How to identify investment opportunities in a changing tech landscape

How is Ontario Teachers’ approaching growth investing at a time when many British pension funds have pivoted to bonds?

Avid Larizadeh Duggan: In the case of TVG, we invest in private venture growth, which is potentially higher risk but can lead to higher returns. We invest in private companies, from Series B post-product market fit (roughly $10 million in revenue) all the way to pre-IPO, and continue holding those assets when they go public, especially if we believe they are great assets. Underlying this is impact, which is a key driver in the way we do business and, in the climate space in particular, a lot of the innovation and impact comes through technology.

Ontario Teachers' invests globally. What are the investment hot spots around the world?

ALD: It's not just where companies are formed, it's where the capital is. It’s also where the deepest public markets, exit markets and acquirers, all exist. In tech, there is no market comparable to the US and, specifically, Silicon Valley. The large acquirers are there and that ecosystem, honed over decades, provides it with a significant advantage. In Europe, the UK, France, Germany and the Nordics are leading from a concentration standpoint. There's also a massive opportunity in India if you look at population growth and the digital transformation that's happening there.

You recently spent some time at Ontario Teachers' offices in Silicon Valley. What are some of the trends you're noticing?

ALD: I was at Stanford University in the late 1990s and early 2000s, right when the first Internet boom was happening. Silicon Valley was an extraordinary place to be, with no limit to your imagination. Today, it’s the first time since then that I feel that same energy, with what's happening in AI, this explosion of data, the tools around it and the cooperative ecosystem. There's still a lot of research that needs to go into AI to create more breakthroughs, but it is already incredibly exciting.

Thanks to the wide adoption of cloud and the ever increasing compute power and mobile technology, we are seeing a continuous explosion of data. Businesses now universally understand how valuable data is. Data can give you insights on your customers, your employees, your productivity and your competitors. It can help you improve your user journeys, your conversions and your sales pipeline. But to do so you need to first collect it, observe it, access it and analyze it. The infrastructure for our data and the tools that are required for businesses to extract value from it is a huge area of innovation.

Data is also the lifeblood of AI. For AI to work well — to give you high quality output — you need high quality data as an input and you need lots of it. It is no coincidence that we are seeing a step change in the AI world as a result of the accessibility of data and compute power. Generative AI is one form of AI which has enabled us to use unstructured data at scale as inputs for lines of text, articles and tweets, thus increasing the models’ understanding of the world. It has also enabled us to converse with machines to question, understand and interact with them. While they still have many weaknesses and limitations these will be addressed with time and with it, we are witnessing the creation of a new ecosystem of companies.

When the iPhone first came out, we were like, "That's great, but what does it really do for me?" But a couple of years later, a whole ecosystem of applications was built for the iPhone; millions of them, because the barrier to coding for the iPhone relative to the PC was much lower. In comparison, today, large language models (LLMs) give you the ability to use language to access certain logic and coding thus enabling millions of coders and innovators. It's still imperfect, but it's a huge step forward.

What’s your take on Generative AI from an investment perspective?

ALD: We view AI as a tool that will allow you to do something, which begs the question, what are you going to use it for? It's not magic — you must do your research and understand its limitations, and I see many companies coming to this understanding. There have been a lot of proofs of concept but for it to be adopted across an organization, questions around compliance, governance, reliability, transparency and ease of deployment still need to be answered.

Taking a step back, as an investor, you want to invest in people or products that are providing solutions to a difficult problem that needs to be solved. When I look at a company, fundamentally I evaluate: Who are the entrepreneurs. Are they resilient? Do they operate in a very large market that will continue growing? Is there a moat they can build? Do they have a unique value proposition and a sustainable competitive advantage? These questions remain the same across every investment.

With AI, we are still at the beginning.  LLMs and generative AI are building blocks within a larger system. The tools and infrastructure required to make the building blocks work together in a scalable way still need to be built. The pace of change is huge. Consumers are AI curious but fickle. Enterprises understand the productivity and efficiency gain the technology will bring and the competitive advantage of data moats. More breakthroughs are required, and they will come.  Over time, the evolution of AI will be more transformational than any other technology tool to date.

So for now, I spend most of my time looking at companies focused on data infrastructure and tools to scale the use, performance and adoption of AI while trying to stay close to the research that will enable more advances.