Data and AI Strategies

I want to draft an Artificial Intelligence investment thesis. The purpose of that would be to set a framework for an approach towards valuating investment opportunities in that domain. In the process of doing so, I wanted to share with you some basic thoughts and observations.

The emergence of Artificial Intelligence has been named the 4th industrial revolution, and its application is expected to be as widespread as cloud and mobile technologies are now.

The impact from Artificial Intelligence can be categorized in three ways:

  • Assisted intelligence, which helps complete tasks, taking away basic work from employees and freeing up time for more meaningful work. This results in increased productivity and profit margins, per employee.
  • Augmented intelligence, which provides additional insights and information, providing guidance in completing complex tasks. My favorite company in this field right now is Daqri. I believe that this field within Artificial Intelligence could be help with specific labor policies like Emiratization.
  • Autonomous intelligence, which allows for independent operation without human involvement. Machines will be deployed to act on their own.

Recent developments in Artificial Intelligence have mostly been in machine learning. It has powered autonomous intelligence, which has outperformed humans at tasks such as reading handwriting and recognizing objects. More recently, machine learning has outperformed humans with a lifetime of experience at complex games such as Dota 2, with only 2 weeks of learning, and at Go which is an ancient board game.

Machine learning requires extensive data to feed algorithms that generate a prediction or response. The accuracy of the algorithm’s predictions and responses increases as it ingests more data. This feedback loop implies that the more data a company has access to, the more successful its machine learning endeavors will be. Currently, large technology companies such as Amazon, Google and Apple are leading the industry in Artificial Intelligence development, with all the valuable data that it has access to, and the talent it attracts. These technology incumbents have a clear advantage in Artificial Intelligence development.

This incumbent advantage provides clear challenges to startups and investors wanting to be a part of the Artificial Intelligence revolution. Louis Coppey, a machine learning enthusiast I follow, suggests three strategies that startups can pursue to overcome this predicament, with a focus on keeping their technology defensible:

  • Collecting data from consumers in an untapped market.
  • Merging datasets sorted using different tools.
  • Proprietary ownership of unique datasets. 

Startups and technology companies, even governments, will have to find a way to use and collect data creatively to set themselves apart from the overwhelming amount of data that technology incumbents own and have access to.

Its worth looking into the Dubai Data initiative. For data focused businesses, its an opportunity to take advantage of data being shared across the public and private sectors. You'll find it here:


The Chance to Explore

“Impossible is just a word thrown around by small men who find it easier to live in the world they've been given than to explore the power they have to change it.” Muhammad Ali

This particular part of Muhammad Ali’s famous quote on 'Impossible', eloquently reminds me of my passion working with startup accelerators.

Tech startup accelerators that provide funding and mentorship are grass root efforts that give the opportunity for young people to build and create something, allowing them to understand and have a sense of ownership.

Without nepotism, but with authentic demand and user adoption, entrepreneurs have the ability to create value in both monetary terms and social impact empowering them to call their own shots. 

They are given the chance to explore.