Welcome to the Tech for Non-Techies podcast

110. Cutting through tech hype with the Actionable Futurist

Conferences are full of speakers saying that the latest tech will change the world, but that often leaves smart people even more confused. Knowing about trends is irrelevant if you don't know what to do about them.

To learn how to cut through the tech hype, listen to this episode with Andrew Grill, the Actionable Futurist. Andrew began his career as an engineer, became a Global Managing Partner at IBM and today is a keynote speaker on tech & business trends.

Learning notes from this episode:

  • “To understand the technology, you need to play with it,” Andrew says. Using new software or devices at home makes you comfortable with trying new technologies. (e.g. try TikTok! you'll see what an engaging algorithm really feels like and you'll have a laugh)
  • Innovation theatre is a problem if there is no clear understanding why a company has a digital strategy. This is usually a leadership issue, not a tech issue.
  • The job title of Chief Digital Officer or...
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109. Do this to become a Digital Collaborator today

To lead in the Digital Age, you need to become a Digital Collaborator. The best way to learn anything quickly is to put yourself in a situation where not doing it isn’t an option.

Listen to this episode to learn what you can do to start collaborating with tech teams and take your career to the next level.

Learning notes from this episode:

  • If you work in a corporate, set up a weekly meeting with technologists and your team to discuss what they’re working on and how it impacts scale, efficiency, and customer satisfaction. This public commitment to collaboration removes your choice to delay.
  • For example, if you work in marketing, set up regular meeting with the data science team and begin by outlining your goals for the year and where you see the biggest bottle necks. While the data science team might not have solutions right away, this will lay the foundations for future collaboration. 
  • Another way to do become a Digital Collaborator is to volunteer...
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108. How to work with a data scientist

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...
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107. Top questions to ask about an app to become smart money

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?...
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106. What angel investors REALLY need to know about tech

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,...
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105. A surprising outcome of Speaking Tech (& a lesson from Apple Watch)

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. 

To get Sophia's monthly business update, register here. 

 

Listen here on Apple...

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104. Do things that don't scale

"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:

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103. How I got into deep tech investing (with Colin Beirne, Two Sigma Ventures)

“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...
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102. When AI goes wrong - Zillow case study

ai and big data Jun 08, 2022

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...
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101. How companies really use AI

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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