5 minutes with: Richard Hoptroff

Awarded a PhD in Physics by King’s College London for work in optical computing and artificial intelligence, Richard Hoptroff has a long track record of technological innovation in AI, Bluetooth and, most recently, time synchronisation.

Today, with Hoptroff – the company he founded in 2015 – he is working on developing hyper-accurate synchronised timestamping solutions for the financial services sector and beyond, based on a unique combination of grandmaster atomic clock engineering and proprietary software.

Could you tell me about Hoptroff and how your technologies help businesses worldwide?

RH: The world today is digital, and digital moves at the speed of light. It needs precise timing globally to be able to function, for example, for media streaming and to be able to know what happened when, especially for financial services. And I mean precise – down to a millionth of a second, in some cases. 

But synchronising digital systems at scale is a tricky business that requires a lot of specialist expertise and hardware. So my instinct was to provide that as a utility that you subscribe to, rather than a complex system you have to manage. And that is what Hoptroff does.  

After all, you flick a switch and the light comes on, pretty much 24/7/365. You don’t think about the supply chains, power stations, and distribution grids that made that happen. Time on tap should be that simple, trustable and reliable.

Can you share with us some tips on how to launch a successful tech company, and what are some common mistakes that entrepreneurs should avoid?

On success, a couple of thoughts: first, look for what other people are doing successfully and repeat it with enough differentiation to not compete head-on – for example, a different location, price, or sales channel.  

Second, look for systemic changes, such as changes in regulations, technology or demographics, especially in big, fragmented, sleepy markets that will be slow to compete or will need you to remain competitive. For example, the introduction of Markets in Financial Instruments Directive (MiFID II) and Consolidation Audit Trail (CAT) regulations in finance were the springboard for our current business.

On mistakes, real successes come from operating as a team. Stick to what you’re personally good at and partner with people who are good at what you’re bad at. The whole is greater than the sum of its parts. And the “fail fast and fail often” philosophy is right; most ventures fail, so flush them out quickly and move on, or pivot the team and relationships you’ve built. When you innovate, there’s very little correlation between the effort put in and the value generated. So, if it’s a slog, it’s probably time to recalibrate.

What do you consider to be the most significant challenges facing tech entrepreneurs today, and how can they be overcome?

For most of us, it’s having the balls to think big. I keep having to remind myself that if I risk and fail, it won’t kill me. As Gordon Moore said, “If everything you try succeeds, you’re not trying hard enough”.

How do you see the role of AI evolving in the tech industry in the coming years, and what emerging trends do you think will drive this evolution?

I’ve been active in AI since the 1980s, when the collective brain trust could gather together in a fairly small conference venue. My first business, in the 1990s, was using neural networks for time series forecasting and modelling for business applications. Back then, as now, availability of data drives what you can achieve.  

In the 1990s, I specialised in extracting the best insights possible from small datasets, because that was all anybody had. With the Large Language Models, such as ChatCGP, and image creators such as DALL-E 2, he who holds the data holds the power. And, even if the dataholders are willing to share data, they may not be able to do so for privacy reasons.  

A company I was a director of, Replica Analytics – now acquired by Aetion Inc – specialised in creating synthetic datasets from real medical data so that AI models and software testing engineers could take advantage of it.

In terms of emerging trends, I contrast the AI world of the 1980s with the AI world of today. Back then, we had crazy, interesting ideas that have never been followed up on. Today, a few big players use a narrow branch of AI to extract value from the data they sit on. I think the new advances will be in revisiting those old ideas. Ones I’m particularly keen on are error estimation (“ChatGPT, are you sure you are right?”) and anomaly detection, where you could connect up an AI system to any input, it learns the patterns, and raises an alert when the data doesn’t fit the usual pattern.