Welcome to the Tech for Non-Techies podcast

Venture capitalist Marc Andreessen famously wrote that “software is eating the world.” While digital transformation is everywhere, and even your coffee shop has an app, this doesn't mean we all need to learn STEM subjects and become coders.

The vast majority of jobs remain non-technical. 

To succeed in today's economy, ambitious professionals need to learn how to become Digital Collaborators. This means learning additional skills, rather than completely retraining.

Learning notes from this episode:

• Microsoft says that "the demand for digital skills continues to grow, and we estimate that digital job capacity – or the total number of technology-oriented jobs – will increase nearly five-fold by 2025, rising from 41 million in 2020 to 190 million in 2025. These numbers are in stark contrast, and they illustrate the digital skills gap that has accompanied the Fourth Industrial Revolution.”
• Being a Digital Collaborator means learning to...

You've probably heard about network effects, but they aren't the only thing you need. Learning effects build the ultimate moat against your competition.

Learning notes from this episode:

• You get better at speaking a language the more you practice and correct your mistakes. It is the same with algorithms: they get better with time and training.
• The more time and data you have to train an algorithm the more accurate the algorithm’s output will be, and also, the more complex the problems it can solve.
• “Learning effects can either capture or add value to existing network effects or generate value in their own right.” – Competing in the Age of AI, by Marco Iansiti and Karim Lakhani
• Companies that have been training machine learning algorithms for longer are at a competitive advantage. Strong learning effects make it impossible for competitors to catch up.

Resources mentioned in this episode:

• Sophia Matveeva’s LinkedIn
• Listen to The Business of AI...

It’s easy to put the tech sector on a pedestal, as we’re constantly bombarded with its power and profits. But “technology is just a tool to affect business outcomes,” says prop tech entrepreneur Sebastian Rivas.

Sebastian runs Andes STR, a which uses machine learning algorithms to find property investments for short term rentals. If you want to invest in a property and rent it out on Airbnb, Andes STR will find the investment and manage the rental.

Sebastian started his career in finance, and created a smart plan to break into tech. Listen to this episode to learn how he did it.

Learning notes from this episode:

• Technology is a tool used in business to improve efficiency, user experience and productivity, but it is not an end in itself.
• Being tech savvy and understanding how technology influences business outcomes is a must have in today’s working environment, almost no matter where you work. Even your coffee shop has an app!
• “The biggest...

You’ve probably heard the term cloud computing, but like most non-techies, you’re not sure what it means. In this episode, you’ll learn what it is and how businesses use it to solve problems.

You’ll learn from DJ Johnson, who works at Microsoft Azure. DJ started his career as an NBA player and transitioned into a career in tech.

Learning notes from this episode:

• Cloud computing allows businesses to rent space to store data. Previously, companies had to store data on their own servers, which was much more expensive.
• The two biggest players in cloud computing are Amazon Web Services and Microsoft Azure.
• As a non-techie, first identify business problems and then see if technology can fix them.
• For example, during Covid when suddenly many people ended up working from home, one of DJ’s clients suffered from major time lags in their communications. Their internal messenger service was taking 3 days to deliver a message! This was making customers...

Big data and predictive analytics can help you make profits, sell clothes and strike oil. But, unless you know how to ask data scientists the right questions and then use their answers, data are just a collection of meaningless facts.

Listen to this episode to learn what data scientists do and how to work with them.

Learning notes from this episode:

• Every senior level professional today has to learn to speak tech: knowing the concepts of how digital products get made is now basic literacy.
• Working with data scientists can be broken down into three steps: 1) ask the right question, 2) get insight 3) take action based on the insight.
• Predictive analytics are based on past data, which does not make predictions future proof and does not take account of shocks to the system.

Enroll in How To Speak Tech For Leaders by 26 July 2021.

If you want to sponsor several employees in your team to take the course and want a group rate, email us on ...

“The biggest lie told in tech is that you that you need to be a coder. I think that being able to understand the user experience behind tech, being able to articulate technology, and being able to get other people excited about it, is what you really need to run a good company,” says Jenny Griffiths MBE, founder of Snap Vision.

Jenny is the founder and CEO of Snap Vision, a visual search company that works with the biggest names in fashion and publishing.

She has been featured on the World's Top 50 Women in Tech by Forbes lists. She was appointed MBE for Services to Innovation in 2015, and in 2019 was awarded the Royal Academy of Engineering's Silver Medal for contributions to UK engineering.

Learning notes from this episode:

• The grass is always greener on the other side. Investors tell technical founders that they’re missing business skills, and non-technical founders that they need tech skills.
• Snap Vision began as a consumer product, and while the Snap Vision...

Sophia Matveeva spoke to Jung Seok Kung (JS) founder of Aizen, a fintech company which uses AI to support decision making and manage risk for banks. JS is a non-technical founder, who now leads a company that processes 10,000+ algorithms in real time.

If you want to learn what AI is in practice and how it's changing business this episode is for you.

We cover how JS went from spotting a market opportunity to creating an algorithm using a spreadsheet, and the ups and downs of entrepreneurship.

Learning notes from this episode:

• An algorithm is just a set of instructions that you put into a computer.
• JS created a prototype of his algorithm using a spreadsheet. The aim of his algorithm was to give out loans to customers. He bought lending data from the Lending Club and tested it against a control group. This means one set of loan applications was assessed by the algorithm and the other was done manually. By doing this, JS proved that his algorithm was making correct decisions....

Harvard Business School professor of AI and digital innovation Marco Iansiti talks about what AI is, how it gets made and how it impacts organisations. Listen to get a case study on Ant Financial and learn how AI adoption has accelerated during COVID.

Key learning points:

• AI is already changing industries and the economy by making simple operations run faster
• A business model is how a company aims to create and capture value
• An operating model is how the company delivers that value to the customer. Prof Iansiti calls this “the hard part”
• AI is already being used by companies like Ant Financial to take humans and cost out of their operating model, by putting an AI factory in the core of their business
• COVID has accelerated the adoption of AI across industries

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... 

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In this episode tech entrepreneur and Chicago Booth MBA Sophia Matveeva covers what algorithms are, how they are made and when rubbish data creates rubbish algorithms. Hear how Amazon’s recruiting algorithm experiment went horribly wrong.

Don't let the term algorithm scare you. It's just another word for a set of rules.

Key learning points:

• Algorithms are just sets of rules for computers to follow
• Algorithms need data to be useful
• Data is by definition historic because it is information on what has already happened
• A combination of AI and algorithms can create better user experiences, which can result in more usage, which then results in even more data and happy customers
• Algorithms amplify the data. If there are biases in the data, they will grow as a result of the algorithm. Rubbish data = rubbish algorithms = useless product

Listen here on Apple Podcasts. 

Listen here on Spotify. 

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