Tech Talks Daily
00:25:28 · Neil C. Hughes
[00:25:28] S0: And we are recording this at that time of the year where we're drinking pumpkin spice lattes, preparing our inboxes for Black Friday emails and Cyber Monday with one eye on 2027. So finally, what... What's keeping you busy at Newton, and what can we expect next year and and beyond? What are you working on?
00:25:47 · Ben Grinnell
[00:25:47] S1: So we have taken all of our work through through live coding hackathons. So everyone from the CEO to the most junior person just to make sure that everyone understands what we're working with because I think there's a there's a need to be a bit hands on, whatever level you are in the organization, to be able to effectively work with this technology. So that's been one thing. We've also been looking very closely at the the war for tokens, as you said. We had token maxing and now we're in... They're getting quite expensive. So we've we've got some sovereign compute. One, because tokens are expensive, and we want to be able to at least run inference ourselves. And two, because a lot of our work is in places where data shouldn't leave The UK, so we need to have that capability as well. And there's a... I'm predicting a big crunch in the inference market because there just isn't enough compute to go around. They can't build it fast in America at the moment, and I don't think we're doing any better in The UK. So, I saw Microsoft pull out of a contract this day, which, set one of our our main hopes of having a lot of infrastructure, go down a bit. So I'm really keen that we protect our ability to use AI in a safe and secure way. So that's been a big focus as well as making sure that, you know, I've got the right things in place to make sure all of my workforce can continue to use it in a... Not an unrestricted way, but a way that's giving our clients the biggest benefit. And one of the things I've done there is I've said, I I I do want people to have access to the latest models, and they might be the most expensive. But I'm looking at... The people who are using it most are already adopted, and they can look at how can we make it more efficient, how can we use more efficient models. The people that I try over the first time, I want them to be using the best model and getting the best experience, because otherwise, they won't become long term adopters.
00:27:48 · Neil C. Hughes, Ben Grinnell
[00:27:48] S0: I think that's a perfect moment to end on. And for anybody listening who just wants to find out more information about you, Newton, anything we talked about today, where where would you like me to point everyone? [00:27:59] S1: Yeah. Please. Newton's website, newtonimpact.com, is a great place to learn about what we're doing and sharing with clients. And, personally, I'm quite active on LinkedIn, so I'll be sharing a lot there about what I'm doing. [00:28:11] S0: Awesome. Well, I will have links to everything that you mentioned there and pop that in the show notes for people to check you out. I'd love to stay in touch with you and see how things evolve next year and see how many more marathons you're gonna be running as well, maybe. But more than anything, just thank you for sitting down with me talking about your story. Really appreciate your time. [00:28:26] S1: Great. Thank you, Neil. That was great.
00:28:26 · Ben Grinnell, Neil C. Hughes
[00:28:26] S1: Great. Thank you, Neil. That was great. [00:28:28] S0: I think Ben's advice today left me thinking about some of the handoffs that we just accept as part of working life. Every time someone explains a problem to another team, something can get lost. AI might shorten that journey provided the people who use it understand when to ask for engineering help and how to protect the information involved. I also appreciate that challenge to how we measure progress. Saving time has value, but so does better services, stronger revenue, and giving people the extra room to do more useful and valuable work. And if employees hear a conversation or hear a rumor about using technology at the expense of reducing headcount, it's wholly surprising, isn't it? The enthusiasm will quickly disappear. So a big thank you to Ben for joining me today. And remember, you can learn more about the team at Newton Impact at newtonimpact.com, and I'll add everything else to the show notes too. But over to you, what small business problem could you start with right now and solve with AI? And what evidence would persuade your colleagues to try it for themselves? I wanna hear from you. Techtalksnetwork.com. Let me know. I'm afraid we have reached the end of this episode. I will be back again tomorrow with another one, but thanks for listening as always. Bye for now.