# The Intersection of AI and Genomic Analysis

2024-03-07 · Bell Falls Search Focus On Talent Podcast · https://www.imaurer.com/talks/focus-on-talent-ai-genomics/

> This transcript is unedited YouTube auto-captions with speaker labels added by hand; proper nouns corrected, otherwise expect missing punctuation and mis-heard words.

### Introduction

**Ron [00:00:04]:** 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 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 I think he was the first placement I made in the executive surch business so was a long time ago I've had the pleasure of working with him as a teammate

**[00:00:37]** 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 Masters at Syracuse University and software development's always been the backbone of his career 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

**[00:01:10]** decade he's been growing a platform focused on genomic analysis in the 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

**Ian [00:01:24]:** thanks Ron and you got my name you said it right and everything that's great thanks for having me on your podcast this this is fun

**Ron [00:01:31]:** 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 and 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 in intentional whether they were serendipitous some of them could have been mistakes and you learned from them

**[00:02:04]** so can you walk us through that

### The nonlinear career path

**Ian [00:02:06]:** yeah sure so started off in computer programming with a Commodore 64 I got it because I thought I wanted to play video games but I got bored with that pretty fast and the cool thing about Commodore 64 is that 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 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 and in the lab there was like a posting for a job and basically 10 or 15 hours a week at locked Martin so

**[00:02:39]** I went in to did the 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 subsid of GE and during that time I've learned how to parse HTML and do cross linking between these documents and a lot of the similar stuff that I'm actually doing today with documentation and and in the genomic space while I was there

**[00:03:13]** I didn't a rotation with the radar department or sonar department and I was almost done with that and the guy hey you should stay here in the sonar Department don't go back to that other department because that's nobody think 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 90s 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 the work on the

**[00:03:46]** 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 and definitely work in your favor and then 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 eny I worked there for about

**[00:04:18]** nine months I didn't really I just didn't like the culture it was like a big company culture I got a couple 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 I know how I got these clients because I they basically popped up at eny 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 long term 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

**[00:04:51]** to be good and I said all right let's give it a try that was eeric 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 it 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 we're closing work making stuff happen grew it to I think about 60 people by 2006 we merged with Brulant at the time Len Pagan who started that company I think we were at 250 maybe 300 people at the time and then through a lot of successful e-commerce projects and

**[00:05:23]** some other things that the company did we grew that to a thousand or so folks with the acquisition and Mer with Rosetta and then finally were acquired by Publicis 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 Greg and and a few other websites and that was a great learning experience got to meet Emanuel Glenny and Jeff Shiner who were two of my colleagues and and learned a lot from them and a few other folks that work at GenomOncology

**[00:05:55]** now it was a great environment for learning 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 are going to take over and run with it from there

### Follow the hard problems

**Ron [00:06:14]:** seems like you intentionally made the decision to follow the hard problem over and over again

**Ian [00:06:20]:** yeah I think that's where I felt like I could make a differentiated impact so the main thing you're try 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 and 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 at in your career like just look for hard problems look

**[00:06:53]** for things that aren't getting done and just do them don't necessarily even have to be asked to do it yeah make your bosses life easier and people will recognize you and you'll differentiate yourself from 95% other people real real quickly

### Founding GenomOncology

**Ron [00:07:06]:** 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 and frame up GenomOncology and and especially talk about how it's evolved over that time

**Ian [00:07:27]:** sure yeah so when we started we actually didn't necessarily know what we were going to build right we knew we could do bioinformatics tools right because uh our founder had been a bioinformation before that was even a word uh so we basically process and analyze information he had read a couple papers that came out in I think 2011 that 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

**[00:07:59]** 6 weeks six months those are meaningful timelines uh in the life of a cancer patient uh and then the other anecdote that was going around was the the 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 so we could see that the first genome cost $3 billion do to to literally sequence and now it's going to cost $200 like by the end of this year and we knew that that was happening so we and so by building something in that domain we thought

**[00:08:32]** we could be ready and the joke was ,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 a Mac Mini at the time to analyze a whole genome and it was it impressed people uh and it got us in the door a few places and it got us in the door places that were doing genomic sequencing for cancer patients to actually make a report so that's

**[00:09:05]** 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 a seemless 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 we were like okay we got the bionformatics part we know how to make a PDF what do we put on the PDF and so then you start pulling back all the the problems

**[00:09:38]** and that's it's basically what we've been doing since then I I got a list of 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 because that's actually the gold the problem that 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

### Biomarkers, drugs, and clinical trials

**Ian [00:10:05]:** 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 80s or 90s they probably got a day of genomics in their teaching so they have to keep up to date on what genomics it are I don't think that's as much of a problem now now the problem is okay you got the genomic test you got these biomarkers what 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

**[00:10:39]** an A to a t which changes the protein in that specific Gene from a v to an E that causes melanoma so that's a very specific thing that just happens to that one patient it's sematic meaning it's happening in your body it's not in your germ line it's not in your hereditary genome so that's just one example and that's for one disease cancer is thousand or 5,000 diseases 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

**[00:11:12]** 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 in 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 that's approved by the FDA but maybe there's a drug that a drug company's 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're we've read

**[00:11:45]** and curated in our knowledge base 13,000 clinical trials at the biomarker level and the biomarker level isn't just oh you have B 600D therefore you get this you're on this trial it's like you've got this biomarker and this biomarker Mar but not this biomarker and not this biomarker and you're this disease type not this this disease type or you could be in these five disease types but not in these two disease types and you couldn't have had this prior therapy so it's like these complicated rules it's basically what what we call an expert system which was something I worked out at Lo Martin in 1997 it's these

**[00:12:17]** 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 we know about the patient all that information is way too hard for a person to 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 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

**[00:12:49]** 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 it's a Precision oncology knowledge base that has genomics and it can scale with genomics

**[00:13:22]** which is hard once again always looking for the hard problems because the hard problems are where there there's value and then after that I can then match a patient to a variety of of drugs or trials and 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 our system can do that too go find me all the patients that

**[00:13:55]** 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 could 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 we found a thousand patients the third and a third have gotten those abnc drugs

**[00:14:27]** and here's a what's called Meer curve or 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 hunting in the dark here because you know it's all so new

**Ron [00:14:44]:** I just wanted to point out a couple things you're so far past these Concepts after doing this for 10 years now for for the basic person like me Y and when I worked at genome ecology there's two things that still stick in my head first first no two cancers are alike every Cancer is different 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 and the specific drugs designed to potentially provide a therapy for individuals because we have surgery we have chemotherapy and we have radiation which

**[00:15:18]** are essentially poisons

**Ian [00:15:18]:** correct yep

**Ron [00:15:20]:** to try to eliminate or eradicate cancer but now the space that that GenomOncology is playing in along 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 and so

**Ian [00:15:36]:** 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 effectively it's at its simplest form but it's obviously way more advanced than that

### Advice for students

**Ron [00:15:58]:** that was obviously very eye openening so can 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 may maybe or maybe not approach it from your lens of a CTO and a person with an extensive software development background or stay higher level what's up to you

**Ian [00:16:29]:** software developer is it's a different business than a lot of things because there's the opportunities to 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 meaning you have to like actually take on a project that's not 10 hours long or 20 hours long because nothing useful or meaningful comes out of a 20 hour project it has to be something bigger it has to be something more ambitious so figure out what that means for you what is

**[00:17:02]** that project my daughter is learning music so she spends a lot of time practicing her instrument 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 maybe the rockman's not going to do anything but that's okay because you can learn those skills and and then apply that to 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 who'll 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 and then

**[00:17:35]** read some books so the books that I like are I like the cal newort book that I mentioned there's another one he wrote called Deep work we live in a very distracted World Tik Tok 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 the ability to actually focus on work and drive to creating differentiated outcomes s 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

**[00:18:08]** or creating software or what have you 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 the person at that center of the vend diagram so figure out what that vend diagram is for you and then be that person and really try to avoid shallow work right emails and meetings and one-off things shallow work is necessary and

**[00:18:43]** you got to do it to be a good cooworker but if you can figure out a way to design your schedule to to not be that in my world that that's valuable but you got to also recognize that there's a big economy there's a web article from Paul Graham from probably 20 years ago at this Point called maker versus manager schedule so that is 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 I'm an individual contributor I'm

**[00:19:15]** 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 that's my job now so I'm very happy about that but the manager maker schedule thing is managers are trying to basically every minute of the day for every single person on their team and the maker 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 schedule me for half an hour at the beginning or the end and let me get my stuff done I I could keep going on with the Deep work thing

**[00:19:47]** but to me it's really developing those rearn valuable skills and then it's a long-term games a long-term game is your network known you for 25 years known Brad for 20 years develop V 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 right and I know there's 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

**[00:20:20]** what you're going to do and do what you say and you'll definitely be better off for it

### AI, expert systems, and automation

**Ron [00:20:25]:** so you brought up AI so let's talk about that I 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 of software development experts where again back to the student lens or back to the college lens how would

**[00:20:58]** 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 is there an answer to that right now

**Ian [00:21:10]:** 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 and then there's another side of things 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

**[00:21:43]** the data at computers and we're going to get magical AI too I actually think the answer is both right you're going to scale up this thing on the side this currently generative AI with more and more gpus your V stocks going to the Moon all that stuff and then you're going to have symbolic a I which is going to be expert systems rules codified knowledge and then those two things working together system one and system to thinking is how I refer to it based on a book from Daniel Conan and the basic gist is that's how you get to the the world where things are bet are trustworthy

**[00:22:15]** 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 and it does things in a way that was more morally acceptable so if we can build trustworthy AI That's what I want to do so that's like the the background if I thinking about it now where are we going with this stuff I do believe that there's an little adage going around the AI is not going to replace me it's the person using AI is going to replace me that's that's certainly a thing to consider IC aai is just the next evolution of automation so the cotton gin

**[00:22:48]** I couldn't think of an earlier example was was 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 aiot un quote 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 so the first is with generative

**[00:23:20]** AI you can describe what you have and what you want and it will fill in the middle for you which is really powerful concept rather than figuring out how to do something like how okay what buttons do I have to press in this application to sort these things and dedup these things you don't have to figure that out anymore you can just say here's some stuff I got sort and dup 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 meaning if

**[00:23:54]** 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's a solution on the internet on stack Overflow or one of these other websites Cora it's going to give you the answer and be like here's the answer that's a known problem with a known solution the cool thing about geni 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

**[00:24:28]** 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 think there's going to be new types of jobs will software engineering be a job in 50 years I think it'll be a a thing but it probably won't look it will not look anything like what it does today Y and the thing that humans can do is have problems and solve new problems and AI has no no problems an AI can't solve something

**[00:25:00]** 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 honestly just more of the search space it's just more of playing go there was move 37 which is 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 a unique solution and it did it so those are like my Lev thoughts of it and one of the really interesting things to it that I've really embraced is

**[00:25:33]** 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 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 stop like i' I I've done that I've done that recently which just basically like I have this idea tell me how I would build it and then I work through it it and then like even just trying to push through and say oh no try it this way try it that way I realized no this is untenable doesn't work and I can stop the idea so I've just saved myself you know

**[00:26:06]** 30 hours of pain and then the other thing I 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 gonna do it I'll do that now I have this 4 Hour investment now I have this tool and then that tool is going to save me 40 hours or 100 hours going forward and that was a worthwhile trade so that's those are the types of things that are really exciting to me as far as just as a partner or 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

**[00:26:39]** assistant is is really what I'm trying to do and that's what I'm actually building right now for genology 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 actually make decisions a computer can't make a decision because a computer can't be accountable so you have have the human you have the knowledge base which actually is ground truth because I've had experts curate it and now the chat bot can actually just make using my knowledge Bas easier which is really the thing I want to do what do I have I have a patient what do I want I want a solution

**[00:27:11]** 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 chat bot help that person understand the context of their situation and then query on knowledge based 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

### Advice for experienced talent

**Ron [00:27:42]:** 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 being recruited what have you seen that's stood out in your mind over time where individual ual have differentiated themselves from one another in that process anything that you could share

**Ian [00:28:02]:** I'll tell you my biggest pet peu first my biggest pet 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 like just fundamentally doesn't make sense for you to know all these things so tighten that up second thing is now you've 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

**[00:28:34]** stuff because 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 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

**[00:29:07]** 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 long-term thinking too that's the other you know other key thing is um 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 uh LinkedIn profile and it's 9 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 there's you know there's a there's another side of it too where corporations aren't necessarily holding

**[00:29:40]** up their side of it but 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

### Book recommendations

**Ron [00:29:46]:** you mentioned a bunch of books and I know you love to read when 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 or to learn AI I bet you that's changed several months later I'm still plowing my way through those on Audible what are the top five books you would tell people to read today it doesn't have to be about AI

**Ian [00:30:07]:** yeah let me check off the AI books real fast so the AI books were genius makers by Kate Mets 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 prediction machines that's what it was so prediction machines was the first one and then the second one was a followup to that the basic just 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 so if you were to go back in time

**[00:30:40]** 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 it Ford but it was really the assembly line where they redesigned and re architected around electricity new systems thinking that was how this step worked and actually made a meaningful impact

**[00:31:12]** 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 or prediction markets wherever something's you're trying to make a prediction great plug it in there's really nothing there or generate a par 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 our AI in Loop and how do we design these things and then also recognize that we're going to design I'm designing my stuff recognizing that gp4 and the smaller open source models are

**[00:31:45]** 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 so I'm designing with that in mind those are the AI books and then as far as like the career books it's so Cal Newport has a new book coming out I haven't read that yet because 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 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 and have a nice life a nice family but his real thing was to be really happy there's

**[00:32:19]** 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 kind of My Own Boss I can kind of 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 I know what quality looks like and 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

**[00:32:52]** not looking and then purpose so I was building e-commerce sites that was fun honestly it didn't fulfill me I've had people in my life pass away from cancer uh very meaningful people and so working on something every day that I think it hopefully make the world a little bit better place is definitely something that I aspire to as well

**Ron [00:33:11]:** appreciate thatan really appreciate your time and thanks for sharing so hopefully we'll see you