Britain’s artificial intelligence startups are enjoying a moment in the sun. As I reported last week, companies developing AI solutions attracted VC cash to the tune of $2.1 billion in the first half of 2024 and according to the Dealroom/HSBC Innovation Banking report, the stars are aligned for record investment over the year as a whole.
However, despite the rush of VC cash, Britain’s AI innovators face the daunting challenge of developing and commercializing their technologies and solutions at a time when startups in the U.S., China and elsewhere are also pitching for customers. And of course, at the top of the tech industry tree, the likes of IBM, Google, Microsoft and OpenAI are spending billions of dollars in order to stay ahead of the game.
So how does a relatively small startup company navigate a course from the lab to the marketplace?
Earlier this week I spoke to Noel Hurley, an IT industry veteran who spent 20 years working for CPU design company ARM over two periods. During his time with the business he occupied Vice Presidential roles in a number of divisions, including CPUs and Incubation. In January of this year, he was appointed CEO of Literal Labs, a spinout from the University of Newcastle in the northeast of England and a company at the start of its commercialization journey. Given his experience working for that rarest of beasts – a U.K.-originated tech company that has achieved real success in the global marketplace – I was keen to get his take on the opportunities for Literal Labs and other research-led AI contenders.
Finding A Market Niche
Let’s start with Literal Labs. As Hurley sees it, Literal Labs has a compelling pitch to potential customers. Rather than using the neural network technologies that underpin much of today’s advanced AI, the company has built on a concept known as the Tsetlin Machine. For those of us not steeped in the theories of computer science, all we really need to know is that this is a technology based on a concept known as propositional logic. According to Literal Labs, it can produce tools up to 1000 times more energy efficient than the neural networks equivalents.
Does this matter? Well, as reported recently on Forbes.com the demands of AI could be pushing the world towards an energy crisis. That tends to be seen in terms of a massive rise in consumption putting pressure on grids, but it perhaps also creates problems for businesses that could benefit from embedding AI into their own systems.
Hurley cites the example of production lines, where AI could be used to optimize processes and drive efficiency – a holy grail of the AI revolution – but the costs may be prohibitive.
So Literal Labs commercial focus is on Edge computing – the kind of processing that takes place not in distant server farms but close to the action. This is where Hurley sees a beachhead.
“There’s a sector of AI called edge AI, and that is about using artificial intelligence in everyday devices, and that can either be from a consumer perspective, but also from an industrial perspective as well,” says Hurley.
In addition to production lines, the company sees opportunities in driverless cars, robots and consumer devices. In addition, Hurley says the Tsetlin Machine concept is more transparent in terms of tracking the relationship between input and output. As such, it lends itself to the demand for so-called “explainable AI. For instance, let’s say a tool uses a wide range of data to approve or reject mortgage applications. In regulated industries, there is a growing demand for technologies that can explain the processes that result in these decisions being made.
So there are niche markets to address – something that is perhaps essential for a startup company – but if solutions based on Tsetlin Machine concepts provide a silver bullet, why isn’t the technology being used more widely? Hurley acknowledges a trade-off. On the one hand, there is speed and efficiency. On the other, the processing is currently less accurate. The focus now is on engineering more accuracy while also finding applications where current levels of accuracy are “good enough.”
Building Confidence
In the meantime, there is the daunting prospect of finding customers and use cases. As Hurley concedes, that will involve a lot of conversations to forge relationships and, in the first instance, build confidence in the technology.
“Building a community around this technology is going to be important as is building partnerships around this technology. Partnerships with neighbors in the value chain are going to be important as well. As you know, there’s going to be a certain amount of evangelism that’s going to get engineers comfortable and enthusiastic about this technique and wanting to build and develop models using this approach,” he says.
As such it’s a long-term play and the same is likely to be true for a great many of the AI-focused companies that are currently spinning out from British Universities.
Holes In The Road
And the road ahead is uncertain. It may be that the upturn in AI investment will turn out to be a bubble that will ultimately deflate, making it harder for new businesses in the sector to get a hearing and raise capital. Then there is the nature of the investment to consider. When earlier this month I spoke to Yoram Winjgaarde, founder of intelligence provider Dealroom about AI investment across Europe. As he pointed out, it’s not just VCs who are interested in AI. “The big tech companies are a massive component of AI investment,” he said.
All well and good, but investment in U.K. and European AI by established big tech companies could undermine the ability of companies on this side of the pond to thrive and grow under their own steam. This was something Hurley talked about in a recent interview for Fortune in which he warned that U.K. AI could become just a sidekick for the American giants.
So what are the chances of a small research-based company finding real traction ?
Hurley returns to his experience at ARM, where the same groundwork had to be done. “Working in arm in my second phase, you could walk into any semiconductor company and get an audience immediately. That was not true in the early days,” he says.
But what the early-stage ARM did have was an ambition to win orders in a global market. Hurley cites the leadership mantra of the company’s first CEO, Robin Saxby. “And he was very strong in saying we’re a worldwide company. We’re going to spend our time and effort with our customers and we’re going to have a worldwide focus. It’s not about being the best in Britain, it’s about being the best in the world,” Hurley says.
Finding The Gaps
But given the cloud of the existing big tech businesses, is there room in the marketplace for companies emerging from U.K. universities today? Hurley says the opportunity – initially at least – lies in identifying markets and use cases that are of little interest to the tech giants.
“Now a lot of these big organizations are not going to pay any attention to a whole bunch of niches at this stage just because they’re going after the big contracts and they’re going after contracts that are going to give them as much return with as little effort as possible,” he says. “There are often gaps that they’re not paying attention to. It’s about looking at the technology, What are the benefits of technology? And frankly, going and talking to the customers that we believe will see benefit from our technology.”
Like many AI startups, Literal Labs is at the very start of its journey and as is the case with every early-stage business no one can predict whether its pitch will capture the imagination of customers. But as Hurley sees it, there is still room for emerging businesses.
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