AI and the future of everything: a long-term perspective

Disruption Week 2023 / 4 minutes

Key points

  • Advances in AI have been made possible thanks to huge gains in computation, driving demand for NVIDIA’s chips and other accelerated computing products
  • New AI models may do more to reinforce the standing of the large tech platforms than disrupt them
  • Healthcare and education are among sectors set to benefit, but long-term investors still need to consider companies on a case-by-case basis

All investment strategies have the potential for profit and loss, capital is at risk.

WATCH: Investment manager Kyle McEnery discusses artificial intelligence and related investment opportunities in his Disruption Week webinar

It’s almost a year since ChatGPT took the world by storm. Many have described the chatbot’s arrival as a ‘tipping point’ for artificial intelligence thanks to its creative proficiency in natural language and, more recently, imagery. But if we step away from this moment and take a broader view, our perspective changes.

“The notion that we can capture intelligence in a machine has been around for a very long time,” says Kyle McEnery, an investment manager at Baillie Gifford who specialises in AI.

“In the 1800s, we had Babbage’s difference engine and its mechanical wheels. In the 20th century, vacuum tubes. Then, we built circuits in silicon. Machine learning got going when we connected the resulting computers to create the internet, generating lots of data. And they continue to get faster and faster and smarter and smarter.

“Yes, it’s amazing that they can now generate artwork and produce text, but there was a time when it was amazing that computers could do partial differential equations. My point is that to understand what’s happening today, it helps to see it as part of a big wave that’s been building for a long time.”

London’s Science Museum built a version of Babbage’s ‘19th century computer’ based on the inventor’s designs. © SSPL/Getty Images

As a long-term investor, Baillie Gifford is wary of getting swept up in the hype around AI but mindful of its transformative potential. It’s impossible to know how the technology will affect individual companies’ fortunes over the next five to ten years and beyond. However, at his recent Disruption Week webinar, McEnery shared some of the emergent themes influencing his thinking.


Brute force

For most of its past, work on artificial intelligence focused on teaching machines how to think by encoding rules and facts into their software. This became known as ‘symbolic AI’. The idea was that by showing machines how to manipulate symbols and facts, they could learn how to reason the way we do.

However, history has repeatedly demonstrated that a ‘brute force’ approach involving massive amounts of computation tends to be more effective at dealing with complexity.

As the computer scientist Rich Sutton observed in his 2019 essay, The Bitter Lesson, while it may feel more satisfying to build knowledge into agents, “breakthrough progress eventually arrives by an opposing approach based on scaling computation” – or, in simple terms, chucking enough processing power and data at the problem that computers can use the examples to find solutions themselves.

“We started getting better results when we stopped trying to be so clever and just started using machine learning, taking our hands off the wheel to see what the AI models could learn,” McEnery adds.

ChatGPT’s underlying large language model follows this trend. Its maker OpenAI has improved on it by building larger models that learned from bigger data sets – effectively all the accessible text on the internet – requiring more computing resources. Humans are still involved in fine-tuning, but the point remains that access to increased computer power is critical.

“This learning paradigm is very hungry,” comments McEnery.

“That means that computing company NVIDIA’s chipsets and other accelerator hardware need to continue getting better because we’ve got to the point that you can’t do machine learning at scale without them.”

Strong demand for NVIDIA’s Grace Hopper Superchip helped the company achieve $1tn valuation status. Baillie Gifford first invested in NVIDIA in 2016. © Shutterstock/glen photo

But McEnery makes the additional point that for specialist AI applications you also need to have access to information-rich examples.

“You can argue that the intelligence is in the data,” he explains.

“So the most important question becomes: where can you get high-quality data and lots of it? And when you answer that you can identify which elements of society can be highly affected by machine learning.”

This is where companies like Ginkgo Bioworks can gain an advantage. It is working with Google Cloud to build AI models to help it ‘engineer biology’ based on its proprietary DNA database, containing about two billion protein sequences.

Another example is Wayve, a self-driving technology startup whose technology is being tested by the grocery delivery firm Ocado. Wayve recently trained a generative AI model using thousands of hours of driving data it had collected in London. The model lets the firm create new high-definition road traffic footage by providing descriptive text prompts. This will allow Wayve to validate how its self-driving system copes with unusual situations and improve it via unlimited examples.


Disruptive or not?

The history of technology is full of tales of disruptive innovations’ destructive effects on incumbent businesses. Combustion engines spelt the end for most horse-drawn carriage makers. Ecommerce has driven scores of department stores to close. And online search has devoured yellow pages business directory publishers.

However, generative AI – the ability to create text, images and other output to human-like levels – may buck that trend, at least regarding the tech giants and their platforms.

“It’s costing about a billion dollars to train some of these models,” says McEnery. “That’s why OpenAI has tied up with Microsoft, and Anthropic, a rival AI assistant maker, has partnered with Amazon and Google.

“But it’s not just about money. Distribution matters. So, Microsoft has an advantage in having millions of users for its Office apps users as it rolls out its GPT-powered Copilot features. Google leads in search, which could help its Bard chatbot. And Meta’s new image-creation tools and AI features for advertisers should benefit from its ownership of Facebook, Messenger and WhatsApp.”

In the words of tech analyst Ben Thompson, generative AI could become a ‘sustaining technology’, improving the performance of existing products, rather than a ‘disruptive’ one that displaces them, at least in its early stages.

“But that’s not to say there won’t be a shake-up,” adds McEnery. “And the most interesting prospect of that happening would be if the distributing channels changed in some way.”


Accelerated advances

More broadly, AI has the potential to create long-term value by making it easier and cheaper for companies to bring new products to market and improve on their current efforts.

“Healthcare’s an interesting example,” says McEnery, “because it’s always been hard and very expensive. But we’re seeing companies like Recursion Pharmaceuticals and Exscientia use AI to rewrite the economics of drug discovery.”

Recursion Pharmaceuticals uses AI to analyse biological datasets that are too complex for human interpretation. © Recursion Pharmaceuticals

Education is another sector where the effects of the technology are already visible. Duolingo, for example, has started using GPT-4 to let its subscribers improve their foreign language skills by roleplaying situations with a bot. Likewise, Coursera has an AI-powered tool in development to help educators structure new classes and generate content for them based on its existing library of online lessons.

And this is just the start. From moviemaking to video game creation, automated transport to smart energy grids, the applications for artificial intelligence appear almost limitless.

As long-term investors, we can benefit from continuing to take a patient approach, considering each business on its own merits and recognising that these AI developments have been a long time in the making and will play out over many years.

“No good companies are based solely on technology,” reflects McEnery.

“They are based on solving problems. So, we’re not going to invest in companies just trying to develop AI or models for their own sake. It all comes back to who will benefit from the technology’s use and what will the knock-on effects be.”


Discover more about Kyle McEnery’s thoughts about AI’s investment opportunities by watching the video at the top of this article of his Disruption Week webinar.

Words by Leo Kelion

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