Chapters
0:00 Welcome Ian Maurer & Introduction
5:53 Making the Intentional Career Move – Find Hard Problems
7:22 The emergence of GenomOncology & Precision Medicine
10:02 Cancer, Biomarkers, Clinical Trials & Genomics
16:24 Software Engineers – Preparing for your first job
20:21 AI Magic Tricks, Symbolic AI, Neuro AI and the future of jobs
27:38 Suggestions for Experienced Talent

Each week, we interview proven leaders from our network, to learn from their experiences, and share their Talent Attraction and Candidate Experience stories with you.

• Our mission is to promote the accomplishments of our guests
• Highlight the companies where they work and the services, and products that they offer
• Share success stories from their experiences and, most importantly
• Provide strategies for job seekers and advice to talent seeking to accelerate their careers.
  

Today’s guest is Ian Maurer, CTO GenomOncology. He’s worked in consulting, he’s architected e-commerce solutions, he’s built and led teams, he’s a writer, he’s an accomplished speaker, and over the past decade, he’s been growing a platform focused on genomic analysis in the oncology space. In our chat, Ian shared several great insights, including:

• His take on the current state of AI and the future probability on the impact of jobs
• Genomics and Precision Medicine on impacting Cancer
• The importance and value on your career of chasing hard problems
• A subset of books that have made a big impact on his career journey

The Intersection of AI and Genomic Analysis: Ian Maurer’s Vision for the Future of Cancer Therapy

[00:00:00] Ron Laneve: Hello and welcome I’m your host, Ron Laneve. Each week we share career stories of tech experts and marketing mavens operational gurus and sales leaders to illustrate how they’ve navigated the nonlinear career path. 

[00:00:14] Ron Laneve: As you’ll hear, he’s the epitome of the theme of these conversations. I first met him as a candidate and sorry to date both of us but it was 1999 and I think he was the first placement I made in the executive search business. So it was a long time ago. I’ve had the pleasure of working with him as a teammate on three separate occasions in the 2000s and then the 2010s.He’s been a client of mine for several years and most importantly, he’s become a great friend. This individual is absolutely one of the smartest people I know. He got his Computer Engineering degree, both his bachelor’s and master’s at Syracuse University. And software development has always been the backbone of his career.

[00:00:56] Ron Laneve: He’s worked in consulting, he’s architected ecommerce solutions, he’s built and led teams, he’s a writer, he’s an accomplished speaker, and over the past decade, he’s been growing a platform focused on genomic analysis in the oncology space. Not to mention he’s a pretty funny guy too. I’m thrilled to introduce to you, Ian Maurer CTO of GenomOncology Ian, thanks for being here. 

[00:01:21] Ian Muarer: Thanks Ron, and you got my name you said it right and everything, that’s great.  Thanks for having me on your podcast. This is fun. 

[00:01:27] Ron Laneve: Absolutely. You and I have talked about this a bunch and I think you’ve seen some of my other episodes. After a lot of these conversations, it’s become fascinating to me to watch and learn about individuals, what I call the nonlinear career path and, for me, I’d love to, can you go through your background and career history and really emphasize, the how and the why around the moves you’ve made, over the time whether they were intentional, whether they were serendipitous, some of them could have been mistakes and you learn from them. So could you walk us through that? 

[00:02:01] Ian Muarer: Yeah, sure. So started off in computer programming with a Commodore 64. I got it cause I thought I wanted to play video games, but I got bored with that pretty fast. The cool thing about Commodore 64 is it actually has a basic interpreter right in there. So I actually would start writing my own games and doing things like that. Took computer science in high school, decided I wanted to be in computer engineering. I went to Syracuse at Syracuse, I was in the lab because they actually had labs for the computers because not everybody had a laptop at home.

[00:02:28] Ian Muarer: And in the lab, there was like a posting for a job and, basically 10 or 15 hours a week at Lockheed Martin. So I Went in, did an internship, got to learn some new technologies. I worked with C and this thing called SGML. They actually paid for my master’s degree, which was amazing. During my time, my first rotation was with this same group that was doing online documentation for aircraft engines for GE because it was a, it’s an old subsidiary of GE.

[00:02:56] Ian Muarer: And, during that time I learned how to parse SGML and do cross linking between these documents. And, a lot of the similar stuff that I’m actually doing today with documentation and in the genomic space. While I was there I did a rotation with the radar department or sonar department.

[00:03:13] Ian Muarer: And I was, almost done with that. And the guy’s Hey, you should stay here in the sonar department. Don’t go back to that other department. Cause that’s, nobody thinks that’s cool, like that department, the smart people don’t go there. And I was like, but I don’t really want to work with sonar ever again, and that other department was doing stuff that was very similar to web technology. I actually floated to that and this is the late nineties. The web is really becoming a thing. And I said, no, I’m going to go work in this other department, even though it’s got lower status, compared to these other departments, because I got to work on the hardest problems because I was really the only trained programmer there. So they would throw me the hardest problems. And so that, one lesson to learn is look for hard problems. If you can find hard problems and solve them and be tenacious, that, that can definitely work in your favor.

[00:03:57] Ian Muarer: And then, and I was graduating from my master’s degree. My girlfriend at the time, my wife now, moved out here to Cleveland. I showed no loyalty whatsoever, unfortunately, and decided to get a job and move to Cleveland. And luckily, I ran into you. I don’t even remember how we connected. You placed me at E&Y I worked there for about nine months. I didn’t really, I just didn’t like the culture. It was like a big company culture. I got a couple of opportunities to do independent consulting. I did that for a little while and I realized I’m not good at the business side of this at the time, like I don’t know how to find the next client.

[00:04:27] Ian Muarer: I know how I got these clients because I, they basically popped up at E&Y but I don’t know how to get the next client and I don’t really want to start calling people. And so I realized I got to go get a job. So I got a job at another small consulting company, realized that company wasn’t probably working to my advantage longterm.

[00:04:42] Ian Muarer: And then luckily you reached out to me and said, Hey, I know this guy, Brad, we’re starting this new thing. It’s going to be good. And I said, all right, let’s give it a try. That was Xteric at the time, April, 2004. That’s where I met Brad. Who’s currently my boss still. I think I was the 10th person. I think my desk was like in the walkway, there wasn’t even a hallway. It was a walkway. There was 10 of us stuck in this little room and I was in the worst possible spot, but that’s okay. 

[00:05:04] Ian Muarer: We were closing work, making stuff happen. Grew it to, I think about 60 people by 2006. We merged with Brulant at the time, Len Pagon who started that company. I think we’re at 250, maybe 300 people at the time. And then through a lot of successful e-commerce projects and some other things that the company did we grew that to, a thousand or so folks with the acquisition and merger with Rosetta. And then finally were acquired by Publicis. 

[00:05:29] Ian Muarer: During that time, I was working on e commerce, which was great. We basically built and launched a bunch of e-commerce websites. I think it was Jared for Sterling Jewelers at the time and Tractor Supply and HH Gregg and a few other websites. And that was a great learning experience. Got to meet Manuel Glynias and Jeff Shiner, who were two of my colleagues and learned a lot from them and a few other folks that, work at GenomOncology now. It was a great environment for learning. 

[00:05:53] Ian Muarer: When Manuel started GenomOncology I saw it as a great opportunity to do the next thing, to learn something new. E-Commerce had been a, fun ride, but it was coming to an end, and you could see that, Shopify or some of these other online platforms were going to take over and run with it from there. 

[00:06:10] Ron Laneve: It seems like you intentionally, made the decision to follow the hard problem over and over again?

[00:06:16] Ian Muarer: Yeah. I think that’s where I felt like I could make a differentiated impact. So the main thing you’re trying to do when you’re starting your career is figure out how to build career capital. There’s a great book called: “So good they can’t ignore you,” by Cal Newport. There’s a book called: “Smart and gets things done,” by Joel Spolsky who had this online blog that I used to read.

[00:06:35] Ian Muarer: And that It’s basically, it’s great to be smart and it’s great to get things done. But if you can do both of those things, you can be a differentiated value for your employer. And that’s really the best way to get started in your career. Like just look for hard problems, look for things that aren’t getting done and just do them. Don’t necessarily even have to be asked to do it. Make your boss’s life easier and people will recognize you and you will differentiate yourself from 95 percent of the people real, real quickly.

[00:07:01] Ron Laneve: Let’s talk about, the last, what, 10, 11, 12 years have been with GenomOncology, building this product and this platform on this set of solutions to hopefully change the way, we approach cancer Therapy.Can you talk about that a little bit more and frame up GenomOncology and especially talk about how it’s evolved over that time? 

[00:07:22] Ian Muarer: Sure. Yeah. So when we started, we actually didn’t necessarily know what we were going to build. We knew we could do bioinformatics tools because our founder had been a bioinformatician before that was even a word. So we could, basically process and analyze information. He had read a couple papers that came out in, I think, 2011. That basically said that basically walked through the process of taking genomic data, analyzing the data and then helping a patient. And then they realized that it would take so it was taking so long to do it that they couldn’t actually help the patient because of, six weeks, six months. Those are meaningful timelines in the life of a cancer patient. And then the other, anecdote that was going around was the cost of genomic processing was dropping faster and really even more impactful than Moore’s law, which is the law that governs like, CPUs and now GPUs, it’s even more impactful than that.

[00:08:15] Ian Muarer: So we could see that, the first genome cost $3 billion to literally sequence. And now it’s going to cost $200.00, like by the end of this year, and we knew that was happening. So we, and so by building something in that domain, we thought we could be ready. And the joke was, 1, 000 genome sequencing, 100, 000 analysis. So that was the problem we wanted to solve. And we knew that cancer was the right spot to solve it because cancer is a disease of the genome and it’s complicated. So therefore, let’s go where the complicated thing is. And so we started by building a research platform. Basically, how could you use a Mac mini at the time to analyze a whole genome?

[00:08:49] Ian Muarer: And it was it impressed people on. It got us in the door to a few places, and it got us in the door at places that were doing genomic sequencing for cancer patients to actually make a report. So that’s how we built our first product was basically working with two or three of these first clients, understanding what their workflow was and really solving the critical problem. The problem is I can’t take eight hours to process a report if I’m only going to get paid 100 to make the report. So how do we drill down and make that process as seamless and quick as possible to get it down to 15 minutes a report? And that’s basically what our software does. And then doing that, we uncovered so many problems.

[00:09:26] Ian Muarer: We were like, oh, okay, we got the bioinformatics part. We know how to make a PDF. What do we put on the PDF? And so then you start pulling back all the problems and that’s, it’s basically what we’ve been doing since then. I got a list of problems that I can keep solving if we need to. And then, obviously AI is going to change a little bit about what we got, but it’s, that’s how we started. And that’s really how we follow it, we get there, show value and then ask the client what’s next? What’s the next problem that you have? Cause that’s actually the gold, the problem with the client has, and if you can solve that problem, that’s how you can actually create value for them. And that’s how you can get value for your company. 

[00:10:02] Ian Muarer: So the core problem is that precision oncology is really hard to keep track of all the potential options, doctors, when, if they went to school in the eighties or nineties, they probably got a day of genomics in they’re teaching. So they have to keep up to date on what genomics are. I don’t think that’s as much of a problem now. Now, the problem is, okay, you’ve got the genomic test. You got these biomarkers. What are those biomarkers mean? So a biomarker is basically a specific mutation. You got your skin. If you had UV light hits your skin in a very certain way, it can change a value in your DNA from an A to a T, which changes the protein in that specific gene from a V to an E that causes melanoma.

[00:10:40] Ian Muarer: So that’s a very specific thing that just happens to that one patient and it’s somatic, meaning it’s happening in your body. It’s not in your germline. It’s not in your hereditary genome. So that’s just one example, and that’s for one disease. Cancer is, a thousand or five thousand diseases.

[00:10:54] Ian Muarer: It’s not one disease. And then you’ve got 24, 000 genes. And we don’t know what all the different mutations mean yet. We got to interpret each mutation, figure out is this one pathogenic or benign? Does this actually matter or is this just, who you are? And then, after that, okay, which drug can I give the patient? There’s a new drug every week, maybe two drugs every week now, with the FDA approving drugs. For specific mutations for specific disease types, so keeping track of that and a knowledge base. We do that. And then there’s what are called clinical trials, which are okay. There’s really no good option for me right now.

[00:11:29] Ian Muarer: That’s approved by the FDA, but maybe there’s a drug that drug company is working on. That’s really exciting. Can I get my patient on that trial so they can get access to that drug? And so once again, we’ve read and curated in our knowledge base 13, 000 clinical trials at the biomarker level and the biomarker level isn’t just.

[00:11:48] Ian Muarer: Oh, you have BRFA 600E. Therefore, you get this. You’re on this trial. It’s like you’ve got this biomarker and this biomarker, but not this biomarker and not this biomarker. And you’re this disease type, not this disease type. Or you could be in these five disease types, but not in these two disease types.

[00:12:02] Ian Muarer: And you couldn’t have had this prior therapy. So it’s like these complicated rules. It’s basically what we call an expert system, which was something I worked on at Lockheed Martin in 1997. It’s these complex rules stored in a database that can then be retrieved. Based on a specific patient at that point in time, what’s their disease? What biomarkers do they have? What drugs have they had in the past? And other things we know about the patient. All that information is way too hard for a person to remember, that’s why doctors and cancer specialize, there’s no doctor that is like at a big hospital that’s I know about every cancer.

[00:12:35] Ian Muarer: I know about breast cancer. I know about lung cancer. It’s the local community oncologists that really need this help because They can see any kind of patient, if you’re not at a big hospital, you’re going to see a melanoma patient or a person with lymphoma. So knowing each and every single one of those diseases and what, how to best treat them is really hard to do without software. That’s where we come in. We have the software that basically does all that kind of processing, cleaning up clinical data. Clinical data is very messy. We have a lot of PDF reports, like they’re unstructured reports. We parse those and use NLP on those and then we help our clients, basically normalize all that information to our knowledge base.

[00:13:12] Ian Muarer: It’s a precision oncology knowledge base that has genomics and it can scale with genomics, which is hard. Once again, always looking for the hard problems because the hard problems are where there’s value. And then after that, I can then match a patient to a variety of drugs or trials. Then there’s the opposite problem, which is I have this clinical trial. Which patient can go on my trial? That’s another hard problem or I have this drug. Are there enough patients that have this mutation that actually makes it worth me creating this drug, going through clinical trials and going to market with this drug? That’s a whole different problem as well. 

[00:13:48] Ian Muarer: Our system can do that too, go find me all the patients that have this type of disease and this, these types of biomarkers and we can bring those back. And then if you store that information along with what treatments they’ve gotten in the past and what the outcomes of those treatments were, we now have what’s called a real world data or real world evidence database. Which then, if you’re a doc and you’re like, okay, I have this patient, they have these mutations, they have this disease type, what are my options? And it says, oh, you can give them drug A, drug B, or drug C. Which one do I give them? Now you can ask the real world evidence database and it can say, okay, we’ve, we found a thousand patients. A third, and a third have gotten those A, B, and C drugs. And here’s a, what’s called a Kaplan Meier curve or a survival plot that says, Oh, drug C seems to be doing the best. So let me go ahead and bring that up and potentially give that to my patient based on that information. Unfortunately, a lot of this stuff is still, we’re hunting in the dark here because it’s all so new. 

[00:14:40] Ron Laneve: I just wanted to point out a couple of things. You’re so far past these concepts after doing this for 10 years now for, for the basic person like me, and when I worked at GenomOncology, there’s two things that still stick in my head. First, no two cancers are alike, every cancer is different. And if a couple different people have lung cancer, it’s not the same. And that’s where the precision medicine part comes in and the specific drugs designed to potentially provide a therapy for individuals.

[00:15:09] Ron Laneve: Because we have surgery, we have chemotherapy, and we have radiation, which are essentially poisons, correct? Yep. To try to eliminate or eradicate cancer. But now, the space that, that genome oncology is playing in, alongside of the developers of these drugs, is the matching Of the drug or the therapy or the clinical trial with the specific genomic makeup of the person.

[00:15:33] Ian Muarer: And so yeah targeted therapies and immunotherapies. Those are really the two key things that We’re managing and we’re managing what’s the what’s called the eligibility criteria How do you like what? What patient would be a good fit for this drug and which drug would be a good fit for this patient?  And it’s a matchmaking service 

[00:15:51] Ron Laneve: It’s at its simplest form, but it’s obviously way more advanced than that. That was obviously very eyeopening. So could we transition into, the two areas we talked about? So students in college or soon to be graduating what advice would you give them as far as, Tools to add to their bag while they’re in college, before they graduate, things to think about as they’re planning to enter the workforce, maybe, or maybe not, approach it from, your lens of a CTO and a person with a extensive software development background, or stay at higher level.

[00:16:22] Ron Laneve: It’s up to you. 

[00:16:24] Ian Muarer: Software developers, it’s a different business than a lot of things because there’s the opportunities to work on open source projects or build your own thing with your own computer and you can work on that. But I think in general, that advice is useful, which is basically to learn anything meaningful you have to solve really hard problems and to solve really hard problems, you have to work on something substantial, so it’s meaning you have to like actually take on a project that’s not 10 hours long or 20 hours long There’s nothing useful or meaningful comes out of a 20 hour project.

[00:16:53] Ian Muarer: It has to be something bigger It has to be something more ambitious. So figure out what that means for you. What is that project? My daughter is learning music so she spends a lot of time practicing her instruments, but she’s also in a rock band. And so she spends time making marketing materials and getting shows and gigs for their band. That’s a super useful skill set.

[00:17:11] Ian Muarer: Maybe the rock bands aren’t going to do anything, but that’s okay. Cause you can learn those skills and then apply that to your future life. So if I was in graduating today, I would try to figure out what that was. And if you’re a software developer, it’s go make something right? Go make a product. Even if. You’re the only consumer of that product because you’re just going to uncover harder and harder problems. 

[00:17:30] Ian Muarer: And then, read some books, so the books that I like are, I like the Cal Newport book that I mentioned. There’s another one he wrote called Deep Work.  We live in a very distracted world. TikTok YouTube. You’re distracted by these things. It’s really hard for folks to have attention spans, honestly, focusing on a two hour movie, some people can’t even do that anymore.

[00:17:48] Ian Muarer: The ability to actually focus on work and drive to creating, differentiated outcomes is critical. So Deep Work is a book all about that. And it’s about designing your schedule so that you have blocks of time where you can actually dive deep into something, whether it’s writing or creating software or what have you. 

[00:18:06] Ian Muarer: Figure out how to design and develop rare and differentiated skills. There’s, different advice that I’ve seen where it’s okay, you’re not going to be the best at any one thing, but maybe you could be the best at two or three things, combine it together, my, my spiel right now is precision oncology, software development and AI. Like I’m trying to be the person at that center of the Venn diagram.

[00:18:26] Ian Muarer: So figure out what that Venn diagram is for you and then be that person. And really try to avoid shallow work, emails and meetings and, one off things. Okay. Shallow work is, necessary and you got to do it to be a good, co worker. But if you can figure out a way to design your schedule to, to not be that in my world that, that’s valuable.

[00:18:46] Ian Muarer: But you got to also recognize that there’s dichotomy. There’s a web article from Paul Graham from probably 20 years ago at this point called maker versus manager schedule. So that is a kind of like deep work before deep work, which was basically there’s two types of people in the world. At least in the business world, managers who are just trying to coordinate and get work done and makers who are the actual people, individual contributors trying to get work done.

[00:19:09] Ian Muarer: I’m an individual contributor, I’m a CTO or whatever, but my differentiated skill is actually creating the next new thing for our company. And it’s really cool that I get to do that as my job now. So I’m very happy about that. But the manager maker schedule thing is. Managers are trying to basically, schedule every minute of the day for every single person on their team. And the maker’s just like saying, Hey, leave me alone for four hours so I can get something done. So recognize that difference and have those open conversations with people. Hey, don’t schedule me for 15 minutes, three times a day scheduled me for half an hour at the beginning or the end. And let me get my stuff done.

[00:19:40] Ian Muarer: I keep going on with the deep work thing, but to me, it’s really developing those rare and valuable skills and then it’s a longterm games. A long term game is your network, known you for 25 years, known Brad for 20 years, develop that trust with people so that they know who you are, that they recognize, that you have integrity, that you’re going to get stuff done, you’re, trustworthy those networks and those relationships are super valuable. And I know there’s a hundred people I could probably call and say, Hey, I’m out of a job help me out. If I had to. So recognize that, treat people with respect, and do, say what you’re going to do and do what you say. And you’ll, definitely be better off for it.

[00:20:21] Ron Laneve: So you brought up AI, so let’s talk about that. And I couldn’t have a conversation with you without bringing that up. I know you’ve gone all in on it. You’re obviously applying it in pretty deep ways to GenomOncology I know personally you’ve posted a lot of articles about it. For most of us in the world there’s generative AI and chat GPT and people like me are fooling around with prompting, but for you and for, the next wave of software development experts where, again, back to the student lens or back to the college lens, how would you suggest that, that cohort dive in, I don’t know, I’ll say the right way to be the most productive and competitive going forward. Is there a, is there an answer to that right now? 

[00:21:06] Ian Muarer: I think there are lots of answers. I don’t, I think every person has to figure it out for themselves. So I’ve been doing AI for seven or eight years, depending on how you define AI, I’ve been doing AI my whole life, because expert systems, which is what I was developing 25 years ago, is basically if then statements. And there were people back then, and there are still people today that think that’s actually true AI. We’re going to have, codified knowledge, and that’s how things are going to work.

[00:21:27] Ian Muarer: And then there’s another side of things, and that’s called symbolic AI, if you’re interested. And then there’s another side of things called neuro AI, which is basically saying, just take a bunch of data and get a bunch of computers and throw the data at computers, and we’re going to get magical AI too, 

[00:21:41] Ian Muarer: I actually think the answer is both. You’re going to scale up this thing on the side, it’s currently generative AI. With more and more GPUs and video stocks going to the moon, all that stuff. And then you’re going to have symbolic AI, which is going to be expert systems, rules, codified knowledge. And then those two things working together. System one and system two thinking is how I refer to it based on a book from Daniel Kahneman. And the basic gist is that’s how you get to the world where, things are trustworthy. Trustworthy AI is what I’m trying to build. Trustworthy AI is reliable AI, something that actually works and does what you want it to do, and responsible AI, meaning it treats people with respect, does things in a way that, is morally acceptable.

[00:22:24] Ian Muarer: So if we can build trustworthy AI, that’s what I want to do. So that’s like the background. If I were, thinking about it now, where are we going with this stuff? I do believe the, there’s a little, adage going around that AI is not going to replace me, it’s the person using AI is going to replace me.

[00:22:37] Ian Muarer: That’s. That’s certainly a thing to consider. I see AI is just the next evolution of automation, so the cotton gin, I couldn’t think of an earlier example was with automation. It was the assembly line. There’s Excel and Excel macros and formulas. Like those are all types of automation and they’re all AI quote unquote. And I just think of generative AI as a new type of automation. The current AI that we have, I don’t think is going to be AGI unless it gets programmed in a way that’s. interesting because and then also you just got to figure out what do people mean by AGI. So the simplest way I can describe the, like two things that it can do that’s interesting to me.

[00:23:12] Ian Muarer: So the first is with generative AI, you can describe what you have and what you want, and it will fill in the middle for you, which is a really powerful concept. Rather than figuring out how to do something like hot. Okay, what buttons do I have to press in this application to, sort these things and dedupe these things. You don’t have to figure that out anymore, you can just say, here’s some stuff I got, sort and dedupe it for me, and it gives you what you want, because you’ve just declaratively, in context learning told it. The other thing that it can do is it can solve classes of problems, when those classes of problems have known classes of solutions.

[00:23:48] Ian Muarer: Meaning. If you have a specific problem and you go to Google and you say, here’s my problem, it’s going to give you, if there is a solution on the internet on Stack Overflow or one of these other websites, Quora, it’s going to give you the answer and be like, here’s the answer. That’s a known problem with a known solution.

[00:24:01] Ian Muarer: The cool thing about Gen AI is you can give it a type of problem. And it can give you back a type of solution, probably the specific solution for you, because it’s mixing and matching stuff, but mixing and matching interpreting that information isn’t true reasoning. It isn’t true intelligence in my mind, it’s a form of automation. That’s a cool magic trick, and it’s, but just because it’s a magic trick doesn’t mean it’s not useful. So figure that stuff out, use it. Don’t be afraid of it. It’s not going to take over all the jobs. I think there’s going to be new types of jobs.

[00:24:36] Ian Muarer: Will software engineering be a job in 50 years? I think it’ll be a thing but it probably won’t look, it will not look anything like what it does today. And the thing that humans can do is have problems and solve new problems, an AI has no problems, an AI can’t solve something radically new. But what it can do is it can mix and match stuff and, generate things that kind of look new because we haven’t seen them before, but it’s honestly just more in the search space. It’s just more of playing, go there was move 37, which was like this move that the AI did that had never been done before and everybody freaked out about. That’s not really intelligence. It just searched all the possible things it could do. And it found a unique solution and it did it. So those are like my high level thoughts of it. And one of the really interesting things to it that I’ve really embraced is the idea of activation energy, meaning I’d have all these projects, ideas. Oh, I could do this or I could do that side projects or just like little tools I could build.

[00:25:38] Ian Muarer: And a lot of times now I can actually just chat with the chat bot and like figure out Oh, this won’t actually work right and I just stopped like I’ve done that I’ve done that recently which is basically like I have this idea tell me how I would build it and then I work through it and then like even just trying to push through and say no try it this way try it that way I realized no this is untenable it doesn’t work and I can stop the idea. But I’ve just saved myself you know 30 hours of pain. And then the other thing but on the flip side I could say oh here’s a problem that would take me 40 hours. But with chat GPT, I can do it in four. And doing something in four hours versus 40, I’m going to do it. I’ll do that. Now I have this four hour investment. Now I have this tool. And then that tool is going to save me, 40 hours or a hundred hours going forward. And that was a worthwhile trade. 

[00:26:22] Ian Muarer: So that’s, those are the types of things that are, really exciting to me as far as, just as a partner or a co pilot, I hate that word now just because everyone’s reusing it, but that is the, it’s an assistant. Trying to build an assistant is really what I’m trying to do. And that’s what I’m actually building right now for GenomOncology is the GenomOncology assistant, which is, I have this knowledge base. I know what the truth is. I have an expert. They’re the person that’s going to be accountable. They’re the person that has to actually make decisions. The computer can’t make a decision because the computer can’t be accountable. So you have the human, you have the knowledge base, which actually is ground truth because I’ve had experts curate it. And now the chatbot can actually just make using my knowledge base easier. Which is really the thing I want to do. 

[00:27:04] Ian Muarer: What do I have? I have a patient. What do I want? I want a solution for my patient to help them. Or, I am a patient, and I want a solution to help me. Which is really where things are going to go. So given that, how does the chatbot help that person understand the context of their situation, and then query a knowledge base to bring back relevant, true facts with evidence. Not just hallucinations. And then help that person guide them through an open ended, open ended is the cool part, like I don’t have to go make a program that can handle every single use case. The chat bot, the assistant really helps us do that. 

[00:27:38] Ron Laneve: Very cool. You’ve done a lot of interviewing over the years. So for experienced talent as they are going into interviews or they’re applying for roles or they’re being recruited.  What have you seen that’s stood out in your mind over time where, individuals have differentiated themselves from one another in that process anything that you could share? 

[00:27:58] Ian Muarer: I’ll tell you my biggest pet peeve first. My biggest pet peeve is people who on their resume either just list a bunch of technologies. There’s no way you’re an expert at all this stuff. You’ve been out of college for three years. There’s no, it just fundamentally doesn’t make sense for you to know all these things. tighten that up.

[00:28:16] Ian Muarer: Second thing is now you’re more experienced and you were on a bunch of teams and you did a bunch of projects. Tell me what you did. Not. I was around a bunch of people doing cool stuff. What did you do? How did you meaningfully impact stuff? Cause that’s what I’m going to try to figure out. Maybe some other interviewer won’t try to figure that out, but that’s what I’m going to try to figure out, which is okay.  You were on this team. You did this hard thing. You solved this problem that no one else could solve. Or maybe it’s not even a technology problem, I’m good at technology. There’s technology problems and there’s people problems and everything’s really a people problem at the end of the day.

[00:28:46] Ian Muarer: I’m not as good at the people problems, but explain the people problems you’ve solved. Those are just as important and just as valid as well. So make sure that’s crystal clear, not just, I was on a team and we did cool things like, what did you do? How did you meaningfully impact things? What problems did you see and what value did you create?

[00:29:03] Ian Muarer: That’s the trade. You’re trading your time. Solving hard problems so the business can do better and then eventually everybody gets everybody wins, that’s the long term goal. So have a long term thinking too. That’s the other you know, other key thing is Don’t just look at everything as a transaction. The last few years have been terrible with Covid and things like that, right people I look at people’s LinkedIn profile and it’s nine months here 12 months there 18 months there. That’s not a good look right not from my perspective, but I also know that the world’s changing too, 

[00:29:31] Ian Muarer: there’s You know, there’s a, there’s another side of it too, where the corporations aren’t necessarily holding up their side of it. I thought I would be more of like an independent consultant person and, look at me 20 something years later, I’ve had two jobs effectively and one boss.

[00:29:44] Ron Laneve: You mentioned a bunch of books, and I know you love to read and I know, I don’t know, was it maybe a year ago, you put out your, hey, here’s the five books I think you should all read for, to learn AI. I bet you that’s changed, several months later. I’m still plowing my way through those on Audible.

[00:29:58] Ron Laneve: What are the top five books you would tell people to read today? It doesn’t have to be about AI. 

[00:30:02] Ian Muarer: Yeah, let me check off the AI books real fast. So the AI books were Genius Makers by Cade Metz just to get a history of things, who were the players? What were the technologies? What happened? What were the big events? Then there were two books by this by this group of academics. There was like I have it behind me somewhere, but basically one was about basically the power of prediction. So the prediction machines, that’s what it was. So prediction machines was the first one.

[00:30:22] Ian Muarer: And then the second one was a follow up to that. And the basic gist is how are we going to make these things actually have business impact? What’s the ROI on AI and how do we do that? And so it really walked through the corollary of electricity.

[00:30:33] Ian Muarer: So if you were to go back in time to when electricity first hit the factory floor, it didn’t have much of an impact. Because the factories were designed for pre electricity. And then when they brought electricity in, they could put it in certain spots where it was like, Oh, okay this is a point solution that could use electricity. And so now, widget a goes to widget B and electricity makes that, that one part better. It wasn’t really until, I’m assuming that Ford, but it was really the assembly line where they redesigned and rearchitected around electricity.

[00:31:03] Ian Muarer: New systems thinking that was how this stuff worked and actually made a meaningful impact. So the thing that’s going to happen over the next 10 years or 20 years is going to be. We got this new tool it’s working great in these like point solutions, prediction markets, wherever something’s you’re trying to make a prediction.

[00:31:19] Ian Muarer: Great. Plug it in. There’s really nothing there or, generate a paragraph for my email. Great. That’s, I can plug that right in, but to actually have meaningful impact, we’re going to have to redesign our ecosystems. Where are humans in the loop? Where are AIs in loop? And how do we design these things?

[00:31:33] Ian Muarer: And then also recognize that we’re going to design, I’m designing my stuff, recognizing that GPT 4 and, the smaller open source models are the dumbest these things are ever going to be. And the slowest they’re ever going to be, in a year, they’re going to be twice as fast and twice as smart.

[00:31:47] Ian Muarer: So I’m designing with that in mind. Those are the AI books. And then as far as like the career books, so Cal Newport has a new book coming out. I haven’t read that yet. Cause I don’t think it’s actually out. So I like him. There’s a book called Drive by Dan pink, which I don’t think I actually read.

[00:32:01] Ian Muarer: I just read the bullets and the bullets. I remember. The bullets are like, what is your long term career goals, aspirations? Obviously people want to make money and have a nice life and a nice family. But his real thing was to be really happy, there’s three things you want out of a job. Autonomy, mastery, and purpose. For me, I have all three. I’ve succeeded in that way. Autonomy I’m kinda my own boss. I can kinda work on cool, hard stuff. That’s what I like. Mastery, I get to work on things that are hard problems and try to get better at them. And the cool thing about software development is I know when it works and therefore I can readjust and reconfigure.

[00:32:35] Ian Muarer: I know what quality looks like. You might not actually know what quality looks like. So that’s actually a great place to get a mentor. Like a mentor can actually tell you. That’s good. That’s not good. And a bad mentor will tell you that’s good every time. And then fix it for you when you’re not looking and then purpose.

[00:32:50] Ian Muarer: So I was building e commerce sites. That was fun. And honestly, it didn’t fulfill me. I’ve had people in my life pass away from cancer, very meaningful people. And so working on something every day that I think hopefully make the world a little bit better place is definitely something that I aspire to as well.

[00:33:07] Ron Laneve: Appreciate that, ian. Really appreciate your time and thanks for sharing. So hopefully we’ll see you soon.

 

 

March 8, 2024