Why Big Tech Is Spending Billions On AI

Billions are flowing into AI, models are getting smarter, and the price of access keeps dropping, but the hardest part for most leaders is deciding what to trust and how to stay secure while everything speeds up. We sit down with Mark Rutherham (CTO at BCN) and Matt Lovell (CEO of CloudGuard) to unpack what the latest AI headlines mean for real business strategy, especially for UK organizations navigating cloud dependence, infrastructure gaps, and the messy reality behind “sovereign AI.”

We talk through the investment race powering generative AI, from chips and cloud capacity to the competitive push for market share. Then we shift to the risks that show up once AI moves from experiments to embedded workflows. If AI becomes part of how work gets done, availability and performance start to matter like any other critical system, which raises uncomfortable questions for business continuity, disaster recovery, and vendor dependency.

We also dig into data privacy and governance where the key issue is increasingly where data is processed, not just where it sits. Tools like Microsoft Copilot and model options like Claude highlight the trade-offs between capability and control, especially under GDPR and PII requirements. Finally, we explore what cheaper AI and open source models change for small teams, plus the infrastructure story behind the scenes: data center power demand, renewable energy constraints, and what comes next.

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Transcript

The AI Investment Curve

Mark Rotheram 0:01

So the investment is absolutely crazy that's going into this, and it's all AI. So you've got a curve that we're on now that is just getting bigger and bigger and bigger. And you can see it happening. The investment is getting bigger. Everyone that is a cloud player is investing heavily into this.

Matt Lovell 0:16

You look at the exponential growth in data center and data center efficiency requirements to support, you know, the scale out of AI technologies, and you would say, where's it going to come from to keep pace with the rate at which technology is growing.

Sinéad Hammond 0:34

Welcome to the AI for business podcast with BCN, where we put AI and automation at the centre of every episode. This is a regular debrief for business leaders to digest the business news and the biggest stories and development in AI as they hit the headlines. And when it feels like there's a new story and technology pretty much daily, we're cutting through all of the noise and we're giving you a rundown of what's important to businesses, why it matters, and what you can do to keep up with the pace. AI news is moving fast, huge investments, better models, and even robots back in the conversation. For UK business leaders, the challenge isn't keeping up with everything. It's knowing what matters, what to do next, and how to manage the risk while you move. Today I'm joined by Mark Rutherham, CTO at BCN, and Matt Lovell, CEO of CloudGuard, who together will help us make sense of what these headlines mean for strategy and leadership and how businesses can keep secure as AI spreads faster through a business. So welcome guys. Thank you so much for joining me. It's nice to see you. How are you both doing today?

Matt Lovell 1:34

Very well, thank you. All good, Sinéad. Always good to see you too And Mark.

Three Headlines Shaping AI

Sinéad Hammond 1:38

It's nice to have you. So we're going to start the episode as we usually do with the top key three headlines and developments in AI over the last month. And then we'll get a bonus one from you as well, Matt, if that's okay. So, Mark, if you had to pick the three that you're most focused on this month, what would they be and why?

Mark Rotheram 1:55

Yeah, so I guess one of the biggest themes, and it's kind of one that's been a trend for a while, is the amount of investment that's coming in from big tech going into general purpose AI. So it's the growth really that's been pumped into the entire ecosystem by Nvidia, Anthropic, pushing the boundaries and pushing into bigger and bigger investments. And I think it's really interesting that whilst we've seen those huge investments happen from a lot of big tech, just this week OpenAI paused its UK data centre projects, which which has kind of put another dint in the sovereign dream of the the UK around regulatory AI. So whilst we've seen some of the big players plow more and more money into this and uh you know that kind of growing capability, we're still struggling a little bit from a UK perspective to get the right level of infrastructure and investment that we need to have that sovereign AI platform. So that's probably the first one. I think hot on the heels of that, you know, we're seeing these investments rewards. So the fact that the models and the tools are becoming more capable. We're seeing some absolutely crazy news out of Anthropic, which I'm sure we'll have a chat with Matt about with the the capabilities of the models that are kind of not quite released but being looked at by security firms. But new models coming out that are extremely capable and extremely cheap. So capability is going up and costs are collapsing. Yeah, it's really interesting to see that that trend that's been happening since the probably back end of last year is continuing with all of the different providers and even the the open source models coming through as well. And I guess third one is you know, we're continuing to see the physical world evolve. So the robotic kind of army marching out of the data centers is is still happening. Uh, we've seen robots being deployed, starting in Japan, you know, they've started actually getting them out there now. And the the thing that's enabling that is these reduced cost AI models with with the more capable thinking and brain. So the the headlines are really uh a continuation of what's going on. It's just again spiralling and getting bigger every time we look at them.

Big Tech Spending And UK Gaps

Sinéad Hammond 4:10

Yeah. We'll dive into each of those kind of a bit more together. And I want to get both of your opinions on all of the news that um Mark's talking about. But yeah, it's definitely a general trend of just bigger, faster, more easily accessible, all of those things. So we'll dive into that a little bit after this. Matt, what about you? What are you seeing at the moment? I mean, you come from a cybersecurity perspective and your expertise in there. So what would you say is kind of the headline that you're really keeping an eye on at the moment?

Matt Lovell 4:39

So AI brings huge opportunities for businesses to accelerate and yeah, depending on your vertical, depending on how applicable obviously AI is to that vertical, and it applies to everybody uniformly, the actual adoption of it and all of aspects of adoption, right? From user awareness and user effectiveness of using the AI tools, the speed, as Mark said, the IO tools are evolving, but also the security implications. And it's not just the tool itself. We've started to see the wave of vulnerabilities come through in the tools, and most of them are high or critical, which is a real concern. But it's actually data sharing and what it's doing because organizations are reaching into the tools and starting to truly understand the capability, and the capabilities accelerating at exponential rates, as we've said. Understanding how you do that securely when user awareness is already probably too low. The organizations investing in helping people on that journey, you know, whatever their position is in an organization, however, AI may be able to make them more effective, and I think across the board it can. But being mature enough as an organization to share data, share data securely, understand how that works within workflows, within supply chains, you know, within partnerships, and who you're sharing data and how they're then managing that data onward, it's a real maturity curve for organizations don't seem very well prepared for right now. That's what we are fundamentally seeing.

Sinéad Hammond 6:12

Yeah, definitely. And I think that's something that we've talked about on this podcast before, haven't we, Mark? We've talked about kind of the curve and trend of technology being available and then how businesses are actually preparing and embracing that as it goes forward. So I think that's going to be a really interesting thing to talk about, and something that'd be really useful for um listeners today. So let's dive into them one by one in a bit more detail. We talked about big tech doubling down on AI investment. You mentioned Nvidia, Anthropic, OpenAI, like who's doing this and what does it signal to you? And this is kind of an open question to both of you, I suppose. Like, what what does this mean for the way that AI is progressing in the landscape right now?

Security Risks Of Fast Adoption

Mark Rotheram 6:54

Yeah, so I think the easy way to visualize it is becoming an absolute dominant player globally. Yeah, so we've got Nvidia becoming a multi-trillion dollar company as their demand for chips is just exploding. You know, they are the people that are building the roads that everything runs on effectively. So that in itself is huge. You know, last year, last year, the year before they became the most valuable company in the world and and they are just continuing on. But hot on their heels, we're seeing all of the big players. So Amazon putting another 35 billion into their strategy, you know. They they actually have their own chip line as well that has got massive consumption for their AWS platform. Anthropic, you know, they they have signed massive deals, you know, they don't have their own infrastructure, they're working with Broadcom and Google, you know, very much focused on how they can buy as much capacity as possible. Meta, they're on the bandwagon now, billions again going into AI, and even Intel, you know, Intel have started working with Google and with Elon Musk in the the Terrafab chip project, which again, if if you look into what's happening with Terrafab, that's predicted to be the biggest chip manufacturer overtaking everything else that's happening in the next few years as well. So the investment is absolutely crazy that's going into this. And over the next year, we're seeing three of the biggest players, Anthropic, OpenAI, and SpaceX, who owns XAI, all IPO-ing, you know, effectively going open up to the public to buy stocks and shares, and that will be um the biggest round of global investment in history, and it's all AI. So you've you've got a curve that we're on now that is just getting bigger and bigger and bigger, and you can see it happening. The investment is getting bigger, the companies, everyone that is a cloud player is investing heavily into this. So, yeah, so what it means for us is all that money's gonna come from somewhere, yeah. They're expecting adoption, they're expecting enterprise all the way down to small, medium corporate, SMB, and person people to invest and spend money in their models. And and to that point was just said, it's gonna be all around adoption now. Who's gonna get the market share? How are they gonna get it? How are they gonna get that adoption going, and how are they gonna get the money back from these multi-billion pound investments that they're all making?

Sinéad Hammond 9:19

Yeah, absolutely. And I suppose maybe a question for you there, Matt, is obviously there's the one side of it where investment and competition is ramping up and everyone's trying to get that share of the market. What tends to then happen in terms of a risk perspective and what do businesses kind of need to be aware of when they are doing this? I know, Mart, you mentioned adoption, and we've sort of talked about guidelines and things in the past. But from your perspective, Matt, what what tends to happen when technology is accelerating at a speed like this?

Matt Lovell 9:50

Uh lots of things, Sinead. I think um, where do we start, right? Let's let's just look at uh the dependencies, right? So I obviously come with my data center hat on here from my previous life. And I would say, you know, you look at the exponential growth in data center and data center efficiency requirements to support, you know, the scale out of AI technologies, and you would say that, you know, rare earth materials, power, uh, cooling, you know, just physical location space, where are you gonna cite that for optimized performance on the globe, the global, you know, obviously communication fabric? You then look at obviously the deployment of all of those, the build of all of that, you know, compute and memory, storage capacity, et cetera. Where's it gonna come from to keep pace with the rate at which technology is growing? You then think about business risk, right? So adoption, doing it securely, having the right training in place, having the right guardrails in place, et cetera. But we're starting to see the next wave, which is organizations leveraging AI capability in workflows. What happens if there's any disruption to service availability? Okay, because that's absolutely critical to a business. We've seen in the last couple of months businesses not even realizing that their workflows are now fully automated and leveraging AI. AI is a huge part of that workflow process, and the availability of that is business critical. Is it in their business continuity planning? Is it in their disaster recovery? If there's even sporadic or intermittent disruption to availability, if performance degrades, for example, and we've seen that because we can't forecast the huge swathe of people switching between the AI tooling and the effectiveness of AI tooling, we've seen in the last six months the huge acceleration of Anthropics platform and Claude and the capabilities of Claude, not just in obviously the AI context, but in code and in cowork. And people realizing that there's different levels of effectiveness between the tools. So if you're AWS, if you're your Google Gemini, you know, you're looking at that and you're OpenAI and you're obviously uh AIX as well. You know, how are these people able to plan for these huge peaks in activity and how does it influence the user experience? And if you've got workflows that are dependent upon that and suddenly they are degraded, what's your plan B? And therefore, you know, how are we plotting this out? How are we then playing that through and measuring the user experience those workflows are delivering to our business customers? Okay. They're the things that we now need to be thinking about and safeguarding against. And that's what typically happens with rapid adoption of technology.

Sinéad Hammond 12:36

Mark, I saw you nodding along there. I didn't know if you'd got anything that you had to say back to that as well, based on maybe some of the clients that we work with and an experience that you're having at the moment as people are embracing more of this technology.

Mark Rotheram 12:49

Yeah, no, Matt's right. I mean, there's there's loads and loads of considerations and issues, and and you know, we talked a little bit about sovereign AI and and the state of the UK from a what can we actually have here? And that's something that really needs a lot of thought and and consideration when we're working with our customers, because everyone would love to have AI native in the UK with all the bells and whistles, and it's just not going to happen anytime soon. You know, we've we've seen a £150 billion investment into AI come to the UK and then go without anything happening. Yeah. So I think when we're working with our customers, it is how do we provide agentic capabilities in a safe, secure, robust manner that you can rely on. And you know, it is challenging. We are finding ways of doing that, but I think you know it's it's coming at a not in the way that we would love to do it. It's it's in a way that we we have to put compromises in. And yeah, and I I can see it as being in that position for years, um, as our infrastructure just lags behind America and China and and some of the other places across Europe where they've got that capacity and they've got that investment.

Matt Lovell 14:02

Equally, sorry, just to come in, I think it will expose, but it will also accelerate our you know our thinking and our capabilities. Because on the one hand, you know, we're saying we're gonna see, yeah, I mean, data center electrical consumption on a on a very broad, high-level global basis is somewhere between 9 and 11% of all consumption globally, right? So that's already a huge number. Although AI and the technology it's using is hyper-efficient in most cases, it is still going to be consuming proportionally more power as an aggregate. How are we going to support that when we are more reliant, more dependent upon renewable energy? And we know very clearly that you know the wind isn't always you know high and the sun isn't always shining, certainly for large parts of the day, depending on what part of the globe you might be in. And therefore, how are we gonna bridge that gap, right? And that will accelerate nuclear fission technology and local nuclear, more consistent, more dependable, more reliable connections that bridge those gaps. Yes, we're gonna focus on renewable energy for sure, but how are we also gonna keep to you know the global warming thresholds that we absolutely need to commit to and even bring them down? So, how are we gonna use this technology to understand it? You're seeing it in the medical industry, you're seeing it in research and development already. We are making major breakthroughs because the technology is finding answers that we've been searching for a long period of time, right? The analytical capability will take us there, but it will need focus, is what I'm saying. What are the principal problems in scaling this technology out? And those are the priorities to fix, and then how is it going to help us fix the other priority issues around the world without causing greater levels of global warming, which in itself will accelerate other problems?

Mark Rotheram 15:50

Yeah, and I think that's where the you know if you look at the the biggest trend that wasn't here a year ago, that's emerged recently, is data centers in space. Yeah, and that that is one answer, but it's not an answer that's going to be here today, is it? It's gonna be a a year, two, three years. We already have the first one, I think the first ones were launched last year as a test. But I think that's that is one of the big answers, isn't it? But that again, it it flies in the face of sovereign AI and sovereign kind of approaches. And I think that's that's where we're being pushed, if I'm honest. As we want to embrace AI, we're gonna have to accept that it's not all gonna be sat here in our data centre in the backyard. It's gonna have to be European, global, or you know, stellar-based uh as we go forward.

Where Data Gets Processed

Sinéad Hammond 16:37

Do you think that'll then change the way that businesses have to think about their procedures and their policies and things as well? Because you know, typically, especially since things like GDPR and you know policies that are in place, a lot of that where the data is kept has been part of that conversation. It's been part of business process. But it sounds like based on what you're saying there, those sorts of things are gonna have to be reviewed. So just by implementing any type of AI, particularly more agenc and more kind of advanced level, it is going to change that.

Mark Rotheram 17:09

I think one one of the most fundamental things that we're gonna grapple with over the next 12 months is where data is processed, not necessarily where it's stored, it's where it's processed. So we look at you know what's happening with Microsoft Copilot is a really good example. Microsoft Copilot had been out for a few years. It was innovative, baked into the ecosystem, and then up came clawed co-work, and it's brilliant, better than co-pilot in a lot of ways. So Microsoft have done a really good thing. They've they've last year they said, look, we're gonna let you use uh the Opus model and the Sonic models within Copilot, but there's a data processing agreement uh because the data will be processed by Anthropic in their data centers wherever they might be. Um earlier this year we saw another flip where they're gonna further embed that anthropic capability into Copilot. So, you know, that started earlier this month with the first co-pilot kind of foundry businesses getting access to it, but it comes with that compromise of your data will be processed globally by anthropic. So we're already seeing it happen, and I think as we build out bespoke solutions, agentic solutions, we're having those same conversations. If you want access to the best models, the best we can do is European processing, but that comes at a tax. More often it's global processed. So we have to think about PII, GDPR, how we control that, how we can do things before it gets sent out, and how we we monitor and manage all that. It's not impossible, it's just like I say, more considerations that businesses need to have. They need to know that they're making those decisions. They need to know what the security boundaries look like, where things are being processed, and and the the what-ifs related to that.

Cheaper Models And Small Teams

Sinéad Hammond 18:51

Yeah, we say this every episode: any changes that come with technology or a business decision, it's not just like a an IT, it hasn't been an IT department decision for a long time. Um, so it's all these knock-on effects that go with that. You've mentioned in the headlines as well around AI cost barriers being pretty much eradicated. You know, anyone can get a any sort of low-cost license. I mean, I think everybody I know has one where you you just kind of access AI with without any kind of barrier there. What does cheaper AI change for businesses, especially businesses without big teams? And what does that then look like when you know new models are being developed going forward?

Mark Rotheram 19:36

Yeah, so the the race for higher performance and cheaper cost is continuing. We saw um the latest open source models from Google arrive, um, Gemma 4. That's something you can run on your phone, yeah, and you get a really good strong model just locally. Never mind having to pay for APIs, you know, it's it's part of hardware, it just runs. So these things are happening all the time. What it means for the businesses we're working with is that they can tap in to this, I call it a big brain, regardless of the provider and the model. You've got these big brains now that you can tap into that can spread across your context, unlike anything you've been able to see before. So you can get much more contextual intelligence at a very low cost that can really help power your business processes. So if if we kind of flip it into the the so what, historically it's been quite hard and costly to automate business processes. It's now a lot easier and a lot more powerful than it's ever been. So we can bring a more powerful capability at a lower entry price, a lower cost to run, that's much better than anything we've been able to deliver to our customers before. So we can turbocharge with a genetic workforce, agentic pipelines and workflows, businesses targeting teams, targeting hotspots, targeting just repetitive work or not so repetitive work in a way we've never been able to do before. So and the opportunity for every business to do something impactful is right there now without breaking the bank. That's the real impact, and it's growing month on month that potential impact.

Healthcare Gains And Robotics Tease

Matt Lovell 21:15

You know, one one of the personal observations I would make is how I travel into town on public transport every day, and you know, typically you would lose that that travel time in terms of productivity, but because the tools and the models are so powerful on your phone, you can be super productive for the journey time, coming in, going home, and solve problems and use you know that mind space in order to be really productive. Now, I'm not advocating everybody does that, not at all. What I'm saying is I choose to do that, and other people I'm looking around me and just being observant are clearly choosing to do that because the tool is so capable, right? And and certainly in the last three or four months, as Mark said, in in the tools there. Those tools have opened up whole swathes of new opportunities for us to be leveraging that. And I'm not advocating that we do it on a free platform because then we have different data constraints, and that data is more likely to be ending up in training a global model at a you know a shared level. So you've got to be super, super clear on the guidelines for people. But then people can be thinking about it and just using that in their hand in terms of accelerating processes. And I certainly found that incredibly powerful, you know, in terms of learning capability, distilling huge swathes of information that normally I'd just have a singular dimension of being able to read it, etc. I'm consuming it really differently, and that makes a fundamental difference. I was talking to somebody else the other day, and they were saying, Do you know what sector grew the quickest in terms of AI analytical use and progression in 2025? And I was like, Well, I would suggest to you, based on my own experiences, finance. And they were like, Absolutely not. That was like 25%, but healthcare grew 65%.

Sinéad Hammond 23:04

Wow. So interesting.

Matt Lovell 23:07

That is a major, major step forward, yeah, in terms of what that can bring to all of us at a global quality of life level, medical advanced level.

Wrap Up And Where To Listen

Sinéad Hammond 23:16

Absolutely. I mean, we talk about it from a business level here because on this, you know, that's generally the kind of people that we work with, but it's got applications so far out of just the business processes and things that it's so interesting that we don't even touch. And I would have said finance or legal potentially, I might have said those sectors seeing a lot of growth in there. But yeah, it's so interesting that it's healthcare. And in fact, conversations about applications of AI and healthcare bring us nicely on to this huge topic which we've touched on before. And Mark, you've talked about a little bit earlier, robotics. So if you tune into our next episode, we'll be diving into it, robotics more, and we're talking about the CISA and how that impacts AI. So definitely tune in for that. However, in the meantime, that's all we've got time for for this episode. So, Mark, Matt, thank you very much for joining us.

Mark Rotheram 24:04

Thank you. Cheers, thank you.

Sinéad Hammond 24:06

If you want to catch up with other episodes, you can do so at bcn.co.uk or subscribe to the podcast in all the usual places. Thank you so much for listening, and we look forward to seeing you in the next episode.