Upskilling with AI – Jonathan Siddharth, Turing

This is Text Strong tv. Hey everyone, welcome back here to techstrong tv. You know, if you're like me, I know you're like me cuz that you're our audience and we're all kind of being, you know, inundated with stories around AI and, and how AI is going to change.

And, you know, and, and anytime something is this hyped, of course there's the hype and then there's the reality. And, you know, it's kinda like that meme. This is how my friends see me, this is how my parents see me, this is how, uh, my wife sees me, and this is what I really do.

Right? And so, getting to the, well, what does AI really do? Part of that meme is, is kinda what our mission is here today.

And I want to introduce you to a gentleman named Jonathan Siddharth. Uh, Jonathan is the c and co-founder of touring. I hope I got his last name right.

But in any event, Jonathan Welc, welcome to techstrong tv. Um, is that, why don't you say your last name, just so we clear it up. Straight up.

Thank you, Alan. You, you got it right. It's Jonathan Siddharth.

Okay. Wonderful to be here. And, uh, hi everyone.

Thank you. And, and welcome Jonathan. So, Jonathan, look, we're gonna get into tour and we're going to get into all of these things, but I wanted to start off a little bit about you, right?

If you wouldn't mind sharing with our audience, kind of your, you know, your CEO co-founder here. How, how did, how, you know, what journey did you go on with this? Yeah.

Thank you Alan. Ha, happy to share my, share my journey. Uh, so I am a two-time entrepreneur.

Turings my second, uh, AI company. My first AI company had a successful acquisition. Um, I started the company when I was at grad school at at Stanford.

And, uh, where, where, again, I specialized in ai. I've had a journey with AI now since, like, since about 2001 when I was first, when I built my first neural network for, uh, for autonomous driving. And it's been so exciting to see, uh, AI and machine learning, uh, go through all its ups and downs, like in the last like 20 odd years.

Um, and, um, uh, Turing today is a, is a company that, um, w wouldn't, wouldn't exist, uh, if not for, if not for ai. Uh, we are building the world's first AI powered tech services company, right? Like imagine an AI powered, uh, Accenture.

That's what we are building. And, um, the, we believe that AI transformation is the new digital transformation. Uh, every company today is an AI company.

And Alan, I spent time with, uh, fortune 500 CEOs, CTOs, and C-Suite. I keep hearing this over and over again that every company today needs an AI strategy. Every job has been transformed by ai.

Every product is an AI product. And, uh, companies are still grappling with this journey of, uh, going from digital transformation to AI transformation and traditional tech services firms, like if you look at an Accenture, tcs, webpro, Infosys, et cetera, they were not really built for this. I mean, uh, the way they find talent to work on your projects or to staff your projects is through manual sourcing and interviewing.

It's manual matching of people to projects, and then it's manual coding and delivery, right? So Turing is tech services reimagined from the ground up with ai. Um, we use AI for automatically sourcing developers from all over the planet.

Turing has about 2 million developers on the platform, 2 million growing by about 80,000 developers a month. Uh, and we use AI for automatic vetting and matching of talent. Um, and we use, and more recently we are using AI for AI accelerated deliveries.

We are using generative AI to speed up the productivity of a software engineer or product manager or data scientist. Um, and it's been amazing. It's been amazing.

Like more than 900 clients have partnered with touring on their transformation journey. Uh, these include, uh, companies like Disney, Johnson, Johnson, Rivian, Coinbase, many, many more, uh, some well known AI labs. And it's, uh, it's, it's, it's such an exciting time to be in ai.

Um, touring itself has experienced two tailwinds that have propelled our growth in the last few years. The first tailwind was, um, uh, remote work, I think when, when Covid hit and every company decided that to go remote first. And with distributed teams, that was a big accelerator for Turing.

The second is, is ai. Uh, more recently we have been raising at a 4 billion valuation cap on a safe. The company has raised about, um, 140 million so far.

Uh, and we are the fastest growing tech services company ever. Uh, and that's all due to sort, uh, building, uh, a tech services company on an AI foundation. Um, and the thing that I find fascinating, like having been in machine learning for a, a, a significant period of time is unlike predictive ai, like I I would categorize AI into predictive AI and generative ai.

Unlike predictive ai where the creators kind of knew the capabilities of the system they were building. Um, I mean, you train a supervised learning algorithm on, on a task, you kind of know what you're gonna get and what the accuracy of the system would be. With generative ai, it feels like the creators are discovering the capabilities of their creation, which is some, and which is something amazing.

Like I've, I've sat across the table from researchers in, in OpenAI and other AI labs, and that's, that's what strikes me. It's like this, this almost feels like emergent behavior where oh, wow, I didn't know it could do max. Like I didn't know it could reason.

And it's just a testament to what happens when you train a large neural network with many layers on a corpus, the scale of the internet, right? Right. And a simple algorithm like gradient descent, like on such a large corpus of data gives magical results.

So I'm, me and the team at touring are like kids in a candy store. Like, it's just so amazing. Um mm-hmm.

I, I, I agree. I mean, look, at some level, it's almost akin to being a parent and watching your, your child's growth intellectually, emotionally, you know, developmentally Yeah. Growth.

And you're just amazed that man, you know, they say the days are long, but the years are short. Yeah. Something, yeah.

Something like that. Yeah. And, and that, you know, the strides that we're now making with the generator of AI and the large language modules and, and yes.

When you have a canvases or a data set as big as the internet, wow. You know, but the, the, there's, there's downsides to it too, cuz not, obviously not everything on the internet is true. Right?

And, and there's a lot of, you know, nonsense that you, if there was a way to weed out, we'd love to weed it out. com. I think we have 20,000 some odd articles over the years here on DevOps.

Right? Forget the whole internet. I can make a tremendous data set around DevOps.

Yep. Right. And dump that in.

And, and, and, you know, these are the kinds of things I think a lot of companies are looking at. One of the things I wanted to touch on though, Jonathan, is, you know, I, I have, I went to dinner last night with, uh, actually a husband of one of our workers here at Techstrong who's in the AI sales space, but not generative ai. He's more in, let's call it traditional ai, if there is such a thing as traditional ai, you know, help desk conversational ai Yeah.

Um, yeah. The financial sectors and stuff like that. Yeah.

You know, and he was telling me a little bit about their journey and where their market is and, you know, what is the generative ai, you know, space LLMs obviously a hot, hot market, but even that kind of traditional AI market is, is really making great strides as we move to true conversational ai. Not just, you know, pick one, two, press three or something like that. But really conversational AI that you can engage with.

And then you could start combining some of that, you know, conversational AI stuff with the, the, the large language mod module, generative AI stuff, and, and Wow. Right. You could do some pretty amazing things.

Yeah. But you know, Jonathan, I said something in the beginning of our conversation and that, like, that meme, that stupid internet meme, here's what everyone else thinks I do. Here's what my parents think I do, here's what my friends, here's what I really do.

Yeah. There is a lot of sizzle hype that, and it cuts both ways, right? I, I think it, you know, you got people running around saying, oh, it's gonna take my job away.

It's gonna be the end of humanity. It's going, you know, all of these negative Nelly kind of things. And then there's other people who are saying, you know, I read, there's another meme going around that, Hey, cha G PTs just mansplaining stuff in a very simple way.

But it, there's really no, there's no depth. It's kinda like shallow how, if you remember that movie, um, I don't think either of those are Right. Right.

I, yeah. And it's just the very nature of, of hype. Yes.

We, we, we hype these things. Yeah. So I, I'd like to ask, and, and we could jump into specifically on recruiting and stuff in a second, but I wanted to ask if you can, Jonathan, where's, where's the rubber meet the road?

Where is the real right for our audience out here right now, and what you're building your touring? Where does rubber meet the road? What's, what's real?

What can they put their hands on today? And it really helps them. Yeah.

Yeah. You, you raise a very important point, uh, Alan, like how do we separate hype from reality, right? Like in mm-hmm.

In ai, especially whether there's so much higher, and I feel fortunate at curing, like having helped over 900 companies build their engineering teams. Like we have, I mean, we help companies with both staffing and with, uh, services. Either we deliver vetted talent or we deliver projects.

So I get to observe what companies come to us for in terms of, Hey, Turing, can we use AI to make our customer support more efficient? Can we use AI to help our sales teams, um, be more efficient? Can we do this?

Uh, can we waive that AI magic wand to make our recommender system, uh, more, more accurate? Right? And so I get to hear a lot of these, um, problems that people come to us with, which gives us a bit of a lens on, um, I would say like where people think AI can help and where AI can actually help.

Right? Like the, um, and I would say there's definitely a lot of, uh, there's a lot of hype, right? Like in and rightfully so in terms of, uh, what people saw in terms of concrete areas where we see generative AI helping.

It's, um, uh, I, I think there's a lot of value in summarization and synthesis when you have a lot of data. You have a human pouring through that data, and you want the AI to deliver an intelligent summary that can save a ton of time. Uh, I think there's a lot of value in these human, in the loop systems.

Not full automation, but human in the loop. Software engineering is one such thing. I think a lot of people are concerned, Hey, will AI take away a software engineer's job?

I actually don't think AI will take away a software engineering job. Every software engineer is now gonna have AI superpowers. They're gonna be leveraged with sort of an Ironman Iron Woman type suit mm-hmm.

Where they're gonna be 10 x more productive. Um, and the ROI to be a software engineer is about to become 10 x. If you thought the value of being a software engineer was X, it's now 10 x.

Uh, and it AI helps by, like, everyone's gonna have an intelligent assistant that's going to help them be, uh, massively, massively leveraged. Uh, I think those types of use cases are helpful. Uh, I think, um, these conversational interfaces, uh, involving natural language for stuff like customer support, for stuff like sales, uh, I feel like we'll, we'll see, uh, a massive acceleration.

Um, certain jobs, like certain creative jobs, like for example, um, a team that does content marketing or co or marketing for s e will now be able to get 10 x done with the same headcount. Right? Now there's a flip side to that, or a dark side to that, which is job displacement.

I think that risk is real in a, in a, mm-hmm. Like if a company can get, um, get done, what can be done with, uh, 10 people, with one person, they'll just hire one person instead of 10 people. Right?

Um, so that's of course. Yeah. So, so I think those fears are real, uh, about for certain types of jobs, like where there'll be, there'll be a big productivity game.

And, um, but, but, but we have to like, take care of the people who are, who are, who are displaced. I do think, um, every person is going to have to learn AI like, uh, maybe from middle school along with foundational computer science, mathematics, uh, and, and, um, and English, like the AI is gonna be, like, all of us are gonna have to use it, uh, to, to, um, in our day-to-day jobs. Um, the in, I, I still see that, um, when companies come to tiering, like asking if, uh, for help with their AI strategy, one thing I find is that often you have to build certain foundational things first.

For example, your application has to be designed in a certain way to log certain types of data so that you can do supervised machine learning or generative AI at some point in the future. So step one is to invest in good product engineering, good data engineering, uh, a good cloud, uh, infrastructure backend so that you have the right foundation to, uh, to train and deploy AI models. Um, it's also the case that I would say, uh, there is a super set of problems that can be solved by good data science and data analytics.

There's a smaller subset that can be solved by ai. It's an even smaller subset by machine learning and an even smaller subset by generative ai. Generative AI is not the answer to every single business problem that you have.

Um, right. And you kind of have to be, um, intelligent about when to apply AI and when to use good old fashioned data science and data analytics. Yeah.

And, and I think what we're gonna see is, is sort of a, an equalization kind of period where, where those boundaries, right. And, and those boundaries are not set in stone as AI continues to develop, you know, they're fluid. Yeah.

But I think people, I think you hit it right on the head, Jonathan. People need to understand, you know, the right tool for the right job. And, and it's not a panacea.

It's not a panacea, it's not a panacea. And in fact, like Turing has our AI advisory and one of the first things our AI advisory does, and these are technologists from Google, apple, Amazon Meta, uh, who've deployed AI in multi at a massive planetary scale. When some of our clients come to us, like, uh, the some, oftentimes what our AI advisory does is also suggest, um, in certain situations not applying ai instead doing, applying certain other data science, uh, techniques.

Um, but, but for the right problems, I think generative AI can, can be really, really valuable. Jonathan, we're about outta time. For people who want to get more information on tiering, where do they go?

com. So if you are building an engineering team, or if you need to staff your team with Silicon Valley caliber engineers, you should come to touring. Touring is the world's largest, um, talent cloud with about 2 million developers, and we're the world's first AI powered tech services company.

com and you can take help from our, uh, engineering team, from our advisory services to help you build, uh, your product. Thank you. All right.

Thanks for being on Text Drunk tv. Come back, keep us posted. Obviously this is something really important to our audience, Jonathan, best of luck with, with touring.

Take care. Thank you, Alan. Thank you having me.

All right, we're gonna take a break here on Text Drunk tv. We'll be back in just a moment.