ The average company measures efficiency. The best ones measure how fast their agents can act.

8️⃣ Risk management is catching up with 51% have already seen AI backfire, mostly from inaccuracy.

9️⃣ The workforce impact is foggy. 32% expect cuts, 13% expect growth, everyone else is guessing.

Agentic AI looks magical in demos, but scaling it across teams and systems (especially in enterprise settings) is where it still breaks. You can do basic single-step automation reliably right now (tool calling).

As Andrej Karpathy recently noted on LLM agents:

"My critique of the industry is more in overshooting the tooling w.r.t. present capability."

Bottom line: we're still super early, this transformation will go on for another decade.

I'm bullish, but this will take a bit longer than we think, having fully autonomous agents. Just like autonomous driving took a bit longer than we thought, but here we are.

E.g. one of the biggest challenges in building AI agents today is agents' long-term memory.

#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

Oliver Molander

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