Welcome to the Tech for Non-Techies podcast

To become SMART MONEY as an investor, founder or corporate innovator, you have to know what questions to ask about a product. This helps you spot signs of early success or early warning. 

Listen to this episode to learn what questions to ask and how to link product innovation to business strategy. 

Learning notes from this episode:

• The questions fall into three buckets:
  1. How do my best customers behave?
  2. What are the characteristics of my best customers?
  3. What has to happen for them to abandon the product?
• For bucket 1, you could ask:
  • What features do my most active users use?
  • What screens do they visit?
  • How often do they open the app?
  • What time of day do they open it and on which days?
• For bucket 2, you could ask:
  • Where did these customers come from?
  • What are their demographics? Are there any patterns?
• For bucket 3, you could ask:
  • What screens tend to be the last screen that people get to before they shut down the app?
  • What prices of other apps they use have?...

Even the smartest professionals who don’t have backgrounds in digital businesses make the same mistakes when it comes to tech start-ups.

They often want vanity metrics, as opposed to what truly matters, and because they don’t know how a tech product gets made, they don’t know how to properly evaluate an opportunity. 

In this episode you'll learn 3 core tech concepts and how they apply to early stage investing.

Learning notes:

• There are fundamental differences between software products, that are especially important at the early stages. This is because, when a product is very new, it is still in development mode. This is why understanding product development is vital at the early stages.

  For example, evaluating Airbnb as a listed company focusses on typical investment metrics: revenues, costs, growth etc. These would have been unavailable when Airbnb first launched, so investors must look for other signs.

• Tech products are always evolving. For example,...

Listen to what happened when Apple forgot a key market and how to avoid the same mistake. When product teams consist of entirely white males, they make products for white males. When non-technical professionals learn to Speak Tech, you get better products, happier customers & better profits.

Learning notes from this episode:

• While there are plenty of programs to get minorities into STEM, they will take years to have an effect.
• In the next few decades, most developers will continue to be white males. To prevent baking unconscious bias into products, the simplest, cheapest and fastest way is to teach non-technical teams how to work with the techies. 
• Bringing diverse voices into product development is not a moral issue; it is capitalist self-interest. E.g. if women are not involved in product innovation, companies can lose up to 50% market share. 

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Listen here on Apple...

"One of the most common types of advice we give at Y Combinator is to do things that don't scale," says Paul Graham, Y Combinator founder. Recruiting users manually and getting feedback is what lets you build a scalable product.

Learning notes from this episode:

• "The most common unscalable thing founders have to do at the start is to recruit users manually. Nearly all startups have to. You can't wait for users to come to you. You have to go out and get them." - Paul Graham

• A product is always a solution to a problem someone is experiencing. The better you understand the problem and the users, the better the product will be. This often means 1:1 conversations with your customers.

• This advice doesn't only apply to early stage start-ups. If you are creating products, you are always looking for customer feedback to make them better. Brian Chesky still books Airbnbs to live in so he can experience his product as a customer.

Resources mentioned in this episode:

• Do things that don't...

“There are things that are much more important about investing in technology companies than technology,” says Colin Beirne, Founder of Two Sigma Ventures. TSV has invested in around 100 start-ups over the last 10 years, and funded 10 unicorns. They’re part of Two Sigma, a hedge fund with more than $60 billion under management.

Colin is surrounded by data scientists and programmers, but doesn’t have a background in programming. Listen to this episode to hear how Colin went from a liberal arts college to becoming one of the world’s leading deep tech investors.

Learning notes from this episode:

• “The winning company is not always the one with the best technology. Tech can be a differentiator, but usually it’s only temporary. The job of a venture capitalist is not to figure out which company has the best tech. It’s to figure out which company has the best business that can ultimately be the biggest impact,” says Colin.
• Data science...

Real estate marketplace Zillow took $500 million in write downs and fired 25% of its workforce in late 2021, largely because a pricing algorithm made a mistake. Learn what went wrong and how to avoid it.

Learning notes from this episode:

• Zillow started life as an online marketplace for real-estate in 2005, and monetised via advertising. The company decided to diversity and get into the business of flipping houses. Zillow used an algorithm to find properties it believed were undervalued and bought them. 
• When the housing market turned, Zillow was left with massive losses, but its competitors were not.
• Behind every algorithm is a set of assumptions made by humans. For example, factors like crime rates and commuting distances affect real estate prices and would go into a property pricing algorithm.
• Machine learning models often assume that the past equals the future, but that is generally not the case in the real world. When the economy changed, the Zillow algorithm did not...

AI is a great tool to help you make decisions, but it's often not sophisticated enough to make good decisions by itself. This is why companies often rely on AI to do most of the task, but leave the final decision to humans. 

• Most tech initiatives fit into one of these three buckets:
  • Reach scale
  • Increase efficiency
  • Increase customer satisfaction
• Fashion retailer Stitch Fix uses a stylist algorithm to select outfits to send to customers, but the final selection is made by human stylists.
• The Netflix content team uses an algorithm to get suggestions on how much to pay for new shows, but ultimately the final decision rests with them (and isn't always the what the AI suggests).

Listen here on Apple Podcasts. 

Listen here on Spotify.

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There are 2 ways to apply this work to your goals:

For individuals, APPLY...

Today, I’m doing something a bit different. As our smart community grows, I know that some of you might not know much about me, my story or how I got into this tech thing.

That’s why today, I’m sharing a little bit about me.

I’m sharing this with you so that you can see that the confusion you feel about tech, or the fear that your lack of tech knowledge will be discovered, does not have to be your permanent reality. I want you to see from my example that there are many more opportunities for you than you probably think.

You will also learn what not to wear when riding a camel.

Summary notes from this episode:

• I always wanted to have a great career, but when I graduated in 2005, tech wasn't what it is today. I started my career in the media, then worked in private equity and became a non-technical founder after my MBA.
• I planned to use my MBA to transition into a career in tech, but this was harder than I thought. Business school gave me business skills...

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...

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...