CodePath EES2024 Opening Keynote DONA SARKAR - Beyond the Code: Distinguishing yourself in an AI era

Wow. Guess who’s back here at the Emerging Engineering Summit for Code. It’s 2024 and yeah, it’s me, Bobby D, your host.

Thank y’all so much for pulling up and let me just give you a quick introduction myself. If you haven’t had a chance to meet me yet, my name is Bobby Doris, but my friends call me Bobby D, and I’m actually one of the coaches here in the career center. Big shout outs to the career center.

It’s so nice what they’re putting us together or putting together here for us here at the Emerging Engineering Summit 2024. And I’m looking at the notes right now and it says that over 5,000 attendees are here today, not just around the country, but around the world. This is one of those amazing events and I really do appreciate you all tuning in.

I have a list of a few things that I’m going to cover more or less what’s going to happen for the next three days. But over the next three days, we will have an exclusive opportunity to hear from industry leaders, connect directly with other companies or our sponsors that are sponsoring this event and also building a community here at Copath. You know what it’s about, building a community, being a part of cohorts and definitely big shout outs for you being here.

If you were able to join us last year, you know that this event is unlike any other and filled with what we like to call moments of connections to rewrite the future. Right? I hope that you all can get to know each other and definitely make new connections.

We’d like to extend special thank you to our EES sponsor. Let me read them off to you. Our ES sponsors are B 10, Bloomberg, Microsoft, New Relic, Pluralsight, Reboot Representation, Salesforce, QuadCom, and Y Cominator.

Now, it’s not just them that are attending this event because we’re excited to be joined by some of the most impressive lineups of companies at this year’s event. Be sure to explore the full list. There are some of the most outstanding companies in our industry today, and they’re here today.

Right. You can visit now these companies. Let me tell you how you can visit them.

By going to the company booths and the booth area right here on this platform where you can connect directly with them, the companies themselves, swing by and drop off and say what’s up, but also don’t forget to leave your resume. The best time for you to be able to access them is during the network breakouts. And if you have any opportunities to visit the lounging section, this is where your attendees and your peers will be hanging out.

And I’ll definitely be there, too. So, I can’t wait to see you in that section. Thank you so much.

Now, I got a little bit more information for you. We encourage you to keep your eyes on the main feed here on the main stage. This is where we will post events updates and share what’s happening in different sessions.

We also want to ask for some feedback. Now, why is feedback so important? Because guess what?

Your boy Bobby D get to come another year because all the great feedback that I received and let me tell you, your feedback really does influence how we actually deliver this event. But we do want to encourage you to share your thoughts because there might be some swag we give away. I know you seen it when we walked in.

All right. All right. That’s all that’s all I have for reminders.

But let’s get into the main event here. We’re excited to bring to you our opening keynote session. It’s titled Beyond the Cloud: Distinguishing Yourself in the Era of AI.

You know AI is coming, so I definitely want you all to tune in. Now, this keynote is by Donna Carr from Microsoft. But first, please join me in welcoming CodePass co-founder and CEO Michael Ellison and for some welcoming words for us here at EES 2024.

What’s going on, Michael? Welcome to EEES 2024. I’m Michael Ellison, CEO and co-founder of Copath.

I’m excited to kick off our second annual Emerging Engineer Summit, which we all know as EEES. This year, we’re bringing together more than 6,000 students and alumni with 50 employers to convene for three days of learning, inspiration, and networking. This is the biggest gathering of the Copath community to date, and events like this couldn’t be more important.

Copath’s mission to reprogram higher education to create the most diverse generation of software engineers, CTO’s, and founders. And to be sure, we’re off to a great start with that mission. Since 2017, Copath has been a leader in helping students bridge the gap between college CS and industry technical excellence.

Many of you students first fell in love with software development in a Copath iOS, Android, or web development course. Others of you participated in one or more of our technical interview prep courses to prepare you to pass technical interviews. Some of you even had your first technical work experience through Copathr run internship programs at companies like Salesforce and Meta.

Now with the acceleration of generative AI technology, our industry is in a state of rapid change. For those who are hungry and prepared, it is the biggest opportunity of our lifetimes. We launched the first EES event last year to help as many students as possible to be ready to meet this moment.

Let me be clear. You are here to invest in your potential. Use this opportunity to hear from industry leaders directly.

Network with over 50 companies in attendance and take advantage of the many career focused workshops offered. I’m incredibly proud to share that last year’s event even won a people’s choice award for the best virtual event of the year by EventX. Recruiters from top tech companies told us this was their favorite event of the year.

Students told us that after EES, they know they have a place in the tech industry. What this means for us and for our partners is that we need to keep bringing this community together. Are tens of thousands of students and alumni, the most inspiring tech leaders and the companies who want to hire the best CS talent, all converging here at this conference.

To our students and alumni, EES is a launching point for your career. By joining us, you are investing in yourself. You’re developing your knowledge and skills, and you are connecting with your community.

This year we have some incredible keynote speakers including Donna Sarcar, chief troublemaker of AI at Microsoft and VC Rabisar, CEO of HackerRank. We also have 30 sessions planned for you to learn and network from at EEES. There are multiple opportunities connect directly with hiring partners, build your network and learn directly from industry leaders.

You have already worked hard in your classes through Copath. You further honed your technical skills and industry readiness. And at this event, we will support you further in developing your industry connections.

When me and my co-founders founded CodePath, we had a vision to transform the default pathways in technical education for all students and most importantly for low-income, black, Latinx, and indigenous students. Back then, we committed to three core principles that are still critical today. First to keep our promises to our students.

Second, that our programs needed to be pathways to excellence and influence. And third, scaling our programs must also change the education system. Now, 10 years later, we’ve grown 50 to 100% year-over-year.

We’re now the nation’s largest educator of college CS students, on track to serve almost 4% of all US computing students this year. We’re fundamentally changing the CS student experience at over 100 colleges and universities. At many of those schools, we the first exposure students have to real world project-based software development.

And what I’m most proud of are the outcomes. 80% Of you are low-income or black, Latinx, indigenous students. And if you’ve taken even just one Copath class, your chances of securing an internship or job have increased by at least 80%.

So far in 2024, Google Google has already hired almost 600 Copatha students. Microsoft and Amazon have each hired over 400. Our plan to change the face of tech by transforming college computer science is working.

Now, as AI native software engineering accelerates innovation across every industry and sector, Copath will remain at the forefront of tech education, we will be there to support you, ensuring you become the tech leaders of tomorrow. So, be present this week. Enjoy every moment at the Emerging Engineer Summit.

This is an opportunity to take risks, be curious, and be proactive in shaping your careers. And who knows, you might meet your future colleagues, boss, or even your future startup co-founder. But before I let you go, I want to thank all of our event sponsors and the participating companies joining us as exhibitors.

You recognize the critical role of emerging technology and innovation in your industries. You looking for the best emerging engineer talent and you found it. Also, a special shout out to our incredible Copath career center team for putting together another great event for the early talent community this week.

To everyone here attending EES 2024, thank you for being part of the Copath community. Please enjoy all the summit has to offer. Now, I will turn it back over to our host, Bobby, to introduce our opening keynote joining us from Microsoft.

Bobby, back to you. Welcome, welcome, welcome. Can you believe it?

Another year here at EEES, Emerging Engineer Summit, and I’m your host, Bobby D. I hope you caught my introduction earlier, but I got something special for you because we’re about to kick off our keynote speech. Now, this is going to be three days event, but I definitely want you to tune in because I want to introduce you to Donna Sakar.

Now, let me tell you a little bit about Donna. Donna is the chief troublemaker at MA Microsoft’s AI and co co-pilot extendability program. I apologize.

She is going to be talking about today beyond the code distinguishing yourself in the AI era. But let me tell you a little bit about her previous work. She has led the Windows Insider program at Microsoft.

She’s a keynote speaker, author, and entrepreneur. Donna defies conventions with ventures like Primadana Studios and Wine Bars. While pursuing similar certification, which means she knows good wine versus bad wines.

Recognized by Fast Company in Cosmopolitan, she thrives on unconventional paths. Eager to connect with audiences worldwide, Donna embodies the belief that embracing diversity and constantly evolving leads to empowerment and success. Please help me welcome Donna to the stage.

What’s going on, Donna? Conversation around Donna’s topic around the era of AI is going to be something that’s really important for you all and definitely get a chance for you to be able to make an impact in your career because I think she’s going to be giving us some really cool tools, especially some skill sets and definitely some insights into How Microsoft is using AI. Where is Donna?

So, I guess we’re working to some technical difficulties. I hope you all are really enjoying yourselves here. It’s going to be a three days of great events, including opportunities for you to meet other companies like myself, but also companies that will have networking booths and opportunities for you all to connect.

Now, while we’re waiting for Donna to get back online, some of the things that I want to reflect on 2023’s EES versus what I hope to see you all do in 2024, which is connect with your community. This is a great platform for you to be able to connect with peers that are very interested in the same concepts that you are and also companies that are looking for engineers just like you. Now, I think that we are just about ready for Donna.

So, let’s see if we can bring Donna on stage. Donna, are you there? Yes, I’m right here.

Sorry, I got trapped to the breakout room. That was very funny. All right.

Hey everyone. Thank you so much for joining us. It’s been such a joy connecting with you on LinkedIn, through the socials, etc. And today, I would love to share with you some of the real truths about what’s going on in the tech industry and what does it mean for people like you who might be career switching into tech or might be just starting your career journey altogether.

I’m going to share with you a set of slides and we’re going to have a very very honest conversation about what it really looks like to have a very very solid career in tech. And a lot of these principles do apply to the AI verse, but most of these are just fundamental career truths. All right, so let’s talk about just like what we all want.

Raise your hand if you’d like to build a great career in tech. Right? It’s me, it’s you.

I think it’s all of us. Because we know that tech is one of the most lucrative and interesting industries that exists today. And the thing about tech is it’s been around for a very long time, not just the past, you know, 10, 20 years.

Anytime we’ve seen a big shift in capabilities, whether we went from weaving fabric by hand or like looms that do it, that was all technological transformation. And whenever we’ve had a big technological transformation, there’s always been a lot of disturbance of the force. Okay?

And there’s always a sense of unease and a little bit of uncomfortableness along with a lot of excitement. I’ve been talking about how people can thrive and succeed in tech for over a decade. I’ve written a book on it called You Had Me at Hello World.

This is 10 years old. A lot of the principles apply, but I know I need to do a refresh for the AI verse. So, one of the things that, you know, we all know is that the tech industry has dramatically changed over the last two years and things have gotten like kind of real real and it feels it can feel a little bit overwhelming sometimes because we’re trying to first keep up with tech, second trying to get a job, and three just trying to maintain our lives.

And we all see that and we feel it, right? We feel it every single day. But I’ve got a five-step plan for you.

And if you follow the plan, and if you really do it, like really and properly do it, you will set yourself apart from everyone and everything that came before you and emerge as someone who’s going to thrive in the tech industry. Okay, ready? Let’s go.

Step one, and you know this coming, is learn generative AI. All right, is learn generative AI. Now, I don’t need all of you to become machine learning experts overnight, but it’s really important that you actually have the fundamentals of generative AI down.

Now, I assume this is most of you at this point, but if it’s not, there’s a ton of free courses out there. There’s many, many, many resources that you can take and learn to figure out which area of the stack you actually want to get into with AI. And I’m not going to go into copious detail, but I’ll tell you a little bit about what I do.

I work at Microsoft heading up an AI AI extensibility program. That means I help customers all over the world figure out how they can use AI on their own data so it’s maximally useful, right? And I wake up every single day since November 29th, 2022 when IP came out monitoring AI news and this is pretty much my day every day.

I’ve been in computer science since 1998. I graduated from the University of Michigan Go and I have always studied things that are a little bit lower in the stack. So operating systems, mobile development, I worked on mixed reality, the hollow lens device, I worked on a bunch of platform pieces for low code.

And now I’m working in the AI verse. And what has been very interesting to me is to look back and say, you know, AI has actually been around a long time. It’s not something that we invented two years ago.

In fact, AI has been around since the 1950s. And that concept of artificial intelligence, humans and machines working together to solve problems has been around since Alan Turing’s day and there was actually a test to say can you tell the output is by a machine rather than a human and that is called a touring test. So this was invented in 1950.

So these things have been around a long time and IBM had that you know that AI model of Watson that beat a chess player and back in 20 years ago right so these concept have been around a very long time the thing that has caused this chaos is this concept called generative AI okay and the reason generative AI has caused this chaos is because we in the tech industry have gone out there and made nonsense statements in the past about what AI I can and cannot do. Okay. Every time there’s a new, you know, a shift in the tech first, a lot of leaders, air quotes, will go out there and make big statements that we we all know are not true, right?

So, in 2015, Elon Musk said, "We’re going to have nothing but self-driving cars in two years." 2016, we had the heads of a lot of companies saying, "Oh, most of the medical industry is going to disappear. Like radiologists, people who do X-rays, they’re going to be obsolete in the next 5 years. And here we are, 2024, we’ve got radiologists driving regular cars to work to do their jobs.

So, no, these jobs that everyone said would go away. These, you know, giant shifts in technology that people said would happen overnight does not happen. It does not happen overnight.

It happens gradually and then it happens a lot quicker year after year. But there’s plenty of time for all of us to get engaged and get involved. Not just from a consumer point of view, but from a creator point.

Okay. So, companies are f finding value from AI. A lot of people are saying, "Oh, okay.

It’s just nonsense and hype." But Andy Jesse, who’s the CEO of Amazon, recently they did a big study. And this is a boring use of AI, but it’s actually really interesting because what they did was they have legacy codebase, right? Amazon.com was 20some years old at this point and it was built on a Java framework that I believe was Java it was Java 1 2 3 they’ve been upgrading it and as you know anyone who’s written code anytime you have to migrate code from one code one framework to another is annoying right it’s kind of a pain so Amazon of course has to do the same thing they migrated from Java 6 framework to Java framework Java 7 framework usually this would take years but they used a coding tool based on their own AI product called Chi or Q Q they used a coding tool and it saved them 4500 developer years.

So just think about that like if one dev had been working on this it would have taken 4500 years and they were able to do that just this year just migrating code from this framework to that framework. Walmart is another example. They don’t have good highfidelity images of all of their all of the things in their warehouses.

So, they were able to use AI to generate images that you could view in 3D. And there’s something that would have taken us 100 times the effort because you’ve had to go to the warehouse, photograph everything, upload them, etc. So, companies are finding the exponential value already. And this is where we need to get involved.

Okay. So, let’s talk a little about what AI even is, right? The thing that we are all talking about when we say AI these days is called generative AI.

And generative AI is powered by a thing called a GPT. Right? It’s a strange set of words.

If you ask my family, they have absolutely no idea what it means. And they think it is one of the strangest names for a product that have ever come out. ChatGPT, right?

So what is generative pre-train transform? Generative meaning this tool, it generates new things. It doesn’t just retrieve or pull or search.

It generates new things. Pre-trained, meaning it’s trained offline. So these models are trained offline six months ago and then we use them now.

Anytime they get real-time data, they’re actually just connected to a search engine like Google or Bing. And then transformer. Transformer is a thing that was invented by a bunch of researchers in 2018 and all it does is it looks at maybe a sentence and identifies which is the most important word there.

Which one should we drive attention to? So transformer is all about being able to somewhat intelligently say in context what is the most important word in the sentence. So let’s let’s talk about how an AI model works.

How a generative AI model works. Imagine there’s a sentence. Okay.

The sentence is we go to work by train. Basic six sixword sentence. Each word gets what’s called tokenized.

Okay. You’ve heard about the word token a lot. What is a token?

Token is just a set of two or three characters. Let’s say. Okay.

So, because these are short words, we go to work by train, we are able to get six tokens out of this. If you’ve got a long word like octopus, it’s going to be three tokens or four tokens. So, each sentence gets tokenized into these little chunks of words.

Now, the language model goes to work and says, where have I seen this word before? So, let’s look at the word work. It will go to the data set it was trained on over here and identify all of the places the word work shows up.

And it’ll say like, okay, the word work tends to be next to words like before, rewarding, creative, hard work, office, downtime, because those are all the things that the word work tends to show up with and articles and posts, etc. Then there’s a set of words that the word work is not near, right? And that’s like poker and octopus and undersea adventures, etc., etc. So there’s now two sets of words and each set each word gets a value. Okay?

And each one of these values is called a word embedding. So what’s going to happen now is you’re going to have this thing called a vector. It’s kind of like an array, right?

With a direction on it. And this array will have a bunch of words in it. And it’s going to look a little like this.

It doesn’t actually look like this, but for for a description site. So, let’s look at the word C. It’s going to have a bunch of vectors.

Anything that’s dark blue is going to be more near and more accurate to the word C. Anything that’s a red, it’s going to be further away. Now, next to the word C, you’re going to have like ocean and blue and beautiful, etc., etc. Not near the word C will be like outer space.

So, you’ll see that words like C and ocean have very similar vectors. Words like football and so soccer have very similar vectors. Words like C and football have very different vectors.

This is how language models know what words are like them in the data set because the vectors are similar because other words like them are similar. So it’s like a giant ven diagram activity, right? So when you break it down, you realize this is not magic.

This AI thing is not reading your mind. It’s not reasoning about the future. It’s really taking a bunch of data, trillions of data points, right, from Wikipedia, from unpublishedbooks.com, from all these places, and using it to reason about what you’re probably trying to do next.

You can think of it like a big fancy autocomplete that is about as accurate, right? It’s accurate sometimes, not accurate other times. Okay, so that’s kind of how AI works.

I want to tell you about two new jobs that are cropping up. Okay. So, one is called the power user.

The AI power user. And the AI power user is someone who probably comes from a traditional background. So, something like finance or legal or medicine or operations, some sort of a non- tech job, air quotes.

And they’re saying, "Hey, let’s figure out how we can actually like use it to do our job better." So say a new finance person or a new lawyer will be saying you know I want to know what AI can do for me. So they’re the people who research and try new prompts. They tr think of new ways to use AI.

They think before starting a new task like writing a new case or creating a new you know a finance report. They think how can I use AI to do this? So they are the ones who are shaping what AI looks like for their industry.

They are the cross. They found their niche on being I’m a finance person who uses AI to figure out what the future of finance looks like. So this is called an AI power user and it’s a brand new job that’s emerging.

The second job is relevant to many of you and this is called an AI engineer. Okay, we did not used to have a job called an AI engineer a year ago. Before they used to be machine learning experts, so people who build and train models and then there’s full stack engineers like me who write code for a living, right?

We know like UX and architecture and DevOps and all of these things. Now, at the intersection of machine learning expert and full stack engineer, there’s a thing called an AI engineer. And the AI engineer is someone who has to be able to build products, right?

Look at like UX APIs, API calls, etc., etc., like old school software development, but they also have to be a little bit upto-date on machine learning. They don’t need to go train models, but they need to understand how to do prompt engineering, how to do fine-tuning, what does it mean to have fault tolerance and evaluation and all these machine learning terms. So, they have to be a little bit machine learning and a little bit of software developer.

Now, this is the hottest job in tech right now. Every company is hiring AI engineers, Microsoft included, Google included, all of them are. So if you fall into one of these categories, you’re an ML person or a full stack engineer, I recommend learning the other one enough because this is the kind of thing that’s going to lead you down a very very good career.

I am becoming this engineer as well because I’m coming from full stack engineer. I’m learning machine learning so I can be good at all of these things here. Okay, because AI is going to be around a while.

So we need to all keep our skills fresh. So how do you keep up? Right?

I’ve said like keeping up is hard. How do you do that? I actually follow a lot of people on LinkedIn and Twitter.

I know, but three people I follow who you might want to follow. Don’t try to connect because they’ve got way too many connections. Just follow them because they publish really good content.

One is Professor Ethan Malik at Wharton who does a lot of tests on new products. Second one is Jeremiah Wang in the valley who puts on a ton of events and has a lot of stuff. And the third one is Rowan Chang who runs a newsletter called the rundown.

And I find all these to be very valuable and I consume their content pretty much every single day so I know what’s going on. So that’s the first part. No AI be involved.

Second part, hands-on tech. You can’t just read about stuff, okay? You can’t just read about stuff.

You need to actually do hands-on because you have to show it. So let’s start with the absolute basics. And this is me writing Python code at the airport just two days ago, by the way.

So that’s me wrangling with Python because C is my language and I have to learn Python to be kind of good at AI engineering. So the second part is you have to start thinking about your niche, right? Where do you want to get into?

When I first started learning machine learning, I was working in accessibility tools. So this is tools to help people with disabilities use tech better. Now what I did was I said, "All right, let me infuse AI into accessibility." So how can AI be a good assistive tech tool for screen reader users, people who are dyslexic, people who are paralyzed, etc. Now what happened was I am not the best person in the world at AI.

No, I’m not the best person in the world at accessibility, but I was one of four people in the world who are looking at both AI and accessibility last year. Now there’s a lot more, but I was the only one, one of four people at Microsoft itself. Very, very few number of people were doing it.

So what happens with a niche? When you make a niche is it makes you unique. It stands out from the crowd, right?

It makes you credible because it gives you something very practical to dig into and become an expert in. Third one, it really does help you connect with others at a deeper level because you’re not just talking about surface cases. You’re saying, let’s look at this problem that’s been around a long time and how can we use AI for it?

This is like our friends in finance who are becoming like finance AI people. And the fourth one is it helps you build your brand because when people think oh how can we you know do stuff with finance or legal or accessibility with AI there’s very few people to think of they’ll think of you so how do you choose your niche right this is always the top question and I hold a lot of workshops doing hands-on and I have come up with three models that tend to work pretty well first one is the end this is what I tend to do so it’s combine two interests or two passions okay so this was AI and accessibility And I also do this in my personal life. I travel a lot for work and I love starting businesses and I also love wine.

I’m training to be a somalier. So I built an AI bot that gives me wine recommendations from all over the world as I opened this bar that I opened last year. The second technique is called the focus.

Okay. And the focus is saying okay I know a lot about this big topic. How can I go deeper?

How can I go deeper? So this was I worked in accessibility. My focus is neurodiversity because I’m neurody divergent and my passion is ADHD and dyslexia.

How can I use AI to do this work better? So that’s focus. The third one is called the bridge where you take your knowledge about one world and you apply it to this other world.

You don’t try to combine them but you try to apply them. So this is me with fashion and tech where I I love tech. I use it all the time.

I also love fashion. Everything I learn about fashion design, 3D modeling, you know, size and fit, etc., I apply to tech. Everything I use in tech, I apply to fashion.

So, there’s really all these three ways to do it. And it’s up to you. Whichever of those methods you choose, it’s an ongoing experiment.

There’s no one answer tried and true. Okay? It might be a combination.

So, the next thing I’ll tell you is get used to AI coding tools. And I don’t care what you use. You can use Replet, you can use, you know, any of those.

I use GitHub Copilot a lot. I use it every single day. I again had to learn from being a C C++ dev to being a JavaScript dev to being a Python dev.

GitHub Copilot is helping me. It took me probably four years to learn C++. It took me one year to learn JavaScript and it’s taken me one month to learn Python because of GitHub Copilot.

Having those numbers and being able to show how you’ve done it really does set you apart. So GitHub Copilot workspace is like the agent version. Now, I’m not going to make you sit through this demo, but I just want to show you that, you know, I go to my repo.

I said, "Hey, I’m going to build a website from for my wine bar. Excuse me, I’m sniffling because it’s cold in here." so I’m just creating a repo and I’m just going to give it a description like create a wine bar for, you know, a place in Seattle, Washington. Here’s a list.

And it’ll go to work actually doing this work. So, here’s something really important. You want to do real projects, not just code samples.

I’ve seen a lot of students just had like a 100 code samples on their GitHub, but they don’t have a real project that they build onto an object. And if you don’t know what project to do, tap local startups, nonprofits, schools, universities to see what they need help with and really lean into helping them. When I was in university, I actually went to the law library.

They were categorizing their books. I know like actually categorizing them and putting making like a virtual or not virtual like a database of all of their their law library books and I helped them do that and it built me a lot of credibility on how law works, how libraries work and it gave me a really good resume builder. So this is what I would be doing right now if I was still in school or going to graduate.

I’d be working with the, you know, local schools to figure out what project I can do for them. And here’s me being angry at Python because I’m building a wine bar website and building websites not my skill. Much more of, you know, a mobile apps sort of person.

So, if I had to start over right now, what I would do is build an AI travel agent because I love travel. And there’s a thing called Autogen Studio. It’s free.

You can go try it. There’s a GitHub repo for it. Autogen Studio.

It’s how you build AI agents. And I used it to actually build this travel agent. And that I’m using to like plan my trips all over the world.

So, how it works is I would just say plan a trip and the agent would come back and ask me questions and get clarifying things, etc. Once I built a travel agent, what I’d be doing is going and contributing to open source projects in my niche. Like, here’s one called Travelmate. There’s a ton of people contributing.

Here’s a good way to stand out because the maintainers probably work at a company. They’re going to say, "This person’s really interesting. They’re contributing a lot of really useful things.

They obviously know a lot about the topic and they’re really here to help contribute not just documentation but actual code itself. Right? So that’s what I would be doing if I had to start over is choose a niche, do my own project and then go out and contribute to open source projects.

Or if you don’t want to do your own projects, just start contributing heavily to an open source project. I really recommend you publish real code. Okay?

So that could be to your GitHub repo. Make sure you have a website with your name on it, right? I have donasarker.com.

I haven’t published code in a while, but that’s what I would be doing. App stores, websites, and put those on your resume because those show hiring managers that you actually know how to build things. You don’t just know how to study and learn, but you know how to apply it to doing something.

So once you’ve done actual tech work and that second part, doing actual tech work cannot be overlooked, then you want to do kind of the online social thing. Okay, so the online social thing is first of all have a LinkedIn. I know many of you have one.

That’s good. All of you definitely need one if you’re applying for jobs. Okay, make sure you have a clear headsh shot of what you really look like, not your anime person.

Make sure you have some sort of a header image. Make sure you say what you do, what you’ve done. And when you connect with people, add a note.

Always add a note. Don’t just randomly connect because they have no idea if you’re a spam or a real person. Say, "Hey, this thing on your profile looks interesting or it was a pleasure hearing from you, etc." And keep in mind that not everybody is going to be able to connect with you because LinkedIn just limit the number of connections they can have.

Like I’m pretty over connections at this point. But if you think someone is inspiring, go ahead and follow them because you’ll still see their content. So absolutely put your startup side hustle project on your LinkedIn as work experience.

Okay? Okay, so in the work experience section, call outside project this contributed this open source project, but actually you have to do the work before you can talk about it. Okay, so my friend Moyo is really interesting.

He’s doing he’s not an AI person, but he’s teaching himself. So he’s built this Calvin AI product and he’s been documenting his his adventure. He’s been saying like, "Oh, here’s feedback I got.

Here’s how I addressed it. Here’s what I’m working on. Try this new thing, server infrastructure.

If you have feedback, get get in touch. This this guy’s wonderful. This guy’s really wonderful.

He’s doing the thing, engaging with customers, showing how he would work at a company. So, you want to talk about your project at least once a week. You can set set up your own blog, which I recommend, your own YouTube, LinkedIn, Twitter, Tik Tok, whatever you want to do it.

Don’t try to do all of them. It will be too much, but you should have at least one property you own, like your own blog or your own YouTube, and then social media. A lot of people find value in joining Discord servers.

Okay, don’t spend all day on them, but it’s a really good way to engage with other people and talk about your project and see what people are interested in. The number of people who formed teams and found people to collaborate with on these Discord servers are really interesting. So, look up coding discord, you find them by your letter.

So one of the things that here’s a model that has worked for me every single time whenever I want to go and kind of shift my brand I do a 100 days of on LinkedIn. Okay. So when I was learning about AI, I did I think 30 days of AI skilling and I went to learning about it and said okay what is AI foundation models?

Here’s some practical things for accessibility neurody divergent people rewrite create and just showing examples. I’d shoot short videos and show an example of me doing it again. This was day seven of me AI skilling.

So it was two years ago. But I went through this. I did it for hollow lens.

I did it for Power Platform. I’ve done it over and over again. But 100 days of something, it shows you as someone who does things and teaches others in real time.

So the thing is when you’re engaging with social, you really want to see what questions people have and you answer them right in your comments and things because you don’t want to just be seen as someone who, you know, is out there projecting. You want to be seen as someone who is actually engaging with people, taking their feedback and answering questions. So I want to tell you the story of Lewis.

So Lewis is my young friend. I met him when he was in high school. He actually reached out to me and I’m just going to take a picture of the screen because he’ll like to see this.

He reached out to me in high school and said, "Hey, I built this app for my high school to help people you know get over their social anxiety a little bit better." And I said, "Oh, that’s really interesting." He used a Microsoft platform to do it. So we talked and he said, "What’s your advice?" And I said, "Make sure you learn coding because low code is fine, but coding will actually give you a lot more options." So he took it really seriously. He set up this website called lewisd.dev and he’s just been following his following the his coding journey.

He started with low code tools then he learned C# and now he’s doing you know Python and all these things. So he uses GitHub copilot to learn a lot about net C etc. And he saw that there’s no Meetup group focused on AI where he lives. So he got together with a few other people and started it.

So it was in Bletchley Park in the UK. And because he does so much work, actually coding, talking about it on his blog, engaging with people in person, he was able to secure a job within a year or two of graduating high school without formal training. And he just recently shifted jobs to work at Amazon Avanon as a solution architect to go work at giant companies on how to implement AI.

So he is self-taught in the last three years just did this in the AI verse. So Lewis is an example of someone I recommend you go follow him on LinkedIn and Twitter. He publishes great content and he’s the poster child of someone who he did not go to fancy school.

He did not go to Stanford. He completely self-taught from high school. Okay.

So here’s something like every post you write comment on other people. Engage them, right? Because people want to be seen and a lot of people are out there, oh well, I pushed my content and then nothing happened.

I’m like, right? Because you need to go make friends with others. You need to go put insightful information on theirs and say like, oh, this is really interesting.

What did you think was the hardest part of this, that, and the next thing, right? Okay. Fourth part, IRL social.

Okay, this gets a little a little funny, but it really does matter. Okay, if you can attend local events and meetups, this was me in Mexico City just a few days ago. And if you get a chance, volunteer to be a greeter or buddy or moderator, speaker, whatever.

Don’t just attend. Be involved in the organization because that helps you stand out as a leader. And again, if there’s none near your area, go out there and organize them.

Like get out there and organize a user group in your area. That’s what I would be doing. I would be organizing, you know, the Seattle East Side AI group, inviting companies to speak, right?

Come and speak, share what you’re doing, and build relationships with the people who work there. Be seen as someone who wants to help out your local community, but who also has all of that backup, right? Because you can’t just be doing events if you don’t know about technology, if you don’t have a online presence.

Make it so people can easily find you and see what you’ve done. And of course, participating in hackathons is a great way to show your tech skills and build relationships. I have hired so many people from hackathons.

I cannot even tell you the last hackathon we did at Microsoft, there’s four people I hired from that hack. It is a thing because you get to really show tech skills. And again, if there’s no hackathons in your area, it’s a great time to find one and follow up with people you meet.

And the fifth part, applying for jobs. Everyone always goes here before doing the first four steps. Okay?

And this is the issue. So, first put your startup side hustle on your resume as you need to write a different resume for every job you’re applying to. I know it sounds strange, but you do get the keywords from the resume from the job prep posting.

Put it in your resume because that’s how recruiters do screening and searching, right? They use tools to say which of these résumés match the keywords. So, put the keywords right there at the top.

Use it to describe your work and have a different version for each job you’re applying to. Now, I’m a fan of cover letters because if you’re reaching out to a a recruiter, hiring manager, they’re going to get thousands and thousands of resumes. But if you’ve got like a really good cover letter explaining like I Donna who built a travel agent who’s contributing to open source project, I would really love to be a software developer Delta Airlines working on their mobile app that I would stand out, right?

Because I’ve already done work in the space and I’ve thought about it. Be specific with your past experiences. Don’t just, you know, carpet bomb throw out your resume to a million sites and hope that it it’s picked up because that’s not the way to stand up.

So, as much as possible, if you can get a warm introduction, meaning you know someone who works there or you’ve engaged with someone, it’s easier. Okay? Warm introductions are the key right now.

And it’s so much easier if you’ve done some sort of a work with them or for them or seen them more than once before going and trying to apply for a job with them. So, I’ve got homework for you. Obviously, first keep in mind we are very early in the AI verse.

None of you know what a Nokia flip phone is, but in 2002, long before these phones, we used to have a thing called a Nokia flip phone. It only did three things. And everyone thinks, "Oh, we’re on already on the iPhone era of AI." No, we’re not.

We’re at the Nokia flip phone era. IPhones and Android phones didn’t become great until all of you got involved and built apps for them. And now it is our the thing we watch movies on, read books on.

It’s our airline flight pass. It’s the thing that we communicate on. It didn’t used to be these things.

The phone era used to be car phones tethered to your car. Nokia flip phones. Then it became the thing we already know.

We need every single one of you involved to take AI into the next generation. So, it is so important that you don’t get discouraged. I know the job market is gh it’s hard.

It’s definitely hard, but the techniques I’ve been through two hard job markets before in 2002 2008. Those were also hard and we had to do pretty much the same thing, but it was harder because we didn’t used to have YouTube and Twitter and LinkedIn and all of these things back in the day. I would love for you to remember that given that people are talking about digital twins and all this human curated, created and delivered information will be a premium price tag.

So the more you can show up in person with people, not digital twin online, the more you’re going to set yourself apart. So my homework for you today, and I’d love for you to spend some time thinking about it, is to figure out your niche for now. And keep in mind, I’ve switched niches five times in my career in 20 years.

That means every any niche you pick today is something that you can pivot from and change from. But I wish you all the love and luck in the world because you’ve come this far. Getting a job in the AI verse is not going to be the hardest thing you’ve done.

You’ve done much harder things. Thank you so much for joining us. Awesome.

Can you hear me, Donna? I can hear you. Great.

Awesome. Awesome. Well, thank you so much for that great great keynote and you’ve definitely enlightened me on a lot of things that I’ve been more or less contemplating in my mind in terms of opportunities that are going to be open up with this era of AI.

And definitely how the cohorts and students here can really level up especially with some old school moves like you know creating a cover letter or reaching out in different mediums. So definitely very much appreciated. One question that I have and I’ve actually was reading your bio and the thing that caught my eye was your title.

So, if you could elaborate a little bit more, what is it to be a chief troublemaker at Microsoft? So, chief troublemaker is actually a madeup title because generally the first rule of troublemaking is you go make trouble. But it’s it’s good trouble, right?

Because what I try to do is tell people the real truth about what they need to know to do well in AI. Because there’s so much hype, so much nonsense, there’s so much like, oh, you have to go back to school for four years and become a machine learning expert. No, you don’t.

You have to go take a free machine learning course on deep learning.ai and learn enough about it to figure out how to apply it because I’m all about the practical. We live in a world where we absolutely do not have time to do things that waste time, right? So, a huge part of my job is working with people like all of you to figure out how you can have fabulous careers and great jobs that last the test of time.

Awesome. Awesome. And one question that just actually came in.

And definitely if you do have some questions, please put them in. We’ll be, you know, choosing the ones that are being upvoted, but one that really came into play is more or less trying to understand how AI will change the landscape for those new grad students. I think the biggest way is that this focus on humanto human connection is going to become more important than ever because a lot of the things that we took for granted before like oh only humans can write this thing from scratch or only humans can do this other thing over here that is no longer going to be the case.

So now having a strong excuse me personal brand and a strong personal reputation will be more important than it ever has been. So to all of the students out there, the more you stand out in your own uniqueness, the more you’re going to stand out in the AI verse. So everything that a human does very specifically, have empathy, is creative, all of these sorts of things are going to change the job landscape.

We’re going to start seeing jobs be a lot more about managing AI and less about doing the grunt work itself. But we still have to tell AI what to do, right? Like if I’m going to write a new website, AI is not going to write that for me.

I need to specify like, hey, this website should have these colors, these images, etc., etc., etc. Here’s the business problem I’m trying to solve. Here’s the kind of group I’m trying to attract. I still have to spec it.

So, all of us are going to be a little bit more engineering manager while we’re managing AI tools. That’s actually kind of really and it’s enlightening to me in terms of just the focus where we’re going to be shifting from. I’m also a developer.

I’m a Pythoner. But I also know systems. So my skill set start off as a systems engineer and when I hear you talk about DevOps and those type of things, I’m like, hey, those pieces are soon to be connected.

So definitely thank you so much for calling this out. And a follow-up question that we have and this is probably something I I actually really emphasize when I have conversations with our cohorts here is about selecting a project. Yeah.

So, and if you could reiterate some of the concepts that you talked about during your slide, but more specifically like what is a project that could really help, you know, new grads stand out in the interview process. I think the big thing is choose projects that aren’t simple, right? Everyone can go and build a calculator and that’s great as a code sample.

Like you can say, hey, I built a calculator as a code simple, but don’t say I’m building a calculator as my project. A project shouldn’t be something that is easily copyable from a GitHub repo. It should be something that sure you get the base code and then you build it up from there using your own brains and your own experience.

So for example I wouldn’t so let’s think about a vertical called like fashion I I described travel already I would do something like this. So say you know for my fashion line Primidono Studios I would like to build some sort of an AI tool. What I would do is go get like a closet organizer open source project.

There’s a ton of them out there. So, I would get that. I would then download it.

I would upload my own images and then I would ask it the kinds of questions that my customers ask me. Hey, I’ve got a wedding coming up. What should I choose?

Hey, I’ve got my first day of work coming up. What should I choose? What goes with this thing?

What goes with that thing? So, I would really tailor it toward my specific scenarios based on code that already exists. So, I wouldn’t ship that code that already exists and just ship another closet organizer.

I would build primadana AI based on this closet organizer, but then I would also give it like, you know, a little bit of automation capabilities. I would add to it and make it more robust and more rich. So, I’m showing off my tech skills on top of code that already exists.

Yeah, that that sounds amazing because I think the thing that you’re really highlighting here is number one, the value of using tools like Git and GitHub to more or less be collective codebase, but also finding an opportunity to contribute to an open source project. Yes, but I think the light that you gave our audience is sometime I think can cohorts think that contributing to an open source project means that I have to get it back into the main branch and what you’re saying is like hey fork your own project working on this and then contribute what you want to which I think is amazing in terms of project definitely thank you so much for that open source is fascinating to me right because it is you can contribute to the main project if you want right for that I would choose probably a smaller maybe something you do with friends and figure out like make sure you know the maintainer right of the project because the first one contributing to big project can be a little intimidating. I would find it intimidating.

So what I would do is choose a smaller project maybe with like five to 10 people involved and I would start by fixing basic things like hey I’m going to write a set of test cases. Anyone can do that. Write a set of test cases.

If you don’t know what test case is, write an accessibility test case. Go look up in coding tool what would be a good accessibility test case for this code. You can actually just do that in the browser.

You can use GitHub for it. First, make sure your GitHub account. It’s free.

Go get one. Two, GitHub also has a thing called AI models where you can try out AI models as part of GitHub itself. So go to GitHub.com/models and you can try out a bunch of them.

And three, you can use them via an a API call into any project you’re doing, right? So you can contribute to like a small or medium-siz project or fork or clone the repo of a big project and use it to build your own because that’s what it’s for. And then, you know, give credit, give kudos back, but then you don’t have to worry about, oh, I need to merge this back in and it’s going to be intimidating because I have to go through a pull request, etc., etc. But at some point, I think just getting your name out there as someone who does work in the open source space is really helpful.

If if I had to do one thing over again, I would have done a lot more work with open source 10 years ago than I have. Oh, that that that’s amazing, Donna, because there’s a story that I tell a lot of my mentees about working at Twitter. So, I worked at Twitter for almost 10 years there becoming a staff engineer.

Yeah. And one of the things that we used to do is we used to hire based off of contributions to our open source project. And nobody knew that that’s where we’re looking first.

Yeah. My mind is always like, especially the way that these streets are nowadays is if there’s an opportunity for you to contribute on a project that another company like Microsoft, Twitter, Facebook is contributing to, why not find a way to contribute? And this actually leads to my next question is is like I know that you mentioned that it’s a little bit intimidating but overall how do you feel like new grads and and those that are getting into the industry should you know be moving towards making those type of contributions.

So one of the things is that I believe in the best way to get out of your comfort zone is to expand your comfort zone. Right? So instead of say your comfort zone is here you’re like I should get out of it and go over there but that’s scary.

I don’t like that. So, I’d rather think of it like an addition to building a house, right? Instead of burning down your house and building a new one, you add a room.

So, you’re like, "All right, I’m good at doing open source projects for travel stuff. Let me go find one like a smaller, you know, a travel bot or something like that. Let me go and add a functionality to it.

Let me add a city. Let me add an XML file. Let me add test cases.

Let me improve the documentation." do something really small that no one’s going to really like ask you a lot of questions about before saying, "Oh, let me add a whole new, you know, agentic capability to it." Start with documentation, test cases, you know, adding like one variable or one case statement onto like a ZAML file or something like that because that way you really understand it’s just not how the code works, it’s how the system works, right? So, how often does the maintainer look at pull requests? How picky are they?

What were the last few contributions that were made? I’m a huge believer in studying the last few contributions. And this is my hack from back in the day is see the last bug that was fixed, figure out what was the bug, why did they fix it this way, and see if I can reproduce that.

So, the second you’re able to fix the last bug, then you’re like, "Okay, I understand how things work here. So now I can go and fix the next bug, right? Reproduce.

Before you can go write a symphony, learn to play the piano. How do you learn to play the piano? Playing notes, playing arpeggios, playing scales, right?

So play the scales before you go compose the symphony. Oh my gosh, that that’s a bar. I hope you all are listening and wrote that down because that right there.

Thank you so much for that, Donna. Now, another thing that I want to draw us back into AI is that there are so many cool things that are happening. I heard you talk about vector databases.

I heard you talk about machine learning. I heard you talk about prompt engineering. One of the questions that come came in is like which of these things out there is probably like one of the most exciting things and avenues for new grads to really get involved in.

So, I’ll tell you three industries that are ripe for disruption because they are they’re ready. They’re ready for it. Ready?

So the first one is education. You’re seeing it now. Education is getting disrupted.

No one knows what to do with it. School admins don’t know what to do. Teachers don’t know what to do.

Students don’t know what to do. 90% Of students are using AI tools. Teachers are saying no, it’s not allowed.

We’re we’re in a moment. So, education is getting disrupted. And you folks at CodePath know that better than anybody.

But I if any of you are looking to work in the education space, it’s a very good time. Especially if you bring new ideas, right? How can what is the purpose of education?

How can we make that purpose still happen but using all these new tools and the new mindset that we’re growing? So, education huge huge huge opportunity. Second thing is the finance industry and the finance industry is ripe for disruption because they actually have their data in order and a big issue with AI is if the data is not good the AI tools are not going to be good but finance has had to because they actually have to first it’s structured data right lots of tables and spreadsheets etc etc and they’ve had to go and present things to boards and governments and all of these things so there’s a lot of opportunity in the finance sector so if that’s a space you’re interested in or have been.

It’s a very very good space to get to get involved in and start disrupting. And then the third one is legal. Okay.

And legal industry is about to go through a change. We will always need lawyers. It’s not like lawyers going to disappear.

But how do lawyers do their job better? How can people be better prepared to go speak to lawyers? How can people better prepared to go to law school?

So these are three industries where AI is right for disruption. There’s three industries that AI has been overdone and I wouldn’t go build a startup in these three spaces. Okay.

The first one is customer chatbots. Everybody and their mother has built a cast customer chatbot and I wouldn’t necessarily go and build yet another one because there’s a lot of competition. The data is not super structured.

It’s in a bunch of like websites and documentation and I I think it will be frustrating and it’ll be hard to stand out. The second one is actually there just two. There’s customer chatbots and then there’s marketing campaigns.

Okay? Everyone’s like, "Oh, marketing. We’re going to disrupt marketing with AI." Like, I recommend we unless you’re in it, unless you’re in marketing, I wouldn’t go get into this industry because we don’t actually know what the future of marketing is going to look like because we will soon be marketing to AI agents, not people.

And if we don’t have a very good idea or a background of SEO and all of these things, learning that a whole new paradigm while trying to, you know, learn AI, it’s a bit intimidating. So I would personally stick to the more traditional industries like you know healthcare, legal, finance, operations, education, things have been around for hundreds of years rather than some of the new industries because new industries are going to get disrupted and what I want all of you to do is to be players in that once this first round of disruption is done so you don’t get frustrated. Roger that.

Roger that. And and definitely some insights around fintech and finance. You are right.

They have to maintain good data, good consistency, and definitely a great opportunity to feed that into AI and build tools around it. So, I really do appreciate that feedback. One more question that came in, and this one kind of like resonates with in your slide, you had one of your mentees that really just leveled up in front of your eyes is, what is some like cool interaction or, experience, that you’ve recently run into from either a new grad, an intern, or someone new to your circle?

So I I tell the story of Lewis a lot because I watched him go from high schooler to self-taught developer in three or four years, right? So I’ve but there’s been a lot of examples like this. My friend AJ is another one.

He grew up in, you know, less than gray economic means and he actually started his career by choosing a tech product. He he chose Microsoft Power Platform. It doesn’t matter.

You can choose whatever product you want. And he went to the forums and he started answering questions. He’s so familiar with the product that he could answer any question on the forum.

He actually gained a reputation as being able to answer any question on the forum within a day. And he really stood out to the team. The company’s like, "Who is this guy?

He’s so good. He knows all of this. He knows everything about this product.

He seems to be like a huge fan of the product. We should get to know him." So now he’s invited to conferences. He’s invited to speak.

He works in the space. He is known as an expert that he DIYed himself. Again, he didn’t go to college for this.

He just got hands-on in the product and got to know it very very well and used it to build things. It wasn’t just like get to know the product, use it to do something. So, he used the power platform which was a low code tool set.

I’m not trying to sell you on the power platform. Don’t use it unless you want to. But he used it to really build a lot of things.

He’s like, I’m going to build an app. It’s going to connect to this data source. Going to publish it here.

I’m going to get these users. He did the thing by building stuff and answering all these questions about it on a forum. So I found that very interesting, but he’s built a very strong reputation for himself early early 20s.

Roger that. And and thank you so much for that example. And and like I tell my mentees all the time and cohorts here at CodePath, it just depends on if you get started.

Like get started, making moves, push forward, work through the challenge, chase the knowledge, and I guarantee you these ideas of you know, bags and wealth and opportunities will just open up for you. So definitely very appreciative of these examples. So one thing that came in as another question is and and this is just kind of reflecting on my journey as an engineer is that I started off as a system engineer and then eventually developed my second skill being a software engineer because I wanted to automate myself out of a job which is one of my mantras.

Yes. The question I have is someone that’s getting started in the industry how do they choose which path to go on? Should they be like a full stack engineer and just go across the spectrum from front end all the way to backend or should they be narrowing into one specific skill set especially with the the AI forthcoming?

So personally I would go the the niche path. The reason is when you try to do full stack engineering which I always found to be very confusing because you’re suddenly having to learn five different programming languages which you don’t want to do right now. So, what I would do is I would say, you know, I’m going to go all in on Python.

If I had to start over today, like I had nothing. I am 18 years old, self-taught. I would say, I’m going to go all in on Python.

I’m going to use Python to build something, right? An AI travel agent, an AI book publishing agent, an AI, something that I care a lot about, a thing that I have either done work in in the past or a topic that I’m insanely curious about. I wouldn’t just build 10 code samples.

I would actually go and build an AI agent or an AI co-pilot or an AI product from scratch that solves very specific problems that I’ve seen in the world. And I would build I would do it over a course of 30 days. So every day I would add new functionality to it.

I’d say you know what my travel agent needs localization into local language. You know what my AI travel agent needs to be sharable with my family and friends so we can see the same shared itinerary. You know what this needs?

The ability to be able to connect to an API and book trips or book stuff directly from viator.com. You know what? This needs the ability to do this.

And I’d build like a fullyfledged AI app or a website or both or book. And I would use that as some I would use it. I would actually use it, make people use it because I would build a reputation as someone who does things, not someone who talks about things, but someone who does the thing.

So that’s what I would do. I would go all in. Really lean into Python.

I would go learn other languages as needed. Like I think to build an Android app, you were going to have to learn Cotlin or Java, that’s okay, but I’ll learn it, right? As needed.

But if I was just going to build a straightup website, stick with Python. If I was going to build like an iOS app, stick with, you know, Objective C or Swift. So, all depends on what you’re trying to do.

But try to stick to one language and one niche and one project and try it for 30 days and see what happens. Roger that. Roger that.

And definitely thank you so much for that insight. We are getting close to the end but I got one more question up and this is going to be for those that are attending that may be considering other paths in the career especially in tech especially around product management. Yeah.

So how do you feel like AI plays in that role of product management and if it changes at all? So the role of product manager the best product managers I’ve ever seen are not people who manage a project. They’re people who really set the clear problem we’re trying to solve.

Right? We say here’s the problem we’re trying to solve. Here’s who we’re solving it for and here’s the steps we’re going to take together.

That is product management 101, right? And the thing about AI is it’s really good tool for product managers. So you say here’s the problem we’re trying to solve.

Think of all of the people who’ve tried to solve it in the past. What have got what has gone wrong and where are the opportunities? Use AI to build that thing.

Who are top 10 customers we can serve that might be unusual or different? Where would I find them? What forums?

What groups? Which, you know, areas of the world, etc. And third, please generate me a plan, a 10-week plan with five people to actually do this. So product managers should be leaning into AI to do each part of their job because it’s they should treat it as a thought partner, right?

They shouldn’t try to use it as a replacement for themselves because they’re going to be able to say, "Actually, no, we’re not going to go after that audience because it doesn’t make sense for my company." But product managers should definitely think of AI as being thought partner, brainstormer. Keep in mind, generative AI generates. So use it to generate new ideas so that you could craft a plan and a product management strategy that makes sense for your people.

But keep in mind, the biggest superpower you have, product manager people, is your human to human contact. So it’s all about telling the developers you’re working with and the data scientists, the machine learning people that I see you. I know your skill set is this.

This is the thing we want to build. Do you want to help build this part? Because that humanto human part that will be more important than ever before.

We’re all getting bombarded by AI generated content. We’ve already tuned it out. When you see AI generated images, you’re like, not interesting.

When you see a human image, interesting. And you right now hopefully can tell Bobby and I are not deep fakes. We were because deep fakes would arrive on the call correctly on time, right?

But the more you we have humanto human contact, the more you’re going to appreciate it because in the AI verse, all the generic will be done by AI. All the specific will be done by you. Roger that.

Roger that. And let’s give Donna a big round of applause. We really do appreciate your feedback, insights into AI, and always always dropping gems.

I hope y’all wrote this down. If y’all didn’t, we we’re going to be posting this stuff on YouTube, so you’ll be able to rewind back. And Donna, before we leave, I wanted to give you an opportunity to give our audience a last final thoughts.

And again, thank you so much for your time today. Folks, thank you so much for having me. This is one of my favorite organizations on the planet.

I’ve attended so many of your things, and it is a joy to be able to keynote this emerging engineers event. I would love for you to remember that it feels daunting right now, but you’re going to have a very long engineering career. This is just the first act.

This is not this year does not dictate how your career is going to go. What you need to do now is build a niche. Get your first job.

Get your foot in the door. Don’t worry about three year, 5 year, 10 year, 20- year plan. It does not matter because the first the next 3 months, 6 months, 12 months are going to dictate what you do for the next 3 months, 12 months, 6 months.

If you’d asked me 20 years ago, could I ever have imagined a career like this? The answer is absolutely not. My first job was being a database developer.

And from there I have built my career every 3 years at a time. So don’t worry about what you’re going to do far far far in the future. Focus very much on the here, the now, your interest, your niche, and building your tech skills and make friends.

Go out there into the world and make real life friends. Thank you.