I learned to make my first index.html in 1996 toiling away from my i386 sitting in my parents’ basement in Saskatoon, Saskatchewan with nothing more than a little help from some ICQ friends whose identities to this day remain a mystery. Since then I’ve spent thousands of hours playing around with hardware and thousands more making websites and apps now used by hundreds of millions.

I vividly remember sitting in math class in 1998 telling my friend Thomas how cool it would be to one day have a laptop (they didn’t really exist yet). To me having the freedom to work from anywhere with a laptop was exactly what success looked like. Fast-forward 26 years and I’m still living that dream (even if digital nomads practicing geographic arbitrage en masse from Bali to Bangkok to Bogotá have made it orders of magnitude less cool).

Like many people that work in product, I enjoy making things other people find useful. There’s a raw fulfilment that comes from getting instant feedback at scale which I truly love. Throughout my life as I’ve worked across various international organizations, corporates, start ups, scale ups, and investor circles, I’ve always seen the world through this geographically detached index.html lens.

Index.html was the building block for my career and as geeky as it sounds, my life. Learning that basic skill opened countless doors and enabled me to travel and experience countless adventures.

Getting access to broadband internet in the mid-90s in Canada gave those who adopted it an unfair advantage in competing on a global scale. And today the invisible hands of ever-advancing tech are at it again with AI.

Anyone with internet access now has at their fingertips the ability to leverage vast amounts of data, automate complex tasks, and create smarter, more adaptive digital experiences at breakneck pace.

AI democratizes our ability to be smart and to innovate, making it accessible to anyone with the curiosity and drive to dream of its potential. It simplifies tasks that once required extensive technical skills, allowing more time for creativity, problem-solving, and improving the AI models themselves.

AI enables a level of personalization and efficiency in development that was unimaginable when I built my first applications. For the next generation, this means the ability to bring ideas to market faster, adapt in real-time to user feedback, and manage systems more effectively, setting a new standard in how products are developed and how businesses operate.

There’s a global AI race with trillions earmarked over the coming years to create astonishingly powerful AI infrastructure and services. Applications stretch as far as human intelligence does permeating global security, finance, and trade.

For the world of digital products, the next decade is going to look very different than the last. Low complexity and medium complexity products now have a range of solutions to speed up discovery and development which will have widespread implications for product discovery and development across thousands of small, medium, and large Private Equity and Venture Capital portfolio companies.

For product design Canva, Figma, Midjourney, and Photoshop are already lowering the bar for new generations of non-professional designers. And while the line between design and development is becoming increasingly blurred, when it comes to execution itself, tools like Builder.io, Vercel, FastGen, Retool, and Webflow are already enabling teams without technical chops to ship and market low to medium complexity eCommerce and SaaS products at scale.

  1. Customer Service Chatbots:
    • Example: Simple chatbots used in customer service can handle routine inquiries such as checking account balances, updating personal information, or tracking an order. AI enhances these interactions by quickly processing requests and providing instant responses, improving efficiency and customer satisfaction. Klarna’s AI assistant handles two-thirds of support requests which is similar to our experience implementing the same at GoStudent and Grover.
  2. Educational AI Tools:
    • Example: GoStudent uses AI to enhance its tutoring with the GoStudent Learning platform. Features include Short-answer Automarking for instant grading, Co-Tutor for real-time tutor support, and a Tutor Lesson Plan Generator that crafts personalized lesson plans in seconds. These tools collectively streamline the educational process, making it more efficient and tailored to individual needs.
  3. Content Recommendation Systems:
    • Example: Streaming platforms like Netflix or music services like Spotify use AI to analyze user preferences and viewing habits to recommend personalized movies, shows, or music. This not only enhances user engagement but also helps these platforms increase content consumption and subscriber retention.
  4. Personal Finance Apps:
    • Example: Finance apps like Cleo or YNAB (You Need A Budget) use AI to categorize transactions and provide personalized budgeting advice based on user spending patterns. This simple application of AI helps users manage their finances more effectively without complex inputs.

For high complexity products, while AI will in the short-term be able to remove some friction, there will still be a need for technical specialists for discovery, development, and maintenance for some time to come.

  1. Healthcare Diagnosis Tools:
    • Example: DeepMind’s AI models aim to predict patient deterioration, providing critical data that can inform medical interventions. Yet, the implementation of these interventions based on AI predictions must be guided by medical specialists who understand the complex dynamics of patient health and can evaluate AI insights within the broader context of each patient’s unique medical history.
  2. Autonomous Vehicles:
    • Example: Self-driving cars, like those developed by Waymo or Tesla, incorporate sophisticated AI systems to navigate and respond to real-time road conditions. However, the AI in these vehicles is not yet capable of handling all driving scenarios safely without human oversight, especially in unpredictable environments or complex urban settings.
  3. Legal and Compliance Software:
    • Example: AI applications in the legal field, such as Harvey.ai, which provides legal research and insights, face challenges in fully grasping the subtleties of law and precedent that require nuanced human judgment. While AI can support research, the final compliance decisions and interpretations often rely heavily on skilled human professionals.
  4. Complex Manufacturing and Robotics:
    • Example: In industries where precise and varied tasks are required, such as in aerospace manufacturing, robots equipped with AI can perform certain tasks such as assembly or welding. However, tasks that require high adaptability, problem-solving, and decision-making under variable conditions still require significant human intervention.

For many, AI is a nebulous shapeless concept which poses a significant threat to their livelihoods at best, and at worst strikes existential dread. But like with any educational endeavour it all starts with overcoming our fears and learning the building blocks.

Here are some of my favourite resources for learning the building blocks of AI:

  1. SuperHuman AI Newsletter
  2. Large Language Model (LLM) Leaderboard
  3. Course to Get Into LLMs
  4. Chat Product Requirements Document
  5. AI Tools for VCs

Work with me or Chat with C3PO (my GPT)