SDS 656: A.I. Talent and the Red-Hot A.I. Skills

This is episode number 656 with Jaclyn Rice Nelson, co-founder and CEO of Tribe AI.

00:27

Today I’m joined by the marvelous Jaclyn Rice Nelson, the CEO of Tribe AI, a firm that is perhaps the world’s most prestigious consultancy for contracting machine learning specialists. In today’s episode, Jaclyn, who previously worked in senior roles at Google and Alphabet’s growth-equity fund called CapitalG, she details for us what characterizes the very best AI talent, what skills you should learn today to be tomorrow’s top AI talent, and the kind of AI project that her clients are suddenly demanding tons of help with. 

 01:00

Jaclyn Rice Nelson. 

Jaclyn: 01:03

You got it. 

Jon: 01:04

Welcome to the SuperDataScience podcast. I have to start this episode off by apologizing to you and to all of our listeners. In a recent episode number 653, we had your friend Carlos Aguilar on the show, and in it, I said that I knew Carlos through someone named Jaclyn Rice Arnold, when in fact your name is…

Jaclyn: 01:32

She seems cool, by the way. 

Jon: 01:34

She’s a lot like you.

Jaclyn: 01:36

Yeah. Weird.

Jon: 01:37

She hosts the same kinds of parties. She runs a similar kind of company. In my mind, you even look very similar, but as far as we know, Jaclyn Rice Arnold is a fictitious character, whereas Jaclyn Rice Nelson is my wonderful guest today on the show. So some kind of weird brain fart where I just took a bisyllabic man’s first name and replaced it. 

Jaclyn: 02:03

It happens to the best of us. But I think it gave us a great foundation for this episode. So for sure. 

Jon: 02:11

And it also, I think the real villain in this story isn’t me. It’s really Carlos.

Jaclyn: 02:18

It’s Carlos. He’s gone down.

Jon: 02:22

How did he not point it out? He knows you anyway. So yeah, so I didn’t really make a mistake. Carlos did everyone, you can send him your angry letters his PO box is… 

Jaclyn: 02:38

I’m highly entertained by this whole affair. So I think it’s great. 

Jon: 02:42

So Carlos I met through you at a party that you were hosting. So you, your company Tribe AI, as well as another great New York startup, Arthur, you co-hosted a drinks event at the AI Summit. I talked about this a bit in Carlos’s episode. There was like the thing that was supposed to be the pinnacle,

Jaclyn: 03:03

The moment. 

Jon: 03:03

Climax of the entire event was going for champagne at, at the Rooftop the Edge, which looks over all of New York City. It’s like every, every girl’s favorite place in New York to get an Instagram photo or a photo for her dating profile. And it was so foggy that day that you couldn’t see a single thing. It was like…

Jaclyn: 03:24

We nearly got blown off the building. 

Jon: 03:26

Almost got blown off too. It was a serious health hazard. But we got a great photo of everyone with like ties and everything, hair flying off to the side. 

Jaclyn: 03:37

All over the place. 

Jon: 03:38

Yeah. So really fun night. Even that experience was…

Jaclyn: 03:43

I was gonna say maybe not that memorable, apparently. But it was really fun. It was great to bring together a bunch of data and AI leaders in New York which is something we do regularly in partnership with other amazing companies or on our own. 

Jon: 04:00

Yeah. And it was Austin Ogilvie who was in episode number 535 who invited me to that party. And so I’m super grateful to have been able to meet you there and to meet Carlos there, as well as to see people like Drew Conway who was an episode number 511 and I understand is kind of another member of this crew with you. 

Jaclyn: 04:18

Yeah, yeah. He’s an official Tribe member and an advisor. Well a member, an advisor, and he’s been a customer which is actually the same for Austin and Laika. So you have all of my favorite people and I know all of their secrets. 

Jon: 04:34

And soon I will know everyone’s last name. 

Jaclyn: 04:40

Exactly. 

Jon: 04:41

Yeah. So you were characterized ages ago to me by Austin as a New York Tech Scene Supernode. And so yeah, super grateful to have you. 

Jaclyn: 04:50

Will try to live up to this.

Jon: 04:52

And yeah. So we almost recorded in-person in New York because you’re in New York and I’m in New York. We narrowly miss that opportunity. But you are calling in from New York, is that right?

Jaclyn: 05:03

I am. So this is our office in Brooklyn. We’re in Cobble Hill. We call this Tribe House, very homey. We host a lot of events in the space right behind me. And it’s both an office for our core team and then kind of a co-working space where members of Tribe so data scientists, machine learning engineers, et cetera, can come in and work from here or just spend time with us whenever. 

Jon: 05:27

Nice. Yeah. Most of our listeners are in an audio only format, but I can assure you audio only listener that there’s a very cool office in Cobble Hill behind Jaclyn in the video. 

Jaclyn: 05:39

Come check out Tribe House. We gotta get you to our events. Cause we host bunch of them here as well. 

Jon: 05:46

Nice. Yeah, I’m down for sure. So Tribe is perceived as the premier AI consulting firm, certainly in the US maybe in the world. Jaclyn, how did you manage to create such a well-respected AI consulting company in just four years? 

Jaclyn: 06:07

It is, it’s very cool to hear you say that. And I hope that that is very much the core thesis of Tribe is that we could be this like truly premier outcomes-oriented firm but that we could do it really differently. And that was what was actually gonna really drive the differentiated outcomes. So we are not a traditional, I mean we don’t even use word firm or agency or consulting, you know, it, it’s really the core idea is that there are people who are amazing, exceptional at data science and machine learning. And then most companies don’t know how to find them, don’t know what excellent looks like, don’t really know what they should be doing once they had one of those people. And we really saw an opportunity to connect the dots. 

07:04

And the way we do this is instead of being one of these large consultancies that it’s hundreds of people who just kind of sit on our bench and a new customer comes in and they need, you know, data science support and we just pull, you know, data science one, Bob and Sarah who are, you know, free. Instead, we figure out what that customer really needs. And we pull from this distributed network that we’ve created, we vet every single person who is accepted into Tribe thoroughly from like a technical perspective and a fit perspective with, with various companies. And and what this allows us to do is actually build our own data asset which really indexes people, skillsets, what they’re best at what types of environments they would thrive in and really what types of work they are really differentiated at and have actually done before from a technical perspective.

08:04

And this way when a company comes in and says, I need X, Y, Z, we really dig in, understand what they need to do right, what does it take to actually deliver that result? So in our case, instead of pulling from our bench of Bob and Sarah or Arnold and Arnold we instead can really pull into our network and figure out for that particular project, you’re really gonna need someone who is a strong model builder has experience with NLPnd you’re also gonna need someone to go build the infrastructure to put those models into production. And so instead we’re gonna go really deep on finding you the right expert and then also an ML Ops engineer, for example. But really have the ability to do what we think is what honestly what I would want if I were the customer which is have the right people working on any given problem across the board.

09:09

So no matter how many projects we’re working on. And the way we do that is because we have a scalable model. And so none of our consultants are full-time with Tribe. The sort of opportunity we saw is that these are people who don’t want to work for Google, they don’t wanna work for Amazon, Netflix, et cetera, even OpenAI. They definitely don’t wanna work for many of our customers full-time. They don’t even wanna work for Tribe full-time. But the sort of arbitrage opportunity we saw is that by really building in a way that enabled this incredible talent asset to work in a way that resonated with them, that we could actually do an amazing thing for the talent, which is help them really live the lives they want to live while still doing really interesting work and making money and drive differentiated outcomes for customers.

10:03

And so hopefully that gives a bit of a sense of how we’re pretty different and where we spend our time, which is both sort of the traditional view of like a consulting firm where we get to see patterns across companies and customers we work with who range from the Fortune 500 to private equity firms, PE-backed companies, and then early, early stage startups. But also get to learn from this amazing talent asset and network which is pretty diverse in terms of skillset functional expertise. And many of them, because they’ve chosen to work independently are quite entrepreneurial. And so they are also exploring ideas starting companies and they’re, there’s just tons to learn from them as well.

Jon: 10:48

Yeah, that was exactly the point that I was gonna make, is that I bet a lot of these people have their own kind of startup incubating. So this is like the perfect thing. You have these great talented machine learning data scientists and engineers who don’t want to be constrained by the nine to five who might have their own thing developing, or maybe they want to focus on family for the time being, or they wanna focus on some other hobby or hiking or whatever. And so they can pick up work with the Tribe network when it suits them. But yeah, they’re not, they don’t feel like they need to be working 12 months a year for, you know, a full work week all time. 

Jaclyn: 11:32

Yeah, I mean, honestly, another good example is someone who wanted to actually work all the time. He wasn’t working nine to five. He was working, you know, crazy, crazy, crazy hour days and taking on as much work as he possibly could, but he wanted to consult because he knew he wanted to start a company. And he felt if he were at a company, he would kind of be stuck. And in his case, actually he was on a visa. And so you really are, you’re really wedded to that company and instead he sort of set up his life to continue to earn at a level that he would’ve earned at any of these big companies to learn a ton by seeing exposure, you know, gaining exposure across all of these types of businesses. And then when the time was right, start his company. 

12:18

And he’s now, you know, the chief product officer and CTO at an amazing startup. And so that, that actually like really worked for him. And there were, you know, countless examples of this. But the reality is that everyone’s really different and they’re optimizing for different things. Work is not built for individuals right? It’s sort of built, you know, based on historical norms and kind of for an aggregate set. And so we’ve really set up Tribe to optimize for the individual and in this case the really talented technical individual. 

Jon: 12:54

Nice. How do you attract these really talented technical individuals to join Tribe? How do they hear about you? 

Jaclyn: 13:00

Great question. I think about this a lot because we are in this incredibly lucky position where we do not do any outbound recruiting for on the talent side of our business. And and that is because they show up every week. We get, honestly, dozens of applications even a day from wildly talented people. And we built a brand that speaks to this audience. And that is because we really did go deep with our customer, our target customer. This is also probably where we’re pretty different from a large consultancy. Our target customer isn’t the person who pays us. You know, it isn’t, it isn’t the company who hires Tribe. They’re for sure our customer. We will do everything to kill it for them, but our main customer, cause in any marketplace, even if you’re two-sided, you have to have one, you’re optimizing for more.

14:01

We are talent first. And that’s because this talent asset is the thing that makes Tribe so unique. And we know how special it is. And so we have spent so much time with this persona to really understand what do they care about? What do they want, and how can we create that for them? How can we create a destination that they want to come to because it serves them and it meets the goals that they have that they can get nowhere else. And ultimately that comes down to freedom. So, you know, the ability to earn money to support, whether it’s your interests you know, technical research that you want to do, perhaps you’re bootstrapping a company on the side you know, are, as we’ve talked about, there are many, many sort of other pieces there.

14:54

The second is really like a technical community. If you actually do kind of step out of a large company, I’ve done it leaving Google. It’s probably scary not just because you lose your health insurance and you know, certainly in the example we talked about with your Visa but also because you lose that peer group, that technical peer group that is so core to your experience, being able to, you know, look over your shoulder and talk to a brilliant engineer who sits next to you. You know, it’s really similar to being in a university like sitting and being surrounded by people who are really interested in similar topics and where you can push each other and challenge one another to continue to grow as a group. And so we also create that kind of community and camaraderie. And then the last being that sort of access or exposure point. 

15:46

And I think this is a really interesting one, particularly for engineers or data scientists who have worked in large fang companies, for example, or just like really sophisticated tech companies. If you are entrepreneurial and you are going to build a product that’s going to sell into some of these, you know, very sophisticated companies, fantastic. That’s still, you know, a very, that’s not the whole market. And so actually exposure to companies that are building in legacy industries is a really big market and something that you wouldn’t get exposure to generally working within one of these large tech companies. 

Jon: 16:27

Cool. Going back one quick point you were talking about how the camaraderie, the kind of the learning that people get from working with other sharp people, I can see really obviously in the Tribe framework, how, if you know Bob is a really strong machine learning engineer and then Sarah is an amazing DevOps engineer or something that they get to work together on a project for maybe a few weeks or a couple of months. You can let me know. Like both, it varies, yeah. 

Jaclyn: 16:59

Both, either, depends.

Jon: 17:02

And then they presumably on the next project, they’re unlikely to be working together again. They’re paired with somebody else, so then they’re learning new things from new people all the time on every project. Yeah. So that was kind of the obvious learning opportunity for me there. Do you on top of that have like, structure around like ways that beyond just project to project people are learning together from each other? Like, I don’t know, like journal clubs or, or some other kind of system? 

Jaclyn: 17:32

Yeah, I mean like community means so many things. The way I sort of think of it is like amazing things happen when you put really brilliant technologists in the same room. And we try to do that both in person and then also digitally. And so that mean sort of event-based programming that happens over Zoom, that can mean async sort of Slack conversations. And of course means all of the events we host at Tribe House or our Brooklyn office. And hopefully kind doing much more of that in satellites kind of all over the world. But I really think that, you know, at the core it’s like if, you know, brilliant things happen from putting people in the same room, like we want Tribe to be that room. And so we’re just always thinking about how to organize around the things we know people care about, which are, you know, earn, learn and build, basically build being, we did a survey recently and found out that 75% of our network had started a company which kind of wild it doesn’t mean they’re working on a company today, but like this is a very entrepreneurial group. And figuring out how to support them on the things they really care about.

18:48

I would say another topic we know they really care about is pro bono work. And so in addition to sort of or or sort of more socially minded project based work. And so in addition to sort of paid engagements, we also kind of try to facilitate some of these other opportunities to either discuss topics that are really topical. Recently we did a topic on kind of education and generative ai which was really highly attended and you get tons of engagement and debate. But then similarly trying to figure out, you know, if there are opportunities for groups to come together around things they genuinely care about, but it might not tie to compensation. Like hackathons would be another example. 

Jon: 19:28

I didn’t know that you did that. That’s super cool. So yeah, so lots of reasons why you get dozens of applications a day some days. 

Jaclyn: 19:34

I know those are really good days. 

Jon: 19:39

So we have talked about you attracting all this top talent. What characterizes in your view, somebody as being a top AI talent? So what kinds of attributes do these people have that set them apart from the rest of the people working in the space? 

Jaclyn: 19:56

Yeah, it’s a good question. First of all, I think we really do try to maintain a diverse network. And so it’s not that there is a Tribe way or you know, if you pick any large consultancy and they put you through some crazy training bootcamp right? Cause this is how you act when you work at wherever you are. We intentionally don’t do that. And it’s because the people who we have in our network are brilliant technologists and we want to create the infrastructure for them to do the thing they are best at and not the other things they are not that good at. So it’s actually like very much a lean into your strengths and specialist model where we are not trying to help that data scientist to, you know, build decks and these sort of lengthy presentations. Instead, we might pair them with a product manager or a project manager who can sort of do some of that work and where they can really stay focused on the thing they, they do best and do better than anyone else.

21:03

And so, you know, when I think about what is that sort of on in a broader sense, it’s really just comes down to experience. So this is a field that of course is incredibly academic, that is an amazing foundation in order to plug in on a consulting basis. You do need applied experience. There are, you know, lots of amazing programs, free care center formerly insight data science that you do the help you get applied experience from PhD programs. But I would say while there is a large share of people in our network who do have PhDs and really strong academic backgrounds, that typically the attribute we do optimize for kind of across the board is really like applied work. So you have actually, you know, built things or I always say like done the thing that you would need to do in a consulting environment in a constrained environment, not in kind of an academic setting. 

Jon: 22:06

Gotcha. So highly specialized skills, you might be the best at some very specific niche of ai. And you have experience applying that expertise in the real world. 

Jaclyn: 22:19

And, look everyone, you know, there are generalists as well, right? I think data scientists actually like often can be generalists. And, I think that actually that’s amazing cause there are lots of projects where that’s really helpful. But if you need someone who’s really amazing at time series or something we need to know we have that as well. And so the really important thing is that people are, are technically excellent that they can communicate well and work well with other people because no matter what you know, you will be working with a team you do have to talk to real people. And and I think that’s a really critical part of consulting and and that we understand what it is that you are best at and best suited to do. And if that is actually like a broad set of things, fantastic. That probably means you’ll be eligible for more projects. But that’s not the case for everyone.

Jon: 23:20

Nice. So that covers kind of the general characteristics that you look for in top a talent. Are there specific skills or tools like hard skills that our listeners should be aware of that you think are like either today must have skills or things that could be in the coming years where if somebody’s listening and they’re like, what is the market looking for? Like whether it’s it doesn’t need to be a consulting firm like yours. I’m sorry, you don’t even describe yourself as a consulting firm. 

Jaclyn: 23:50

No, it’s ok.

Jon: 23:50

Project-based.

Jaclyn: 23:53

Yeah, I mean we’re, at the end of the day, it’s consulting work is the foundation of our business. 

Jon: 23:59

But yeah, so whether the person is thinking about making themselves more employable as a consultant or as a full-time employee, are there specific hard skills that they should be thinking about learning today? 

Jaclyn: 24:10

Yeah, I mean, this is a really interesting moment with generative AI and all of these new terms based on, you know, things that have existed for a while.

Jon: 24:24

Prompt Engineer.

Jaclyn: 24:24

Yeah, yeah, prompt engineering. There are all kinds of weird job descriptions I’m seeing, many of which don’t actually make sense. But I do think familiarity with large language models is in very high demand right now. Will that be in the future? I believe, yes, I do believe it will become more of a known quantity. I think this is the direction the market will likely move. Today though, there is real scarcity. And so having experience, it doesn’t mean you have to have worked on something for a company does mean you have to have built something, done a thing done a little more than flying around. And and I think like that is differentiated today. In addition to that, we are seeing with this generative wave that actually there are other skillsets that are incredibly important to complement that, you know, generative AI engineer. We are seeing huge differentiation on things like UX design and right user interface and full stack engineering because these, you know, are great, you have a generative AI engineer that was really hard to find. Well, what do you do with it? You know, how does it plug into your product? How does it how do your customers engage with that experience? That’s so much of the work. And we are also like actively staffing up on those areas because they’re so core to building successfully in on generative projects.

Jon: 26:12

That makes perfect sense to me. LLMs are certainly the talk of the town totally with models like ChatGPT even being the most talked about topic by far, apparently at events like Davos. So world leaders politically and business wise are like, how is this going to disrupt my business? And so then they come to places like Tribe probably to try to catch up

Jaclyn: 26:36

And those are the questions they’re asking. They’re coming here, they’re coming to, you know, any of the big names in the space, and they’re asking the really fundamental questions that they’ve asked for a long time in a sense. Which, I mean, this was actually my insight in starting Tribe, I mentioned I was at Google for a long time. I then worked at the late stage venture fund under the Alphabet umbrella called CapitalG. And we invested in all these amazing high growth tech darling companies. And I saw that many of them came to us for help with data science and machine learning because it was something that Google was really differentiated at. And the questions weren’t, you know, how do I, don’t know, do the latest in computer vision engineering. It was it was how do I assess a data scientist if I don’t have one on my team?

27:35

And “Hey, Google, you’re getting a lot of value outta machine learning. Where could I apply it to actually do something for my business?” Well, so to, so those were the questions that were, have always been asked, and it isn’t just by these teams that are Silicon Valley based, have huge engineering teams, Google as an investor, that’s true for the market as a whole. And that, that’s really why we started Tribe is to help accelerate this. And the adoption of technologies that we know are incredibly valuable. It’s proven, it’s, you know, just said it’s been concentrated in just a few companies historically. And so now with generative on the rise, the questions are actually quite similar. It’s like, how do I understand this? What’s the mode, I have IP concerns… Like how do I think about really leveraging my data? 

28:28

And everyone’s really just right now dipping a toe in the water and trying to figure out what to do. That’s true at the top end of the market. So enterprise, and then at the bottom of the market, the startups aren’t wasting no time. They know that the big companies have data but are slower on technical capabilities. They see the technical capabilities, the ability to jump on this new generative technology and speed is their only advantage. And they, the pace of sort of like new companies coming up that are focused on problems in like incumbent industries is wild. And that I would say like big companies dipping a toe in the water, experimenting, learning, small companies building and you know, overall there’s just a lot of velocity. 

Jon: 29:24

That was such a cool overview of what’s happening in the market right now that’s being caused by these watershed moments that seem to be happening every couple of months with large language models. Which we could also call foundation models. So things like DALL·E 2 obviously ChatGPT and other GPT series, architectures, Midjourney, these amazing foundation models that we can interact with, with natural language that produce incredible generative results. I loved your overview there of what of how big companies and small companies are racing to stay ahead of competitors in a broad range of verticals. Super cool time. 

Jaclyn: 30:08

It’s also, I think like there, it’s a really interesting time because there are so many narrative violations, right? Narrative violation, number one, everyone thought AI was coming after blue collar jobs. Well, it turns out right, really [inaudible 00:30:22] process-oriented roles. Well, guess what? It’s coming after the thing, we thought humans were most unique at creativity, really, you know, high paying elite roles. And it turns out that actually these models are really good at it. They, I don’t think of it as replacing, I think of it as like augmenting people who are incredibly creative. But like that’s already been like an incredible narrative violation or deviation from this sort of narrative we’ve been told about AI. And I think that, you know, the latest is, or I would say there are two more. One is like, whoa, this like cloud race just got really interesting. 

31:11

Microsoft is back, right? I think I was at a dinner recently with a bunch of tech people here in New York, and someone asked me who I thought was gonna win Google or Microsoft, and like, I just marveled at like how wild that question would’ve been to ask even six months ago. And so like, I think that is a fascinating competitive dynamic happening. And then the third is who is going, right, this all kind of comes down to who’s gonna win, there’s a race on and who’s gonna win? Is it gonna be big companies or is it gonna be small companies? Right, because there are a lot of people who are actually taking the argument that this could be the time that actually big companies win and crush the startups in this space, because the thing that’s differentiated is gonna be the data and big companies have it, but the only way they can actually capitalize on that advantage is if they can build the technical capabilities.

Jon: 32:08

Yeah. This isn’t a winner take all race, right? It’s like a, this is one where I think the rising tide lifts all boats and it’s amazing. Well, maybe not all boats. 

Jaclyn: 32:24

Yeah, I think this is, I just think it’s an interesting question. Like yes, in a sense that I think actually in some ways AI has had the best branding of any industry ever, right? Sometimes to its detriment. So this is another cycle of something that I think gets people really excited about what’s possible with AI and makes it impossible for business leaders to ignore it. I think that is like, to your point, something that lifts all boats, like that’s a really good thing for particularly in an economic downturn, what happens, people cut R&D spend while given this like generative trend. You can’t really do that right now. And so AI really is this kind of bright spot in tech right now. 

Jon: 33:15

The other reason why I say this particular tide lifts all boats is because this AI community unlike, you know, to pick another industry that is like the opposite in financial services, it’s about keeping everything to yourself. Yeah. Like not only company to company, but an individual trader might not want the trader, that works at the same company, the adjacent desk to know what the strategies are because this trader that’s sitting next to you could take your ideas. Whereas in AI, there’s this, most companies, including almost all the big tech companies, Apple is notably absent in this, but lots of big tech companies, including Microsoft, Google, Meta, Amazon, they publish so much they publish papers on how they’re doing things, they make data available. OpenAI makes these APIs two foundation models available. And so it, yeah, it, I don’t know. In my view, this race isn’t so much about like, oh, there’s like one master algorithm that whoever cracks it first is a Google or Microsoft, they just crush everyone. It’s like, well, they, you know, the big tech companies come up with these big foundation models that it cost millions of dollars to train, but then smaller companies can take advantage of those big foundational models and come up with highly verticalized solutions. And so I think there’s a lot of room for success for a lot of people including all of your clients.

Jaclyn: 34:51

I very much agree. I do think this question, I really do, I do think this question on like the big company, like where, who’s gonna be able to actually capitalize on this, who are not tech company incumbents, right, is the question to me, right? Because like, okay, Google, Amazon, Microsoft, like, it’s just an ordering question, but like, you’re all gonna be quite large. But I think, you know, does an incumbent law firm win a big, big law firm or a startup building a generative law like legal company? And I think that those are gonna be the really interesting dynamics. I think that is like what really creates a lot of urgency right now, because the velocity at which those startups are moving is pretty compelling. And we just know that big companies, while they have this incredible data asset, have historically been under leveraging underutilizing their data assets and kind of slower to change there, despite a lot of effort. And then the second is that these, they’re not speed is not the friend of a big company, right? It’s the advantage of a startup. And so like, how will that play out? And how will that change the market? 

Jon: 36:18

Cool. Now we are definitely on the same page. 

Jaclyn: 36:21

Yeah, no, it’s really interesting. It’s just an interesting time because we know where the puck is going or where we believe it is going. And we don’t know how it’s all gonna play out. 

Jon: 36:34

Yeah. And new things come along every few months that, or you’re like, holy smokes, I can’t wait to do that now too. 

Jaclyn: 36:40

Yeah, yeah, yeah, yeah. Right. GPT-4 is gonna be released in a few months. Like from all the reports I’ve heard, it is a step function change. So, you know, that makes a lot of things possible that based on GPT-3, right, I’m reading all of these articles, I point out all the things it can’t do. Well guess what? In just a few months it probably can.

Jon: 37:04

Yeah. It’s crazy. It is. It has never been a more exciting time to work in AI. I started off the year by saying the 2022 was the biggest year in AI history, and 2023 will probably be bigger. 

Jaclyn: 37:22

I think for sure. Like truly, I mean, just anecdotally, we have probably seen more demand in the first two weeks of 2023 than we saw in the first two months of 2022. So, right. We are just talking about step function change. And it is largely driven by, I think two things. One, this like broader mainstream awareness, like what you’re talking about, it’s the topic of Davos, et cetera. It is like dinner table conversation. Parents finally know what their kids do, right.. But I think the other is this competitive pressure. 

Jon: 37:58

Yep. Super cool. Well, Tribe sounds like an amazing place to work. If our listener is interested in applying to be in the Tribe network, how do they do that? 

Jaclyn: 38:11

Come on over. We can go to our website Tribe.ai/apply or /join if you want to learn more. And yeah, just I think you’ll sort of see we ask a lot of information about your skillsets because as I mentioned, that’s what’s most critical to figure out, you know, how to sort of assess you technically, but way more importantly, how to figure out where you’re gonna be a good fit and do your best work. 

Jon: 38:38

Nice. And then how about you personally? How can people hear your latest thoughts, Jaclyn, after this podcast is over? 

Jaclyn: 38:47

Awesome. Well, I think the easiest is probably LinkedIn. So Jaclyn Rice Nelson on LinkedIn I am tweeting less and less, but Jac S. Rice, J A C S Rice on Twitter and otherwise I’m really starting to, I think we’re publishing a bunch more through Tribe as well. And there’s a spot on our website to kind of subscribe to our newsletters and events as well, which is another great way, particularly for anyone who’s in New York or who just wants a pulse on what our amazing community is working on. 

Jon: 39:25

So I can find out and get invited to these parties without Austin Ogilvie.

Jaclyn: 39:29

You’re already, you’re already getting on the list. No, the one you came to was a very exclusive one, so. That was like, you know, that didn’t go out to the normal subscriber. So you’re already in the elite crew as long as you don’t s*** my name. 

Jon: 39:49

Fair enough. And I would deserve that for sure. I would not be offended if I get your name wrong again. I will completely understand The Black listing. Well, Jaclyn Rice Nelson, it has been a treat having you on the program. It’s been so great. I’ve learned so much, and I’m sure our audience has as well, and they’ve gotten excited about what they can be doing with AI and particularly LLMs. Thank you so much for coming on the show and we’ll have to check in with you again at some point in the future. 

Jaclyn: 40:21

That sounds great. And I really love what you’re doing. I think it’s amazing for the broader community and just really enjoy spending time together. So thank you. I’m so glad we finally made this happen. 

Jon: 40:32

Nice. Thank you, Jaclyn. All right. That’s it for today’s super informative episode with Jaclyn Rice Nelson, which was packed with invaluable information on how to attract the top engineers and data scientists. How specialization with LLMs and generative AI engineering are extremely in demand, particularly because companies are clamoring over each other to make the most of the powerful new foundation models that are being released to the public at an increasingly staggering pace. If you’d like to learn more about foundational large language models like ChatGPT coming up on March 1st, I’ll be hosting a virtual conference on just that. It’ll be interactive, practical, and it’ll feature some of the most influential scientists and instructors in the large natural language model space as speakers. It’ll be live in the O’Reilly platform, which many employers and universities provide free access to. Otherwise, you can grab a free 30-day trial of O’Reilly using our special code SDSPOD23. We’ve got a link to that code ready for you in the show notes. 

41:31

All right. I hope you thoroughly enjoyed this episode with Jaclyn Rice Nelson. Until next time, keep on rocking it out there, folks, and I’m looking forward to enjoying another round of the SuperDataScience Podcast with you very soon.