54. AI Should Become Boring – Tech Field Day Podcast

AI's been in the news, it's been in the news way too much. AI should really just be a feature, and it should be so boring. We don't think about it, we just use it.

Join me on the Tech Field Day podcast to hear how AI has to become boring. Welcome to the Tech Field Day podcast, where we bring together a group of IT technical experts to discuss a single idea around key concepts in the industry. This podcast features a variety of perspectives from members of the Tech Field Day delegate community, and is often recorded in association with one of our events.

Tech Field Day is part of the future and group, and this podcast is also published on our sister company's website, tech Strong tv. On this episode, as we head into AI infrastructure field day, we'll be discussing how AI should become boring. But before the discussion, let's meet who's on today's panel.

Hi. Ah, I'm Guy Career. I am an analyst at, uh, FU Group, um, as well as Chief Analyst for Visible Impact, which is the division of Futura.

And, uh, I, um, actually read about and, uh, get briefed on and talk a lot about AI and AI infrastructure. And, uh, so far it hasn't been boring, but I kind of wonder if maybe it should be. And I'm Jay Kre.

I'm the Chief Product Officer at N Nexus Tech. I have, uh, done a few of these now, I think, and not only should AI be boring, it should be boringly available in more places at competitive prices that are affordable for everyone around the family, spending time together, enjoying their ai. Hey, and I'm Max Ro.

I'm, uh, head of research at Osmium Data Group and analyst as well. And I concur on the fact that AI should be boring and possibly transparent and everywhere provide. And of course, I'm Alistair Cook.

I'm the event lead for AI Infrastructure Field Day at Tick Field Day. Uh, also have a, a background on infrastructure build out. And that's kind of an interesting thing because infrastructure's supposed to disappear.

If you're doing infrastructure right, it disappears away and nobody even notices it's there. It's kind of tough when budget time comes around. But really that is the role.

And I guess as we're starting to look at ai, the future of AI where AI is going to be in our lives, um, maybe we want it to disappear. Maybe we want it to not be the center of our focus and the center of our cost as it's been for a while, guy, you've seen the sort of, um, excitement in AI that really we'd like to get past. Well, let's start with where infrastructure becomes visible to those not monitoring it.

It's typically when there's a problem. And so one way we definitely would like AI to be boring, and it should be boring, is that it us do stuff we don't necessarily want to do to help us do more stuff we do wanna do. But if it becomes interesting because it messes with us, I think that's probably a bad thing.

One of the other elements, so when you think about, um, whether AI should be boring or not, though, is, uh, innovation and development. I mean, we're really early in ai. And so a lot of what makes AI interesting, especially from an infrastructure standpoint is, uh, that much as, uh, the general strategy in AI has been to throw as much as you possibly can at it, starting with as much data as you can, uh, I should add, without necessarily curating that data.

Um, but also storage resources, of course, um, bandwidth compute, that's what the GPU's involvement is all about. Um, that's a pretty boring approach. I think a way more interesting approach can get a lot more out of ai.

And that's one way that maybe AI should, AI should not be boring, as in if you're hosting and trying to get AI to work within, uh, your organization within applications work for the users. That's actually quite interesting and should be interesting for a while as we figure out all the kinks. I'll, I'll, I'll jump onto that one.

So let's assume that in the past we had business intelligence centers of competency back in the old Gartner days. Um, so we're talking decades and then everyone's, oh, we can do embedded intelligence. And so that embedded intelligence, we feel like that intelligence word now has this word artificial tacked onto the front of it.

And the key question might be for all the wonderful, whether it's a, a beautiful magical donut saying how compliant we are or not compliant we are, whether it's a gauge saying how empty or full we are on our goals or what we're trying to do for the business. Is there anything that, whether it's agentic or any of these other terms that are coming forward, MCPA to a and other things that purport to help connect all this stuff together towards an outcome, uh, why isn't that happening faster? You know, what, what, what, what could be, what could be more beautiful than seeing all that promise of what happened decades ago becoming actually more real with the AI that's available in market right now today?

And so that's the part where I think there's a, there's sort of a disconnect. Um, there are CEOs that see this huge opportunity, and maybe they're a little overconfident in that from some of the other future research that we've heard of. But at the same time, there is, to your point on the infrastructure layers, there's everyone just kind of manically trying to stitch all this stuff together to stay ahead of what they believe this next model is going to require, let alone the application scaffolding on top of it that has a product experience that the users will not only be attracted to but actually adopt, use, and then make that more of the DNA of how business gets done.

So that's, again, I think AI needs to be more boring than it is currently. And and the only way I think to get there is to mature all the people tools and processes around that to where we're going from the possible to the permissible to the repeatable, you know, sustainable, then repeatable, then advisable getting all those ables and enables outta the way that's when we're cooking with Crisco, uh, to use the old Crisco term. But, um, those, those are just my thoughts.

'cause you kind of like guy, you lit me up a little bit. That's that's exactly this, this this point that you made is well taken. We, we sort of almost had to put away some childish things and get on with the, the, the order of a bigger higher order business.

Thanks, Jay. Um, I will say, uh, that what you reminded me of is how AI became boring very fast after about three months because everybody was talking about it. And by everybody, I mean all the vendors, all the vendors were talking about, and they were all saying the same things and they were all incorporating ai.

And every now and then you would see something interesting. For the most part, you would see the same thing over and over again, which itself was, I guess, interesting, nonetheless boring. So I almost feel like you're talking about a cycle.

It gets boring and then some, someone does something interesting. I, I think one of the things we're seeing is that it still feels very early in ai. I I still think we're just at the beginning of ai, and maybe it's not time yet for that to be boring.

Maybe this is still the time when we're seeing a lot of innovation and a lot of change. In fact, I'm pretty sure we're getting past the point where you just slap the word AI onto everything and your stock price goes up the same as it it used to be when you slapped crypto on it. Yeah, Absolutely.

Max, are you seeing vendors just actually getting to the point where AI is the thing they're actually doing rather than a label that, uh, that they apply to what they've always done? Yeah, I think that Jay and Guy had really great points at the beginning. The first one was around, you know, getting your data in place.

I think that's, that's the alpha Omega of starting to do anything with ai. So whenever I hear someone in my organization starting with huge projects about, we're gonna do this, we're gonna do that. The thing I'm thinking about first is do we have curated data sets?

Are we able to do something with the data before we start, you know, putting the, the, the, how do you say, the cart ahead of the horse and start, you know, thinking about what infrastructure we're gonna design, what tools we wanna use, and so on. The other thing, you know, moving to the part which is a bit more familiar to me, because of course I'm also, uh, looking at what's happening in the AI space using GPTs and whatever you wanna call it. But I try to look at it from a practical perspective.

You know, I think Jay said it before, organizations are trying to, uh, kind of deal with those budgets, which are getting, you know, uh, g every year, year over year, they're trying to kind of sometimes keep these huge deter fire, you know, kind of, uh, preventing it from spreading. And, uh, when, when you hear, you know, some vendors which are talking about AI and slapping AI on whatever product that they have, you're kind of thinking, you know, what the hell is going on here? I mean, those guys here on the trenches are just trying to, to survive.

So to me, uh, it's a long introduction just to get to the point that what people want is boring AI that helps me solve a practical problem. It's where I think that the most, I mean, in my opinion, the most practical, the most successful implementations of AI and products are co-pilots. Things that help you, you know, accelerate your job, identify what's going on, but also there's enough safety in there so that, you know, the AI is not kind of hallucinating and telling you that everything's fine while you have some major infrastructure problems.

You know, I don't know what you guys think about that When we're talking about problems, it means that we have not achieved a level of maturity in a particular, uh, could be a layer in a particular domain. Um, uh, I I think we could be very provocative as a group and say like, oh, networking is the problem. Oh, storage is the problem.

Oh, the compute and the availability of the compute, or what we think we may or may not be experiencing with our supply chains over the next, you know, several years, uh, those might be, oh, no, no, no, it's actually the software, it's the abstraction layer. We don't have a better scheduler, need a better scheduler. We now need the next, next level, which is, oh, we need product engineering.

We need product by design. We need secure by design. We need all these by design things to be baked into all these.

And every vendor will be bringing us a, uh, a shiny, shiny, shiny sprocket. And those shiny, shiny sprockets are amazing. Um, those are great stories, and they're small part of the, if you think about like a large epic and the smaller little stories and vignettes that go into it.

But what we have to have for a w boring is we have to have that look like a consistent system, a machine that's actually operating for the benefit of those that are going to be utilizing that machine. Um, so if, if, if technology is a response to a perceived need to be q, like we need to see more perception within the larger mass of the market, say like, an need my a boring, I need it to talk to other ai. I need it to, uh, just be something I've taken for granted.

And I think, uh, some of the challenges we're running through right now is within, uh, whether it's observability, which guy brought up earlier, max, your points around where challenges might be. I think we still need to progress the art, uh, towards more of a science, you know, a deterministic outcome. Um, so in our data centers right now, we've heard vendors in the networking space, not to pick on networking, by the way.

Um, no, no one gets a free lunch in this, but, you know, deterministic, power, weight, cooling and geometry, you know, when, when we had Arista in a prior event, tell us about the math, the sheer like terror, terrifying math of what's required for this planet to light up all the things that we required for the AI models and what their hunger is. Um, we have to do something about that. So whether we're exploring material science, where we're looking, um, at how we could be better at turning things off when they're not in use, um, that, that the promise of that cloud and elasticity, are we bringing that to practice?

Um, you know, as, as, as practitioners are, are we embracing that? So, um, again, between Max Guy and I think Alistair, to your earlier point, there's probably places we could kind of poke at it to say where, where the laggard is, but I think it's still about the system. And so any one thing like a chain, you know, it's, it's that weakest link.

I think that you are reminding me of something I learned a long time, but go, if I remember rightly, it's the work of Carl Poper who said that we build up these structures and, and build up and build up our understanding until we get a point of clarity where we collapse things down and start building another paradigm of, of construction. Um, I'm hoping, and I've been hoping this for probably the last year, we will build a new or come to a new insight on a new paradigm for building ai. Because simply moving to these larger and larger models with more and more resource requirements to, to gain a business outcome, um, I just can't see that being sustainable, continuous growth forever, and particularly ex exponential growth.

Continuous is not a sustainable, it's not attainable situation. I'm hoping we will have, um, a sort of collapse of technologies into a mature set of top technologies we can simply use and engage with, with far less, um, resources that seem to be being consumed without necessarily a lot of business value. I think Alistair, well, and Jay, I think that's the, that's what something like MCP, the latest craze in MCP and the craze in particular, what it promises MCP is essentially at in spirit, certainly, and well in, in effect, it's, it's an open system of interconnection and integration, not a to i to AI or AI to data or, so all of the above.

And what does that do? Um, I mean, A to a does something, I, I don't wanna leave that out, but j just taking MMCP as an example, um, that makes things boring in a certain way, which is that you can build to the build two MMCP, uh, Alistair, I don't know if you were at the tech field day where I gave a little presentation on the three life cycles, three a three AI life cycles, which was, that was the subject of my, uh, uh, uh, mini talk. Um, but I started with a rant, which I had maintained, started before and maintained ever since then, which is that there's no such thing as an AI application, and maybe we'll be allowed to do a podcast with that provocative title.

My point was that there is no AI application. There are AI services, AI services or one AI service may be the service that supports the application, which is a chat application. So 90% of it might be that service, but the chat application, if you're interacting with it, uh, for all, you know, uh, uh, may have an army of people behind it as opposed to an AI service could have, you know, other kinds of services.

So what's my point? My point is, well, you know, first of all, models are getting larger and larger, but now there are small language models, SLMs, and, you know, other small models have existed for a while and an understanding of how those work and where those work, that kind of diversity was completely inevitable. But if you don't have some kind of standard framework in which to, uh, uh, you know, present the ai, sir, your golden AI service trained however you like, um, then it's too interesting and it's interesting in a bad way.

So you would rather have this be boring, which is, oh, that looks interesting, I will deploy it because it's MCP compatible or whatever compatible. I can simply deploy it, incorporate it. I don't have to worry about the ecosystem.

I don't have to worry about the provider. I don't have to worry about this, that, this, that or the other thing. I can be inventive and keep the interesting part, my application, not the ai.

I think we were both discussing all of that, uh, before the fact that you want your AI system or systems to be interacting together. You want them to be able to talk the same language, to have, you know, some kind of API integrations and to have a way to change the pieces in case something goes wrong. I mean, the current situation in the world is really interesting in terms of, you know, uh, uh, moving from a, um, what say free trade, global, you know, environment to, uh, more, uh, country centric sovereign approaches.

So I think that this is also important in terms of what solutions you're, you're selecting. Of course, this is going towards the use of LMS or s SLMs, uh, whereas we might be also considering, uh, specific implementations of AI as a feature within some products. Um, the, the other thing as well, which was really important, what Alistair Alistair said before is that, you know, we, we have very, very complex infrastructure systems, or at least I have this kind of view at the customers who, who might interact.

And we are adding yet another layer in the hope of solving that on top of, uh, you know, dozens of other layers, right? So, and to what, what Jay said before was really interesting to me, it's really how can we use AI in a boring way, which is, how does this helps me simplify, flatten out everything, look at the things, make things simpler, uh, AI that is not here, you know, to generate, you know, stupid pictures, but ai, which is here to help solve the problems of the world, the mathematical problems, how to have cleaner energy, or how to have better sources of energy, how do we optimize our systems, whatever, you know, for me that is the kind of you ultimate, you know, value that we get. Not that I don't get a ton of things from chat GPT, right?

But getting something which adds to the greater goods, basically. Yeah. And just to tag onto that, I, you know, I agree us taking all of our pictures here and then avatar us to where we look like, you know, Japanese animation, which is, uh, in, in some ways, if you're part of that, uh, community and you believe in that as an art form, you might believe they're perceived that to be very insulting.

And I think even the, the, the, the creator of that, that that style, that form is mortally offended by what, what is actually happening. Um, so when we think about the application of what we'd call this, this new, uh, boring AI that we're waiting for, it, it would be, there's the knowledge worker requirements. And so, uh, you know, if we, if we said that, like what's the toil in a given day that by adding this essential boring AI to it, that toil is removed.

You know, I've, I've referred to it before as the roofless removal of annoyance. And so in our day-to-day workday, um, if you're in the physical world where AI might be assistive is by combining things like computer vision, other types of remote sensing telemetry, where atoms are involved. I think we also get heavily rotated around this whole knowledge worker only view of the world where we're just passing around bits.

There is absolutely a real world made of atoms out there and how that AI interacts at the edge, where literally most of this data that's meaningful is being created. Those are the things where the boring AI has to be boring. Ai.

Um, I don't want to work with a machine, for example. Uh, that has not become very boring and very well understood. If I put my hand in there, I want to know that it's going to determine, yes, that's in fact a human hand and not the actual part that needs to be stamped next.

So, not to make it awful, but we need to make sure that we're not introducing the worst of the lessons learned from the industrial revolution and the prior industrial revolution. So in this next quote unquote industrial evolution, how can we have the most boring AI possible, all the safeguards in place, all of the other elements that I think we have poked around at the edges of, again, going back to that laggard, settler and pioneer view. That's, that's why I think what's sort of missing, how, how do we, how do we remove the relevant toil of today?

And that's because the AI that we're using to solve for some of that has been applied and it's, it's, uh, it's almost defacto. So, um, anyway, that, that was, that was my thought based on what you talked about Max and, and weaving in guy and then thinking about Alistair's earlier points, we have to think about where the use case is that's real world, and it may not be knowledge, it may be actually in the physical world. And there, there is a huge, uh, existing set of applications where AI is just a feature where, in particular predictive ai, not the generative AI that's gonna create a, a new sentence for you or a new video for you, but the, uh, old fashioned machine learning what we've had for the last 20 years where it's essentially statistically this is the most likely next event in the physical world.

If the temperature is changing in this way in our greenhouse, this is the point at which we need to change things to get optimal conditions. So there absolutely is boring AI out there. Yeah, and I guess that's a good thing is, is, yeah, it's a good thing, is what we're, what we're kind of, you know, AI should be boring.

And I'm just wondering though, maybe everything should be boring. Maybe it's a general principle. I mean, certainly to your point, Alistair, when, when you introduced this topic, um, infrastructure should be boring.

Um, I guess, I don't know, like are we talking about for the operator or the creator of AI or infrastructure or are we talking about for the users of it or for both? If AI gets boring enough, then I guess we'll have AI to run the AI because it will be the boring things AI does for us so that we can do the more interesting things. I think that it's a good principle in general, are you building infrastructure to support ai?

Are you building AI models and seeking infrastructure to run it in the end? Um, what you wanna do is to uncomplicate things for users or, uh, even if the users are operators or the users are developers, uncomplicate things for them, make things simpler and more straightforward. Allow them to use the tools that struggling with the tools, tools.

That's a lot of what AI promises to do. And in that sense, if it just becomes second nature, it's boring and it's helping you. Well, we are running towards the end of time as always.

We could spend a lot more time discussing this and we probably will spend quite a bit more time discussing this at AI Infrastructure Field Day. By the time you're watching this video, we might already have had that discussion. And, uh, guy Jay and Max will be part of my delegate panel for the four days of AI Infrastructure Field Day.

If people would like to continue that conversation with you, where can they find you to carry on that conversation? com where you'll see my, uh, research and analysis. Um, but where you'll, where you will see the most from me, uh, is LinkedIn.

You can find j Kroll at all major retailers. org, which is my newsletter. com, which is my main website.

You can find me on LinkedIn. com and you can find me there. com.

Uh, we post regularly on, uh, LinkedIn. So there again, follow us at oum Data Group. Same on YouTube.

Uh, social media wise, no, with ai, uh, on Blue Sky at Max Moro, I'm also on Masteron at Max Moro, but, uh, seldom there, if you want to read some insights about me renting about stuff, which is non 80, go to kechi com. I'll send a link towards thank you. And of course, I'm Alice Cook and you can find me on LinkedIn and across Tick Field Day properties and the wider future in group properties, in particular Techstrong.

It is one of the places that you'll find things that I've written about. One of the interesting things we're doing, if you do want to find, uh, your way to Jay Guy and, and Max and all of my other awesome delegate panels for AI infrastructure field date. So get across to the Tech Field Day website and find the event AI infrastructure field date.

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