AI Will STEAL Your Job by 2028?

Hi Jerry. Nice to meet you.

Hi. Hi. Nice meet you.

it's a pleasure to finally get to meet with you. I know you're the expert in the HR tech space and you're building cutting edge tech that's going to change the whole Human resource management industry.

Can you tell us a little bit more about what you do and why you are doing it?

Okay,

my name is Jerry. I just graduated from Purdue, and maybe just a few months ago, with my PhD. now I'm doing a startup. we are going to like building, agents for the future hiring, which means that we are going to find a end-to-end optimized solution for both. Applicants and, companies,

in our plan that, maybe in the future you don't have to find jobs by yourselves. All agents can do it for you. And the companies do not have to find the candidates, themselves. They can just tell what they want, to the agents and agents will.

Do the rest part of the hiring. And, the only thing that applicants and the companies [00:01:00] need to do is to join the interview and the rest things. So that's what we are doing.

which means that if I wanna find a job, you're saying that I don't need to hit a submit application. I don't have to go to any company's website. And I can just, give your platform everything about me, like resume or portfolio, GitHub or whatever.

And then you can find a job for me and jobs will come to me. Cool.

that's our slogan. Like where dream jobs find you, it is not you to find the jobs. The dream job will come to you.

Because we can analyze all your submitted information and then we are determining which kinda jobs, maybe should for you. for now, you can apply by selves, but in the future, agents can do it for you.

Yeah.

That sounds like a some, use case that I can imagine that we can use AI agents to, to automate, Because we're doing a lot of those kind of things, manually and on the business side, it's the same. is this a consumer product or It's a B2B product?

actually it is both sides for the customers and, the, the B [00:02:00] side. Also, we provide both size products. yeah, for the companies. Maybe they want to save their cost. maybe reduce the cost in hiring part because, the HR they do not have to spend a lot of time in finding applicants and analyzing their resumes. the only need to do is to tell agents what they want to find, and then the agents will do the rest So the HR is basically you can save maybe 90% of the regular work for hr.

you're saying that the scheduling and organization and,maybe the more basic screening, and finding talent part, those things are more of the low hanging fruit can be automated. Right. Okay. that makes sense.

and do you have any co-founders and how long have you been, working for this company?

actually we launched just three months ago. I have three co-founders. Yeah.

what are each one of yours expertise?

Oh, the first one is for the business, the whole business,BP or,

bd. And another one, [00:03:00] me, I'll be responsible for, the tech part, developing the products and, find the tech map, And another one is for the, product design. Yeah.

Three of us.

Okay. Yeah, Sounds like a dream team of typical Silicon Valley favored startups, type right. you have a PhD in computer science, on the technology side, and you have domain expert in hiring space, and also a design, person,

Alright. Okay. I'm glad you're here.

I'm really honored to speak to someone who's in Human resource management industry. I have some questions, around the current state of the job market.

I know you are in the computer science space,uh, I'mI'm in data science space,I feel like these two roles are blending in, especially in the current state of the Gen AI era.

where do you see these engineering roles heading in the next one to two years? are companies shifting their focus from the traditional software engineers, to more AI specific roles? or they're merging these skill [00:04:00] sets?

I think, the engineering roles are experienced and like in. Interesting convergence phase over the last one or two years. what I'm observing is that, it isn't a complete replacement of traditional software engineer roles with the A roles, but it is a kind of like fusion of skill sets.

Yeah. That's what means merging the skill sets. many companies they are seeking hybrid talents. those individuals who can do engineering part can also, do the AI part. So those talents are very welcome in the job market. Now,

the traditional software engineering fundamentals remain, I crucial, pretty important because you have to know what you should do to develop apps and the software assistance, right? But we are seeing engineers, expected to understand and integrate AI components. This isn't a either or. [00:05:00] solution, but a skill extension for an engineer, you have to learn something, about ai, right? engineers need to know how to build systems that are both, reliable and, leverage, the AI capabilities.

Yeah. Yeah.

do you have a rough estimate of what percentage of those engineer roles are requiring some level of ai, capabilities.

I think for some entry level engineers, you only have to know how to use the, how to leverage AI APIs. that's the basic thing, right?

And for some, high level knowledge you have, maybe you have to know how to,implement a, AI model, for example, implement a large language model, on multiple, GPUs and provide stable, services to others. And, if you are, maybe you want to be expert in a large language model or AI area, maybe you have to know how to train such kind of model. You have to know the structure of the model, maybe for [00:06:00] example, the transformer structure or other structures. Yeah.

Mm,are these roles still distinguished in a way? oftentimes we see that roles defined as frontend engineers and backend engineers are they still called in those names? with AI enabled component in the job description.

Yeah, they still like those things. it still has. back end and the front end engineers. but maybe the difference is that from the previous,job description is that they maybe require some, AI knowledge. For example, if you are a front end, engineer, maybe they will require you to know some basic knowledge of how to use AI to write some code.

You, for example, use the cursor or use the claude to write some very long and very exactly the same code. you can use the AI to finish this kinda part job.

That's interesting. You mentioned about using co-pilot type of things to accelerate, right? is that being part of the interview process? you are saying it's in the job [00:07:00] description, so actually companies want engineers to be able to leverage these AI tools really.

Yeah, I've seen more and more, companies or teams welcome some kind of skills because especially for those small team, they want their employees to be more productive. So the co-pilot, like cursor, those things can definitely help the small team to grow up pretty fast. Yeah.

how do companies or your platform distinguish like good engineers from, the not so experienced engineers when you are allowing that right in the interview process?

no, that, that's not a allowed because this little bit of cheating, I know there's a lot of interview copilot will help you like to the same thing when you are interviewing. They will provide with the answers, but those things in our platform is. Definitely, forbidden because we do not want our, B side customers to be like, fooled or whatever. Yeah. but in the reality, in the job market, those [00:08:00] skills are important. You have to know how to use the copilot, but it's not welcome during, interview.

so it's not used in the interview process, they will put in the job description and, prefer people who are actually familiar with those tools. So But is this something that hiring manager will ask the, candidates?

do you use, cursor or those kind of co-pilots or things like that? How do you evaluate my skills in using these tools?

you mean during the interview to,

Right, right.

oh,that's simple because, for example, using cursor, there's some like pathway. some certain commands, you can use those things to evaluate whether they are familiar with for, that's very simple actually.

If you want to, you can give him a case, right? for example, help me develop a. Small apps, then tell what should he should do step by step. What kind of prompt he should do and even he can show a live, a demo on how to build such kinda apps with the [00:09:00] copilot. Yeah.

I see. Okay. on the same topic, I read an article recently, and I can share, and I'm wondering what you think it's talking about.

technical debt from, engineers striving for faster development using, those copilot tools. there were some debate around using it without putting a lot of, thinking in it because people want, try to chase the speed of delivering.

and it does help, right? But then, less. Attention is paid to the quality of the code, which drives, an accumulation of technical debt. do you think this is true? And, and are people doing something or.

actually, I've talked about this topic with some, co-founders or, CTOs in some startups. we agree that, some employees may. use the co-pilot to finish up pretty quick. they do not see they do not care about reliability. They just put a code and, push the, code to the GitHub. that's sometimes risky. [00:10:00] so we think that we should train our team to how to use, such k copilot in a proper way. we should let them know the speed is not the most important thing. The reliability is the most important thing. they can use such kind tools of course, because it is definitely very efficient, right? but they have to know, they have to check the code. They have to verify the code, and the leaders should be responsible for the code, code review. I think, it is not a big problem actually, for a small team to use, co-pilot. only if you can build a,workflow to do the code review, to train the team to how to use the co-pilot.

Yeah.

So it sounds like it's more of a management, like a culture thing. you still have to abide by those best practices of engineers from, many years of development. that part is not, change is not really changing the core principles.

It's not changing.

Yeah.

do you have a sense of which industries are driving these demand [00:11:00] for, AI literacy in engineer roles? Is there some industries that specifically want that skills vertical in.

actually, for now, I think the. The most popular for those engineers. Maybe will go to some startups like who may focus on AI coding because this. AI coding is definitely the, biggest success in the, Generative AI in the recent years. this area

there, lots of startups in developing such kinda of, coding tools. they need more talents in the AI area. sometimes they do not need software, like traditional roles. but they need AI engineers. That's very interesting because. I knew some team. they only have the AI experts.

they don't have like software, the back end or front end roles.

That's very [00:12:00] interesting.

So they start with let's hire people who are experienced AI engineers and who grew from the AI side of things, not from the traditional software. Is there any problem with that when you're.

Yeah, that's, yeah. There could be some problems. Yeah, because I think a team should not be like this. you should have some, traditional roles. they can help you build up a reliable system and with the AI expert, you can develop your AI applications. Those people should work together. I don't know, maybe there are talents, I dunno. they can handle everything.

Yeah.I'm seeing data science roles also merging towards ai. 'cause we train models, so we understand the training part more and less about maybe the, the software part, Yeah. We are all merging.

As far as I can see, data scientists is actually a part of, the life circle (cycle) of,AI engineer because as a AI engineer, you have to clean data, you have to analyze the data, and then you can decide which kinda model you use, And you can train your model.

[00:13:00] So it's not kind of merging, it's kinda like to include into the life circle (cycle). I think it is. More likely like that

to include the, responsibility be included in the AI side.

Do you think the skill sets are employers want are more end to end now? because you do have to deal with data and data can be quite complex. You have the data engineer and then the modeling piece. And then you build, integration with any of the AI APIs or the AI modeling, what whatever's required.

And then you have the machine learning ops side, right? AI ops side as well. And then you have the deployment and integrating to a software, the user interface as well. So do you think these roles are gonna merge into you just want. People who have end to end full stack. Yeah. Yeah. I think job market expecting more second of roles, who can do data analysis and, AI training and AI deployment. Yeah, those people are pretty welcome in the job [00:14:00] market now. And, the public salary, for those are also very promising.

tell me how much would you pay for end to end AI engineer, including from the data side to the application side?

if it is in Silicon Valley, I think I can pay, 30 KA month.

30 KA month. Okay.

KA month,

Salary. Yeah.

are those talents, like having this end to end experience more rare.

Uh, very rare. Very rare. But I think there'll be more such kind of people will join that team because it is like those, students realize that such kind of role is very welcomed. So more and more people will learn the basic stuff of data science and machine learning and the machine engineering. Yeah. And more.

which programs should those students come from? this is really important because, data science and statistics have their own programs at school. And then software engineer has [00:15:00] their own program. and I think a lot of universities are having AI related programs as well.

which blends in different things, right? which program do you think are more likely to have to educate these end-to-end full stack engineers

I think it still should come from traditional computer science because as a engineer, regardless, you are traditional ones or the AI ones, you have to know some basic things about how the computer is working, You have to know the basic fundamentals. Things of the computer architectures, and you have to know how the CPU GPU is running and how to, allocate their resources.

That's very important. And as for maybe some, very, SOTA (state-of-the-art) knowledge of, ai, you can read some from papers, you can join some conferences or you can learn it by your self. You can go to YouTube to see some like channels just like you,

more about it.

Yeah. having a strong foundation is really important.

Yeah, very

I just realized those things after I [00:16:00] graduated because as a team leader, you have to know everything about, the life circle, about developing such kind of tools, when you're deploying on the cloud, you have to know the basic things, right? If you just know how to coding code some like algorithms, it's not far away enough.

Right. if you only know training models that it's not, definitely not enough to build an, a product.

so now

we're seeing a lot of, layoffs in big tech now and startups as well. Google is no longer like the golden bowl of you have security for life,

and also big companies are freezing hiring as well.

those companies, don't guarantee that anymore. At least it's not a public perception anymore. new grads from computer science programs they're facing fierce competition.

They're saying it's a lot harder this year to find jobs.

Can you tell me what you think about the current state of the job market, and is it gonna change.

I think current job market is a little bit better than the previous, two years. first more companies are [00:17:00] recruiting more software engineer related jobs or CS related jobs. and

AI has definitely impacted the job market because more traditional industry like healthcare, they are recruiting software or CS related, experts to help them to join such AI revolution.

So I think the market is. becoming better now, but it's still cannot be compared with before the pandemic. Yeah. for those, new grad, I think,you, you can do some like open source projects to gain more experience because most of the new grads, they don't have an internship. but the companies, they all want to find some like experienced new grads. that's a paradox, right?

You cannot gimme the job. How can I get my experience?

So for those, new grads, if you want to be more competitive in this day, in this years in the CS related job market, do some [00:18:00] open source projects and, Even you can do a long-term projects or just you can start a very small company by yourself. You can, do it as well. You can do a startups and even though you failed, but you gain some experience. Yeah. That's my suggestions. before this project I'm doing now, I did three,startups during my PhD, but very small projects, but I learned a lot from it.

Yeah.

Oh, I wanna ask more about that. But before we encourage all our audience to jump off the cliff to build their own company.

I wanna ask like if you're a CS major graduate or you a software engineer already, and let's say if you don't have the AI skills. Then you can learn some of that on the side, do open source project or read something, watch some YouTube videos.

There are tons of resources out there.

And, if they wanna transition a little bit to move on to the next level or catch up to stay ahead, you're saying that. The industries that they could be looking into is traditional industry. because they wanna [00:19:00] transform, right?

So they may need a lot of those roles, versus the big tech, right?

yes. Yeah, the new grad can spend more time on new, startups or some traditional companies. if you just focus on the big tech companies, then it is very competitive. Lots of people will want to be joining those companies. So just choose another industry or in other companies that would be better.

Yeah.

when I graduated many years ago. everybody's like,you gotta go to go Google or the big tech, Facebook, and,or iBank, those kind of things are more desirable versus there are a lot of startups Compare with,

[00:19:38] Zhilin Wang: Yes.

Yeah. Now the startups are pretty, pretty active that because, they're recruiting more and more people. And I know in USA even there are some,the new grad or the current students, they want to join the startups. They even want to do volunteer job.

Hmm.

that's very interesting. And they want to do no payment [00:20:00] job in startups. my opinion is that, only if this startups like a full star company, full star, startups, you can learn a lot from it and you can do it.

what does that mean?

because if their startups are very famous or very welcome in the market by those capitals, then you can definitely join this team as a volunteer and you can gain some experience in that team. And, those guys maybe excellent. They can teach you something. You can learn something from it.

Those are still very, competitive as well.

[00:20:33] Angelina: what do you think is the difference between working for a startup versus working for, bigger companies?

in a startup you have to be responsible for more parts. It is not just only very small part. If you are in a tech big company, you only have to do what they want you to do. For example, just, to maintain the database. that small part of the whole life circle.

But in startups, maybe you have to be responsible for like deployment, [00:21:00] deploy the database and optimize the database, and you have to, help others to use the database correctly. that's. totally different than in the big tech companies you will learn more about how to develop a product, but you definitely will be more stressful.

if you were to give some advice to someone who's, a new graduate or people who wants to transition or move on to the next stage for their career, what's your suggestion

Do you have any suggestion on where they should begin?

Um, there's no best answer for this question actually, because different people has different interests. for those CS students currently maybe they just want to find a job. They do not care. kind of roles they are working on. I know some of the excellent PhDs, they graduated.

They just jump into the, maybe do the front end engineer, some of them doing the soft, the back end engineers. And most of them, definitely do the AI [00:22:00] part or the data science part.

My suggestion is in recent years we face such kind of terrible job market. find a job first, and you can find maybe some interesting positions in the coming two years, and then you can jump to that position. But you have to get a job first. Yeah.

that's when we are graduating, we always think that, okay, whatever is out there, accept us, we're just gonna do it

Yeah.

Yeah.

or sell our hope. So do you have any prediction for the next, let's say, three to five years,around the job market?

what do you think is gonna change

I think more positions, regardless, the software engineer positions, the other tech related positions will require more knowledge about ai.

E even if you are a product engineer or manager, you have to know, more knowledge about AI than today 's. so [00:23:00] more people will maybe learn how to code with ai, learn how to even how to train AI and how to deploy ai. Yeah.

so sounds like AI literacy is part of a growing demand. you probably have to learn it. So do something now. Right.

yes. But another prediction is that, the AI related jobs will like increasing, but the, traditional ones may decrease. Yeah.

Can you tell more? what kind of roles are gonna be more.

Um, some very fundamental or very basic, roles like front end engineers, very entry level, very entry level maybe. Decrease because AI can do this part in the future. Yeah. And every day actually we should ask ourselves Will my work, be replaced by AI coder, because every day we should ask ourselves actually.

That's a really good advice. so [00:24:00] we talked a lot about the tech roles. What about the non-tech roles do you think, product managers, other, domain, professionals, do you think their roles can be affected?

They should know. They should know because for example, if you're hr, you have to know some. have to know how to use ai, some advanced AI usage tools. you have to know these things, right? You have to know how to use AI to speed up your work. And as for those managers who are like financial managers or whatever, you have to also know about ai. first thing is that it can definitely help you to to speed up your work. And another thing is that as a manager, you have to have the AI sense. Because in your product or in your team, having AI sense is important. Yeah. In the future,

Yeah. what's your suggestion for non-technical people What about ai They should learn, How should they begin?

they can learn some basic things. they only have to know some concepts and they can watch some like channels like yours. [00:25:00] And,they can. They can like, go to YouTube to find some, very fundamental and very, easy to follow channels to learn some basic concepts. they do not have to, to know the very, very fundamental things about ai. They do not have to know how transformer is working. They just need to know how to use the tools. I think that's enough.

do you think they might have to learn a little bit about coding or, more about tool usage?

only

Yeah. They only have to know. Of course, if they want to learn coding, that's good. But, maybe in the future, everyone is the product manager. You can use the AI copilot to develop a small demo pretty soon. Yeah. You don't have to code out. It only have to, tell what you want. Yeah.

for the companies you're working with, do you know if there are any reallocation of hiring budgets towards different roles, like more AI towards roles or more traditional, roles? Is there any changing those [00:26:00] budget allocation?

Yeah, our customers shows that. Because, they want more AI related jobs. not only the AI engineers, maybe some AI scientists or data scientists. Yeah. But, the software or traditional ones, they, I can see number is decreased.

what's the percentage? Can you give a number?

In the past, two years I saw a stat that shows that, the AI related positions doubled compared to two years ago. Yeah.

the demand is very overwhelming in a sense because we now are required to learn, A whole lot of things in the full stack pipeline. And I'm gonna add one more thing,

domain specific. knowledge are the engineers gonna be required or, more desired if they have domain experience, let's say healthcare or finance.

Yeah, because, I have [00:27:00] such kinda feelings that, the companies are recruiting those people who can solve the problem. who can solve a problem is that those guys who know the business, for example, in healthcare, you not only know how to coding you also know what's happening in your business, right?

In healthcare industry. those people are expecting are welcome in the job market. Yeah.

the thing is there's still a lot of applicants for each role.

how do you think an engineer or a, candidate can really stand out?

There's so many steps I need to pass, right? I have to get my resume, be reviewed by hiring manager, let's say you, and something has to grab your attention, and I have to pass the interview stage as well. how do I present myself in a way that you think it is gonna, give an advantage.

so the instructions for someone to stand out in, like a job [00:28:00] application is to show your benefits. because all the companies. They are using like ATS to help them collect and, screening those candidates. in your resume, you can highlight some skills. They are desired in the job description. You can highlight those kind skills and you can,highlight your. highly related work experience, then those you are having more chance your resume will be having more chance to be selected.

And during the interview, you can talk more about the details, what you are doing, your previous, work, to help the company solve the problem and to reduce the cost or whatever. think, yeah, for all the job applicants in the recent years is very. very hard. And they're like, they are be selected. They're not like to select jobs. They are selected by the jobs. for everyone it is pretty, pretty hard. Yeah.

I wanna ask 'cause a lot of our audience are very technical. [00:29:00] so in terms of more detailed skill sets in ai,what is more in demand, for instance, knowledge about things like rag. a retrieval, augmented generation, search related or multimodality or, large language models or, let's say vision models,deployment.

Which one is more in in demand?

sure. now as far as I can see, those in the job market, one kind of skill, are very welcome and very important is, model inference. you should know how to speed up the model inferencing, right? but this role requires very fundamental understanding of, large models and, computer architecture, especially for the GPUs or CPUs. So this kind person in the job market is very welcome. They get a lot of payment.

oh, this is

a the job market secret. What about the second one?

Second one is, I think, [00:30:00] is how to use the database, the vector database. If you can use the vector database, then you can. like RAG, right? You can do some non RAG, retrieving or the search part because in a searching area you can also use the vector database and in the recommendation area, you can also use, the vector database. But currently, using those database is still in a very early stage. we have to know how to use a vector, use vectors. In a proper

Mm-hmm.

including how to use the vector database. Yeah,

Interesting.

the second thing. Yeah.

what are you seeing missing about candidates not understanding how to use a Vector database? What's missing? are you seeing any pattern.

Oh, I think is that they just follow what, the others told them to create a data, a vector database to create writings. but for some specific area, for example, in the [00:31:00] hiring area, the database, the vectors may fool you or may just, cannot, work as expected

Because in different areas, those embedding should have, specific domain knowledge. They have to be fine tuned so they can work. But most of people just use a general or a universal embedding model to do the embedding. So sometimes the similarity may fool you.

Right.

my suggestion is to fine tune your own, embedding model in your area so that you can get better results.

model fine tuning is a pretty technical area as well, the same as

inferencing, right? what about model evaluation, like observability and evaluation?

that's important because most, I have seen lots of people like just fine tune their models. they generated, maybe they use the advanced models to generate some data, right? they want to improve those, the model's performance in that small area, but they forget that maybe [00:32:00] even, fine tuning may disrupt the performance of the model in other areas. So we have to have a, like better evaluation metrics. you not only have to evaluate the performance in your small area, you have to see whether this model is not corrupted or, poisoned by your generated data.

Exactly. Exactly. that's a whole, very deep topic on, and maybe another time.

how important do you think soft skill is for these roles?

Communication is always the most important thing in a team. you have to communicate with your leader. You have to communicate with your coworkers because you only have to share your ideas, your AI ideas or your software, sense to the other. Then you can build a reliable system I know some people who can work by themselves. Just, I just want work alone. And, they just spend days, weeks or even months to build a module. But this module cannot work, cannot work with the other modules [00:33:00] that's terrible. So communication is important. Communication should be like in time communication.

You should only, if you have any concerns or. Questions, talk to your coworkers and the leaders and we can discuss it together to solve the problem. Yeah.

Oftentimes, I think a lot of the candidates or the students that I talk with don't realize how important communication skills are. you say that communication skills are, in comparison with the technical skills, how important the communication skills.

Yeah. Because in a team, we want those people who can communicate with us. we can find like some who are pretty, pretty professional in pretty good in, coding, but they're not going to talk with you. that's not good we want some people who can share the ideas, who can communicate with others who can, for example, you can explain your code to your coworkers.

that's important. You can not only leave some comments, that's not enough. You have to explain those things [00:34:00] to, the others. Yeah.

And then would you say communication, technical skill. they're at least equally important,

in this day and age, I think we need to stress a little bit more about how important that is. And people are focusing heads down on learning new things and building things, but they sometimes forget about right.

They will.

Yeah. if you are still in school, you cannot just focus on technology.

focus on developing yourself skills, but in your team you have to work as a team, right?

Absolutely.

Mm-hmm.

okay, what is one thing that you wish every AI engineer knew before applying for jobs?

they should read the big lessons, the very famous ones. The big lessons that now developing a stable or reliable AI systems requires still is the computing powers.

lots of AI engineers try to optimize such kinda system by use some, like they developed [00:35:00] algorithms to optimize the workflow, but those things cannot work.

those may not work in the future because if you want to make sure your system can work, stable or as expected, just use more computing powers or use more tokens. Yeah, that's my suggestion. Read the big lesson. It still works.

Oh yeah. for our topic around job market and the future of AI engineers and the blending of roles of different like traditional software and data scientists and also the AI engineers.

Did I miss anything? Is there any questions that I should ask you that I haven't?

I think that you have asked most of the questions. I think that, yeah.

Awesome. Thank you so much. it's really insightful, session with you.

Thanks for sharing all your wisdom working in the hiring industry and being in this boat with all of us. like looking to adapt to the AI era and finding jobs or finding career transitions [00:36:00] or progression for us.

What happened to your three projects when you were in PhD?

oh.

Bonus Question.

When I was in my third year, I started a company is also about hiring. In hiring industry. I developed a, resume, tailor, tailor, resume, tailor mission. like kind of tools to help you get a better resume. very early stage, of how to use the large language models to do such kind of things.

I have to believe I'm the first one. To do that. because just I'm a student, I didn't spend a lot of time on it. when I realized that, when I found, when I realized that is important, I found that in the job, market, a lot of similar tools. So I give up. the first thing.

Another thing is that I 'm did a project, to help non-residents, of the USA to help them to get green card. I developed tool to help them to automatically or to smartly to [00:37:00] prepare all the documents.

Yeah.

that sounds useful.

But I. Yeah, but it is not controllable. It is. no. People would like to try those things because these AI generated documents.

So give it up. the third one is we are going to build a, next generation communication apps. this apps is like you use AI to use different, agents in the social network to help you communicate with each others. you can talk, you can communicate with, your friend by yourself. And when you are like, asleep, your friend can communicate with your agents like these

So like your digital twin, so you're available to chat? Yeah.

Yeah.

Okay.

but yeah. But this project,the other guy is my co-founders. they not want to continue. So we give up.

I see. Yeah. That's one of the challenge, right? In founding a company. But It could be a very useful tool. [00:38:00] Yeah.

Yeah, probably. But yeah,

now I'm all in my current project because, we have a better team. Everyone's all in this project, so it is more promising. Yeah.

Yeah, good luck with this project. And, maybe for another time we can chat more about founder journey, if you're open to it,

I have lot of, have lot of experience in

horror stories and, uh, love any stories. We'd love the, we'd love to hear that.

Yeah. We talked to hundred investors, maybe 90, 90% of them we are like, fuck. They'll say fuck,

off.

Oh my goodness. Okay. Great. Yeah, we could use some of those lessons as well so that we don't make the same mistakes again.

Really nice. chatting today, Jerry. we welcome you to this podcast and I hope to talk with you again soon.

Okay, sure.

Sounds good. Thank you. See you next [00:39:00] time.