Welcome to the Tech for Non-Techies podcast

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...

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...

“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. 

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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...

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:

• Sophia Matveeva’s LinkedIn
• Listen to The Business of AI...

It’s easy to put the tech sector on a pedestal, as we’re constantly bombarded with its power and profits. But “technology is just a tool to affect business outcomes,” says prop tech entrepreneur Sebastian Rivas.

Sebastian runs Andes STR, a which uses machine learning algorithms to find property investments for short term rentals. If you want to invest in a property and rent it out on Airbnb, Andes STR will find the investment and manage the rental.

Sebastian started his career in finance, and created a smart plan to break into tech. Listen to this episode to learn how he did it.

Learning notes from this episode:

• Technology is a tool used in business to improve efficiency, user experience and productivity, but it is not an end in itself.
• Being tech savvy and understanding how technology influences business outcomes is a must have in today’s working environment, almost no matter where you work. Even your coffee shop has an app!
• “The biggest...

You’ve probably heard the term cloud computing, but like most non-techies, you’re not sure what it means. In this episode, you’ll learn what it is and how businesses use it to solve problems.

You’ll learn from DJ Johnson, who works at Microsoft Azure. DJ started his career as an NBA player and transitioned into a career in tech.

Learning notes from this episode:

• Cloud computing allows businesses to rent space to store data. Previously, companies had to store data on their own servers, which was much more expensive.
• The two biggest players in cloud computing are Amazon Web Services and Microsoft Azure.
• As a non-techie, first identify business problems and then see if technology can fix them.
• For example, during Covid when suddenly many people ended up working from home, one of DJ’s clients suffered from major time lags in their communications. Their internal messenger service was taking 3 days to deliver a message! This was making customers...

Big data and predictive analytics can help you make profits, sell clothes and strike oil. But, unless you know how to ask data scientists the right questions and then use their answers, data are just a collection of meaningless facts.

Listen to this episode to learn what data scientists do and how to work with them.

Learning notes from this episode:

• Every senior level professional today has to learn to speak tech: knowing the concepts of how digital products get made is now basic literacy.
• Working with data scientists can be broken down into three steps: 1) ask the right question, 2) get insight 3) take action based on the insight.
• Predictive analytics are based on past data, which does not make predictions future proof and does not take account of shocks to the system.

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