Welcome to the Tech for Non-Techies podcast

130. Lessons for Digital Leaders from the London Stock Exchange - Microsoft deal

This December, Microsoft (founded in 1975) took a 4% stake in the London Stock Exchange (founded in 1698). As part of this deal, the LSE will spend at least $2.8 billion on Microsoft’s cloud related services in the next 10 years.

Big Tech and finance have been getting closer and closer in recent years. The CME has a deal with Google, AWS has a deal with Nasdaq, and almost all banks and insurers now use big tech’s cloud services.

This is a sign of things to come for all industries, and carries lessons for Digital Leaders.

Lessons for Digital Leaders:

  • The senior management of the London Stock Exchange today has to have a different skill set to what it had 10 years ago, because digital technologies are now an integral part of the business.

    • The same logic either already applies or will soon apply to other industries. If you want to have a future proof career, you need to learn to Speak Tech and collaborate with your technical colleagues.
  • This is a case study...

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128. Business reality doesn’t match AI hype (yet)

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...
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122. What's a Digital Mindset & how do you get one?

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...
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114. What is Deep Tech?

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...
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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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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

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99. 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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94. Learning effects: why getting more users isn't the only key to success

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:

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