5 min read


Photo by Vesta Rugilė Nausėdaitė / Unsplash
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Lithuania Tech Weekly #118
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work in progress

rounds and capital


Railsr - CEO (Lithuania)
Genome - BDM
Eneba - Head of Design
WAcademy Global - CTO
Bored Panda - Operations Manager
Orbio World - TV Marketing Manager

founder's guide

I am suggesting that more transparency, more vulnerability, and more honesty is the winning formula and when you are choosing between the two, choose these things.
  • The Complete Guide to Running an M&A Process as a Founder

further insights

A weird thing in life is that everyone strives for a good life because they think it will make them happy. But what actually brings happiness is the contrast between what you have now and whatever you were just doing.

Background of 100 European unicorn founders (does not classify as proper research, many caveats):

85% of founders have a university degree and 23% a Master/PhD degree. The average age at which a founder starts a future unicorn is 33 years old. 10% have worked and Goldman Sachs and 7% at McKinsey. Oxford University is the school that create the highest number of unicorn founders (7%), followed by LSE (6%) and Cambridge University (5%).

Seriously fun because much truth

You may be regarded as a daft ignoramus in London or Paris, but in Chișinău or Tartu you can still be a brilliant thought leader
+44. Never accept calls from the UK. You hope it's Balderton, Index or a cool startup that calls. It's not. It's "Microsoft" or "Investment Advisors".

three questions

Domas Janickas, Co-founder at edON

What have you learned/seen in the past that triggered this project?

I have interviewed, hired and (too often) fired bootcamp graduates. Most of them were over-promised and under-delivered, were told it is enough to spend a month-or-so to become a software engineer, had unrealistic expectations and were far from ready to take on even an entry-level role.

Choosing a career in tech does not have to mean becoming a developer. Unfortunately, too many people see this as the only possible path and do not get proper guidance to see other options, such as QA, project management, process automation, analytics, UI design, etc. When coding does not match their personality, existing experience and soft skills, it leads to failures when studying or getting used to the new role.

The effort required to start a career in tech has recently changed as the tech world itself is becoming simpler thanks to easy-to-use tools, no-code solutions and generative AI and I believe this trend will remain.

What's the key difference between edON compared to the typical "IT academy" model?

The founding team is combining experience in the high education and software industries to design a human centred re-skilling experience. Our focus is personalised role mapping and career planning and making sure existing skills and personality become a key strength in the new career. To make the career switch easier, we will not only train for particular skills, but cover aspects such as culture of international organisations, client expectations management, decision-making, etc. We’re also developing an AI-driven career mapping and planning software which will be offered as a SaaS solution for tech companies.

Given the rise of AI, no-code - what do you think are widespread misconceptions about future jobs and careers?

I’m betting on polarisation of future careers: (i) an increase in jobs such as nursing, where human interaction is the most important aspect and (ii) automation affecting not only the jobs dominated by routine manual tasks, but the creative ones as well. The unknown-unknown is the jobs that currently do not yet exist, but I’m certain that the education models need to change as everyone will have to re-skill in their future careers more than once.

Is it motivation and engagement that defines the pace of learning? Will AI help us have students more engaged?

Motivation and engagement define the pace of learning as well as familiarity with certain concepts based on prior knowledge, quality materials and teaching methods that match the preferred learning style, the learning environment and social factors like healthy competition and peer pressure.

AI can help with creating personalised dynamic content adjusted according to aspects like skill gaps or career plans of each individual student; intelligent adaptive training systems; early identification of learning and more. However, when it comes to student engagement, I believe that intrinsic motivations matter more than what the tools or learning materials can offer.