This talk was delivered by Aravind Srinivas on October 3rd, 2026 to the Cambridge Union Society. The Cambridge Union Society is the University of Cambridge’s largest student society and the world’s oldest continuously running debating society.

Hello, and thank you. It’s an honor to be here.

One of the things I didn’t expect about founding a company is how much time I get to spend at universities. I enjoy it. As a CEO, universities tend to have the only audiences who argue back. So we’ll reserve more time for discussion than I plan to spend speaking.

Let’s begin.

Normally, when I’m invited to speak somewhere, they ask me to deliver a predictive talk titled with something generic like “the future of AI.” I’m not going to talk about the future of AI. Instead, I’m going to talk about the future of you. Your future is more interesting to me, and it’s probably more interesting to you, too.

First, a little bit about the past. Cambridge is the perfect place to talk about the past at the same time as your future, because it has both of them.

As the story goes, what we now call the peer review system was invented here at Cambridge. The accurate truth is that it was just a Cambridge professor, and he proposed it at the Royal Society of London, not here at the university. That was in 1831. Today, more than 3.5 million scientific papers are published every year under the peer review system.

About 160 years later, two PhD candidates at Stanford were working on network structures and data mining. This was when the Internet boom of the 1990’s was just kicking off. The World Wide Web was all that anyone could talk about. Sort of like “AI” today.

Also like today, most of the people talking about the boom were investors and media. The “tech community” then was the telecommunications industry. There weren’t a lot of academics in the discussion. (Some, but not a lot). And these two PhD students weren’t exactly the insiders of the Internet boom.

But they knew one thing that people outside the university system didn’t really care about.

Like most academics, they understood the purpose of the university. The purpose of the university is to advance knowledge.

Which is why we use peer review. It’s also why academic writing has so many citations. And these two students knew that the amount of knowledge in the world (proxied by the number of peer-review quality papers) only grows. So it was easy for them to see the Web would also only grow with human knowledge.

They also had experience with an old problem that was “new” to the web: how to find the right knowledge, or the best knowledge, if it’s always expanding. In the old model, academics judge whether somebody’s research is useful or credible based on the number of times it’s been cited.

So they had the idea to build an algorithm that evaluates every website on the Internet based on how many other websites cited it, or linked to it, in relation to the content of those sites.

They called the system “Page Rank," because they were ranking every page on the web and because one of these PhD candidates was named Larry Page.

Larry and his partner (Sergey Brin) took this one little academic principle and made it available to the mainstream. Google became one of the most useful tools in history, and one of the most successful businesses, because Larry Page simply adapted what universities have already been doing for a hundred years.

As I said, the purpose of the university is to advance knowledge. I am going to tell you three things about knowledge in this talk. My hope is that we will come to a better working understanding of artificial intelligence by thinking about some of our own.

First, knowledge has a life. It’s born, it decays, and it dies.

It has a lifecycle. And because it has a lifecycle, knowledge undergoes evolution.

As what we know evolves, we’re all getting smarter. It’s called the Flynn Effect. The Flynn Effect is the observation that humanity has been getting smarter by about 3 IQ points every decade.

IQ tests are constantly re-normed so the mean stays at 100, so an easy way to think about the Flynn Effect is in percentiles. In 1950, someone in the 92nd percentile of intelligence would only be in the 50th percentile today. We all got smarter.

1950 is also the same year the Turing Test was invented. That was the first test to determine if or when a machine is smarter than people. That means the Turing Test in 1950 was a lot easier for a machine to pass. Because the bar for “human” intelligence was lower.

And that’s the power of evolution. Knowledge always leads to more knowledge.

The downside is the world can get smarter, but you as an individual can get dumber. Knowledge decays. Think of it like a half life. The half-life of a radioactive element is the amount of time it takes for half of it to decay. The half-life of knowledge is the amount of time it takes for half of it to become obsolete.

Here are some examples, from right here at Cambridge.

In 1978, Sir Roy Calne helped introduce cyclosporine into transplantation. This was important for preventing organ rejection. The first clinical trials were at Cambridge. By 2001, another drug, tacrolimus, had become the predominant treatment in its class in American kidney transplantation. So you could say the medical knowledge of cyclosporine treatment had a half-life of 23 years.

It’s an imperfect measurement, but it’s a useful model for evaluating engineers in fast-evolving fields. We do this in tech. The implication is, “at what point in this engineer’s career is half of what they know fully obsolete?”

At what point in your career will it happen to you? The answer depends on how much time you spend learning new things.

Sadly, in rare cases knowledge also dies. There are languages with no remaining speakers. There’s wisdom that dies with an elder.

This is why, for more than 4,000 years, humans have built libraries. And when the web came along we put infinitely more of what we know onto it. So knowledge dies, but it doesn’t have to. Perhaps as some innate aspect of the human condition, people have felt compelled to preserve knowledge. We’re constantly saving it, and growing the world’s total knowledge. That’s a good thing.

I should note: knowledge is like a living thing, but it is not living. Only you are alive. I just think you’re more alive when you’re learning something new.

This is why Perplexity exists, by the way. But I’m not here to talk about Perplexity. I’m here to talk about you, and the economy of knowledge. Which brings me to my second point.

Second, knowledge is not money. But it behaves like money.

There is a time value of money. Interest makes it grow, and inflation makes it shrink. With enough time, both interest and inflation are compounding.

When a hundred thousand pounds has the buying power of forty thousand (probably in about 23 years), you’ll need £250,000 to break even. 23 more years after that and you’d need £625,000.

When things compound, the gains or losses grow exponentially with time.

That’s why the most basic financial principle is that you should always be compounding interest at a rate greater than inflation. Because if you’re not growing, you’re shrinking. Knowledge is the same way.

Just like the value of a pound, the value of what you know today will depreciate over time. And just like regular depreciation, it’ll do this only a small amount every day. It will be so small that you hardly notice it. Until one day you wake up and have nothing. Your knowledge will no longer have the same market value. This is inevitable. Remember, the Pythagorean Theorem was once something only elites were allowed to know. But everyone here learned it in Grade Seven.

We all get smarter, forever. As I mentioned, this is the whole purpose of the university. With luck, someday far in the future, what you studied here at Cambridge may be the curriculum for seventh graders.

That’s a good thing if it happens. It means progress. But it also means your economic goals require you to never stop learning. You must learn with an interest rate greater than the inflation rate of knowledge.

Put another way, you must double what you know at a rate greater than your own half-life of knowledge. This takes time. But when you view the world through the lens of compounding, you have to view it with a long future.

That’s why the best trick for compounding your wealth is to start young, and the second best way is to die old. Warren Buffet started investing at age 11, but 99% of his wealth accumulated after 65.

Which hints at the final similarity of knowledge and money. You have to spend it.

When you have capital, you deploy it to do work. When you have knowledge, especially exceptional knowledge, you should put it to work. Do something with it. That’s the difference between invention and innovation. Invention asks “did you make something?” Innovation asks, “Did it make a difference?”

And if you want to make a difference, you must take action on knowledge. Only action triggers new questions, inquiry, and new knowledge. You must always be learning, and always be doing.

This is where artificial intelligence comes in, and it leads me to my third and final point.

The purpose of AI is to accelerate human knowledge and prosperity.

Consider these first two things I’ve said this evening. I said that knowledge has lifelike character, and we should nurture it and advance it, because broadly people benefit. Then I said knowledge has an economic character. That we should cultivate it, because broadly speaking, all people benefit.

If you believe these two things, then you have to also think about AI slightly differently.

Instead of being obsessed with achieving AGI or making technology smart for technology’s sake, you would conclude the reason to build better, more-accurate AI is to serve people.

You would conclude that the highest and most important purpose of AI is to make people smarter and more prosperous.

If this sounds a lot like the purpose of the university, well here we are.

Think about peer review again. It was invented in 1831. This year Perplexity launched a very simple product called Model Council, based on an idea posed by one of OpenAI’s founding researchers. You ask three AI models the same question. They each research and answer, and then a “chair” model evaluates where they differ and agree, and reasons a final answer.

Model Council does not replace peer review. Instead, it’s purpose is to bring the spirit of academic inquiry to more people. We can expand the ethos of a university beyond these walls, and all over the world.

We could help more people be right than just feel right or sound right. To scientifically seek truth and knowledge. The arc of the universe is for everyone to have more access to more knowledge. It is good for people to have knowledge. And it’s people who benefit.

I’ll close with a short story about my company.

When we started, we were just three AI researchers, with more questions than vision. Then, for a 4th person, we decided it was time to get health insurance. Health insurance in America is complicated even for Americans. I’m not from America, so it was even more confusing to me.

The Google results were no help. And the normal thing a founder does in this case is they call someone who’s done it before. Like an investor, or another founder. But it was late at night. I wanted to get it done, so I had to find the knowledge myself.

The problem with that is doing the research yourself means you Google through SEO-optimized insurance sites that are all bad advice competing to look the most credible.

That’s when I built the first version of Perplexity.

It did the research for me. And I made it cite the answers. So I could verify, and keep asking more questions. And then I set up our healthcare. It was solved. On to the next problem. And the next problem, and the next problem. And here we are today.

The moral of that story is you don’t need to “know a guy” anymore. You can just learn stuff and do it. I’ve already told you the promise of AI is that you can access any knowledge. The moral of this story is that any person can access any knowledge.

This is the ultimate promise of AI. Anyone can now be curious. Anyone can build anything. Anyone can now enter the temple of knowledge. That benefits everyone.

And it’s good news for you. It means the most important thing you can do, for the rest of your life, is to be curious. The most successful among all of you will be whoever has the most questions. And the best questions.

So my advice to you is to become as curious as you can. Do not settle for scrolling social media on your phone. Do not settle for an AI agent that just makes dinner reservations–do some real work with it. And never believe a chatbot that tells you you’re already right.

Believe in the spirit of inquiry. Believe in what you can learn with AI. Believe in what you’ll make when you do. And for all of us, make it good.

Thank you.