← Bald Ambition
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- [00:00]
Peter Swim
Foreign.
- [00:05]
Mookie Spitz
Welcome to the Bald Ambition podcast. I'm your still very bald host, Mookie Spitz and the one with all the ambition today is Mr. Peter Swim. He is the founder of Toilville. No accident, I think in the selection of that startup name it's a a place to heighten efficiencies around, coil around work through the lens of AI. But I'll let the maestro himself, Peter, describe describe his baby. You're probably the fifth guest I've had over the last few weeks related to AI optimization in the workplace. I've had a guest optimizing salesforces and sales management, the dispensing of beverages in the mobile Starbucks like setting operationalizing AI for everyday business tasks. How are you different? What do you offer? Peter, we're all ears.
- [01:13]
Peter Swim
So I'm a person who kind of fell into the AI industry, but I did it 15 years ago and so this is probably my third or fourth AI revolution so far in my career. And I think like what a lot of people hope to happen and have been describing is what would happen in this has only become true very recently. So in my consultancy I took my experience of working for big companies like Walmart and Microsoft, of how AI pilots start and fail and it's largely because a lot of people build towards the hype window as opposed to the practical four. So I help a lot with with my clients one kind of shaping their expectations of technology and creating a set of rubrics that allows you to evaluate what work cost, you know, irregardless of like if it's AI or not and how you can like find low hanging fruit and transform that stuff with or without AI.
- [02:09]
Mookie Spitz
So it sounds like your optimization first with or without AI and now that you've got the big guns of AI, especially after the chat GPT revolution, what was it, November 2022? The world seemed to flip almost overnight. Tell us a little bit about some of your background in AI prior to that because I think most folks think that AI is a new thing and it's been with us for quite some time, even in application. Everything from recommendation engines on your favorite streaming and shopping service to all sorts of back end stuff that's been going on without most people ever really understanding it or knowing it's there.
- [02:54]
Peter Swim
I think everything like that we think of as the algorithm is basically what AI has become today. So when you're looking for carpet tiles and then every single ad around you is carpet tiles and all that stuff has been in motion for a long time and now it's being under the brand of AI because tools like ChatGPT and other large language models has much fuzzier edges. So like you remember back in the day, you would go to a website and you type in the wrong URL and it's file not found, doesn't load. We've now have the ability to kind of like correct and sit you with, oh, you meant this or here's something similar. And when you put that in application, your application no longer says, you know, your chatbot doesn't say, oh, I don't understand that because it understands everything. And so we've kind of solved that understanding problem without really understanding what understanding is in a technological sense. And so my background, a decade ago application called Slack came out and they announced their API and the company that I was in, Botkit, was launched with the API chatbots. And chatbots were basically dumb versions of things you, you use on the telephone where you press one, you get a thing and all that. Our chatbot ran on regex, which is for people who know regex is basically like matching patterns. So like if I say it's a name is going to be capitalized and have one or more extra letters and that's probably a name. And so our chatbot, which probably can run on you could probably run an entire service on the power in your cell phone in your pocket right now. Was the state of the art in 2016, right? That's the best you can do. And the vision and the value what people are selling sounds a lot like what ChatGPT is offering today. And the difference is, is that it's finally fast enough and close enough that you don't need to like have a data scientist set it up for your company. You can like pay for a service with credit card and put it down and you kind of have understanding, but that's where the problems run in. Because something that demos very well, like you can run a demo and get people really excited. When you run it across a larger population of people, you run into problems. So later on I went to work automating contact centers at Walmart and you know, the contact center is very interesting problem because most people don't want to call in to anything. You know, they would love to just get the answer. So if they're calling in, it's already a problem, you've already made a mistake somewhere along the line where the information wasn't findable. And so their problem is every time a person picks up a phone for nothing other person who costs $5 and that person picks up 150 calls a day, and there's a room of 200 of them. And so it's money on the table that you see there. And so their goal was to automate this contact center. And there are some things you can't automate, right? Like, Mookie, you're going to vent a problem and you're going to be like, wow, I've never heard that one before. And a chatbot wouldn't definitely know what to do with it. So that's when I started to kind of see the patterns of like, oh, in the five minute phone call that costs $5, you spend two minutes on verifying a person's identity. You just find out who they are and what they are. And all that computer could do that. Like, it's not a question about like my personal life or whatever. So let's find the low hanging fruit to do things. And that's kind of, kind of put you down like this rabbit hole of like, okay, if everything is a series of like levers and pistons and things you can automate and then things that require a personal touch, how do you arbitrate that kind of working place to get people to do stuff? And so a lot of the tooling I did, I was working at Microsoft when my product became Copilot Studio, which was the thing to design Copilot experiences. And so I was working directly with customers every day of like turning legacy applications into AI applications. And a thought occurred to me and my clients would, you know, my people I'd work with would point it out too, is just like, oh, we're automating something that was originally meant to automate something that's something real, right? So you, you don't event the contact center because you like contact centers. You've been a contact center because you have questions and people need to answer them. And so instead of put sticking AI in something that's already an abstraction, what if you bring it closer to source and make it so the person who knows all the answers is distributed better and not wasting time. On what time does the office open? Because that's on the website. You know, what's the phone number that's on the website? And it's like, oh, my airplane ticket got eaten by my dog. You know, something weird that has a person has to intervene, okay, that needs to talk to a person. So instead of like wasting all the time that you're paying for $5 a call on answering questions that read the manual, let's get people into more deep conversations that highlight your business and the personality and your culture of your company.
- [08:02]
Mookie Spitz
Well, you're breaking the need, the business need into two distinct categories. The one is deterministic question, response, need to check customers information or background. And even remedial machine can do this because it's basically if then. And you mentioned that that's the lowest hanging fruit and that's stuff that you can automate. And there are other functions better left to a real human which are much more complex, interwoven and unanticipated. But ChatGPT and their kind, all the frontier models, have shown us that it goes beyond just if then. And they have very sophisticated machine learning, deep learning machinations that mimic human behavior to the extent where they could take the role of an operator, at least to a certain extent, they can pass the so called Turing test for quite some time. And this is where a lot of the hype comes in. So if I'm hearing you correctly, if you really want to optimize your business and even look at it through the lens of this AI stuff, start with the basics, build up from there and don't let yourself get bamboozled by a lot of the hysteria about plugging in a bot that's going to solve all your problems. Am I, am I hearing you properly?
- [09:32]
Peter Swim
You got it right, right on it. Just like if I paint the face on a rock. It's not a pet, you know, And I think a lot of people get really parasocial with their applications because this thing that is juice to be agree and to think that you are making stunning observations. Often it's like, wow, everything I say, this bot, I'm a genius. I'm smarter than Einstein and Lee Iacocca combined. And this is going to be the best thing since sliced bread. And then they go into court and they present their case and everything they cited is made up, you know, or
- [10:09]
Mookie Spitz
even if it's not made up, it's prioritized to blow smoke up their butt. It's programmed to be sycophantic. And many folks fall in love with their bot. They conclude that not only is it sentient, akin to their own consciousness, but the bot actually cares and they create an emotional reaction. And I think it's that same kind of sentiment that I'm hearing from a lot of business owners, business managers, where they want these kind of miracles to happen operationally in their businesses and, and they expect fingers to be snapped, magic happens and then all their problems go away. But where I think you're basing your startup on is this understanding that you need at least a remedial understanding of the core business issues, which are typically, if you look at the lowest of low hanging fruit, very deterministic and operational. We need to just figure some of the nuts and bolts together stuff out. And as we increase in our relationship with you, we get to know you better and we get to plug in increasingly sophisticated technology inclusive of this AI stuff. We can work together to do that. But most folks do this back ass words. They start with this delusional notion that these superbots will save you immediately. And, and I think what you're getting at again is no, no, no, no, no. Let's start with understanding what you're doing, what some of your issues are and let's crawl, walk, run into more sophisticated solutions if they're appropriate.
- [11:43]
Peter Swim
Yeah, yeah. And I, and I think it is for a lot of people it's a sea change. It's not ever overnight revolution. Like a lot of my clients find out that things they've been carrying as like technical debt and things that they're part of their job are not part of their job. It's part of being a client of Salesforce or Slack or any other tooling that makes you do it that way. And so if you had your drawers to go back in time to when you started your business and you did it from scratch, what would it look like? And for a lot of people it's like, oh, I can fire three of my SaaS services because I no longer need to copy paste an email into a Salesforce thing that sends a thing to a Slack that does this, does that, this orchestration. And so a lot of these tools that are middleware that are designed to like facilitate the orchestration between things becomes irrelevant. And so, you know, a lot of my clients, they increase people and using the money they save from technology to get more people because really they just need five more people of them. And if the tooling is that much cheaper, then great, I can send the virtual self of me along on the calls and record the notes and I don't have to go to every meeting and I have a good idea of the pulse of things and delegation becomes trivial. And you don't hear a lot of stories like that because these Companies are spending $18 billion a quarter training this stuff. And you're not going to make that back unless you get $18 of value out of the labor pool.
- [13:12]
Mookie Spitz
And so the SaaS Magedon, which has hit investors in Wall street, which is software as a service, Salesforce, even Docusign, big companies they need a lot of subscriptions. And businesses have become increasingly reliant on these, to perform all these functions, some of which are redundant, they're interconnected, overly complicated. And what you're recommending is if you deconstruct your operations in a sense from the ground up, you'll realize that A, you might not even need all of this and B, your, your whole setup is probably a spaghetti bowl mess of redundancy, interoperability challenges. And you're recommending they go back to basics and build it back up again in a way that makes, makes more sense and if necessary uses some of the, some of the technology that you could bring to bear.
- [14:09]
Peter Swim
Yeah, I mean, I've worked for companies that had everything. They've had, you know, they've had Slack and Teams and WebEx and, and it's just like each division has to like, so you have to, if you were in a position where you worked with everyone, you had six clients and four email applications and six video calls. And it's silly, right? It's like you should have a phone number and the phone on your desk rings and you pick it up and you answer it like everything else is just like unnecessary cruff. And then you have to like go into a meeting like, well, this organization, our Artifact director is PowerPoint. And this one, the artifact record, is a ticket in jira. And so now people are copying and pasting stuff from Jira into PowerPoint into Word documents. And all this like domain switching and stuff is like the rhythm of business. And no one ever talks about that cost. No one ever talks. You know, you always talk about like shadow quitting and employee responsibility and being in the office or not in the office. But they never talk about the fact that I had to have three meetings about one email. And so we're kind of at this opportunity now. Like, okay, what if we just redo the entire social contract where all my employees work autonomously up to the point where it's time for a decision to be made and I thumbs up and thumbs down after I read their report. And I think that is a much better way to work. Like in my company, internally, we are effectively four day work week because we budget five days of work. And if you find a cool way to do it and you only need to work three days, that's, congratulations, you won the week. You know, I'm not like trying to like track my burn down rates and, and seeing what people are doing every hour a day. I just like, I want them to be happy and healthy and Ready to work when we need work done. And I think this is structurally a much better way to work than, you know, in China, they have the six, six, nine. Six days a week, you know, nine to nine every day. And it's just like if you have to work more than 40 hours a week, you don't know how to plan time. You don't know how to, like, work effectively. And the solution for technical problems isn't more work, it's smarter work. Yeah.
- [16:27]
Mookie Spitz
And it sounds like central to that is once again the discovery that you do. You. You analyze how businesses do what they do before you make any kind of recommendation. Can you take us through how this relationship begins and what's involved? Like, let's say I'm Mookie Spitz and I have the Bald Ambition llc, all right? And I've got all this. All this nonsense that's going around with processing my videos and distributing them and responding to customer service. And I've got to your point, I'm using, with my. My 10 employees, I'm using Slack and I've got email and I've got teams, and I just put out podcasts.
- [17:14]
Peter Swim
Yeah, yeah. And I think, like, a lot of people are like that. So you. I think a lot of people I'm in competition with for contracts. You'll get a sow. And the sow is just like, here's what I want. Like, sure, I'll build you what I want, but I really want to understand why you're building what you want and why isn't what you have today not working for you? Because it's easier for me to bring the price of sow down from understanding how much redundancy you've built into it than from cutting my hours. And so I rather like, you know, if I'm going to slash 40 hours, I want to slash 40 hours because it actually is 40 hours or less of work versus, like, me, like, working nights and weekends and helping people out and stuff. And so that kind of goes down to, I have to understand what keeps you up at night. So you're told me all the things you want, so I understand why you want it. But what will happen to you if you don't get it, you know, and what happens that is keeping you from realizing your vision with the existing tooling, because sometimes the problem started 18 steps ago. And so now you've accumulated all these toolings that are trying to work together better, but maybe less is more, and you can cut versus add to that pile.
- [18:32]
Mookie Spitz
I work in business consulting too, and it's often seemed like a cat chasing its tail or a doctor charging for the treatment before the diagnosis is even conducted. So to your point, you give a statement of work which outlines, let's say, scope and billing, but you don't yet know really what you're fixing until you do that initial discovery phase. So many more responsible and adept consultants will issue two sows. There's an sow for what's considered the discovery phase, where you diagnose the patient, as it were. And the other one is once we figure out what the hell is wrong, then take it to that next step. So what you're saying is very relatable. It seems inevitable and necessary to really understand a business before you can fix it. But a lot of folks are doing, in a sense, what you're doing. So citing again, some of the guests I've had before, most of them cite the need for extensive discovery. There's some overlap in terms of some of the value prop that you're offering. What does Peter Co. And Toilville do, which adds that special sauce to this kind of technological infrastructure optimization?
- [19:56]
Peter Swim
So I've worked with companies as small as three or four people, and I worked with companies that is the largest employer on the planet. And I think the major difference between all those things is risk aversion. Right. And what do you like? So you have your goals and what can you get away with before it comes to pay the piper? And so a lot of the times I, you know, you're talking about discovery phase. I make my discovery phases very expensive and I do it as a canard in a way to like, force people to think about, okay, what are my non negotiables? What do I really need and don't need? Because the better they define it in the sow, the lower my price goes, you know, because I can, I can say, okay, I can do this in a day, I could do this in two days. And because you're able to book me three solid days with all the information, I will cut you a break versus hanging around with you and feeling it out and finding out like, oh, okay, oh, we're actually building a hovercraft, not a helicopter. You know, because I could do that. You know, we could hang out and vibe and go play tennis and do whatever it takes. That's my day rate. You know, like, I'll hang out and do things. But if you want to, like, get down to brass tacks and do stuff and you know what you want and you have a way to measure the before and after already, it's very, very affordable. And I think that kind of a relationship is much more appreciated in small entrepreneur because everyone wants to have a guy or a gal to go to when they need something and then they could go sit on the shelf. I don't want to be like service contracts. I don't want to be like coming to your Friday meetings every week for the rest of my life. You know, I want to be like, hey Peter, this guy wants to sell me this thing. Is it real? Like no. Okay, thanks, bye. That would be the perfect relationship for me with a client. It was just like I, I talk so many people out of stuff, you know, and when you, when you work for a big company and you're selling stuff, it's harder to talk people out of stuff. But I think like less is always more. And especially for very personality driven businesses that are like individuals, they had left the big guys because they were stifled. And so the last thing you want to do is create new stiflement that like interrupts your ability to be, you know, the special person that you are that drives your business.
- [22:11]
Mookie Spitz
Let's dig in just a little bit more. A prior guest was Martin Martinez, Marvin Martinez, a band saw AI. Now I bring him up specifically because he has the opposite approach, diametric opposite approach. He's talking about he makes a call, he shows up and really only in a matter of hours. He's done his discovery and he's making fixes. Now his point of view is he goes for the lowest of low hanging fruit instantly and obvious recognizable problems that to your point might or might not take an AI solution and then he kind of burrows himself in and works up from there. Now the reason I showcased him in contrast to you is to highlight your own value prop and the next question is what kinds of solutions after you do this deep discovery dance which to your point is extended in time can be expensive. What kind of solutioning do you do in contrast? Let's say to. Well it's obvious that, you know, 56% of a call is wasted on rigmarole and if we just put in this little thing thingamajig then you'll be better off.
- [23:37]
Peter Swim
Yeah.
- [23:37]
Mookie Spitz
How are, how are you different that way?
- [23:39]
Peter Swim
So one, I don't think it's either or it's like a and but because I can absolutely take the sow and show up to the meeting with a proof of concept now and it's almost free for me to just because I have universal tooling and my tooling is agnostic to the Point where I could just label it to your tooling. And so you give me the specifications, I could build my first guess of what you want and bring it to the meeting before you sign anything. And so that is a. My strategic advantage and it's your disadvantage. Right, because that is such an easy thing to do and demonstrate that you may not realize this is only ever going to be 87% good because of all that load bearing stuff. Like, you know when you buy an old house and you tear open the walls for the first time and you kind of hold your breath and see what the situation's going on inside there
- [24:31]
Mookie Spitz
with mold or no mold.
- [24:34]
Peter Swim
Yeah, yeah, that, that is, that is everyone's, everyone's stack at their company right now. You'll find out like, oh, this one server has been online for 22 years and if it goes down, who knows if it'll come back up again. And so there's a lot of things like that that don't get. You don't even find that in discovery. Sometimes you find it six months into the engagement. And so how do you arbitrate the renegotiation of the contract or whatever? You got to have a good relationship and you got to understand it's like, okay, this is structurally very bad and here's what we got to do to fix it. And AI is going to fix it because this is a fundamental technological problem. And then that is more like a traditional software project than it's ever been. So what you do is then you make every bit of work independent of the other bit of work. So if, you know, ghosts are over here and gold is over here, we can keep working on the gold while we work at the ghost and we'll get to that problem in due course of time. But if you're predicated on that because SOW forced you into the path of the ghost, you run into an issue where all work stops until you fix it. And I think that is also like the biggest problem. Software is like the side quest, kind of like debt, where it's just like, oh, I thought we were just building a new deck. It's like, yeah, but your foundation sinking, so now you need a new foundation, you know, and, and I think a lot like, you know, I come from family contractors obviously, so I think a lot about these things where it's just like, yeah, in addition to a house seems like a simple cheap thing until you start digging into stuff you didn't touch and you didn't architect. So I don't want to like, I don't want to, you know, make it tough for me and over promise something that I can't like in good conscious promise. But also I want to be honest and build a relationship with you that, hey, maybe this addition is something you should put off until you fix that leaky roof. And I can help you with the roof, but that's a different conversation and that's free advice. And I think that is a better business standpoint that a lot of technology and services companies have where they're like, they say yes to a relationship they may not be interested in keeping forever versus me trying to find clients and partners that are in it for the long haul. And we have a good relationship and maybe we only work together, we're 18 to 24 months.
- [26:57]
Mookie Spitz
Let's make that longer term relationship real to our listeners and viewers. With some use cases, can you share at least in, in abstract or anonymized some of the, some of the stuff that you've done based on this deeper dive, based on creating that relationship and based on spending some time under the hood before you really make a move?
- [27:24]
Peter Swim
Yeah, like for example, I had a client and they wanted to have a chatbot on their website that answers emails. Right. And makes sense. You know, I. Answering emails is hard, but there we're a broker for health insurance. So it quickly became apparent that you can have a chatbot ask 167 questions because that's excruciating. You know, that is just like not a good experience. So instead of having the chatbot ask 160 questions, let's get you to fill out the form with 160 items. And then you come to the meeting with the form filled out. And so that kind of like, it's like, oh yeah, fill out a form makes a lot of sense. But then you run into the problem of like, oh, let's answer the questions around the form and all that. So we take this project that is meant to sit in front of customers and now it's a failure path versus like you pick up the phone and you press once, like, hey, you're waiting on hold for 12 minutes. While you're waiting on hold, you want to fill out some information that'll speed up this call. And so when they pick up the phone, half of it's filled out, right? And so, or maybe everything easy is filled out and the things that require a little digging are done by the person. And so one, it helps the user, you know, making better use of time, but more importantly, it helps the employees because they're no longer juggling random systems that change arbitrarily. They're handling their system and now when they pick up the phone, a little baseball card pops up and says, hey, this is Cindy. She just had a root canal last week. And so you say, hey Cindy, how's your mouth feeling? And that little bit of glue and stuff. One calls are cheaper and they're better, but also like improves customer satisfaction and improves employee satisfaction because they're not flipping around at eight different windows and they're not like trying to make, you know, everything happen. And it kind of surfaces the idea that a lot of people and people don't find this out until they start trying to automate their business. There's a lot of load bearing people inside organizations. You know, you'll have a person, your company's been there 30 years and they know everything. They have speed dials for everyone and they have everyone's personal home cell numbers and they make things happen. And the reason they have to do that is because your blind spots as a leader not being able to simplify the process for them. And so if I can pull that out into visibility for organization, then that's an entire roadmap of work for years possibly of transforming your business into a data driven unified brain that knows that the president of the United States is Donald Trump and not Joe Biden, because a document was uploaded six years ago and can tell you the office hours and also collect as much information as it can before it bugs Cindy, who's really quite busy. So that's kind of like my, my career pitch for a long time. It's just like I've had head of contact center say, can you have the AI tell jokes? I'm like, all the problems you have telling jokes is the one you focus on. And it's like, well, what kind of jokes? And there's all these questions that come out of these problems. And I think technologists say yes as opposed to probing deeper into that question because they know this is like unending banana stand of money for them where they can. It's like, yeah, this is gonna be a problem that we're gonna get to 80% quality and it'll be stuck there for months or years and I'm not interested in doing that anymore.
- [31:04]
Mookie Spitz
So human beings in general, regardless of their station in life, have this knee jerk tendency to be emotional about stimuli and that forces folks into the tactical bucket almost instantly. So if you're showing concepts to a client, rather than think about the brand strategy and the overarching story, you want your company to Tell they're going to instantaneously respond that they don't like the color of the logo or this image needs to be smaller or larger. That's natural. And in operations what you're describing is analogous where you know, you do, you do an evaluation of their infrastructure and their first question to you is, we want to make a funnier bot. Maybe we should put a joke in there because they're on hold for 10 minutes and maybe we can entertain them when the missed opportunity is they're on hold for 10 minutes. And the series of questions that the operator will need to endure with the customer once they are on could be addressed with, with the utility of a bot, given today's technology. And it could be a fun, engaging experience facilitated by a user interface that's friendlier and more fun to use. So if I hear you properly, stop thinking tactically, stop thinking about knee jerk, instantaneous, oftentimes useless solutions just because they seem to be something that might have a quick fix. Step back and look at the flow of your information throughout your organization. Go back to pain points and need and let's build it back up from the vantage point of that kind of optimization rather than running around with a bunch of band aids and plugging in holes and relying on a 30 year veteran employee who grew up with this infrastructure and has been hurt. 90% of her job or his job is filling holes, right?
- [33:11]
Peter Swim
And I think like it's counterintuitive because a lot of people come up to and say, well, what's the ROI of this thing that you described? It's like, well, how do you measure work now? And they don't have an answer. They don't know what the cost is of like changing the text on the can of soda is. Well, it's about 47 meetings across six different organizations and hundreds of hours of billable legal time and all that stuff. But you'll ship AI in two weeks because you want to be fast and agile. And so what how do we, how do we make change management for work have as much liability and attribution as the text that goes underneath the logo on the soda can? I think is like a unanswered question. A lot of organizations and so this is all stuff you get for free when you work at Toy Build because you know, we don't like doing work at your behest because we say so. And so maybe I talk people out of surfaces with us, or maybe I choose not to work with people because I see no daylight to work that way with them. And I think that has to be okay because, you know, it's part of the twofold system. Is the change from a place of wanting to change, or is the change coming from wanting to rubber stamp a regime of decisioning and make stuff happen? And I think that is also something that, like, a lot. You know, I think if people wanted that, they would not have left working for the big guys and started their own companies. So I think that's a very easier conversation to have outside the enterprise than is to like when I'm talking to Jimmy the insurance broker or Cindy the veterinarian, who's starting a veterinarian application for our website. Because I think they realize that, yeah, I could have just bought a SaaS product to do this if all I wanted was get it done. But I think there's a better way to do it. And so that is like the little glimmer of light that I start this engagement with people. It's just like. Yeah, like, it's all. It's all kind of arbitrary, and you just got to do it the way you like it. So you want to do what?
- [35:16]
Mookie Spitz
Or they could have bought a joke bot.
- [35:18]
Peter Swim
Right.
- [35:19]
Mookie Spitz
And solved their problem. Now you're the change management guy.
- [35:24]
Peter Swim
Yeah.
- [35:24]
Mookie Spitz
And change management for upper management might embody some of the characteristics that you're describing. Optimization, heightened efficiencies, roi. They're thinking along those lines. They see the spreadsheets. They're the big picture, the chess master moving the pieces around. But folks throughout the organization, they equate change management with unemployment. So how do you. How do you deal with this notion that. I believe, in a recent poll, only 27% of the population approves of AI or thinks that AI will benefit themselves, society, and the rest, which I believe, if you do the math, is at least 73% hate AI and think AI is going to destroy their life and eventually kill all humanity. So. So change management has a lot to do with. To your point earlier, building trust. But this trust building happens at senior management. They're the folks who hire you, and they want the changes to be managed. But then how do you assure folks throughout the organization that you're actually a knight in shining armor and not executioner?
- [36:42]
Peter Swim
Yeah, you know, and. And I think that's a huge part of. You know, I've said this before on podcasts where no one is better at the slow walk than the American worker. You know, if you have an American worker doing something they don't want to do, they'll be like, they'll do just enough of it to keep their job. And I think AI is in that position because its branding has been horrible, its implementation has been horrible because no one has ever posited the value inherent in the process to the average everyday information worker or developer or coder. And you know, we've seen this in time immemorial automation going back to the Luddites, going back, you know, hundreds of years to, you know, the steam engine that people are afraid of technology and it's up to the technologists to find something that works for everyone. But in AI and in tech, the customer is not people, it's venture capital and investors. And so they have no skin in the game whether or not you'd like it or it does your job better than you or not, because they're trying to sell it to your boss. And bosses have never needed a reason to lay people off in the history of work. And I think like all these things kind of point to a picture of if AI is a poster child of work versus employers, of course it's going to have a blind eye. I once had, I worked for a taco company, a taco chain and they created a way for you to order tacos via Facebook and it had horrible approval ratings. And I was on this two hour call and then I unmuted about hour into it and said, what happens when the food comes cold and they're like, oh, they have to call the restaurant. So there's nothing you could do in Facebook to get more food sent to you or anything? It's like, no, that is all because it's just like the, this Paul Varilles is a philosopher in from Paris in the late 20th century and he said the ship was the invention of the shipwreck. So sometimes technology events, problems that weren't foreseen or were very foreseeable that didn't exist before they invented and existed after they meant it. And it's your responsibility as the inventor to deal with them. So technologists who push AI systems should have solutions for workers that produce things like universal basic income or other things that support humanities and families and workers. And if we don't, then there'll be other societal problems that will cause AI to be insignificant in comparison. And so I think like when it comes down the end of the day, I'm not, I don't, I don't have stock in ChatGPT. I don't care if they succeed or fail. I want to people to work well and be happy at their job and do as much as they can to do the American dream of owning family and, you know, having a home and sending your kids to college and not having to go fund me when they get sick. And so if you look at things from that standpoint, there's a lot of opportunity there. But I think there's no marketing positing that to people in a way that is, like, believable or acceptable to the populace. So until that happens, AI will be kind of a dirty word.
- [40:01]
Mookie Spitz
Tons of mixed signals. You mentioned UBI Universal Basic Income. To me, this is a real mixed bag of tricks. On the one hand, you're espousing that the technology will generate so much capital and heightened efficiencies to the extent that there'll be all these trillions of dollars that the government could tax out of these companies, which could then be redistributed throughout this population. But it begs the question of the population losing their damn jobs, which would necessitate this kind of payout. So I'm always quizzical when people cite UBI as the savior to this problem because again, it begs the question of AI taking away millions of jobs. On the flip side, the messaging sucks. I agree with you. Because a lot of people's work is grunt work, remedial stuff that they hate, but they know they need to do. And what AI can do, at least initially, is automate a lot of the dumb drudgery. And it'll open up more hours for that employee to do things that are ostensibly more human, which is connect with other human beings, perform more strategic functions, and presumably add more value to the company than doing these rote behaviors that constitute now maybe a half of their job. So I think that's, that's a missed opportunity right there, where we're not here to take your jobs. We're here to improve the quality of life at your job, which we're hoping and designing for you to keep. And that. That's lost in the. In the mix. Frequently, however, the question is again begged by companies like Stripe. You got Jack Dorsey with wearing a hat that says Love on it and laying off 90% of his workforce on a slack call. And then you've got companies like Meta who have just unloaded a huge percentage of their workforce. And you have these big tech players who on the one hand are boosting their cap X in AI investment by literally hundreds of billions of dollars, and on that same investor call are boasting that they're laying off tens of thousands of their workforce. So it's one big convoluted mess. I. I agree. The messaging is just terrible any way you look at it. But let's go back to you. Let's be pragmatic. When you come in a company, how do you onboard in a way where you're not bringing revolution? You're the doctor. We've been using your role as an analog for being the physician of business operations that are sick due to all these problems. Instead, a lot of staff will look at you as, you know, the guy with the pitchfork and, and, and riot mob coming to burn down the, the factory, burn down the office.
- [43:18]
Peter Swim
You know, we don't have to get into like praxis and work stuff here. But I do say that revolution is the outcome when all else has failed. Right. And because people see no other choice to do things that they feel they cannot survive without. And so if we have a problem of malaise, I think most companies suffer from malaise and not revolution where they don't feel like their voice matters or nothing changes or new system, same problems. Those are things that, where the worker and I don't think, you know, there's a lot of thing in the workplace where they want work to be a family. And I think a lot of people are happily to happy to have a job that gives them their insurance and their food and money on the table and they go home and they don't think about it until the next day at 9. I think that should be fun. Yeah.
- [44:10]
Mookie Spitz
And easily 95 of 100.
- [44:13]
Peter Swim
But then you have Jack Dorsey's of the world. Who has he ever dug a ditch you know like he did.
- [44:21]
Mookie Spitz
He did dig a ditch and that's called Twitter.
- [44:25]
Peter Swim
Oh yeah. Yeah. But he sold it to even bigger ditch. So. And I think like, I think like that is just like we have a lot of millionaires and billionaires right now who don't have a very strong sense of one the value of work and the sense of what is the cost of work. Right. Because anyone that says firing 80% of your company is the hardest day in their life is suspect in my eyes because that is, it's already, you've already decisions already happen. There's no, you're not going around each person individually and looking in the eyes and saying they're doing. I've been, I've been not fired as many times as I've been fired by layoffs. And the not fired I think is the hardest thing because everyone in the room loses their job but you and two other people. And so they all line up to get their hand in their stuff and you're sitting there just Trying to work like that's crazy. That's a crazy thing to put people through and to, and, and then to go on your blog and say I was the hardest day of my life.
- [45:33]
Mookie Spitz
The bad news is you're fired. The worst news is you're not.
- [45:40]
Peter Swim
Right? Yeah, all their jobs are now your job, you know, like and oh yeah, you're not going to get a raise this year. There's no bonuses here either so. But you still have a job.
- [45:49]
Mookie Spitz
We're bringing in the AI guy.
- [45:52]
Peter Swim
Yeah, we're bringing. Well so, and so I come in this situation where it's just like, okay, I have the boss who's out of connection with the workers and I have the workers who have been suffering at the hands of the boss's whims for many years. And so I think the best thing for me to do in a situation like that is one to shine light on data problems. Like we need to understand how the work is happening and we need to have accountability for decisioning even from the sea level down. And I think like light on shadows is one way to do that. I always encourage people to work is not a family like I have. My nieces and nephews are getting in the workplace and I tell them to be promiscuous as they possibly can with employers because if you spend 20 years of one company, you won't learn anything that'll help you at the next company when they lay you off in your 40s so you know, dig as many ditches and you know, paint as many pumpkins and whatever weird stuff you can do before you half the family and all that stuff to worry about where it's harder to be mobile. Mobile. And because worker mobility now like we went from the COVID times where workers had a lot of leverage and now the pendulum swung the other way and workers don't have a lot of leverage, but they do. They can slow rolled AI projects, they can quit the job. It's dangerous to quit a job now. You know, everything's tied to health insurance and all that, but that's always on the table. So you have to treat people with respect. And I think if I come to the table and I'm Karl Marxing and I'm banging my shoe on the table, I'm not going to do anything. Anything. But if I can like familiarize myself with the complexity of relationship between labor and their employers and technology, there's surely some sort of thing that makes things a little better and the process isn't to solve it in a day. It's to create a process where you can start chipping away at the problems systematically. And so if we can go in and do that and we can use AI to like, it's always easier to see it in motion than to see it on a whiteboard or see it in a blueprint. So if we can like just bring it to life and look at it and say, oh, this stinks, instead of spending six months on it, then we should do that and then we should like embrace things that make work better instead of making work more work.
- [48:14]
Mookie Spitz
There's a pragmatic aspect to humans being left in the loop and humans keeping their jobs that the doomsayers ostensibly say they're all going to lose. One aspect is just customer service. You could have a Turing tested bot do a lot of it for you. But at the end of the day, what greases the wheels are the interpersonal relationship and peer to peer functions of so many companies. Companies hire people to relate to other people. It doesn't matter how sophisticated the bot or the robot, whether the predictive or reinforcement learning will do the shot or not. You need people. It's a people business, doing service, creating products for other people. That's one saving grace, I think. And the other is the adoption curve, where the technology could be there, but it often takes human beings, a generation, sometimes two, to really adapt to the technology. Remember Friendster, the social network that had a lot of the functionality of a Facebook, but people weren't really quite ready for a social network and sharing. And the tech might be there for a lot of this AI plugin and adoption, but people don't get it. They have trouble learning it and implementing it. And it's a lot slower, I think, than people realize in terms of plugging in and letting it take control of an organization. So I think we're getting a little bit ahead of ourselves with the hype curve that you've been talking about. Not only from the vantage point of business optimization, but actual implementation, utilization and cultural transformation.
- [50:10]
Peter Swim
Yeah, well, I mean, if you think about it, everything is repeated modalities of stuff from the infancy of computers. Like if you go back to look at Xerox PARC in the 70s, it looks a lot like Windows still. It looks a lot like OSX still. We're still clicking on icons. And so people. So people make, people make blogs that are like a online version of a magazine and they make Twitter, which is online version of a text message, which is online version of a telegram. Yeah, yeah.
- [50:40]
Mookie Spitz
When we look at our phones like 500 times a day. You know what we're really looking at as far as the UI goes? Yahoo.com 1998 it's like, remember email sports when people didn't understand how to use the web or what it was? You couldn't like navigate to anything. There weren't real search engines, so you needed a button to press to perform a function. And apps on a mobile device are pretty much the same to your point about it repeating and iterating the technology, the ui, how people access tech in the way that's most functional to them based on their learning curve.
- [51:21]
Peter Swim
Right. And I think a lot of it, at the end of the day you can feel very busy doing all this, but you don't get anything done right. And I think a lot of technology at work feels the same process where, okay, I'm going between slack and teams and I'm clicking all the buttons, I'm responding to all the things. It's like, oh, two hours left, actually do my work. I've had my powers of meetings and I clicked on all my things and I responded to everything and I made my inbox empty. Now time to write the report I had to write. I'll have AI write it and I won't read it and I'll submit it and you know, they'll, they'll laugh me out of high school because I said Abraham Lincoln was the first man on the moon or something. Right. And, and I think like that's also the, the thing when we talk about ROI is like, oh, if just because you can ship code 10 times faster, you shouldn't ship 10 times more code. You should take the time and do all the things you cut because you couldn't have time to do it like accessibility and making sure all the bugs are out. So 1.0 is a real 1.0. You don't have to ship 1.1 the next day.
- [52:25]
Mookie Spitz
Let's flip this conversation around. We were talking about use cases that are very pragmatic and fairly immediate and really erupt or emerge from your discovery in a way that's useful. Let's say you do your discovery and a company like Mookie Spitz bald ambition. I actually need a proprietary LLM. You did the discovery and you just concluded I need a deep seq cranking on my laptop and I need agentic technology to help me distribute my podcast across all my channels and I need agents to do the processing, the editing of my videos and then we work together and you envision a high tech solution like really cutting edge like you suggest. I Get Nvidia's Nemo on my desk and you suggest that I get claudebot to start automating my functions and we get serious, we get deep about AI in 2026, you. You conclude that that actually would work for Bald Ambition llc.
- [53:39]
Peter Swim
Yeah.
- [53:40]
Mookie Spitz
How does, how does that work? Where you bring, you bring the big kahuna. Let's, let's flip it around. Let's stop talking about just plugging in Slack in a more efficient way and talk about bringing in the big guns and doing it in a way that actually adds value instead of just blow smoke up up everyone's posterior for the sake of jacking the sow.
- [54:03]
Peter Swim
Well, I'll tell you what we do at Toy though. And so the first week of Toyville, I took the week off because that's a great way to start a business is take the week off. So I took the week off and I did nothing but vibe coding for a week. And at the end of the week, I had 3,000 computer programs on my computer and I did an analysis of the code and it was five programs. And when I thought about it a little harder, it was two programs. But that 3,000 computer programs was like 20 gigs of stuff on my computer taking up a mess. And all it was was change management. It was crud, which is create, read, update, delete, operations. And so we started building a system is basically governance tool that wraps around things like Claude. And so whenever you use AI like this one, you should own the model. And if you can't own the model because it's too expensive and you don't have $12 billion a quarter to burn, you can rent it.
- [54:58]
Mookie Spitz
That's what Apple did, by the way. Apple didn't want to do that.
- [55:01]
Peter Swim
I think Apple's very smart about what they're doing because you notice they haven't released anything yet because their company run by designers and it isn't designer ready yet, this technology.
- [55:12]
Mookie Spitz
But all the capex that all these companies are burning through to create their own frontier models. Apple's like, you guys do all the heavy lifting and when you finally figure it out, we're gonna plug it in. At first I thought Tim Cook was out to lunch about AI, but the more I think about looking back, he saved Apple a trillion dollars by doing nothing.
- [55:33]
Peter Swim
I agree, I agree. Because like we our internal tooling, we run Apple computers and they're ready to do this stuff without buying the stuff you described. You can any computer that says Apple intelligence is more than adequate for most people for AI tasks nature. And so Our tooling that we build in house, it starts off with stupid computer programs and then it goes to Apple AI and then it goes to cloud AI when it's really stumped. And the key difference is every time we go to cloud AI, we count how much we use and what it costs, and we use what we learn to write computer programs to avoid doing that the next time. And so instead of 3,000 computer programs, you have one computer program and everything becomes like a practice or praxis or ritual, whatever you want to call it. And so then your entire business is, you know, it is a, a personal Google that lives in your computer and they'll never use it to train ChatGPT. I remember, and you probably do too, when Amazon first came up and they had the stores within a store. So all these companies like, well, we can't beat Amazon, so we'll havetoys r us.Amazon.com and look how that worked out for Toys R Us, you know. And I think a lot of people who build their business on these tools are going to run to the same trap again. So I teach my clients, yeah, absolutely. If you can afford getting Nvidia computer and run it locally, you can get most of what you need done. And if we need to like do a Sprint, we'll spend a couple hundred bucks on Claude this month and get it to write the program and leave Claude. And that's what they know. That's why, that's why they want to own your output. That's why they want to make you hooked on being paying rent for AI for the rest of your life.
- [57:19]
Mookie Spitz
This is to me, the biggest tension in AI. If we're looking at trending and future scoping, I liken it to the IBM servers succumbing to the PC and the Apple personal computer or even the Pro Tools example where you had a lot of musicians who were renting studio time very expensively from the big companies and then the software came and they could just do it all at home on their, on their, on their PC. And that's basically this, this big picture to local. So you have all these companies investing trillions now building these expensive big frontier models and then they charge you for tokens. You're renting the computing time from them and they own your content and they're going to monetize your engagement with them. They're going to provide advertising. It's going to be jacking you for everything, including your subscription dollars. And the alternative is get a local LLM to your point on your own laptop, on Your own PC and you own your own content and you do everything locally. And this battle between the servers versus the PC is going to be the defining dichotomy, I think, in AI as it moves forward. And not enough people, I think, are talking about this. The investors are all about the big frontier models, I think with the dangling fruit of AGI. If we keep pumping this up, given the existing LLM architecture, we're going to have a hell no. 9000. And most computer scientists and even egghead bald guys, podcaster consultants like me are like, no way, Jose, you're not going to get artificial general intelligence from a large language model. You're just not going to.
- [59:15]
Peter Swim
No, no. I mean, you know, getting artificial intelligence from a large language model is like getting a gravity predictor out of a pachinko machine, right? It is a, is an input and output and all it does statistically bias the result. And so if you look AI like that, we have things called computer programs and we've had them for decades that are biased result machines, right? So if you tell a calculator and you type in a number plus a number, it gives you an output. And so that bounded state machine can be made for hundreds of a cent now. So why not? You know, we have all this technology in our house and our lives in your phone that is more than powerful enough for a private AI cloud. And why hasn't it happened yet? One, because the entire market is perhaps owned by a, a oil company like consortium of RAM and CPU and GPU and tech companies that have decided this is the way it has to happen.
- [60:16]
Mookie Spitz
Maybe they're patting each other in the back and eating themselves. You know, it's the chihuahuas and the cats. You know, it's like you, you know, one's eating the other's excrement. So it's like, you know, OpenAI buys Nvidia chips. And in OpenAI's eyes, OpenAI is dependent on the data centers, and the data centers are dependent on the frontier models and you know, round and round and go.
- [60:42]
Peter Swim
Yeah, I mean, like if you buy ChatGPT Pro at the $200 a month, by my estimation that's 300% loss for ChatGPT on Pure Computer. So why would you do this as a company and you know, a company that's lost, you know, $18 billion in a quarter has never ever succeeded in the history of mankind. And I think their thing that they hope is either one the blunder on the AGI and they will be the only game in town and they'll own the AGI and but you know, someone will use the AGI to train another AGI, they'll be cheaper and that'll happen
- [61:17]
Mookie Spitz
the next day, then take over the world.
- [61:21]
Peter Swim
So I think like all these do discussions, I think distract from am I going to lose my job? Right, because we're debating about is Peter Pan real or not. Meanwhile, tech companies are laying off people and saying you have to come in the office and do all that stuff. And so these conversations are like, okay, I love to have a spitball conversation about is AGI real or not? It's not. And, and do that all day. But like you're getting paid hourly to do a job. So how do we get that done today? And I think that is like a lot of the tension people have at work where it's like, well, it's cool to play with this new thing. I have two hours I'll never get back. And now I have to go back and doing the job in predictable way because it's due at a predictable time. So I'm going to go back to the non AI way to do it. So I think there's a lot of like context switching you have to do until this technology says what it says on the tin. And also like a company is prepared to make the changes they have to make to make it work better. And until that happens, I think we're kind of at the whims of like tech companies and functionalities and leadership.
- [62:31]
Mookie Spitz
There's two big developments though that do have pragmatic import and the first is agentic technology that just blew up with Anthropic. So you mentioned profitability. Anthropic's bottom line increased by 30x. They were predicting about a billion in revenue and they cranked to 30. And they're actually predicting profitability in maybe a year or two they're actually going to make money maybe. And then the other one is this local LLM stuff that we've been talking about. So agents can do stuff and they're amazingly adept at it. And then local proprietary LLMs are walled gardens of content that turns the model upside down from these data data centered server minds that charge you for every token.
- [63:23]
Peter Swim
I do think there's going to be a market spread of personal AI and industrial AI, right? And so the AI that a company like Microsoft needs to do a day of work is structurally different than the work you do to produce a podcast. And you'll probably have enough AI in your machine that you get for paying the $2,000 for your laptop or whatever that merits, the ROI and its cost. And it'll be good enough for many things. And the AI that you take to make like a Marvel movie or whatever would have to be bared by the cost, by the people who do it. And that's fine, but I think, like, we have to get out of the speculative industry where, like, okay, so at Toyoville, our AI system, we basically wrap anthropic Claude and we divert it whenever possible to an application that does what I ask to do. So I don't send anything in the cloud that already exists. And because of that, only 13% of Claude things actually make it to Claude when I'm using Claude code, because I'm just rebuilding stuff on existing structure. And of that 13%, it's 100% cache effective because I rewrite the prompt to not be different every time and only say the stuff we need to get to the next step. And so I think the future is something like that where, okay, I'm using a dollar thirty of CLAUDE a day and I'm using the rest of the computers I paid for all day. And Instead of spending $500 a week on Claude, I get by on like $15 a day. And that to me is, that's more sustainable as a business, as a business owner. And I think it's more sustainable, like, environmentally, because, like, we shouldn't have to, like, you know, it's like using terry cloth for toilet paper. A lot of these AI processes where it's just like, hey, I need to be able to write an email to someone's like, okay, I'm going to write for you an email. Then we're going to like, figure out how email works. And all this happens if you look at the eternal memory every single turn. And so if, if AI is expensive, we should price it so, and we should make the tooling more efficient. And all that stuff has to happen. And I absolutely don't think any of my clients should be on the hook to build or write that code. So I, I do it for myself because that's how I retain my competitive advantage. But I think it's not ready for primetime for a person to just use in, like buying a suit from Walmart, you still have to go to Brooks Brothers and get it measured and tailored to you in a way that requires people like me, if I'm hearing you
- [65:50]
Mookie Spitz
correctly, it's local, not these big servers, as best you can. And then you need an Expert like you, Mr. Swim at Toilville, to plug it in, to customize it, eliminate redundancies and overkill and optimize it to your work streams that you determine through your rigorous and detailed discovery process. So that's kind of a good bow that we could put on your value prop, which is come to Peter with your business issues. He'll look under the hood in a way that's detailed and comprehensive and thoughtful. Only after he determines really what. What's broken will he try to fix it from a holistic level. And then he'll apply the tools of the trade in a way that's most expeditious without the hype. And when he does implement more sophisticated AI solutions, he'll ensure that you maximize the control of your data, and he'll continue the relationship with you to customize these ever evolving technological miracles so that it keeps working. It taps into the pulse of the latest technology and isn't giving away your data and your soul to these huge frontier models who are gobbling up the world. Is that a decent overview?
- [67:23]
Peter Swim
You know, I'm gonna need to send my pitch deck to you for you to punch it up, because that was great. Thank you. All right,
- [67:30]
Mookie Spitz
that's my. That's my free service to your guests who come on Bald Ambition. We'll wrap it up into. Into pitch because, you know, essentially this podcast is about consultative selling, storytelling, and business. But you take the complex, you make it simple, and you make it resonant in a way that's logical, inevitable, and differentiated. And it sounds to me like you've got. You've got this going on at Toyville.
- [68:00]
Peter Swim
Thank you. Yeah. And of course, like, all right, I do emails for free, so if you have a quick question or whatever, you can hit us up. Our website is peoplemakeitbetter.com I'll have links
- [68:11]
Mookie Spitz
in the description below of the audio and the YouTube video.
- [68:15]
Peter Swim
And I. I work a lot with people who don't think they can afford someone like me. So, like, not for profits. Religious groups, community groups. We can make something happen to help you out. So reach out to me and happy to talk to anyone.
- [68:29]
Mookie Spitz
Great. Thank you. Peter Swim from founder of Toilville. And you should rename it after you. You initiate a relationship, it goes from Toilville to What's. What's desirable, end state, from Toilville to Happy, Happy Opolis.
- [68:49]
Peter Swim
You know, like, I'm a son of the soil, and I don't. I don't turn my nose down to toil. So. So it's all right if I own the harvest.
- [68:58]
Mookie Spitz
That's right. We reap what we sow. And it sounds like your farming is very thoughtful and deliberative and good for the business environment. Thanks so much Peter for your time. Like subscribe, comment and share and check his links below to initiate at least the beginning of a discovery. Call for Peter to check out what you got and see if he can help. Thank you so much.