Welcome to Motivka

The early years

Born in 1994 in Melbourne, Australia, my early life was split between Australia, Kenya, and the Czech Republic. My desire to travel and explore the world definitely comes from these early years. I’m also a 90s kid through and through: Computer games, Saturday morning Disney, MAD Magazine and chip packet collectibles are memories I look back on fondly. I even remember visiting public libraries to access the internet in the early days, discovery sites like Miniclip, Neopets and Limewire.

Alfred E. Neuman from MAD Magazine. Illustration by Tom Richmond

Technology changed rapidly during my schooling. I feel quite fortunate to have lived during this transition, where I know what it was like before and I know what is has become. It honestly makes it a lot easier to pick up new tools when you have context and understand what might work best.

University life

I ended up studying an undergraduate degree in International Studies. Asking an 18 or 19 year old what they want to be for the rest of their life is such an incredibly daunting question to answer, and I ended up thinking more about the places I wanted to go and the languages I wanted to speak rather than the skills I wanted to learn.

By the end of the degree, I was living in Spain having completed several courses of a degree, with a body of work relating on topics of technology rather than the typical development and politics of an international studies graduate and realised that I didn’t exactly have a logical next step. This led me to return to Australia, where I was starting to feel a tug toward something relating to technology.

I toyed around with some courses in computer science, cybersecurity and then data analytics. Finally, I ended up completing a Masters of Data Science after being exposed to the world of informed, data driven decision making, in a role at Uber. I’m someone who loves to figure out how things work, organising them into logical and neatly defined categories, so this ended up being a perfect fit.

The timing of this degree also coincided with Covid, meaning I was able to work and study full time for a couple of years and fast-track getting the piece of paper, while also learning lots on the job.

Work life

After completing the Data Science degree, I found myself working in engineering, consulting and architecting roles in the Solutions department of data companies. For those not in the know, Solutions roles are technical roles within a Sales department of companies, where the key responsibility is to move prospective customers through a successful sales cycle by communicating value of the technical products you are selling. It’s essentially a technical problem solver able to communicate and translate requirements between those who are buying software and those who are building and selling software.

These roles honed my ability to speak with a hugely diverse range of people. Sometimes strategising with executives on the direction they want to take a company and how a product might help, other times collaborating with an engineer to figure out the best way to do a data integration. Often building proof of concepts to communicate how something might work in a customer environment, and sometimes providing education through interactive workshop. Finally, it provided a great insight into the inner workings of those particular businesses and industries.

This was all while maintaining deep technical knowledge in particular domains such as data science or marketing.

That is a long winded way to say, I’ve learned to be a pretty good communicator.

Living through an AI revolution

I believe we are living through an industrial revolution. I am observing a rapid and violent shift in the way that we perform work, similar to what was experienced through the digitisation of everything in the 2010s, although now at a much faster pace and at a larger scale. Although this transition is scary and is going to result in lost jobs in the short term, I do not believe mass unemployment is the long term outcome.

The key change I see is that in many industries, the listed skills or tasks listed as part of a job description are now able to be automated, leaving the employee responsible for managing these processes rather than executing them. Paradoxically, this should also mean that employees are able to work less with greater productive output, but I think companies are getting this rather confused.

The expectation that companies should be adopting is that employees are able to do more, not that there is less to do.

The reason I believe we are seeing short term job loss, is that older companies who typically employ more people are perceiving these changes as cost savings rather than productivity gains. This is causing short term shock, while spooking other companies in similar situations. These companies then start to panic as they begin to be outperformed by AI native companies as they do not have the infrastructure to rapidly train their employees on AI tools.

There might have been over hiring over Covid, but I do not believe that there is a race to a zero employee company as some people seem to think. There is plenty in growth in brand new roles such as AI Engineer, Design Engineer and Forward Deployed Engineer at disruptive AI native companies, the issue is that this change is occurring incredibly quickly.

So, for those in my network who aren’t receiving this education, I want to help in preparing for this change in work. Follow along for tutorials, explanations, tool reviews, workflow breakdowns and the occasional hot take.