Welcome to the Tech for Non-Techies podcast

Despite the many press releases touting Silicon Valley's diversity efforts, the majority of funding from this innovation will still go to white males in 2023. 

While this reality is not what many of us want, it is an opportunity for investors and innovators to capture overlooked user markets.

 Learning notes from this episode:

• A product is a solution to a problem a human is experiencing. Innovators make solutions for problems they are familiar with, which means that homogeneity in founders means homogeneity in products.
  • This means there is more competition amongst products that solve problems experienced by male founders, such as laundry services and food delivery apps, than in other niches, such as female healthcare. 
  • While funding will be harder to get for products not aimed at the white male market, competition in these sectors will also often be lower. Ultimately, this means big untapped opportunities.
• Companies ran by founders who are not white...

The biggest gains and innovations will come from digital technologies in healthcare in 2023.

Big Tech firms have already entered healthcare, with Amazon buying One Medical and launching Amazon Clinic in 2022. But, healthcare tech innovation is happening across the board: from start-ups to hospital systems.

Learning notes from this episode:

• As Big Tech firms' valuations have fallen, they have become less attractive places for people to work. This means that working at a promising health tech start-up is now a much more tempting option for talented managers and engineers at Meta and Alphabet.
  • All things being equal, most smart experienced people would rather contribute to saving lives rather than driving attention on a social media feed.
• The gap between innovation and adaption is narrowing in healthcare. Many startups with innovative technologies that have been around for years are seeing an increase in demand, as hospital systems go digital and and more doctors use...

There is plenty of hype about AI, but most organisations are still using old precesses to make decisions.

We are  in the Between Times: "after AI's clear promise and before its transformational impact," as described in the book Power and Prediction: the disruptive economics of Artificial Intelligence. 

In this episode, Professor Joshua Gans, one of the book's co-authors explains why organisations are not yet adapting the full power of AI and what will happen when they do.

Learning notes from this episode:

• Artificial Intelligence is a prediction machine, which supports decision making.
• Today businesses often use AI for one or two processes, but most decisions are still made by humans. Technology first companies and start-ups often have more AI-based decision making, because they do not have to replace legacy processes.
• Business leaders should not accept AI as just a black box. In fact, Professor Gans argues that business brains might be better...

Why do you keep checking your phone, even when you’re trying not to? It's because the apps on your phone use the Hook Model. described by Nir Eyal in his book Hooked: How to Build Habit-Forming Products.

To learn how apps like Instagram, LinkedIn and Vivino keep us coming back to our screens, listen to this episode.

Learning notes from this episode:

• Habits are behaviours done with little or no conscious thought
• If your product becomes a habit, people will use it more. You will spend less on advertising and make more money.
• The greatest return on investment generally comes from increasing the product's ease of use.
  • If you want to improve a product, first look at the design: where you can make it more simple to use. E.g. can you require less information from users when they first sign up?
• The Hook model consists of 4 steps 
  1. Trigger: you get a notification that someone commented on your post
  2. Action: you open LinkedIn / Instagram to see the comment
  3. Variable Reward: you...

To be digitally savvy, follow the 30% rule – this is the minimum threshold that gives us just enough digital literacy to thrive in the tech age, says Professor Paul Leonardi.

• “To have digital transformation in your company, you don’t need to know how to code, but you need to know enough about coding to be dangerous. This means being able to talk to the people in your organisation who are working with your codebase, so you can understand the opportunities and challenges of your platform,” says Professor Leonardi.
• “When you are getting a recommendation from a data scientist, it is only ever based on available data. Most data that are available are those that are easiest to get. We systematically bias those data and overlook metrics that may be just as valuable or more important to our decision making, but are excluded from the process because we never digitised or collected them,” advises Professor Leonardi.
  • Whenever looking at a report from a...

Software updates can have weird unintended consequences that the company doesn't even know about. Existing features that worked perfectly can stop working, leading to lost revenues and annoyed customers.

Listen to this episode to learn why this happens and how non-technical leaders deal with it when it does.

Learning notes from this episode:

• A developer could write a line of code to affect one outcome, and there could be a completely different unintended outcome that they don’t even know about it.
  • When an app, site or algorithm gets complicated enough, these unintended consequences are more and more likely to happen.
• To prevent this, make sure that different people test the new version on different devices and browsers.
  • In tech teams, this function is called Quality Assurance.
• Remember that these unintended consequences are inevitable. The key is to catch them early and correct course.
  • Create a process for your users to quickly tell you if something goes...

Technology is a tool, not an end in itself. The quickest way to bridge the gap between tech and business teams is to relate business outcomes to technology. 

Learning notes from this episode:

• In every company, you always have two sides: the people who make the product, and the people who sell the product.
  • The aim of both sides is to grow the business, but they solve the same problem using different expertise. (It’s like Oceans 11, but legal)
• As a leader your job is not to know everything, but to set a vision and break it down into goals. You need to learn, but you also need to know when to stop.
  • This is how non-technical founders build tech ventures and how corporate executives transform traditional organisations into digital leaders.
• One of the biggest reasons non-technical leaders struggle to collaborate with their technical counterparts is fear that they will not understand what the technologists are talking about.
  • To solve, this, you need to learn to ...

"Successful entrepreneurs don't have better ideas, they have a better process," says Eric Reis in The Lean Start-Up. To learn how to innovate with speed, listen to this week's episode.

Learning notes from this episode:

• “A start-up is a human institution designed to create a new product or service under conditions of extreme uncertainty,” says Reis.
• Do not to apply your corporate experience to start-ups.
  • Corporates have:
    • Departments
    • A known business model
    • A known problem
  • Start-ups have:
    • 3 people and a dog
    • No proven business model
    • A problem hypothesis
• To test new ideas in conditions of extreme uncertainty, follow the Build-Measure-Learn cycle:

(Diagram from The Lean Start-Up)

• This process is not only for tech products. Use it to invent new products and services, and if you get traction with existing tools, then consider investing in tech.
• If you do not have a technical background, you will not know how to build a product so you could...

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...

The porn industry is behind many of the innovations that drive e-commerce and the consumer internet today. If you want to know what new trend is going to be the hottest thing in tech, the makers of smut probably have the answer.

Learning notes from this episode:

• The adult industry pioneered streaming video, tracking devices and online credit card transactions.
• Even before the advent of the internet, porn drove consumer tech. Author Patchen Barss  says that without porn, the VCR might have never taken off as a consumer product.
• Pornographers are not necessarily the inventors of new technologies, but they are  the first to use them and thus drive consumer adoption. Once a technology works for porn users, they often flow down to the mainstream.
• If you are a tech investor or a tech innovator, seeing what new products or use cases are happening in the adult industry, can help you spot the next big trend. The more you can pick up ideas from wherever they...