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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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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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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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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

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

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My story: ambition, tech and the camel incident

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...
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The top skill you need to succeed in the Information Age

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...
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Feature creep – why apps get too complicated

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...
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How I built Make Love Not Porn - with Cindy Gallop

Would you leave a high flying career in advertising to set up an adult content site? Most people wouldn't, but Cindy Gallop is not most people.

After leading one of the world's top advertising agencies, BBH in the United States. Cindy decided to try her hand at tech entrepreneurship. Her venture, Make Love Not Porn, is in the new category of "social sex" and aims to revolutionise how people talk, share and watch sex. 

As a non-technical founder of an adult content business, Cindy had to learn how to work with developers, get users despite being banned by advertisers and create a troll free online environment.

Learning notes from this episode:

  • "You don't have to be a tech person to build something absolutely phenomenal in tech," says Cindy. Instead, you need a strong vision, the right team and the determination to keep going. 
  • "You do not need a technical co-founder from the beginning." In fact, delegating your vision to the tech person simply because they...
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How to innovate at a corporate: lessons from Apple and Intel

Every company wants to be innovative, but how do you balance the risk of innovation with the need to keep the lights on? Listen to this interview with Kapil Kane, Head of Innovation at Intel China, to find out.

Learning notes from this episode:

  • Most tech innovations die because they do not have a solid business case. As much as non-techies need to learn to speak tech, techies need to learn to speak business. “No matter how smart you are, if you are not able to get your idea across in the language of a lay person, you are missing out a lot,” says Kapil.
  • To structure creativity within an organisation, Kapil advises learning from Apple, where teams often worked on projects that other teams did not know about. This meant that they could focus on their work, while upper management connected the dots.
  • The innovation accelerator at Intel China Kapil set up brings in revenue, but that is not the only benefit. It serves as a training ground for ambitious people “If...
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Top mistakes non-technical founders make in UX design

Design is often at the core for why products go viral or flop. But, how can you tell good design from bad right at the start? How do you hire the right people and avoid costly mistakes?

That’s what you’ll learn on this episode.

Learning notes:

  • User experience designers, not developers, should be your first hire in the vast majority of cases.
  • Learning how to use design software does not make you into a designer. Learning to use a kitchen knife does not turn you into a chef. This is the same logic.
  • Great designers mix human psychology and the design process to make products that people want to use. The best designers are well versed in behavioural economics and human insight, not just tech tools.
  • Great designers are partners, who question your assumptions and sometimes tell you that you are wrong. Someone who only agrees with you isn’t going to help make your product great.

Resources mentioned in this episode:

  • Introduction to Design for Technology: Listen on Apple...
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