Your Customers Are Running Your Proposal Through AI

Your buyers are already running your proposal through AI before you hear back. Tech entrepreneur Anthony Mittelmark unpacks what that means for anyone who sells anything.

He breaks down generative engine optimisation: running your own products as prompts through ChatGPT, Perplexity and Gemini to see whether you turn up at all. Why 50 years of trading data may be worth nothing, while the messier patterns most businesses ignore could become a real asset. Why one tactical automation fix can quietly wreck profitability somewhere else. And why boards can't hand AI architecture to IT and hope for the best.

Plus the 2030 exercise every leader should run now. Follow the Dumb Monkey Show for AI for business that actually pays for itself.

Resources & Links:
📲 Dumb Monkey AI Academy → https://dumbmonkey.ai/
📱 Dumb Monkey App → https://dumbmonkey.ai/dl
📘 The CEO Who Mocked AI (Until It Made Him Millions) → https://mybook.to/dumbmonkeypodcast
🚀 Enterprise Monkey → https://enterprisemonkey.com.au
🎙️ Anthony's LinkedIn → https://www.linkedin.com/in/anthonymittelmark/ 

Transcript

SPEAKER_03 0:00

Hi everyone and welcome to a very special episode of the Dumb Monkey Show. Today you went to see us looking directly at you because we are looking at a fantastic tech entrepreneur, really interesting thinker in this space, someone who's worked through the BC pathways, someone who's very good at Exods and someone who understands how AI is disrupting and erupting into our workplaces and into our business ecosystems. Anthony Middlemark, welcome and thank you for being part of the show today.

SPEAKER_01 0:27

Hello, Davina and Amir. How are you? Very good. Thank you so much.

SPEAKER_00 0:32

We are really excited to have you uh on this show.

SPEAKER_01 0:35

Thanks. I'm I'm excited to be here.

SPEAKER_02 0:37

And you know, that your audience didn't hear us talking before, but but yeah, we we we're already we're already pretty much connected. So yeah, I think it'll be a a good conversation.

SPEAKER_03 0:50

Well, it's it's always exciting when we get um interesting thinkers in and you know for for the outtakes that we won't be sharing with everyone, we've already had some really interesting conversation, as Anthony um points to. But Anthony, if we can kick off the conversation, um tell me a little bit about how you have seen AI change the landscape in your field in the past say six months.

SPEAKER_02 1:18

Wow, okay. This is uh this is almost an infinity. Um starting with the really long questions and just working. Yeah, yeah, yeah. Leah, yeah, exactly. Let me let me start with the beginning, right? In in 2022, 2023, we saw Gen AI start, right? And we we saw we we we first experienced a prompt-based interface, you know, so a chat bot. And it it was amazing, even even the first models. And the and the first the first big disruption was obvious, and and I'll I'll explain to you what it was. So so we way back in the in the 90s, right, we had search launched, right? Just search. And what what search did was it it effectively uh changed the entire way people looked for things. But that that was based on keyword depth. So if you were searching for a bicycle, you searched for bicycle, it looked for sites that had a lot of mentions of the word bicycle, and that that determined relevance. Um, and so intent, human intent, you know, I want a bicycle, was linked to keyword depth. And and for and for years and years and years, that keyword depth made Google billions and billions of dollars, right? So that that's that's point one. Then Gen AI launches. Now we can fully express intent in in a natural way, and that intent can be you know pushed into uh you know a large language model. You have semantic alignment. I know you don't want the the jargon, but let me just explain to you. Semantic is meaning, right? And so it looks for meaning alignment. So you go red bicycle, red road bike at this price with this gear. So all of a sudden, your intent, your expression of intent is so much more descriptive than it's ever been. That means that the you know, the 10,000 applicable retailers of that bike now become five. Um, and so what we saw in the last couple of big Fridays, Black Fridays, was a huge shift into gen AI search for discovery and research. So this has created the need for retailers and financial services and insurance companies and anybody who does anything to think about how those products and services are described. So when a generative engine looks at them, it determines do you semantically align to the user's prompt? This this is one of the biggest things that happen. And and and if you look at it next to everything that LLMs can do now, it's it's it's kind of trivial, but it's still monumental, right? And and the companies that I talk to, a lot of the companies that I talk to, they they struggle with this. They struggle with this GEO generative engine optimization or answer engine optimization. And the the best way to understand it is look at your product, think about every way anyone has ever bought it, create the prompts, put them in perplexity and chat GPT and Gemini and see what you get back. If you don't come up as number one that or or in the list, then that's the area where you do not have depth of meaning, right? And you you've got to fill that, you gotta fill that gap. So that's that's kind of ink to the that's the first part of the the answer to your question, right? Then the second thing is um the way tech services businesses work, that all the tech services that that every business in the world consumes, they they sell you a service. Some go in vertically, they sell you a thing that does one thing, uh, but a lot of them uh sell you services where they go in vertically and then they expand horizontally. This this is called systemization, right? And this has been a major strategy for tech companies forever, right? So, you know, I it the the more systemized I am in your business, the harder it is for you to get me out and replace me with something better. You know, you gotta you gotta reach a point where you know it's too expensive to operate, or you're not your your customer experience is compromised, or some other really big metric is being compromised, and you and you pull them out. So in in AI now, for companies that want to utilize AI in a in a materially beneficial way, that systemization, that position in the customer's technical stack is the biggest contested battle uh uh for of all time now. They're all fighting for it. Because what happens is between uh you being able to code your own stuff and you and and and and and do it quickly and not even need any experience, uh, but you also being able to ask for tasks to be done by third-party agents, you may not need that stuff anymore. And so there's a big war on now to either I get you to keep my stuff and fully integrate it. Some companies will do that. Um, and uh and and some companies will try to do it on their own. And then the the third part, the third part answer to your question is everybody sees AI right as money savings, you know, unit economic advantage reducing the cost to deliver a service to a customer. Um they they they see it as um you know completing completing tasks, automating tasks, and and potentially improving customer experience. But what they don't look at is things like compound learning, where they have some aspect of how they operate that they could compound, they could layer learning, and they could utilize that learning to provide uh overall value to the entire business, including the end customer. But what's most important about the idea of compound value is in the next few years, investors and acquirers are going to ask you questions about your data architecture and your potential for compound learning, right? So these are the three biggest, but I literally could go on forever about this, about about how how how big uh a disruption it is. I think you know, we'll look back at this period in time and this will be like an inflection point for for humanity, pretty much. Yeah.

SPEAKER_00 9:08

Yeah, I think three great points. Uh the first point about search actually reminded me of a recent uh interaction that I had with my Claude. So in my Claude, I've actually not trained it, but provided custom instructions to it to be very blatant about things, to proactively suggest things, and so on. So I was sharing something that my lawyer had done for me or shared with me, and there was a mistake that caught it. The second time there was another instance, where I said, My lawyer has said this. What do you think? So it caught the mistake, but said, Hey, Amigir, this is the second time your lawyer has actually not considered this thing. I think it's time for you to switch the lawyers. I was like, Okay, uh, what would you suggest? So it then came up with a bit of list of the lawyers that would be suitable for me. So I was thinking it's when you say uh you need your business needs to be optimized for the prompts that uh people would put to search for it, but you would be entering in an era where even if you already have existing customers, your AI would be challenging you and saying, hey, you are buying something from them. What about someone else who can provide a better service or a better product?

SPEAKER_02 10:28

This that is a beautiful example. You know, I I I talk to companies all the time, and I go, you know, all your customers are using AI for procurement, and you know the minute you send your proposal in, they're gonna put that back into AI too. So yeah, I mean, um, yeah, the point you just made 100%. Um, you know, is is Claude being ruthless because two must does two mistakes merit merit switching? But you know, you you kind of do, you know, pay certain services for, you know, you have an expectation of certain services to do to do certain things. So, I mean, do you change? Maybe you do, maybe you don't, but are you glad that it called it out? Yes, because it forced you to kind of think about what's important to you in the context of paying for that service. And and and and that point can be applied to everybody who provides a service.

SPEAKER_03 11:28

And AI gives us the time and the space to do this. I mean, that it being able to check these things critically, these things were were constant pain points in in business and in workflows. But if it's a quick search now, that pain point's gone. So that opens the field.

SPEAKER_01 11:43

And well, and in the in the you you go, Amir.

SPEAKER_00 11:47

No, I was saying in my case, I I know that Claude can confidently make mistakes as well. So I would take its uh advice uh sort of like I I wouldn't sort of fully act on its advice, but the normal people who are using chat GPT or Claude, and they are actually taking some of that advice extremely seriously all the time without even applying their judgment.

SPEAKER_02 12:13

This is a great point as well. So remember when I said semantic alignment, so there, so LLMs want semantic clarity. So when we learned keyword searching, it was like one and done, bicycle, home loan, you know, new job. That that would get you a list, and then you would click around so you found the one you want. And the the the kind of the the challenge of a large language model is um one and done does not work. So there's two strategies. You can you can prompt and refine, prompt and refine, prompt and refine until you get to you get a really good answer, or you can write big long massive prompts and uh and you know if if you can connect all the concepts together and and and that also works. And you can also uh have control prompts, uh, which you can include with your prompt about accuracy. Um, you know, don't don't cite any, don't don't create a a cited report of something that doesn't exist. It it must be true. And all the behavior to really refine on stuff that's harder than finding a new bicycle, um, that that semantic clarity is it can be very challenging. You know, um, you know, normally when you when you buy a complex service like you know, getting your house built, your your scope of thought around it, unless you're a builder, is is limited, right? So um so it's hard to write, it's hard to write a prompt that covers that. But I think we will see that behavior shift relatively quickly because people will learn that they get way better results with with with way better clarity. But but we're we're in the shift time. You know, the if you're if you're good at prompting, you have a good at you have a big advantage now.

SPEAKER_03 14:32

Yeah, definitely. And and Steve, you I think something that I'm hearing coming up in the in in the messaging and the things that you're saying is this idea of how to how to differentiate, how to um be you know, provide value, to be seen to be providing value, how do you be found? In a market where everyone has access to LLMs, one of the differentiators is of course gonna be how you're using them. Um but another one is going to be are you using just LLMs, or are you looking at doing some custom AI as well? And where's the value lines between that for particularly probably larger businesses? But as as bills become more affordable, this is gonna be an everyone question. Do we make a custom AI and what and where do we use that? Do we just use a yeah?

SPEAKER_02 15:21

This is a big question, you know. So there's this concept of sovereignty, right? Where you you run a model for your own benefit. I I don't know, I'm gonna get jargony again, but there's there's a concept called data leakage, right? So if you're using a commercial LLM and you're running your whole business on it, you're running, you're running a generative interface and everyone interacts through that. Your your data and uh all the loops, the feedback loops that need to be completed to buy something or do something, that all that all goes that go all goes straight to the LLM and it and it trains them, right? Um, and so that's a potential risk. Um uh it it it's very hard not to be jargony. It if you have stable patterns, which uh so some companies have years and years and years of data, right? And they think they're sitting on a big asset. But if that data is stable, then a very small bit of that data gives the same result as years and years and years. It's rederivable. Where you have unstable patterns, and most businesses do, but but you you you know, you kind of have to be an expert to figure out what they are. Those those patterns you you want to keep, right? And so um there's there's two concepts. There's still before you go ahead.

SPEAKER_00 16:54

I I would need you to explain what's stable button versus unstable button with an example, please.

SPEAKER_02 17:02

Okay, um I do I do have an example for you. Um, so uh a convenience store, right? I can think of a convenience chain. We won't say any names, but we all know the name that comes immediately to mind. They have 50 years of trading data, right? And they think it's super valuable. It is not. That's stable. The patterns repeat. Um, I'm trying not to use the mathematical term for what for for the curve that it represents, but it just repeats, it's not worth anything. And you know what? I can just say to an LLN, uh, give me 10,000 records uh based on based on a traditional um uh convenience store, and uh and and it'll just do it, right? And and and I don't I get no extra value from from having the five years, just only 10,000 records was enough. That's that's stable, right? Unstable is things uh where um finance, right? You you have compliance, right? So so you you you have to you have to comply in if you have a service that's operating across um uh different geographies, you have different uh different types of compliance, right? And so for the customer, those different compliance modes mean different experiences. Sometimes compliance can make an experience pretty awful, as as we all know. So an unstable pattern is how do you optimize customer experience for differing compliance models? So I still want to acquire the customer, I still want them to transact, I still want them to do stuff, but I have to comply. My compliance in Japan is different from my compliance in New Zealand, which is different from my compliance in Australia. So I want to use AI to manage dynamic customer experience so that even with that compliance in place, I still provide a good experience, but that that gives me what I need, you know, turnover, basket size, transaction buying. That's unstable, right? And that's the value in that. Yeah, because if if I if I compound the learning on that, that means that that's one an asset for my business. That's an asset that I can talk to investors, and if I'm a public company, I can talk to the market about, right? Um, but also that layers, I get, I get more and more and more value, and then I can utilize that value. And if I if I this is again techie, but I I don't know how you can get away from these concepts. If you separate the what the LLM does from memory, you can keep the learning separate and utilize it for your own benefit. Or if you don't think it's a differentiator, you can syndicate that learning to other companies and charge for that. So either way, you get an asset or a new revenue stream. So yeah, that but but but yeah, that's that's stable and unstable patterns. And I recommend for your listeners, even though it sounds like you know, I'm a data scientist, it's it's worth, it's worth you know, using an LLM in the context of your business to kind of figure out where you're at, because if you're not looking at compound learning, context engineering, there's other things, I won't mention the other ones, but there's a lot of there's a lot of AI processes which um the objective that they enable is straightforward, but but how they do it creates data as well that you can store and also also use as compound learning, right? And if you don't know about these things, if you focus only on tactical AI, like automate this right now, you you kind of open yourself up to people who can build a platform from scratch that does all of that stuff, including the automation. And immediately it becomes kind of hard for you to compete because they can do for five cents what you're doing for three dollars. You know, and that that's a very hard economy to match. And that's that's the the the compound learning and the and the unide economy. Economic difference is what really is the challenge. For AI disruptors, right? That's what they're going to be able to do. If you're a small company in a remote geography, you don't got to worry about it. Don't be LLX in cars. And you, you know, someone's talking to their car and where's where's the best place to get you know um speed or or something. You're something that's unique and geographically specific, then that's good enough. You don't have to do anything else.

SPEAKER_03 22:41

My favorite search for your coffee in the town I'm going to. Yes. Send me to the good coffee.

SPEAKER_02 22:47

Yeah, well, well, well, well, yeah, exactly. I mean, it if if but but but you know, sometimes you don't you don't really have to do anything because if if you're if you're the good coffee and you're rated well, peep people people do find you.

SPEAKER_00 23:00

What what we're talking about is businesses that, you know, like uh uh accounting firms, financial services companies, banks, you know, big, big enterprises where you know they were like, well, we're the only game in town, but um, you know, that that there's going to be people coming for those sectors, you know, so it's so it is yes, and and you would be aware that Y Combinator, instead of funding just the products, they're saying build rather than building the software for a law firm, you build an agentic law firm and we are going to fund that. So you've got all of these startups who are building AI native companies that are coming for you as well.

SPEAKER_02 23:45

Yeah, yeah. I mean, what what would you be doing if you were Y Combinator?

SPEAKER_00 23:49

Oh, absolutely, yes.

SPEAKER_02 23:50

Yeah, absolutely, yeah. Yes.

SPEAKER_01 23:52

Yes, yes.

SPEAKER_00 23:53

Uh, you talked about compounding knowledge and you also mentioned sovereignty. I have formed a view after a lot of conversation thinking I wanted to test that view, is I felt that sovereignty in terms of us having our own model from the scratch. And when we talk about sovereignty, we're talking about individual sovereignty, national sovereignty, which is it could be so many like definitions, but I'm I'm more talking about like if you look at it, it's like if you're an organization in Australia, like having an Australian model, or just having an open source model that you place on your servers or things like that. I found a view that there's a the race to actually build models and build better models, but there is a is a if I have to spend my energy and my resources on it would be actually on building a knowledge that is owned by my organization. That though that thing that you talk about, compounding knowledge or an AI brain that sits outside the model, and model is just like electricity, which we can you know switch the providers at any point of time, as long as that's yeah, yes. So as long as the knowledge uh remains with your organization, and what you're saying is it's compounding, which means uh everything that happens within the organization is fed back into that brain so that it grows. Uh it sort of solves the purpose. Is that what your view is?

SPEAKER_02 25:21

I I I agree with you. I I think in most cases, businesses do not have a case for sovereignty. Where you have a case for sovereignty is everything you do is so massively unstable and prone to variation that it's worth having your own model because if you build your own model, then you can sell that utility to other people who have the same problem, right? But most of the time there's no case for it. And and the and what you just said about separating inference and memory, so you know, reasoning and memory, that sounds like a future concept, but I'm telling you in three years, that'll that'll be an architectural pattern that is just common because everyone will have learned by then that memory is something you want to keep.

SPEAKER_03 26:17

So what should what should businesses be doing now, Anthony, to prepare for three years when this is ubiquitous?

SPEAKER_02 26:27

Here's what I do. I go think 2030 or beyond, right? Think about every way you make money now, and think and and and imagine what a competitor might do to your customer experience, your employee experience, your unit, your unit economics, you know, which is which is every piece of your provision of service and how much that costs, and what might a competitor do in the hard to understand AI stuff, compound learning, um, context engineering. There's a few more that I I usually like to rattle off, but uh, but I won't because it'll just it'll just frustrate people. But but do all the thinking, think it through, use LLMs to understand competitive advantage, red team. Bring bring in a bunch of people that you know that are expert and sit in a room and get them to just ruthlessly attack your business, and then figure out what the priorities are and what the sequencing is. And then the next thing is understand technically what you need to compete. So resisting the technical shift drives the unit economic challenge, right? So where you have a case to fund shift, do it soon and get the capability of your organization around that. Um, and and where you don't need to do the hygiene stuff that makes you appear modern to generative AI. You know, so like the the the coffee house example or the or the you know the geographic the geographically you know placed specific supplier of X, just just do the GEO, the generative engine optimization. But but if you think you're gonna get competed out of existence, either well, this is the this is the alternate strategy, do nothing and just ride it to the end because you you you either don't want to invest or or or you or you you don't want to get your head around what it takes to change. And believe me, that that that you know you you think I might be saying that that's a bad thing to do, but it it is very challenging. It's very challenging for companies that are totally into it and have the money. They they're still all over the place, right? So um, you know, figure out what it takes to compete in 2030 and and and go for it, but it's gonna be a vastly different world. You know, the the refrigerator's gonna order its own food. So, you know, I and I either you're just gonna get a green button on your phone, go approve, and that's gonna be it.

SPEAKER_00 29:29

Your your your approach of how you pursue it is very interesting and very contrasting to my approach, uh Anthony, and I'm really liking it and learning learning from it. I generally approach it like you know, when we initially talked about uh we try to not use jargon, but you said it is important for people to understand those jargons because their competitors will be using that. But then what I've seen you doing throughout the process is like I like to ease into the conversation when I'm doing it. I'm saying it's okay, let's take one step at a time, let's see the efficiencies. What you are saying is, and this is where I want to go with it as well, and I know the reality. Uh, but what what you are saying is this is what's going to happen. Either be prepared, go all in, or you know, just just ride it till till the end. It's it's it's very interesting uh to see uh that approach. I want to hear from you if when you say this to businesses, what reactions do you get?

SPEAKER_02 30:35

Yeah, so my my approach has always been like pretty explicit. Like, you know, I look, I know this is gonna be challenging, but um, you know, how much time do you think you have before you really start to get some real competition? 12 months? Okay, if you think it's 12 to 18 months, then we need to get to the facts, right? And I know you're worried about shifting the capability at your business and um, you know, general AI maturity and uh and and and you know how positions will change, how role descriptions will change, who may, you know, rise with the company and who may not, but you've got to focus on some things which take that long to move. And the architecture is one of them that you're gonna need a lot of time on, right? So, so you know, we let's talk about architecture, let's get, let's get, let's do the 2030 scenario, let's figure out what AI first means in the context of your organization. Then let's define the architecture, then let's give that to you to figure out how you're gonna kind of enable, right? Then let's go back and look at operating model and all the other impacts on the business, and we will we will try to enable you the best we can to handle it. I mean, a lot of times I work with organizational psychologists because the the the combination of the intellectual shift and the operating model ship is yeah, it's a lot. It's it's it's a lot. Yeah.

SPEAKER_03 32:28

Tell us about that, uh, about that, Anthony. What value does an organizational psychologist bring in this process? Because so much of where we talk about AI ends up just talking about how people think and how we respond and how we absorb information or change or deal with all of those big things.

SPEAKER_02 32:44

They just uh work with you know groups within the organization to help them contend with change at this scale, but also they work with leadership to help them understand how they need to lead through this kind of change. This isn't this isn't voluntary change, you know, this isn't the old definition of transformation, like you know, we're gonna paint some graffiti on the walls and you know, we're gonna shift the cloud. This is a forced, violent, rapid uh transformation. And um, you know, you're you're competing. Yeah, that's right. Yeah, yeah. You're you're you're competing not only with startups, but in in in many industries, you're competing with the LLMs themselves, right? Because they their goal is to monetize token usage. And when you have those kind, when you have that kind of power, um, it's very easy to just, oh, you, you know, you want to you want low-level research for legal, boom, there it is. You want health information uh diagnostics, boom, there it is. So, so um yeah, there's there's there's a lot of pressure. So um, yeah, you know, the the the the the people part is hard, and even even you know, everyone doesn't know what it means for them, and so that creates a lot of angst as well. But I work with a lot of companies where it is really severely existential, and so um, you know, being very explicit is it's just you know, you just kind of have to be that way because you gotta get on up to speed on concepts now. It's it's just like this podcast, it's like we're resisting, we're resisting jargon, but but not understanding the architectural patterns that drive those advantages, that's the disadvantage.

SPEAKER_00 34:56

So yeah. And are you almost saying that understanding architectural pattern is now the role of the leadership rather than some IT people over there?

SPEAKER_02 35:07

You are my you're my favorite person ever. Yes, that's exact that that's exactly what I'm saying. I this is this is based on what you guys said in the beginning. I I don't want to make this too fine a point, but I don't know how you can govern a business without having a rudimentary understanding of these concepts, because isn't it your your job to focus on customer value and and business value? So, yeah, like I mean, it's not it's not that hard, but you gotta commit to it a bit. But I I do think Amir in the next few years, you know, we're gonna see the most common leaders are conversant in this stuff. Yeah, I think that's conversant, not they're not architects, they're not, they're not engineers, and they're maybe not even you know vibe coding, but they but they understand they understand architectural patterns that give advantage and valuation, right? You you have to. I don't I don't know how you can avoid it.

SPEAKER_00 36:17

So I'm coming to a bit of operational question over here because this is how like this is where the conversation comes to is like once you have sort of convinced a business as well. A question that people ask is okay, what is the budget that you would do we need to allocate towards this activity, or what is the percentage of the budget do we need to allocate towards this?

SPEAKER_01 36:39

Yeah, yeah.

SPEAKER_00 36:40

Have you found an answer to this question yet?

SPEAKER_02 36:42

No. This you got to do the 2030 thing first, right? And then you you're either looking at like an incremental path, which means you want to spend less money, but it's gonna end up probably costing you more to get there, or you're looking at leap. Leap is risky. Where do you find the capability? Blah, blah, blah, blah, blah. But the money part is very much dependent on what you think the big buckets of challenge are going to be in the in the 2030 scenario, right? Uh, you you may be able to do it incrementally. Uh, for some businesses, especially like fintechs, you will not be able to do it incrementally. You're going to have to leap because all the interesting question, I think, too, in itself.

SPEAKER_03 37:31

Um, Anthony, if we can just jump in there.

unknown 37:32

Yeah.

SPEAKER_02 37:33

What are what are those fields?

SPEAKER_03 37:35

What are those businesses that need to jump now? I mean, fintech retail. Who needs to move fast?

SPEAKER_02 37:42

Accounting, legal, yeah.

SPEAKER_00 37:45

But who doesn't need to move fast as well? Like maybe just uh utility services, uh, maybe they don't need to move fast, but like a business who is in money of making business and have competitors and have customers.

SPEAKER_02 38:00

There's no law into disruption, though, is there are some where where you own the physical infrastructure, you're relatively, you're relatively, you know, safe, right? Because if I need if I need a uh connection to your service and it's physical, it's very hard for me to disrupt you. However, for those companies to be competitive, they have to look at IoT, you know, uh Internet of Things sensors in the physical environment to allow them to manage asset life cycle, maintenance, predictive maintenance, um, where they have a third-party workforce. Oh, you know, those third parties are generating data that you're paying for, but you're not getting it because they're not instrumented to give you that data. So there's still a challenge for them, but it's not as existential as um as you know, pure, pure services businesses. Yeah.

SPEAKER_03 39:07

So basically, if you're if your service value, if you the values that you deliver is derive from people sitting in front of a computer, get cracking.

SPEAKER_02 39:15

Well, yeah, or or or if if the value comes from like talking or research, um diagnostics, those, those ones. But but I mean, the the point you guys made about that that that every business will be challenged to some degree is true. Because even if the only thing you have to do is GEO, that's not set it and forget it. That that's you got to check and check and check and check and check. And then also, OpenAI wants to have an advertising product. Uh, obviously, you know, Gemini, Google, Gemini will as well. And if they allow um the semantic alignment to be artificially manipulated because someone's paid, that changes the game as well. So there's a challenge for everyone. It's the it's the level of challenge.

SPEAKER_00 40:14

Yes, because we do a lot of work in construction and manufacturing, although you would treat them as pretty traditional and archaic industries, but it's probably not an existential, but it's more about them being competitive when the cut their competitors are actually embracing and adapting AI, reducing their costs, increasing their profitability, improving the customer experience as well.

SPEAKER_02 40:37

And you almost supply chain optimization using AI just so you only order what you need, it's ordered at exactly the right time. Um, you know, it it automatically manages the compliance or terrorists or whatever happens when it crosses borders. You know, if if you uh you know it might manage sourcing for you, so you never you never get to the point where that one thing that you need to build, the one thing that you build gets so expensive that you can't competitively build it, that you know, yeah, there the if you if you dig down, there's always a way. And if it once again, if you're if you're the only kind of supplier of that thing, am I gonna drive a hundred miles to buy it? You know, uh, you know, some percentage cheaper. I I I might. Um, but but if you're in a very competitive space like car production, um then yeah, it's important. You know, I I heard you know that that um BMW had chips on on every single part. They knew where every single part was at all times. Um so uh yeah, there's there's a scenario probably like this for most businesses of a certain size, you know, where you know once you hit a certain size, you get more prone to to comp you have more competitive levers than than a smaller business.

SPEAKER_03 42:10

And we've we've talked a bit about and you've been fantastic in digging into uh future thinking and immediate future thinking in a lot of ways, but things that are that are needing to be planned for, conversations that need to be had and a whole lot of a whole lot of getting moving. But what about things that are happening right now? So in the space of you know thinking it could be retail or it could be you know fintech, could be anything else. What are the investments that are that are in AI that are delivering return on that investment right now? And what are the ones that are interesting to you?

SPEAKER_02 42:45

Yeah, okay, so um well the ones the ones that are most interesting to me are what the big some of the the big tech company strategies. Obviously, the the competition between the two big LLMs is really interesting, like what they're doing and how they're evolving. I mean, they're both about to go public, but you you you're seeing them start to broaden the service for applicability, which which is quite interesting. Uh conversely, uh Google has connected Gemini to literally everything. So it doesn't it doesn't matter if you're using their LLM because you're already using Gemini and absolutely everything. And I think that's a very interesting strategy because they had so many tools that so many people used. They had a distribution advantage, they block you from buying a service on someone else because you're you're kind of already getting it from them. So I thought that was pretty interesting. But um, but but the the other part of your question is a lot of people, as I said in the beginning, a lot of companies are um they're just Going very tactical, right? Um, so there I I need to improve this, you know. Mostly it's automation. I just want to automate this, you know, and I want I want to get my costs down and I I want to eliminate a lot of people, which which which normally doesn't work. And and and for the for the point you made, Amir, it it doesn't work because you didn't understand the architectural pattern that would have delivered what you wanted well enough to execute it correctly, specifically governance. If you're if you're if you're working with agents, you know, you the the there and I I I'm not talking about like board governance, I'm talking about you know uh agent governance, like so what what happens if, what happens if, what happens if. But but the other the other thing about um investment and and and why a lot of it isn't working is because if if you have a value stream and retail is the easiest one to understand, you know, the customer does discovery, then they find your your then they find your store or your website, then they look for what they want, then they put it in the basket, then you buy it, and then then then you pay for it, and then it's delivered, and then there's some reacquisition activity. Hey, Davina, you bought this, would you like this? Um they are doing a lot of vertical AI integration. And if if you look at a value stream and you improve one chunk, it often negatively affects all the rest. And the rules about improving that one chunk can drive uh improvement in that objective, but actually reduce profitability, right? So if you are optimizing for churn, you don't want people to churn, it may keep unprofitable customers, right? That that that's one example. But those that all those activities in that value stream are linked. So if you do one vertical optimization, it can mess things up. If you do multiple vertical optimizations, they just can compete against each other and drive absolutely no benefit at all.

SPEAKER_03 46:11

Even though I've seen this, are you seeing this, yeah repeatedly? Yeah, without without throwing any names under the bus. Can you give us some examples of of you know an implementation and then where it's gone patched?

SPEAKER_02 46:25

This this happens mostly in marketing, right? Because marketing metrics are very um, yeah, they're they're very explicit and um and and and and uh uh in influencing customer behavior is like really, really important, right? So so you know, acquire, convert, and then once you're in you bought one thing, how can I get your basket size up? What's your total lifetime value? You know, how do I reacquire you for less than I acquired you before? I don't want to have to buy you again off Google. I want to reacquire you myself. And so we we see departments that go after just one of those objectives, and then maybe one thing looks like it's working, and then and so then they buy uh different tools that do three of those objectives. And and you you if you if you want, you you can go to Claude even and ask for legitimate examples where this happens and it'll tell you, but but specifically in retail, I I've seen this, I've seen this activity, right? So it's not it's not a mistake, it's a very hard thing to understand the conflict of the optimization, you know, models running in each thing. It's it's it's it's hard to understand it, right? Um, but but uh if you actually want ROI, you have to think holistically, not vertically, right? There there are times when there are times when like your CX is terrible and you like you really need to fix it, and you're prepared to accept that your unit economic will be more expensive, but you just add to fix your CX. Fine, that's fine, that's tactical. You you can do that, you know. Going back to everything we said in this conversation, if you want to use it, if you really want to get advantage from it, you've got to consider it holistically and you got to do the work to understand, you know, in a system, you know, when you throw a rock in a pond, you know, the ripples, the metaphor that we all use over and over and over again, that those ripples eventually, you know, hit the shore. And so you you you've got to consider that stuff. And and and where where leadership in a business goes, fix it, fix it, fix it, fix it, we see tons of tactical activity, right? And the tactical activity, it wastes time, it costs a lot of money, but it also increases your propensity for disruption because while you're not actually fixing stuff, um, somebody else is trying to cut your mustard. And and and and and in retail and financial services, the cut your mustard is coming from the Gen AI searches themselves. Because uh, oh, the other you asked me the question about stuff that I thought was really interesting. Google and Shopify built the agentic um protocol. So they recommend, they have an agent, the agent goes out, it buys, and they own the entire transaction. As a user, it's great because I told you to find those speakers, you found them, you bought them at a good price, I get a good result. But but but for a lot of retailers, that's that's not gonna be that's not gonna be great. And if they're forced to use it, that means that they then have to figure out what they're not spending money on. Because that is not that's not gonna be cheap to deal with. Excellent.

SPEAKER_00 50:09

My favorite question, what are your thoughts on predictions about super agents? We talked a bit about aliens before we started this podcast.

SPEAKER_02 50:21

Um, it's it's it's really interesting. Like, you know, do I think everyone will utilize agents? Yes. Are you probably to some degree already? Yes. Um, will you will you have an agent on your phone that is half companion and and half tax doer? Like a hundred percent yes. Um, does your phone itself become an agent? So not not something that has an agent on it, but but the phone itself is an agent? I'd say pretty much yes. Can you transfer that agent into your car? Yes. Does that agent live in your house as well? Yes. Um, will will companies be run partially by agents? Yes. Um, you will will they be led partially or entirely by agents? Yes. I I you know I don't know, I don't know when. I mean, a lot of infrastructure has to be built to make agent-to-agent interaction work, but we see the scale of the money going into it. And um uh all every major tech company um wants this. And I don't know if you saw, you know, Jeff Bezos has a new company called Prometheus, and basically what he's trying to do is put agenc engineers into your business, right? And this is this is the beginning, you know, like um does that take off? Will everyone fight for that space? I I think yes, will he win? I don't know, but in answer to your question, yeah, they'll be they'll be everywhere. That agent that at some point that agent will be able to go into a you know a robotic chassis and you'll be able to walk around with it.

SPEAKER_00 52:11

I mean, and and just and your thoughts about the theory about that these organizations are trying to build superagents, and they have admitted they they are trying to build it, but one of the organizations would actually reach to a level where they would be able to do the research so quickly that would outdo all of the other organizations. Uh, have you have you heard of it?

SPEAKER_02 52:36

I mean, yeah, I mean, this the reason why um anthropic went from 350 billion to a trillion dollars in like two days, it was like three months, but it was a very short period of time, is because the biggest bets in the world are on who can corner intelligence, right? Um, and everyone has a bet that that intelligence gets to a stage where it can solve massively valuable and complex problems. Um, that you know, uh AWS massive investments in open AI and anthropic, Google investments in itself and anthropic, um, everybody in in open AI, but it's the but the biggest shareholders in those companies are doing it because controlling intelligence is going to be the new thing. And on your point on sovereignty, if you're using a third-party LLM as your as your government LLM, then that LLM has the ability to very subtly influence policy, right?

SPEAKER_00 53:50

So these are all things which and we have also seen, Anthony, that uh uh if uh an LLM is developed in US, the US government has the power to stop other countries using it, which means if you're relying on uh an LLM developed in China or US founding everything on that basis, they can stop that at any point of time and the businesses can be halted.

SPEAKER_02 54:13

We we're just at the beginning, right? There's the two big Xanthropic and OpenAI, and but you're gonna see we're already at thousands of models now. You're gonna see like thousands and thousands of models. They're gonna become you have very generic models now, and you're gonna have you're gonna have highly vertical models, you're gonna have large spatial models, large action models, and um, and I think you know, uh, you know, countries will have to will have to figure that stuff out. I I I do think though that the the turn off of Fable and and Mythos was political, not I don't I'm not sure that they really did anything wrong. And I'm sure somehow they'll they'll work it out and you'll see that come back on. But but yeah, it it it it is a concern. You know, you it it it's just like the internet, you know, you you were running all your store processes on on on the cloud delivered stuff and the internet goes down, what do you do, right? And everybody eventually built stuff that would, if if the internet went down, it would still work. Um, I think you know, you'll you'll see a strategy like that where you know you you you shift from uh third-party inference to local inference until stuff gets fixed. It may not be as good, but it's good enough to just keep going. But I mean, this is a this is a question that I don't I don't have any real answer for. Yeah, you know what?

SPEAKER_00 55:41

I sleep at home every like every night I sleep at 2 or 3 a.m. in the in the morning because I'm doing some work. And just said I slept at 10 because my claw was not working, so I didn't have anything to do. So it's uh it wasn't a working like so and and we're in this space, aren't we?

SPEAKER_03 56:00

I mean, it because there's the big tech crisis uh happening now, and because the the values that had been driven by um by by these leading development companies, so it's anthropic, it's Gemini, it's open AI, it's all of the above. And in a world where it's gonna be really expensive to use AI to do these things, if we assume that there is gonna, you know, it's gonna be a whole lot of your budget's gonna go towards implementing AI. How, if you're a startup and you're looking for an exit, how do you structure yourself?

SPEAKER_02 56:31

Oh, this is the greatest question. So I I love this. So two things. One, I think you'll see the prices a bit, they'll they'll they're going up, but they'll go down, right? Because it'll it'll become a utility, right? Just and it'll commoditize. But if you're a rapper, then that's the term for that, what you just said. So you you just have a have a layer over a commercial LLM. There's a great example from the past. There, Twitter used to have this thing when it was Twitter, they used to have a service called Firehose. And Firehose gave you all their data and you could you could buy access to fire hose. And there was this very popular um startup in the UK that that utilized fire hose, and then one day Twitter just shut it off, and they went from they went from a $400 million valuation to zero. Um if if you are a rapper business, you do have to have a peering strategy for your for your reasoning, for your inference, right? So you you can start with one, but look just like you peer cloud, you know, no really smart business relies on one. You know, if you're small, it's okay. Um, it could go down, and you know, maybe that's not so bad. But big businesses, they peer. They they they take, they have redundancy with multiple cloud providers, and this is the exact same strategy that you'll do with with with with models. You'll peer the and and and there there's another strategy for peering too, which is sometimes you don't want to use the commercial LLM because it's too expensive. And some tasks can be done by smaller local models, right? So you so part of your of the control around your agents says, agent, use use this one for reasoning. You know, use use the LLM for reasoning, but but for this test, use the small model because it's cheaper. And and and part of that, part of that control of agents is understanding how to navigate the agents so you get a cheap price. So you not not a cheap price, but you get a price that makes sense to the task you're trying to trying to deliver. But yeah, wow, this has been the best.

SPEAKER_00 58:55

Well, that's have been an amazing conversation, and I think we can just keep going at the end.

SPEAKER_03 58:59

I feel like we've just scratched the sips. I think there's a good 150 conversations we need to have on the back of that.

SPEAKER_00 59:05

Yes, we definitely need to get you on a sequel, a second podcast.

SPEAKER_02 59:09

Yes, I I I I love to because when when the like it's exciting when the when the questions are exciting, you know, and I think you guys navigated the conversation really well. Like, you know, you you ask good questions, you ask the the important questions as far as I as far as I'm concerned. And yeah, you just it yeah, without any without any script, I because I I've done a few of these now, and I think this this was the this was the most natural but most compelling, um, because it's it's not all you know jerky because you're you're you you're like, what's my next question? But yeah, and and and and and both of you have have have both a very instinctive knowledge, but obviously you're studied as well. So so you you both understand it, but but but you followed the flow, and yeah, that's good. You guys should you guys should help companies if that if you don't do that already.

SPEAKER_03 1:00:09

Well, he he definitely does, and I'll I'll have my moments. But um, but I think it makes it an awful lot easier, Anthony. For us, yes, it's so great when when you know having someone like you in here because we're learning constantly, we're absorbing all this knowledge that that you're sharing with us and absorbing your your viewpoint. Yes, and it's just so interesting.

SPEAKER_00 1:00:30

Yes, I just I like it feels like the world opened. I felt like just picking you up and just put putting you in front of like every every sort of yeah, yeah, business leader who's still sort of thinking about what to do next.

SPEAKER_03 1:00:43

Yeah, and if people can take away one thing from this conversation, rewind it, yeah, share it widely, is Anthony talking about the importance of leaders understanding the architecture.

SPEAKER_00 1:00:54

I know. I just want to say, listen to this, uh listen to this.

SPEAKER_02 1:00:58

Just put it on right I love it when you ask that, Amir. I I I I loved it. I was like, oh, thank, oh, yeah. I mean, you know what's you know what's this in the same in the same you know kind of line of thinking is boards. I'm like, um, okay, so let me take it through blah, blah, blah, blah, blah. How are you, how are you providing organizational governance that you have fiduciary responsibility when when the agent either eats five million dollars worth of tokens or does something really, really wrong? Yes, that's that's on you, but you don't understand any of these concepts. You know, so the so yeah, it's that that that's that's in the same line. But yeah, I I loved that comment because I I totally believe that. And um, and I've actually kind of believed that throughout my entire career. When when you see a leader delegate the decision making to someone who is uh responsive to the business and not proactive about building um you know technical infrastructure which supports the goal of both the customer and the business, yeah, you don't you get a big mess, you know.

SPEAKER_00 1:02:19

So Anthony, how can people reach out to you or your organization?

SPEAKER_03 1:02:23

How can they learn more from you? Where do we find more?

SPEAKER_02 1:02:26

Uh I I'm on LinkedIn. That that that that would be uh a primary contact point. Um or or they or they can they can ask you and and and and you you can connect me. Yeah, I don't I don't I don't mind, but I I loved I love talking to you and and if if we do another one I'll make sure that I that I can get down there, although you know we would have probably been wild in person. So that's right. Yes, yeah, yeah. But but yeah, like a like a great feel from the two of you too. The texture is uh, you know, it's like um it's really inviting, you know, that the the two tones, the two, the two viewpoints on the questions. Yeah, I thought it was good. You guys got a good podcast. I like it. Oh, thank you. Oh, thank you.

SPEAKER_00 1:03:23

That's a good kind of idea.

SPEAKER_02 1:03:24

I'd be someone likes.

SPEAKER_03 1:03:26

Uh it was awesome. Thank you so much for for joining us, Anthony. Um, we will absolutely be chasing this up and coming back to you another time. But you've just you've delivered some really fascinating things for people to think about and and your thoughts on leadership and and structure and how you target ahead to that 2030 and really think about where's the disruption coming and what do I do? How do I get organized now? I think that's that is the that is the conversation that everyone who's in business and really in organizational positions everywhere needs to be having right now. So thank you. Thank you for bringing that to us.

SPEAKER_02 1:03:59

You you you are welcome. I've loved talking to you both, and I look forward to talking to you again. Oh, and yeah, when when when you're when you're done, send me the link and I'll post it as well. Oh, fantastic. Yeah, I'll give you I'll I I'll post it on Substack and um and LinkedIn.

SPEAKER_00 1:04:16

Very good.

SPEAKER_03 1:04:17

But for everyone who has joined us on the podcast, uh, thank you so much for your time. I hope you enjoyed that as much as we did. I'm not sure that's entirely possible, but do you hope it was fun for you too? And we'll see you again next time on the Dumb Monkey Show.