Welcome to the Tech for Non-Techies podcast

Cloud computing powers most of the digital services you use today. Listen to this podcast episode to learn what it is and why it matters.

Learning notes from this episode:

• The cloud computing is expecting huge growth: the total cloud computing market is estimated to be reach $1,614 by 2030 from $545 billion in 2022, according to Precedence Research.
• Before the advent of cloud computing, technology companies would have had to have their own servers to store data and run computations. Having your own servers is very expensive because you have to pay to
  • keep them secure
  • keep them cool
  • keep them running 24 /7 and pay high electricity costs
  • have a space to keep them
  • maintain them with the help of a specialist
• Cloud computing means that companies do not have to buy and maintain their own servers. Instead, they can just rent space on another company's servers. 
  You can just pay for what you use and expand as you grow. 

This is a picture of servers ran by a cloud...

Companies like Deep Mind fascinate investors and innovators, but what is a deep tech company really and how does it differ from other types of tech firms? Listen to this episode to find out.

Learning notes from this episode:

• Deep Tech is a sub-sector of the technology sector where the emphasis is on tangible engineering innovation or scientific advances and discoveries. It includes artificial intelligence, robotics, blockchain, advanced material science, photonics and electronics, biotech and quantum computing. 
• Deep Tech is usually B2B: these companies usually sell their innovations to other businesses, rather than directly to consumers.
• Deep Tech companies are usually founded by technical founders, and sometimes have non-technical co-founders who help them commercialise the innovation. A good example is biotech tech start-up Vitro Labs, where a scientist teamed up with a fashion industry expert to create laboratory grown leather.
• The biggest risk to Deep Tech...

Some problems that annoy you daily could be solved by AI, but most business teams don't know that because they’ve never discussed them with a technologist. Listen to this episode with Dr Catherine Breslin, a machine learning scientist with a PhD from Cambridge, to learn how to make the most of the AI revolution.

Dr Breslin was one of the first people to work on Amazon Alexa and today leaders Kingfisher Labs, a consulting company.

Learning notes from this episode:

• For AI to have the biggest impact, data scientists need the input of domain experts, who are usually non-techies.
• To collaborate successfully with a data scientist, Dr Breslin suggests that non-technical teams bring their business wish list to a data scientist. Some of the items will probably be easily solved by technology, while others will not. Having regular discussions between tech teams and business teams will widen your scope of what’s possible.
• Buying data to build models is a significant...

When an app has too many features and pop ups, most users get confused and frustrated. This is feature creep: when the product’s core functionality becomes hidden in too many options and things to do.

Feature creep happens when a team is determined to stay productive, but loses sight of its strategy. Sometimes stopping is better for the product than doing more.

Learning notes from this episode:

• Feature creep is problematic for two main reasons: it confuses users and it costs money. This is because product teams have to be paid to design and code, and you also have to pay cloud costs to store your pointless features.
• Feature creep happens when there is a pressure to produce, which is contrary to the ability to focus. It can be easier to present new features as productivity to investors and corporate bosses, rather than saying that the product team took time to review results and reflect.
• To prevent feature creep, go back to the fundamental product development questions...

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...

Facebook disrupted the media market forever. The Apple App Store created the app economy, valued at $6.3 trillion today. What makes platforms like these SO successful?

In this episode, you will learn the core concepts behind platform businesses, so you can identify platforms in the making or build them yourself.

This is the beginning of a mini-series on platform fundamentals at Tech for Non-Techies.

Learning notes from this episode:

• Platform businesses have been around for millennia. A market square is a platform business. Technology just allowed these businesses to reach more scale and make more money than ever before.
• Most traditional businesses are pipeline businesses. A pipeline business employs a step-by-step arrangement for creating and transferring value with producers at one end and consumers on the other. For example, Ford makes cars and sells them to consumers.
• Pipeline businesses grow and prosper if they have Supply Economies of Scale. If Ford makes lots of...

The terms coding and programming are ubiquitous, yet many non-technical professionals do not know what they mean in practice. Why are there different coding languages? What do developers actually do?

This is what you'll learn in this episode.

Learning notes:

• Data is information that you can use to do something with. For example, your shopping list is information that you use to remember what food to buy when you go to the supermarket.
• Technology is what you use to create, store or communicate that data. In our shopping list example, it could be pen and paper, or the notes app on your phone. 
• In order to make software, you have to tell a computer what to do. You do this via a programming language. All digital technologies are created by using specific coding languages to turn data into useful outcomes.
• Python Java, C++ and Ruby are the most popular languages  today. But, there are 256 coding languages in use today.
• Binary code, also known as ...

”Most people make the mistake of thinking design is what it looks like. People think it's this veneer — that the designers are told, 'Make it look good! ' That's not what design is. It's not just what it looks like and feels like. Design is how it works,” - Steve Jobs 

In this episode, you''ll hear from Sarah Doody, a UX designer who has worked for the likes of Vice Media and Dow Jones. Today Sarah runs Career Strategy Lab, a school for UX designers. 

Learning notes from this episode:

• "User experience design is how you interact with a product, whether it's digital, physical or a mix of both," Sarah Doody.
• When hiring a UX designer, think about the outcome that you want, rather than the process. E.g. Do you want a prototype to test an idea? Then you don't need the same level of attention to graphics as you would for a design you would give to developers. 
• If you want to transition into a career in UX design, your experience in another...

If you have an idea for a new product in a traditional business, you will probably have to work on an extensive plan before you do anything else.

This is not how it works in tech companies. When the likes of Airbnb and Slack bring new apps or features to market, they use the Sprint Method. It is a methodology developed by Google Ventures to bring new ideas to life and test them quickly and cheaply.

Learn how this works in this podcast.

Learning notes from this episode:

• The aim of a sprint is to test an idea for a new product to find out whether it is worth investing more money in. For example, you can use a sprint to test an idea for an app by creating a prototype. If users like what you’ve made, only then should you hire developers.
• Each sprint should focus on one idea to test. Do not try to test multiple ideas in one sprint.
• To figure out where the biggest risks in a new idea lie, ask yourself: if this time in a year, this project failed, why would it have done so?
• A sprint...

Agile is now a ubiquitous management term, but few people understand what it means in practice.

For some products, agile is THE BEST system, for others, it is THE WORST.

Listen to this week’s episode to find out what it is, how it works in practice, when to use it and when to avoid it.

You’ll hear how WhatsApp used this methodology to release its first product, and learn how to use it yourself.

Learning notes from this episode:

• There are two methodologies to make things: waterfall and agile.
• Waterfall came from manufacturing and construction and emphasises planning in order to release a perfect product. For example, if you’re building a house, you need to plan ahead and only let people move in to live in it when it is complete.
• Agile methodology is an iterative process, used by software developers. Simple changes get released frequently, in response to user needs or technological changes. The product is by definition never complete, because it is always changing.
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