AI Scientist And Entrepreneur Shanif Dhanani Shares How To Leverage AI To Create A Market Disruption (#419)

419 Shanif Dhanani
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Jeffrey Feldberg: [00:00:00] Shanif Dhanani is a software engineer, data scientist, and founder of Locusive, a company that's creating an AI powered customer success assistant for SaaS. He's built machine learning at companies like Twitter, Booz Allen, and Tap Commerce, and founded Apteo, a SaaS company. Shanif loves creating software and AI products for startups, and has learned the ups and downs of starting new companies from scratch.

Through the Locusive he's helping SAS companies leverage the power of generative AI to provide their customers with extraordinary experiences while freeing up their CSM teams for more strategic work, like driving renewals and upsells.

And before we start this episode, a quick word from our sponsor, Deep Wealth and the 90 Day Deep Wealth Mastery Program. Here's Jane, a graduate who says, and I quote, the Deep Wealth Mastery Program prevented me from making what would have been one of the biggest mistakes of my career. I almost signed on the dotted line with an unsolicited offer that I now realized would have shortchanged my hard work and my future had I [00:01:00] accepted that offer. Deep Wealth Mastery has tilted the playing field to my advantage.

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Deep Wealth Nation, welcome to another [00:04:00] episode of the Deep Wealth Podcast. Well, you heard it in the official introduction. We have a fellow entrepreneur in the house of Deep Wealth, but not any entrepreneur. We're talking a mega smart entrepreneur. We're talking generative AI and all that wizardry that goes on behind that.

So Shanif, welcome to the Deep Wealth Podcast. An absolute pleasure to have you with us. And there is always a story behind the story. Shanif, what is your story? What got you from where you were to where you are today?

Shanif Dhanani: Hey, Jeffrey, thanks so much for having me. that's a big question. I'll try to distill it down, so I'm a techie. a coder, software guy, data scientist. I've always loved building and I always loved just analyzing numbers. So that's where I started. But. The flip side of that is I always hated working at big companies.

So my background mostly started in the world of software. I started teaching myself how to code at 14, eventually started building a bunch of products and tried to build a company around those products that I built and realized I had no idea what I was doing. So. Yeah. Like most entrepreneurs, [00:05:00] I eventually figured it out.

I ended up going to business school for that. I don't know if a lot of people need to do that, but in business school, I helped start a company. It was called tap commerce and one of the world's first mobile ad tech platforms. Built a system that handled about a million ads per second.

So part of a great team that did that, sold it to Twitter, worked there for a while on their ads. And then I started a couple of other companies. One was predictive marketing for e commerce. And today we are creating agents for companies, particularly in the world of e commerce. So that's past 15 years of my life, just distilled down into a quick two minutes, hopefully.

Jeffrey Feldberg: What an entrepreneurial journey you're on the not cutting edge with the bleeding edge of technology. You're selling to some of the key logos that are out there and just really making a difference. And just for the benefit of those in DeepAuth Nation, when they hear you say the word agent, I know for myself, If I wasn't immersed in AI, I would think of the Matrix and those agents, but that's not what we're talking.

Hopefully we're not talking about that. [00:06:00] So when you say agent, not yet scary, but when you say agent, as we are here today, talking about AI, does that really mean?

Shanif Dhanani: No, it's a good question. When you're immersed in this world, you use a lot of terminology that maybe isn't as common. So an agent or an AI agent, basically just a software application that can essentially take actions and accomplish an objective more or less by itself. It's an autonomous piece of software that can essentially think through and reason through a couple of steps.

in order to reach an objective. Now, there's a lot that goes into that, but that's sort of the high level way I would describe one.

Jeffrey Feldberg: So a lot going on with that. But let's take a step back for a second and just big picture wise. And I know everything with AI seems to be changing not by the day, but by the hour. What's going on with AI from your vantage point? I mean, you've been right there for quite some time and you're ahead of most of the people that are out there.

So what are you seeing? What would you want us to know about AI, where we are, where it's heading? And what that means [00:07:00] is entrepreneurs, what we should be thinking about.

Shanif Dhanani: I'll sort of start off by saying how I think about it as a techie and then I'll start off by how do entrepreneurs and business people think about it? So like you said, I've been in the world of AI before it was AI. It used to be called machine learning. Before that it was data science.

Before that it was, predictive analytics. So it's had a bunch of different terms and I've been there probably for the past 15 or 20 years, today's AI is actually. More interesting and more capable than anything I've ever seen, mostly because it can create and it can sort of think and reason. I hate to use those terms, but it can approximate thinking and reasoning.

And so when we think about AI today, we are really talking about the most cutting edge. Automative data science that we've ever had. Most people are going to be familiar with it from the perspective of something like chat GPT, where it can essentially create long form content, help you think through [00:08:00] problems, help you reason, we never really had that before a few years ago.

And I think where we're going is we're just going to take this concept of being able to do what humans have traditionally able to do and only been able to do and. Turn that into something that machines are really good at everything from creating music to generating art, obviously writing text, thinking, reasoning, automating.

I think we're just getting more and more advanced there, which is fascinating to see coming from a world where no more than five or 10 years ago, the best we could do is predict what ads people are going to click on. So the world is changing. And what that means for entrepreneurs is I think you're going to be able to get a.

A lot more done with a lot fewer resources. If you've got an idea, you can quickly take it to market with maybe just a team of one or two. You can spend far less than you were doing before, and small and solo entrepreneurs are going to be able to spin up their own very large businesses quickly. Much less large businesses are going to be able to [00:09:00] automate away a lot of what they're doing.

So there's a lot to unpack here, but I'll pause there.

Jeffrey Feldberg: It's incredible with some of the things that you're saying, my goodness, each one of those statements could easily be an episode for the entrepreneurs out there, though, what I've been hearing a lot is with AI, where we are now, you could even do this today, but especially where it's going to your point, the days of needing a hundred, 200 person company to get into the millions, gazillions of dollars, a single entrepreneur is really right now with AI on the precipice of being able to do that through AI of scaling a relatively large company.

And not having the headcount to do that thoughts about that fact fiction. Is that something we're going to leave for the movies? Is that here today? Where are we on that? Okay.

Shanif Dhanani: we talked earlier about how quickly AI is moving and if it just keeps that trend up, then I actually fully agree. So I mentioned I'm a coder. I've probably been able to write. 50 to 60 percent more code just with the help of AI just by myself accomplishing things I probably not never could have before, [00:10:00] but I'm also able to create, SEO content and push on marketing by myself or with maybe one or two people.

So I think that the world that we're moving into does enable that sort of lean approach to very large businesses, which I think is it's the right time for it now to, four or five years ago the way people evaluated businesses was talking about how many people they had, how big is your head count now?

I think it's going to be, how much money are you generating and how small is your team? How lean are you running? And so we're entering a world where I don't think this is going to be fiction. I've already started to see it happen. It might take a little bit longer than some people think, but do think it's coming.

Jeffrey Feldberg: My goodness. So with all that said, why don't you share with some of the things that you're now doing, because you're out there, you're actually some of these, what at one point in time would have been the theory that we're talking about. It really is. It's reality. This is now fact. And Shanif, you are leading the way in helping other companies do this.

Through what you're doing on the back end with AI and your knowledge and expertise. [00:11:00] And hopefully I'll remember to circle back to ask you if people are still going to need to know coding with AI and what goes along with that, but share with us what's going on with what you're now focusing on of how you're helping other companies.

Shanif Dhanani: I appreciate that. My background is mostly in data and automations and that's what i've stuck with So I mentioned I think at the start of the call. I started a recent company a couple of years ago We are building agents. I mentioned what an agent is earlier we started basically building agents that could do things like Analyze your data pull data from databases, understand and interpret trends and patterns.

And we are now essentially applying a lot of that core technology to one particular vertical. We're actually building AI agents for e commerce. Now, the e commerce agents we're building, they don't have to do maybe as much data analysis as what we were focused on before, but what they can help you do is essentially help your shoppers find the right products.

At the right time, understand what products go well with what they're [00:12:00] doing. We're even building a styling assistant. All of this is done with AI and its ability to sort of understand intent, understand how to think through and how to rationalize from, one particular starting point to an end objective.

So, wrapping it up in our case, what we're doing is we're helping e commerce brands essentially improve the customer experience by making product discovery easier. But we've also built agents in all sorts of other spaces.

Jeffrey Feldberg: And so practically speaking with what you're doing on the back end. So effectively, if I have a car, just to try and simplify this down to the simplest kind of analogy, if I have a car, you're putting the engine in that car. So what does that look like? Someone comes to my website and it's now your intelligence behind the website.

What would I see as a potential prospect or even a customer?

Shanif Dhanani: There's a few, from the user's perspective, we try to keep it as simple as possible. I've been in the world with product and software for 20 years. And one thing I've grown out of is trying to make [00:13:00] really complicated, super smart seeming things that end up falling flat on their face. So as a user, what we want you to do, you go to a website.

We wanted it to be super simple for you to find what you're looking for and to get essentially what you need at the time that you need it. Now this could look like a chatbot. Everybody has seen them. I don't think anybody really likes them, but they're out there. It could look like something where you open up a chatbot and you ask it to find you.

For example, in the case of e commerce, the right product, you might ask it something like, Hey, I'm going to a wedding in Mexico. Can you find me the right dress to wear? And so it'll go through all of the products that site's got available and it'll find you the right dress, but it might also find you the right shoes to go with it.

So again, heavy e commerce use case. The concept here is you as a user and the end user should not have to make a big effort to find what you're looking for. And even to accomplish your goal, which in this case might be to find an outfit really quickly and then check out the agents that we're building allow you to search for your products [00:14:00] or in a case of maybe a SAS company or an information company search through your website, take action, maybe that looks like checking out.

Maybe it looks like finding an associated product and then moving on with your day, getting what you need and then allowing you to go forward. So that was a quick e com example I gave. But you can assume that essentially what these agents are capable of doing is talking to a user, understanding what they want.

And then getting them what they want as quickly as possible.

Jeffrey Feldberg: And so with that said, from what you're seeing, how good is it from this kind of interaction on the back end? And we're still, I'm still going to say early days. You can correct me. Hey,

Shanif Dhanani: Oh, yeah.

Jeffrey Feldberg: long. Early days were well into that, but relatively speaking, early days from where we are right now, what's been your experience with that?

I mean, how's the whole side of things from, okay, I'm on the site, here's what I want and it's coming back. Is it pretty good with what's coming back or no way would not in a million years would I do something like that in terms of what it's showing me.

Shanif Dhanani: We are in the early days. And so that's going to come with all sorts of little hiccups and nuances. We are [00:15:00] in a world where we have this capability, which is something that is leaps and bounds beyond what we had even three or four years ago, where you can talk to something and have it take action for you.

I actually write. newsletter on agents. And one of the things I focus on in my newsletter, one of the things I love to say about these things is they're smart, but they're not that smart. They might do something really stupid that a human would never do, but then they might turn around and they might find a solution to a problem that you would never have thought about.

You might ask them to find a product that goes really well with a particular type of jacket in the winter, and it would come back to you with something really dumb, like shoe shiners. Now our agents are a little bit more optimized. Then that. But on the flip side, you might come back with the most interesting sort of fleece leather jacket that you've ever seen.

So what we're doing, we're in the early days, folks who are building agents are in the early days, and we're putting in a lot of work to try to make these things safer so that they don't maybe screw up any experiences. Accurate so that they can actually find what you're [00:16:00] looking for. Easy to use.

So they are capable in many ways, but there's still a lot of issues. And when I talk to folks who are building these things, I generally say yes, these things can reason and they can sort of accomplish a large amount of tasks. But They're still not that great. So one way you can essentially deal with their shortcomings is focusing in on one small problem.

Make your agent more vertical instead of horizontal, make it focus on one thing. And Jeff, you've probably heard, this is the same thing we've done in SAS for the past 20 years, go from horizontal to narrow. So it's a long way of me answering your question. They're smart, but they're not that smart.

They're capable, but they're not a hundred percent capable. That's where we are today.

Jeffrey Feldberg: And so when we're talking about this, as we've been seeing out there in the headlines, as different websites get to know what my preferences are, what I'm all about, it then takes that and my goodness, a lot of potential damage could be done in terms of marketing or targeting me or fraudulent kinds of things.

And so when you're talking about taking this now to the next [00:17:00] level, what should we know about this? And how do we make sure that we're on the positive side of AI and really appreciating and benefiting from what it has to offer in a good way?

Shanif Dhanani: that's a big question. It's funny. My previous couple of companies. Or maybe on that dark side. So we were using really heavy duty machine learning to understand what you were doing on the internet and essentially showing you ads, which would then convert into people purchasing. So am I super excited about the concept of, using AI to drive more commerce?

Maybe not, you could probably be doing a lot of things with it, but from a consumer standpoint, I do think that there's a world where you can become a lot more productive. Let's say you're an information worker, or you're just somebody who's trying to accomplish more tools like chat GPT, which have been trained on the world's data are really fantastic at helping you solve a problem, research think through things.

So I think that's one thing you can do to make yourself a little bit more effective, more productive from a safety and security standpoint, we're entering a world [00:18:00] now where machines can be trained to talk and act like humans, we're going to see a lot more scams, we are going to see a lot more sort of questionable content.

We're going to see a lot more media that looks like it might have been generated by human but it was generated by AI. So to be safe, I think it's becoming even more important to be more skeptical, which is kind of a bad thing. Like it's kind of annoying that we all have to double check everything now, but that's, I guess the world we're living in, it's important to double check where your media is coming from.

It's important to make sure you're double checking any links you're clicking, for example, on emails. Whenever you're browsing, I don't know there's much you can do about that. The internet's capturing all sorts of information about you. I built systems to do it, so I know, but I would say just double check everything.

Be careful about what you're clicking on. And then when it comes to money, just be careful where you're putting in any sort of payment information.

Jeffrey Feldberg: So like with anything else, just proceed with caution, be careful, don't take everything at face value and just verify everything. So that's a little bit on the [00:19:00] dark side, but on the positive side, at the accuser, when I was preparing for this, Shanif. I mean, my goodness, some of the testimonials that you have, and I may be off a little bit on some of the percentages.

I don't think I'm far off. One of your customers was saying, wow, what Locusive did for us in terms of our response time and our cost, it was improved by 85, 90%. I mean, we're talking substantial numbers here. We're not talking 5%, 10%. We're talking huge in terms of saving time, saving money, increasing profits.

Customer satisfaction, all those kinds of things. So what's going on behind the scenes with that? What's some of that secret sauce, if you will, should need that you and the team are putting out there.

Shanif Dhanani: I get super excited talking about this because it basically, I know who you're talking about, Ryan from GrantExec, one of our best customers. What we are seeing today with AI is the ability to automate tasks that you were never able to automate before as a human, which means you have to put manual labor behind it.

In the case of this one customer, they were essentially [00:20:00] trying to comb through thousands of grants, federal grants and state grants in order to find the best ones for their customers. When we implemented an agent via Locusive, the first brand that I built, we allowed a machine to find essentially the most relevant grants for their customers automatically across thousands and thousands of grants.

And instead of a human having to comb through a bunch of data now in two or three days, a machine can find these for your company for your organization within the course of a few seconds, it can re rank them so that the most relevant ones are at the top and it can get you a report right away. So now instead of you having to spend a lot of time and money hiring someone and a lot of Training efforts in terms of getting them up to speed, you can train an agent to do this for you.

So what we're seeing today is a world where you can start to automate away a lot of tasks that might have [00:21:00] been tedious or manual before you can start to automate away a lot of tasks that require. Some minimum level of reasoning or pattern matching. And then because you can automate it away, you can start to build the processes on top of them.

You can build products on top of them and essentially start creating offerings for your own customers that are more powerful, quicker, faster, better, stronger, that sort of approach.

Jeffrey Feldberg: So from what you're saying, it sounds like through AI and what you're able to put on the backend is doing a few things. So some of the manual, literally just the grunt work instead of visiting a gazillion websites, and maybe it's government grants or RFPs or whatever the case may be. But to quickly go through that and based on some criteria, I would imagine it's coming back saying, okay, Jeffrey, of the 10, 000 websites that I checked, here's five or 10 opportunities that you should really know about.

And then on the front end, depending on what kind of information you're provided of really making a, an immersive custom [00:22:00] experience for my customer, or perhaps a prospect who's coming there to really get it down for them. But on the back end, what I'm also hearing you say is. The people that I have doing some of this now, perhaps I can redeploy them to go deeper into the company and perhaps we're overlooking on renewals or having some more upsells or hate have you thought about doing this?

Because I know you're working with us in this area. Are you aware that we're even doing this and really taking customers to the next level because we're freeing up our people from some of the manual, the intensive, the time consuming, the boring. Things that need to be done, but now we're deploying our people for higher level, better kinds of things.

How am I doing with that?

Shanif Dhanani: It's funny. I give the same pitch. I mean, I don't even need to say much more, it's great. exactly how I think about it. A lot of, I'm sure this question is going to come up, so I might as well just preempt it, are we going to lose jobs because of this? What I try to tell folks is I think that.

We're going to maybe see fewer folks being hired for tedious work [00:23:00] and more folks being allocated to that service component or the human component that AI isn't great at solving, the deeper relationship building. And so exactly what you just said, Jeffrey, we're going to take folks who are working on stuff that is maybe low value if nothing else, or low sort of minimal amount of value, but still requires a human.

We're going to start to see them redeployed in terms of. different tasks, different ways of building companies. And so I'm fully of the opinion that we are going to start to see a lot more efficiency. As we talked about earlier, you're going to have fewer people being able to do a lot more things.

Jeffrey Feldberg: And Traneef, in addition to the question that you shared, sure, let's take the elephant in the room. Okay, are we going to be losing jobs? Are people going to be out of work? I would take that question and flip it on its head, and the question I always ask is, look at the past five years, even the past two or three years.

What jobs didn't exist two or three years ago that now exist today? Because of technology and with the AI, I can't tell you what jobs are going to exist six months from now or a year from [00:24:00] now. And sure, we don't take lightly that people, their livelihood, their lifestyle, their income, the ability to put food on the table for them and their family.

I get all that. But at the same time, if there's gonna be new opportunities that really have people in, I would hope, more interesting types of activities and jobs, I would think that's just a win for everyone. Thoughts about that?

Shanif Dhanani: because I've been in the world of tech for, this long, I've seen this trend where something new comes out. People start to worry if it's going to take over their jobs, but in reality, four or five years down the line, it's created a bunch of new jobs. It's created entirely new functions.

The world of SAS customer success did not exist 30 years ago. The world of AI engineers did not exist. And so what you've got now. And I strongly believe this is if you look at the course of human history, every time something new comes out, I think people overreact and they tend to worry a little bit too much.

But in reality, every piece of technology that makes our lives a little bit easier ends up creating new jobs in ways that we can't anticipate. And I, fully believe AI is going to do the [00:25:00] same thing here.

Jeffrey Feldberg: And so with what you're doing behind the scenes, Shanif coming to you and okay, Shanif heard you on the DeepAuth podcast. Can you take a look at my company? Here's what we're doing and I'm not sure what we don't know. We don't know what we don't know, but can you help us find the gaps? I mean, time wise, how long would that typically take for you to say, okay, Jeffrey, here's what we found.

Here's what we're going to do. Here's how we're going to give you back some of your time and dollars and for your people up to do really higher level kinds of tasks, more money generation types of tasks, what does that look like?

Shanif Dhanani: it's a great question. I used to do a lot of AI consulting before I started on the product focus work that I'm working on now. What I found was there were sort of two types of one are in a group of people who know they need to use AI, but they don't really know what they've just heard about it.

Their board is pushing them to use it. And they're sort of coming at it from, I think what I would consider a backwards approach. The other group tends to know what problems they have within the organization, where they're spending a lot of human hours on tasks that maybe they don't need to, [00:26:00] or where they can open up new opportunities for revenue by automation.

For those folks, I would say AI is a great. Approach to use for things like automating away tedious tasks improving features or product opportunities that maybe you couldn't do before because they required some minimum level of human reasoning. I would say if you're in the first bucket.

You don't really know what you want to use AI for. You just heard about it. I would say, start by looking at where your internal hours are going and what are your problems that you're trying to face. Move yourself into that second bucket. Once you're in that second bucket, it's just a matter of applying the right tool to the problem.

So whether that tool is one of the ones that we've built or something else that exists already out there, the time between getting getting educated about what's out there and being able to deploy something now is extremely small. It's the smallest we've ever really seen in software. You can go from problem statement to solution within the course of a few hours, if not days or weeks.

So, [00:27:00] that part is really simple, but I think the hardest part is understanding where you could use AI to grow and solve problems. That takes a little bit more thought.

Jeffrey Feldberg: Let's take it from this perspective, because I know there's some listeners of deep automation, they're just going to bury their heads in the sand. Yeah, it's all sounds good, but it's too much for me. I'm going to take a wait and see approach and see how this all lands. And that's fine. And that's everyone's perspective to do that.

But on the flip side, the competition. So if I'm not doing this, if I'm not reaching out to you, shouldn't my competition likely is. And so from one entrepreneur to another, if I'm not doing this, but my competition is doing this paint a realistic picture for us, what does that mean? picture wise, maybe not next week, maybe not even next month, but certainly within a few quarters from now, and certainly within a year or two or more from now for the companies that really are embracing this.

I mean, what does that look like? Because you've been on the dark side, on the light side, and with what you're doing right now, you've been all over, you have that kind of perspective.

Shanif Dhanani: I think is a very easy analogy for this. I think it's for back in 2020 10 2009 [00:28:00] 2008 when companies went mobile, they either went mobile and they adapted or they stayed non mobile and a lot of them, failed. Similarly, back in the 2000s, early 2000s, you were either. On the internet or you weren't like, that's crazy to think about now, but if you're not going online back in 2000, your competitors are, and they're suddenly able to start selling more, shipping more.

So we're in a world today where everyone, I mentioned that first group of CEOs who I talked to, everyone is trying to figure out how to use AI because they're worried that the competition is going to be using it. And the competition is using it. It's just a matter of figuring out how to best apply it to your business.

That's something. That you as a CEO or you as a VP or whoever you are at the company needs to understand. So I'm not going to sugarcoat it. I do think that AI is going to permeate sort of every service and product that we use within the next 10 years, particularly generative AI, if not the more traditional stuff.

So if you're not using it, I would say, you might be okay for the [00:29:00] next couple of years, but long term healthier business. I think you have to figure out how to use it to become more efficient, how to use it to make. Automation's quicker, better, faster, how to use it to make more money or how to use it to cut money where maybe you don't need as many things that are non automated.

Jeffrey Feldberg: And so Shanif, with what you're doing at Locusive and how you're going into different companies, if I want to start in one area of my company to do this, I'm wondering in a situation like this, and I know you would be right if you said, well, Jeffrey, with the question that you're about to ask me, it really depends on where a company is, what their journey is.

Every industry is different. Every company is different. And I get all that. On the flip side though, you have the 80 20 principle or Pareto's law that says, okay, 20 percent of your actions are likely generating 80 percent of the results. And maybe it's not 80, 20, it might be 90, 10 or 95, five or 99 one.

But a small group of actions has big repercussions, possibly in a good way, [00:30:00] possibly not in a good way. Or the flip side, hey, 80 percent of your challenges are coming from 20 percent of the same issues. So from that general pattern in mind, I mean, are there certain patterns that you're seeing that if I was going to dip my toe in the water and work with you, Hey, Jeffrey, why don't you start over here?

Most of the companies that we've worked with, they've had a huge ROI across the board in a number of different areas by focusing on this. What would this be?

Shanif Dhanani: There's a few, I think there are, like you said, it all depends, but there are, few low hanging pieces of fruit here. Customer support tends to be one of the more obvious ones. If you have a chat assisted assistant on your site. There is a very straightforward path for you to allow your customers to use something like that, particularly if it's a new age chatbot, where maybe it's plugged into all of your systems and it can actually answer questions.

Unlike the chatbots from a few years ago, which were really dumb. That's one area where we've seen, and we've heard people cut a lot of costs. Anything related to workflows also sort of falls into this. If you've got. Something that can [00:31:00] be kind of automated or an RPA robotic process automation approach that just didn't quite cut it before.

Now you can, because you have this tool that can essentially reason so that it can automate away a lot of the things that you may have had to use human for and then, if we're going into the world of e commerce, which is where we've sort of spent a lot of time. Product discovery is something, discovery in general, whether you're on e commerce, for example, looking for products or you're on a website that just has a terrible search engine, discovery becomes a lot better with the world of AI.

You can now find what you're looking for. You don't have to dig through as many pieces of information. So discovery becomes a huge opportunity for folks who maybe have some sort of offering that requires more in depth analysis of a lot of information. Now you can have AI do it for you. Again, depends on everything, particular about a company, but those are some of the early areas where we've seen a lot of success,

Jeffrey Feldberg: Okay. So customer support, customer service on the back end, talk to me on the front end because the dreamers out there are saying there is going to [00:32:00] come a day and that day isn't a decade away. It's not even years away. It's just around the corner where your outreach to prospects, whether it's calling, whether it's people calling in, it's not going to be a person it's going to be.

An agent and sure, maybe some of the higher ticket items, higher level kinds of things, there'll be people, but certainly a lot of things it's going to be agents is that. Realistic today. Is that something down the road? Is that something that you're working on behind the scenes?

Shanif Dhanani: that's not something we're working on. It's definitely something I considered when I first started my company, but we've seen companies do that today. It's not even in the future anymore. We have seen various companies offer things like AI agents for outreach, not only just for email, but also for calling.

Like you said, there's a couple of companies out there who will auto dial and let you chat with the customer or essentially let their AI chat with the customer as if it's a live human being. The agent might be tied into their internal systems so it can answer questions, for example, about pricing or refunds.[00:33:00] 

But ultimately we are starting to see sales enabled AI both from the BDR side, also the AE side, if you're in SAS sales. And so we're entering a world where it goes back to what we said earlier. if you are a maybe more ethically minded organization that's building these things, you might make your AI identify itself as such.

A lot of companies might not do that and might make it. seem like you're talking to a human when you're not. So it's happening today. We've seen that there was an initial surge of success with these companies, but I'm starting to hear anecdotally that a lot of the approaches they use might be falling by the wayside.

Now, for example, an AI can outreach to thousands of customers all at once. But what happens when the customers that are most likely to convert have been already contacted four or five times? You might not have an AI system that knows how to deal with that, and it might start to show worse results. So it's a double edged sword.

You can start to automate away a lot of the outreach that you were doing before, but that might also cause [00:34:00] you to burn a lot of bridges. It just kind of depends on your approach as a company for lead generation, for outreach, for building relationships with your customers.

Jeffrey Feldberg: And so Shanif, a bit of an unfair question. I'm going to ask it anyways. Knowing what you know, but now you don't have that programming ability and you're an entrepreneur who's great at working with companies like yours. What would you be doing as a business owner to say, okay, Shanif, you know what, here's the one area.

Let's just focus on this. Let's hit it out of the park, do really well there. And once we're doing this. We can then look at doing some other things, but I want to get a quick win. I want to see some meaningful results. Tangibly speaking, what would that look like? And by the way, subtext of this, where I'm going with this, when we read the headlines, they're so grandiose, so big picture, and that's wonderful.

We need to have that. But a lot of times the strategy, the specific action items is just missing. That is very difficult to fill in the dots.

Shanif Dhanani: Yeah, it's super true. And one thing AI is really good at is I like to say, and actually I think the chief data scientist at Google said it AI is [00:35:00] very good at tasks, but it is not good at jobs. So what that means is we as humans probably are responsible for coming up with a strategy, let's say you're a marketer and you want to increase your SEO.

Well, you can start to use AI to automatically generate content, blogs, videos, what have you. But the AI is going to be good at generating that one piece or that one, video. It's not necessarily going to come up with the best way to do distribution, the best way to do a higher level multifaceted approach.

So if you want a quick win, I would say use AI to generate some deliverable that you would need to do otherwise. I'm a coder. So in my world, I can use AI to come up with code or unit tests. For those of you who are technical much more quickly than I could with By myself, but an AI even, today, it's not capable of writing the very large application that I've created over time.

So use it for tactical things, or creating deliverables, generating content [00:36:00] that fit into a larger picture that can happen extremely quickly within the course of minutes. And you actually get a quick boost as the human who's responsible for the objective. You can get a quick boost by using AI that way.

Jeffrey Feldberg: And should if I'm going to ask a newbie question for you and you're going to roll your eyes from most people though, who aren't even familiar with generative AI, they've heard of perhaps chat GPT, but they've never used it. So big picture wise in a sentence or two, what's the real story about AI? Who am I really dealing with?

So I'm asking you to do something that would be like, what that would be similar to. Well, if it wasn't AI, it'd be like asking the world's best management consultant who's available to you 24 7. I mean, what would you say to that?

Shanif Dhanani: I tend to say something relatively close. I tend to say it's like working with an intern right out of school who's got a fantastic education, but who knows nothing about your business or the way that you do things. It's got a very good high capability of [00:37:00] Reasoning and understanding and thinking, but maybe it has no clue what your playbooks are for reaching out to customers.

It has no clue about your internal systems. So when you're dealing with these things, you have to treat it as such. You have to be able to expect it to be able to reason and think, but you also have to give it context about the things that it just can't know. Maybe because that information is behind a firewall or your, private network, or it just doesn't know anything about your business.

So Treat it like an intern from an Ivy league school, or maybe not an intern. Treat it like a college grad from an Ivy league school, really bright, sharp kid, but maybe knows nothing about your particular industry or your business.

Jeffrey Feldberg: As you're talking about that, it's actually a great segue for one of my other questions. So you've built Locusive to, in part of the many things that it does, it can also tackle AI security. So for the listener, what would be the biggest risk that a company would face when integrating AI into their operations and how's Locusive protecting them from that?

Shanif Dhanani: Yeah, it's a good question. So I want to make sure we're super clear. Security is this [00:38:00] huge, broad umbrella, the way that we've worked with security on agents and locusive is by giving them, giving the agent itself, the ability to understand and determine if a user is trying to do something malicious.

So I don't know if you heard the story a couple of years ago where a car company put an agent or like more of a chat bot onto their website and a user convinced it to sell them a car for a dollar. And I think a judge actually upheld the ruling that they had to sell the car for a dollar because it was on their system.

So this was in the world where chatbots were new and AI was new. And so what we've done with AI security is, there's a couple of things. One, we've built in guardrails. AI will do essentially whatever the user tells it to. So you have to be able to build in guardrails to prevent it from doing that. One guardrail would be something like an AI trying to analyze a user's request before actually responding to it.

Straightforward, but, not a lot of people do it. Another one would be preventing the AI from executing any malicious actions. Preventing it from [00:39:00] trying to delete any data. So, like, it's explicitly checking for that and stopping it. preventing it from doing anything that are talking about any subjects that you don't want it to talk to.

So these sorts of guardrails go a long way in the world of AI. Now, keep in mind, there's a huge surface area here for vulnerabilities to come to light for insecure actions and applications to use AI. And so In our case, when you're implementing an agent specifically, it's important to have those guardrails.

It's important to have things that the agent is allowed to talk about. It's allowed to do and things that it's not allowed to do. And so that's how we've approached security, at least from the agent specific world.

Jeffrey Feldberg: Okay, and Deep Wealth Nation, please take note of what Shanif is sharing, because good intentions can often go awry, particularly if it's new, you don't know what you're doing, and you don't have the security in place that malicious things could be done, or information that you don't want to be shared is now out there for public consumption, not what you want, and can certainly hurt you.

That said, though, on the [00:40:00] flip side, Shanif, really, same question, but in two different contexts. If we were to look five years forward, where do you see what you're doing? You and the team right now at Locusum, best guess of what that looks like five years from now, and again, best guess big picture wise with generative AI, what that's going to look like.

Shanif Dhanani: I, as you might've guessed it already, I am a huge proponent of this concept of an agent or an autonomous agent. I think that. Every business worker, if not every organization is going to have this agent that essentially they can outsource all their tedious tasks to it. It can go and do research.

It can go and create presentations. It can go and run data analysis. And so what we're going to see is, I think, a world where Agents take over the world of software, agents are going to be talking to each other, they're going to be transacting with each other, maybe over some sort of blockchain, maybe with regular payments, they're going to be executing tasks, and then reporting back to their human bosses, quote unquote, about what they've done, and That [00:41:00] could also extend to the world of individual consumers, you might have an agent who goes and orders groceries for you every day or who might order you food or book a trip or, whatever it is, I think that we are going to move into a world where we are using software more as a, go do this for me.

And it goes and does it versus let me go and click around and do a bunch of stuff and think about how to do this and then go do it myself. I'm huge in the concept of agents and I think that they're probably going to start taking over pretty quickly.

Jeffrey Feldberg: And Geneva, if I were to take what you just shared, very exciting, by the way, to hear your vision, and that'd be terrific for that to come to pass, and perhaps not a great example, but I'll try and simplify it, not to confuse simple with simplicity. If we were to look at the personal computer, and I know most people are now using their phones and not the personal computer, whether it be a Windows or Mac or whatever the case may be, but once upon a time, nobody had a personal computer.

And then everyone had a personal computer, and instead of us having to add things up manually, well, we had a spreadsheet that could do all [00:42:00] that. Instead of having to type things out or write things out manually, well, now I have a word processor that can do all these things for me. And so, in a very simple way, the computer helped to automate, helped to save some time for me.

And I guess it could be looked at, well, as all, really almost like having some assistance along the way. Someone who's going to be doing I'm going to be talking, they're going to be typing or writing. And so what I'm hearing you say is in the very near future. All of us have the potential to have an AI assistant of some sort, depending on what we're doing in life.

It could be on the business side, maybe multiple types of agents or assistants doing some complex business things for us on the personal side, looking after our, in your example, grocery needs or our personal things or shopping or whatever it's going to be, but like a mini version of us, a virtual version of us, that's taking the friction.

Out of life or out of business so that we can do much higher level things. How am I doing with that? Am I getting it right, what you shared? 

Shanif Dhanani: I think so. And I don't know if it's going to be in the next five years. I think it will, but I think it's [00:43:00] going to be in the next 10 years and 20 years for sure. Now I might be totally wrong. Humans are terrible at forecasting in the future, right? So you never know, but I just kind of see the writing on the wall and I've actually, you can tell I've sort of bet my career on this because I've invested this much time into building agents and building an agent builder.

So yeah, I think you're on the, money, Jeffrey.

Jeffrey Feldberg: that, let me ask you this, and this is certainly not the focus of the Deep Up podcast. It is relevant to what we're talking about here. Once upon a time, you had the Cold War and you had the arms race. Before the arms race, going back a century or two, it was a battle of the ships in the sea. Whoever controlled the oceans controlled the world.

And now we're hearing, and we'd love your thoughts on this, well, whatever country. is ahead in AI, can really master the world of AI. That's the country that's going to dominate for the next century. And we have obviously us here in the US, we've also been seeing what's coming out of China, and we have open source, and [00:44:00] then the closed source, and all these other kinds of things that are going on.

Where are we today? Who's leading that? And where do you see that? 

Shanif Dhanani: There's a bunch of different things going on there. So I'm going to start with closed source and open source. Two years ago, if you had asked me, what's going to be the best thing out there, I would have said, well, probably closed. These guys have all sorts of money and GPUs.

Now what we're starting to see is open source is starting to become just as good, if not better than many of the closed source models. So from the perspective of closed source, open source, we are in a world today where we've got this crazy advanced AI. for free sometimes at our fingertips. I can't sort of imagine like thinking about this five or 10 years ago.

I think that trend is going to continue. I think we're going to start to see more and more open source or low cost free applications that can do things that we could only dream about a few years ago. Now that actually transitions into thinking about something at a bigger. bigger level and zooming out like countries who control this probably will [00:45:00] end up having a lot more control of economic growth and resources.

But you might also argue that everything becomes decentralized. Open source is available to some random Joe Schmo building something in the basement and he can serve his, AI to someone in China or Japan or the U S. And so I think what's gonna happen is we're gonna start to see a lot more highly productive applications come out.

A lot more hardware probably also come out that will benefit both consumers and businesses. And I think the way nations are gonna benefit is through taxes. I think they're gonna be able to essentially reap the benefits of what their citizens are doing through taxes. The US has traditionally been, one of the best.

countries to do this. They have a lot of innovation. We have a lot of innovation and we have a lot of support for entrepreneurs. But because this technology becomes so democratized, I think you're going to start to see smarter companies come out from lots of different places in the world. I think the bleeding edge will still be in the U S and maybe China is probably [00:46:00] China.

They're very, very smart and good at what they do. But I think that we're going to start to see a lot of countries benefit. It's going to be one of those rising tides lifts all boats sort of situations.

Jeffrey Feldberg: the interesting time will tell as it always does, but hopefully here's for the home team. Hopefully we just take this, don't look back and keep on going forward and just lead the world with that. And so, let me ask you this. I know there are so many questions that I have not yet asked. We're bumping up against some time.

So let me ask you this. Is there a question that I haven't asked that you'd like to put out there or even a topic we haven't covered or even a message that we haven't yet broached that you'd love to get out to the default nation before we go into wrap up mode?

Shanif Dhanani: The big thing that I think people need to, there's two things, right? One we're still early, you hear all this hype about AI, but I think at least the last time I checked the majority of the world still hadn't used chat JBT. That's still sort of a minority. Now that may not be the case anymore, but we're early.

And I think what that means is. If you're thinking about using it yourself, don't freak out. This [00:47:00] is just another business problem. Figure out where you can gain the most traction by automating something that was traditionally hard to automate. That's where I would focus and then just lean into it.

And the other second thing is you can start leaning into it by taking something very small and applying it within the course of 30 minutes or an hour, you don't need to make a huge capital effort. You don't need to make a lot of commitments to getting started with AI. That's a Testament to the folks who have trained these models.

They've made it very easy. So start small. See where things pay off and then double down on what works. Those are sort of the two things I would recommend you do for your business. Don't worry. Don't freak out, figure out the problem and solve it using the solution.

Jeffrey Feldberg: It's interesting as you're talking about that, you actually take me back to Deep Wealth Mastery and in the nine step roadmap, one of the things that we talk about with step two X factors and also step one big picture. So step one big picture, it's generative AI. It is [00:48:00] something that, hey, if I don't.

If I don't follow this, if I don't do something with this, it can put me out of business. Step 2x factors is, well, how can I leverage a potential issue from the big picture, in this case generative AI, and actually put it to my advantage that I can grow my company and create a market disruption. And Sinead, what you're saying is spot on because like you were saying, Hey, make it an experiment.

This is not a bet the farm kind of thing. It's a small experiment. You can change it if it doesn't work, not a big deal. And when we use even the terminology experiment, when the team hears that. It takes the pressure off. Okay, it's an experiment. It's okay if I don't quite get it, or if it doesn't work out, not a big deal.

It's an experiment. But as you're talking about that, circling back to one of my other questions that I mentioned I would ask, I'll ask it now, just before we're going to wrap up. So with AI, maybe not exactly where it is today, but in the very near future. Programming skills, programming languages. Is this occupation going to be like making the horse buggy?

It's just going to be put out to pasture.

Shanif Dhanani: gone back and forth on this. So I mentioned I'm, [00:49:00] I've been a coder since I was 14, I'm a software guy. And the first year or two of me using generative AI, my mind was blown. I was like, I don't know. If I was still working at a company, I don't know if I'd have a job in five years. Then I sort of circled back around and I said, there's this term that's come up recently, which you might've heard of Jeffrey Jensen's paradox.

The easier something it is, the easier something is the more demand for it, that there will be. So when it comes to programming, I have seen AI be able to do some amazing things, but at a smaller level, and I've had to admit me as the human I have had to corral it. I've had to build a lot of the guardrails.

I've had to build a lot of the infrastructure to support it. So it's hard to say going forward if you believe that AI is going to continue on the same trajectory that we're seeing today, then you would be. I would understand how you could think that these things could create their own applications without any human guidance.

I suspect we're entering a world where we might be [00:50:00] leveling off in terms of the AI's ability to hold large amounts of context and reason through it. So you might've heard that you can only enter a certain amount of data into an AI before it can't process it. These are called context windows. When you're programming, you might have.

Thousands pages of code, the equivalent of thousands of pages of code that you need to keep in your head. And humans are very good at navigating that. AI might not be. And so if you believe that we're hitting up against that wall, then yes, absolutely. We're going to need coders. I think the demand is going to grow.

I suspect that AI will probably be used for a lot of the tedious work when it comes to coding, but you're probably going to need well versed engineers to build the guardrails, build the infrastructure and create the ecosystem that the AI can work within. So I think we're going to see fewer engineers, but I think the ones that we have are probably going to be really good and probably very well paid.

Jeffrey Feldberg: Interesting. What's ahead? Exciting, because this is the unknown and the unknown is [00:51:00] the unknown. Who knows what kind of jobs are going to be there? Careers, opportunities, market disruptions, just waiting for us. Exciting times. So that said, from the future, we're going to go back to the past. And I'll tell you what I mean by that in just a moment, because it's a tradition here as we close things out, that it's really a ritual for the DuPont podcast.

It's my privileges, my honor, where I ask every guest the same question. And it's a fun one. So Shanif, let me set this up for you. When you think of the movie back to the future, you have that magical DeLorean car that will take you to any point in time. So imagine now is tomorrow morning, and this is the fun part.

Shanif, you look outside your window. Not only is the DeLorean car curbside, the door is open, it's waiting for you to hop on in, which you do, and you're now gonna go to any part of your life in the past. Shanif, as a young child, a teenager, whatever point in time it would be, what would you tell yourself in terms of life lessons, or life wisdom, or hey, Shanif, do this, but don't do that?

What would that sound like? It's

Shanif Dhanani: I probably going back in time, I'd go back to like every point I could and be like, Hey, don't do this, do that. [00:52:00] Okay. If I had to pick one, I think what I would do is take myself back to when I was in my twenties and tell myself, let's say two things. One, you are going to be way happier.

When you're not so focused on yourself and you're worried about the wellbeing of others, and you are concerned about doing more good in society than you are just building wealth. So focus more on others and your happiness will come. And two along the same vein, focus your career on the one or two things that you're super passionate about, because it requires a ton of time and a ton of effort to grow something to a point where it will be.

Generating a lot of wealth or life changing. So basically don't go and chase money. Don't go and chase the things that are fleeting, focus your time on the thing that you really care about, something that you can spend 10 or 15 years building. And then in your personal life, focus on others, not so much on yourself.

Those are maybe two things I'd tell myself in my early twenties.

Jeffrey Feldberg: great advice, and, [00:53:00] as you're talking through it, I'm thinking about that, Shanif, and to me, and you can share, Jeffrey, on base or off base, they're really related, because When you're focusing on helping others, I mean, to me, that's what always an entrepreneur has been. We find a painful problem for other people, solve that problem.

And when we're world class, when we do it for enough people, help them get their goal, eventually we get our goal as opposed to, and I've been, by the way, I've been on both sides and I've. I've crashed and burned miserably when I've done this, when I've chased the money, not the passion. And so you're absolutely right.

When we're really focused on helping others, being a positive change out there, getting them what they want. Yes. Over time, we can get what we want, the lifestyle, the freedom, the wealth, whatever else we want to get. We can do with that. So it's some terrific advice. And she, before we wrap up, somebody has a question.

They want to reach out to you. They want to have you come on in you and the team, and really with locus of make a difference in their company, where would be the best place online to reach you?

Shanif Dhanani: I try to respond to every email I get. So, email is the [00:54:00] best way. My email address is Shanif, S H A N I F at locusev, L O C U S I V E dot com. Feel free to email me. I'll try to get back to you. If not email, I'm always on LinkedIn. You can check me out there, Shanif Locusev.

And I try to just be as accessible as I can because I love talking about this stuff and I love talking to folks who love it.

Jeffrey Feldberg: The population Shanice gave his email address. Take him up on this. He's been involved for decades, a very successful entrepreneur and a smart one at that, and some wonderful intentions of really making things better for all of us. So email him, ask him, speak to him. You'll come out a whole lot better than when he came into it.

Well, it's official. Congratulations. This is a wrap. And as we love to say here at deep wealth, may you continue to thrive and prosper while you remain healthy and safe. Thank you so much.

Shanif Dhanani: Thanks, Jeffrey. Appreciate the time. 

Jeffrey Feldberg: So there you have it, Deep Wealth Nation. What did you think? 

So with all that said and as we wrap it up, I have another question for you.

Actually, it's more of a personal favor. 

Did you find this [00:55:00] episode helpful? 

Have you found other episodes of the Deep Wealth Podcast empowering and a game changer for your journey? 

And if you said yes, and I really hope you did, I have a small but really meaningful way that you can actually help us out and keep these episodes coming to you.

Are you ready for it? 

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Thank you so much. 

God bless.