Thank you.

Speaker 2 

What you’ve done to kind of shape the company to where it is and from where it start? 

Speaker 1 

Well, I think that my major contribution has been. 

Speaker 

Oh. 

Speaker 1 

On culture, really imposing a culture of, of innovation, of failure is acceptable, and that you really, when someone tells you that something is something, is a is a urban myth or something is something that. That you cannot change really grow and challenge steps. And that started with the at the end of the 90s, there was a perception that software. Late projects were effective life. So you talked with a lot of CIO’s, a lot of it a. Lot. Of software developers, and they usually blame the business because they couldn’t get the requirements right, so everything was always late. There were missed the misconceptions about what was built and the when we looked into the. Problem is that why is that everyone accepts that projects are late projects take long. You cannot predict them and so we kind of almost look at the problem and the reverse engineer until we got into the Asus platform and then add those. Of challenging the status quo has always been part of the culture. But not only with me. Most of the innovations do not come really from my head anymore, OK, so. 

Speaker 2 

You. Driven your company to a point now where? Can you? You’ve developed? Low code so that it is now accessible to. Everybody. But now you’ve you’ve brought in generative AI. To become a little. Bit more accessible and more effective for for enterprise applications. So can you talk about some of those use cases and scenarios that you’ve done using the combination of these two technologies? 

Speaker 1 

Yeah. What what we’re facing now, we’ve been. So we’ve been using AI and Gen. AI especially. In order to enhance our products, so there’s a lot of agents and generic technology inside our platform, but what interests also is is. I’ll do a business applications change with inclusion of AI agents in the middle, because today the components are things like portals, workflows, logic, business rules, repositories of data. And where is the rule? Where can you put an agent? Where can you put the AI so that you change the way digital systems are built? And what’s interesting is that we building a new agent. Is, is, is, is, is is one step. That needs to be surrounded by a bunch of logic by a bunch of software until it becomes really effective. And so we’ve we’ve been having a lot of experience with our customers as they deployed agents and Gen. AI. That a lot. Of the work, and that’s by eventually becoming a mixed. Of the typical. Additional software like a portal on top of an agent or a collection of policy rules. With that control, an agent access to data, so a lot of these things create are are are necessary so that we can create a system that’s actually usable and adoptable. 

Speaker 2 

1. So you’re you’re talking about, when I heard the word policy. So it seems like. They’re trying to inject almost rules and processes in a more automated way in the system, through an agent that allow it to be what we what do we say trustworthy?

Speaker 1 

Yeah. For instance, we have a. We have a big customer rush, OK? Or decided to create. In this first instance ChatGPT, but the safe ChatGPT for the whole company. 

Speaker 2 

Is it The thing is a safe ChatGPT? 

Speaker 1 

Yeah, because what happens is that that particular ChatGPT can access internal data from rash. So what they did is that they they they deployed that in the multi set up depart. And for instance, when they reach HR, you can ask any questions about your benefits, about a bunch of stuff that otherwise you’d have to call somebody in HR. But one of the things you can ask is. How much does my boss make? Are paid in terms of salary and so one of the things that they had to do was whenever you accessing and emerging these agents with access to internal data, they put a series of policies that have to do with the what is the data that you actually can access and mesh. That with the bonds, all of that was done with the low code and it was very iterative. We have solutions that can be deployed in one week, but in the meantime there are probably 50 to 60 versions and so being able to iterate. Test them and see if they are. If they really are respecting policy access rules. If they don’t, go into too much hallucinations, you know all of these things are crucial to build a highly adaptable, adaptable. 

Speaker 2 

System. OK, I want to change a little bit. And talk about. The. The ability for you to actually tackle impossible projects, and when I say impossible, let’s talk about the legacy systems because we know a lot of companies have it. They’ve invested millions and millions of dollars on it, but in the past it’s been difficult to integrate transformation. Transformative solutions against legacy systems. Can you talk about how that’s? 

Speaker 1 

Changing. Yeah. What what we find today? A lot of organizations have a large number of legacy systems. That are fundamentally end of life. There are 1520 years. We’re just talking with the custom that has a system that was built in 70. 4. And so you can imagine the language. Yeah, it was a global system mobile RPG. 

Speaker 2 

Mobile. 

Speaker 1 

But all developers have disappeared. There is no manuals, there’s nothing. And so a lot of these systems are chain balls that actually are attached to the to the feeds of the the business. They cannot evolve, they cannot. They cannot build new systems on top of it. And what? The the potential. Of this disruption of of compressing the time that you develop is that instead of looking into a two to four year development project to replace something. You can now do it a project of three to four years. You can do it. In seven months, right? And when you do it in seven months with the technology like for instance out systems where the cost of change is so far. Best you can actually not only compress the time, but any type of surprise that you might find like requirements that you software a particular way kept on changing. You can incorporate them in real time and so suddenly you go from a two year plus project that you didn’t know if. You’re going to deliver. The seven months project that you deliver on time and on budget and that has made a lot of these legal. Projects that were considered impossible because they have no way of being transformed. Suddenly it becomes possible to rewrite them. 

Speaker 2 

So from your perspective, does that mean over time, even with the use of your systems and a couple? Regenerative AI that. The cost of development will come down significantly. 

Speaker 1 

The will and the. For instance, what we’ve seen is we have so many, so much experience with this that we’ve been compressing projects of about four years into between seven months and four. And what we see is that a lot of the Genii capabilities in a lot of steps today done manually can actually further compress at least two ex and so reduce something that would otherwise take three years to about 3 months. And so we’re seeing orders of magnitude of compression. That’s certainly for a lot of organisms. That are looking into. I want to build this. I want to build a new project or a new system because I want to release it three to four months and it’s pretty complex. Certainly it’s possible to do this with a relatively small number of people and a lot of tooling and a lot of help. Assistant school, right? 

Speaker 2 

Now Jen AI is still very nascent. It still has its issues. So what are the current risks that you see right now that you’re trying to solve for? 

Speaker 1 

Yeah, one. One of the the ways training is being used for these these type of platforms, the software development platforms. Is really this this concept of copilot where you have a companion to developer that helps you generate pills. And what we’re seeing already is that the code that’s generated is 5050%. Longer than what you actually need if you write. It by hand. So you’re right, you’re already creating a large chunk of technical debt in the code that’s being generated, and we were expecting this because we faced that back in 2004 when we started deploying our first versions of the platform. And so one of the issues that we realize is that as you build these systems that you generate more code, the code becomes more opaque. It’s more difficult to understand. What it does? To a point where after a while you start to have a bot that explains you. What the code does? And so you have a bot that generates the third another bot that explains that the code becomes less and less important. Is it knowledge transfer mechanism and So what we find is that it’s extremely important to be able to explain how did a Genii model created the particular piece of code that why? It what was the rationale behind it and that we believe that that that is a a fundamental function of research in the next year. 

Speaker 2 

Umm. 

Speaker 

Well. 

Speaker 1 

Because otherwise it’s going. To be very difficult to apply. 

Speaker 2 

This. Yeah, absolutely. 

Speaker 1 

It’s a it’s a problem we solve with the combination of techniques that involve local the Genii, but it’s a real problem for very large systems. 

Speaker 2 

It’s been a problem even in narrow AI, especially when it comes from an IP perspective. Start to see you say you start to see companies who want to protect what’s in that. Black box, right? But if there is a lot of organizations are going to trust these systems, there needs to be a level of transparency that you’re talking about for it. 

Speaker 1 

Right. And you can do it with tests. But it. But we prefer in the software development arena. One of the things about software developers is they want to understand why. And when they write it, they understand why, when it gets generated, they don’t understand, they don’t really understand why. That’s why it’s very important. The systems need to kind of reverse engineer and explain what they’ve done. If they become very, very automated. So there is an explainable ality factor. 

Speaker 2 

Perfect. 

Speaker 1 

That’s a crucial aspect of this system that’s very difficult. 

Speaker 2 

So from your perspective, the significance of generative AI in in the application of of software development in the future it it is going to be a catalyst to change. So so you you believe that the speed, the power, the agility. 

Speaker 1 

It’s massive. 

Speaker 2 

Expected to see is that going to be a standard? For our future. 

Speaker 1 

I think it’s going to be a yeah. Because if you think about. What we did from. From 20 years ago was really a modernization and and remove automation of the craft of software developing not only software development but also managing of the cycle of change and deployment. And there’s a lot of oil. There’s a lot of bad stuff. Stuff that you don’t really need to do. And Jenny I produces the capability of automating more of the tasks, more of the functions of that cycle. And so as the as the potential to elevate the role to a much more strategic, much more gratifying role of developers. If I miss it so our comma. Which is that 10s of thousands of developers today, 50% are proud developers. They are computer scientists, a lot of them from grade school, and one of the things they like is the fact that tools like these allow them to elevate to a point where they become more architects. They become designers of. Very large systems. They absolutely compress the time it takes to deliver business value. They like that they’re like, they’re like that, the pregnant is getting stuff done weekly. 

Speaker 2 

Right. 

Speaker 1 

And at the same time being. Able to design at the much higher level than marks the marks of. 

Speaker 2 

It it’s a significant shift if you think about it. If you think about the common developer today, they don’t necessarily go to school to aspire to the role of a. 

Speaker 

So. 

Speaker 2 

A development arc. They love the idea of coding and figuring out and challenging themselves on on solving problems. So what? Do. You. How do you speak to those that are coming out of school or that have been doing this for many years and they’re quite satisfied with have they been? Developing and letting them know, by the way your role was going to change, right? 

Speaker 1 

I’m a computer scientist, right? Like came out of time, I. Went to several schools. I’ve always felt software development as a mixture. Of artistry with engineering. There’s a lot of creativity going on. And what’s going to happen is there’s going to be more needs for software developers. But software developers that can really add value from a strategic point. 

Speaker 

Of view. 

Speaker 1 

In terms of high level design. Time. Thinking about what is the right construction of the system to provide a particular type of outcome, a real understanding of how some disruptions can be integrated to get the maximum benefit. And if you think about it today, if you. A lot of our. Communities made out of people after 10 years that have 10 years of experience, that have become tired of becoming support engineers. And being waiting up at 4:00 AM to go and fix the big attached to a piece of software for 10 years because there’s no one else who can maintain it. They don’t like that. That’s style. That’s not creative. And so. The future of this is going to elevate the profession where we think it’s going to be more strategic in one way and in some areas where you need very precision. Still you need to code, you need the. To go down a little bit like like what happened with the Elon Musk when the over engineer that tells the fact. 

Speaker 2 

Right. 

Speaker 1 

That he thought everything could be done with bots and then a lot of screws and a lot of precision work actually had to be complemented by humans. And we see that also in this new generation of the way software development is. Going to evolve. 

Speaker 2 

OK, perfect. I could talk to you forever, but I think we’re running out of time, so. Thank you so much for joining. 

Beth Nevins 

Thanks Hessie for that amazing introduction. Yes, my name is Beth, I’m founder of developer dot IO and I’m based in London. So what do we do? We support founders and the founding team in C2 Series C Venture Capital backed tech startups and specifically across the. People and talent expertise arena. There are three sort of core services that we really help with. One is candidates and the talent strategy that comes with that and actually sourcing talent. For the founders, the second one then is more on people, projects or consulting bringing in strategy around good design as we scale regarding people and talent. And the third thing that we support then is coaching, which is more on leadership development and specifically a founder program that levels them up on their recruiting. And leading leaders capability. Uh. So that’s us and I I love the idea that you said Founder HR hacks coverage to get into that. There’s plenty of. 

Hessie Jones 

No, exactly. So I would, I would say you have the credibility in the chops to actually answer a lot of these questions. OK, so, so let’s start about the definition of human resources, because you and I had a discussion earlier that this term is actually a little bit antiquated. So tell me about it. 

Beth Nevins 

Yeah, I mean. Personally, I’m not fanatical around language, although I see the value in in. Changing that over time. So let’s break this down. So traditionally, when we think about work and and and how work used to operate more in the industry. Sense there’s a lot of focus on output and productivity and measurement, so traditionally HR supported, you know, you know, productivity efficiency, you know, administration, back office type support and so forth. But now the shift is focusing much more. On what’s called. X which is people. Experience and the irony of of that is focusing much more on cultivation rather than purely focus on on measurement. So what goes in to enhance that output and ideally the outcome of what we actually want to get in the right way for sustainable growth. So people experience is all about good design and healthy design around that input. Enable us to to cultivate high performance and the only place that I focus slightly differently, different to some of the people in my community is. Some people would equate PX to people first in their definition where I personally. Would say that PX and people experience is all about alignment. There’s no first involved. It is aligning the business need and the people the employee team need at the same time. That gives us the best outcome, having weighed up all needs and all trade-offs involved and the right design that goes with. That is, is important. So yes, there’s a new branding. I think that’s important. Historically, HR has been seen and has certain associations with that language. So I think it is healthy to have a a new brand and a new definition of the. Way forward for for. People, teams and the value that they can bring. 

Hessie Jones 

That’s excellent. Thank you so much and I’m happy to hear that this has evolved as as we discussed and it’s always been one sided and the goals of the organization tend to tend to, I guess be prioritized against, you know compared to you know the health and happiness of the organization. Overall, so I’m glad to hear that that has changed. OK, so let’s let’s tackle the 1st. So there are founders that that wear multiple hats in the very beginning. So they take on many tasks including the the role of HR or even as you say people management despite the fact they don’t even have any real training. So they bring on their Co founders, they bring in employees they. They established these contracts and processes used, but they may not necessarily be compliant and so like the role of HR, may actually be either outsourced or it’s actually attributed to, let’s say, one of the senior managers role. So at at this stage, what kind of responsibilities should, let’s say, the person that that’s executing on this role have? 

Beth Nevins 

All right. Well, it depends on the stage. So I would start very early onwards and be sensitive to to my risk appetite. But very early on if the founder is pretty much you know, 0 to one and what I mean. Why? That is fundamentally the most critical thing that businesses do or die. So they want to prove the market potential by having early stage customers with a minimally viable product. So they need to build that in the beginning. So what that means is the fundamental concern at this point. To be very honest is talent. It’s hiring. So what can HR help? Advisory at this stage is fundamentally getting them to understand what stage fit is and in the very early days when they’re finding their founding team, in my opinion, stage fit is pretty much synonymous with the world with the word culture at this point. So what I mean by that is fundamentally. This one thing. Thing. So let’s just say we were using their recruiter as an example, and the recruiter was coming into a seed stage or a very early stage company and we’re interviewing them and we’re saying to them, OK, so you need to find us some people, what do you need fundamentally that you think is the lever to help us find these people their. Answer will be too late now. They’ve answered with tooling because they are from a 500 to 1000 person company. And they have a lot of. Resources so then. We have to go to that candidate. OK, well, we have nothing. We have no money, no resources, and all the constraints. So now, what would you? 

Now, if they can answer that, that’s demonstrating first principles thinking and that’s what we want. But the difference here is if we already had the right stage fit candidate. Who had already. Been in that zero to one environment, they would have already taken us to that answer naturally. And that’s the fundamental thing in terms of. Getting that right fit in the culture very early on. Is making sure we’ve got that raw first principles mindset that can literally box their way out of any problem by hook or by crook, to still give us the outcomes with with pretty much next to nothing. And you need that across the board and your first 10 to 20 highs at the very least. So that’s what we’re looking for there from a compliance perspective though, you’re right, there are I think we’re developer. My company is useful even at this stage as we have done the people HR element down the line. So one of the things we keep an eye out for where there is some risk early on is the contract side of things where of course the founder will be issuing a contract themselves. And to save money, they can sometimes be using some automated or some off the shelf SAS tools that do do contract management, but there’s some risk in that if you don’t. Know the nuance to. The clauses that we oversee and can advise them on, for example, making sure the housekeeper and on location, depending on how they’re operating hours, who they report into. Is important, but more important than that on the contract or spotting things on the nature of the business. If they are an AI company, IP is something that we can spot. If that’s pretty weak or that can attract some lovely developers and. And also looking at things like have they actually integrated their position on share options or equity as part of the employment contract? And if you speak to most employment lawyers, that’s usually not advised, not necessarily shouldn’t be done from a compliance perspective, but it’s not always beneficial. The reason for that is. You will not be putting all the terms and conditions around share options or equity as part of that employment contract that will exist as a separate agreement. And so if that is the case, it. Should remain separate. Particularly when you may change things in the business down the line that impacts equity, you don’t want to be going through a jurisdiction compliant consultation then with all employees around that change. If it simply can be changes changed based on the nature of the the share option agreement, which is a separate agreement. So that’s certain nuances that. Only we can kind of spot because you’ve got the HR hat that that an external recruiter might not spot. So there are those types of things that we look out for. And then once your post product market fit. Then the founder needs to take off some of those hats. So there are a few things that they start working on the company and their team, their leadership team rather than all the minutia of every single task that needs to go under them. And the thing you’re coaching founders on at that point is then how to make the most impactful fewer. Decisions and decisions on everything. That’s the mindset shift. You have there. And then the role of HR and the skills they can bring as you’re scaling. Is focusing on a few things, not just the obvious life cycle management. You know what I call people, operations, design, training and compliance, but they are very useful for three things. One is business, partnering with managers, particularly when you may have a lot of first time managers that come with scaling startups and that’s a benefit of joining those types of companies. Two is they should have good change management skills, ideally better than most because the rate of change now is growing at different rates across multiple teams and could be in different territories and that’s a lot to control and facilitate leadership to keep an eye on how we have a bit more consistency and how we manage the rate of change and navigate through that going forward. And the third skill set that HR people people can really bring in this scale up transition post product market fit is being able to speak different languages of different teams and different micro cultures and territories. But also interpret the languages to each other and bring those perspectives in the same room. Then when making decisions and the reason that becomes very important is not just from multi functions, but actually the founder will no longer necessarily be day-to-day with employee number. You know 87104 for example. And you want to make sure that there’s not a disconnect, or their perspective still being facilitated and brought to the table when we’re trying to make those decisions. So as I go back to my original. Point on PX and the evolution is it’s those perspectives that we can bring to the table together and facilitate that as really. Useful as you scale. 

Hessie Jones 

1. That’s great that you’re able to to actually show the distinction and on how that rule changes over the different stages. Let’s let’s go. Back to, let’s say the company size where where you are very small, so you could be you and your co-founder or you can have let’s say up to 10 employee. These what are the key things or element HR elements that that founders cannot absolutely neglect in in this stage because regardless of you know, budget constraints, these are the things that that they have to. They they must have in order to minimize their risk down the road.

Beth Nevins 

Yeah. I mean, they will have to spend money. On a contract. So I think that that’s the first thing that they should seek some advice on, and OK, they may decide to use an automated tool or get some, you know, legal advice from an employment lawyer to build you one. But I think that that is spend that you can’t avoid, you know, contractually upfront. So that’s the sort of main thing and and potentially depending on the nature of the business, you may think it’s very important even to have an NDA. You’ve seen how some companies around competition and AI and how. Competitive some of these things are that can be very important in technology. So I think you know those two key areas where spend would have to go on from a compliance perspective. And then I think you know to be honest, you know I say this throughout the talent who you hire in is the make or break regardless because they design the strategies. But very early on, right. If Co founders don’t align and haven’t got the same goals, they may break down, but if you have not got the right. Element in the first sort of 0 to 20. You’re not getting anywhere anyway, so working out how you can use that budget creatively is going to be important on getting the right initial founding team. Now you may hack it and leverage your investors other founders in your network. You know, you might have to hustle, get creative with what you can do with, you know, the best agents or. Which people you know, where is it equity? Is it that we make an agreement down the line that we can give them more roles in the future? Is it that we can do some sort of collaboration but get creative in terms of trying to see the long term partnership or opportunity that could be created with suppliers that gives you the the best, more cost effective strategy, leverage your existing team to be recruiters? For you, you know, three people may have 10 connections. Each is already 30 connections. You can sort of tap up. So it’s really making sure that you’re leveraging your inner your outer circles as much as. Possible. And you know, there’s also the skill and being a great communicator. If you can’t translate that vision on free platforms that are to your disposal and articulate that really well from a copywriting perspective, then it’s very hard then to attract the people that you need when you’ve got low brand, all you’ve got is yourself and your words. So that’s really important to really refine that articulation as early as Poss. Go. 

Hessie Jones 

That’s it. That’s a really good point, because I think in the in the, in the age of social media, this is one of the best ways to actually when I say hack in hack the HR role it’s it’s it’s actually doing that on LinkedIn etcetera and you’re talking about. A little bit about the the importance of brand and how that gets established very early on in, in developing some of these early messages on social media and how that starts to find you not only as founder but founder of. Of what kind of culture you’re you’re planning to develop? 

Beth Nevins 

Yeah, I agree. I mean, I I tend to to be more intentional, branded a little bit later on the line because I think a founder goes through such a transition of who they are and then how that plays out in the business. You know, that’s something that I, you know is even a separate offline exercise to be very honest that you need a safe space with the founder on to to do that and that that’s more a coaching. Conversation. But you’re right. You know, cash is king. But these days, content is. OK, the strongest, the strongest brand these days will will tend to win. So you need to be thinking about that far earlier than before where OK, first of all, you’ve got a good product you know no matter how good the marketing is, the product’s not great. So please don’t you know spend all your time on LinkedIn and not on the not on the product. But you know it is important when you’re. Thinking about building up that awareness a bit earlier than you might have done, or your personal brand to attract, yes, I mean. The challenge is it’s a self perpetuating like prophecy. Everyone’s on there, so you’re kind of not in the game or part of that game you’re you’re kind of behind through the peer pressure whether you like it or not. So you kind of if you don’t wanna beat the game, you kind of have to join it at at this moment in time and the way social media’s going. So you know, it’s. It’s kind of something we can’t ignore these days. 

Hessie Jones 

You. You’re helping me segue into the next question which which is really about the role of, as you say, people management and how to help startups attract and retain some of the top talent especially now. With with I guess technology evolving the way it is and the skill sets also changing every single day and the requirements and and. Also, the cost of getting some of the top developers out there, how what? What would you say the role of people management would be at this stage? 

Beth Nevins 

So I think the like the playbook or the strategies of how you go about thinking about this hasn’t changed. It just means what we’re looking for and what they need has changed. So. So I’m talk about the state of play right now because I think I think we’ll go into future work in a moment. But you know, if you’re attracting, you know the role that. Talent and HR together and. I. Mean people here, people. Things can support founders with thinking about is OK, like, what does the business do? Then. What do we need capabilities wise? Who do we need? And that’s what’s called a candidate persona. So like what typical type of person will be successful here? And then, you know, looking at. Where do we find them? You know, where do they hang out? That’s kind of our attraction strategy. And then people teams can really help them define what’s called your value proposition. So, like, what makes us different to our competitors that will meet those needs of the markets and the talent? You know, we, you know, we want to find employees. And what are we going to double? Down on because. I think that’s. The the key thing I try and coach people teams early on is we literally can’t do everything as much as we want to be the best at learning and development. We want to be the best at reward. We pay everyone 100 percentile, blah blah blah. We just can’t. So like what are the two or three things? That really we want to be known for at this point in time and really excel at in that messaging. In addition of course first and foremost, the vision and mission early on is where you kind of need to focus the the playbook on at that stage and time. And then ironically, there’s like 2 roles really, with first time founders and the recruiter skills that you need to look at with attraction. So first time founders in my experience like core capabilities is they’ll be able to pattern recognition reasonably well based on some early training that they can sell. You know that’s the fundamental role of what they’re doing in attraction. And what that? Means then is that the mantle on the recruiter and expectation of their responsibility is massive, so founders need to hire absolutely unbelievable recruiters because they need to do the. They will probably have very high weighting and experience then to have to navigate through the situational relevance. Have they actually done the right projects, the right technologies? They will be fundamental in EQ and leveraging that through their training experience, which the founder won’t have yet in terms of assessing for risk. Assessing for motivations of the candidate, assessing for that stage alignment, asking the right type of questions, which is situational questions, behavioral questions, different techniques, and they’ll need to deploy. So there’s they’re doing a lot of assessment and being relied on for that plus then. Doing a lot of selling themselves too. So that’s the big mantle that good recruiters need to be used for, which is why you need to be very careful early on that you get an absolute a player recruiter partner with you because they play such a bigger role than the founder capability. Maybe at that point in time. And that’s not all founders. That there is a big. Gap there sometimes in the training that’s needed because they may not have done that before simply through, you know, not enough candidates through yet to to be able to build that recognition up. And then on the retention side, that’s where you know the founder and the people team play a big role together and that’s on a wider strategy. But you’re thinking a lot more on if we’re thinking of what’s called people as a product. So the company is a a product. In the beginning you’re focusing much more on acquisition and activation. You know can we get them to the effect of through on boarding and do their first sort of impact piece. But the longer to thing about retention is you know are we improving impact as kind of like a North star? And in doing that then it is this cultivation piece of thinking about their engagement that leads to sort of performance and retention. How do we think about then the enablers to that through, you know, management, training, development, all these other things that we have to start looking at well-being are different variant pinpoints. But again all of the retention piece. Uh comes down to a bigger strategy piece of how far the business needs to retain people, which is a very commercial discussion to be had. And and there are different unique business needs at different times based on uh, whether a company should be restructuring or redesigning. And I don’t mean right sizing or over hiring, that’s a separate problem. But what I mean is the org design of where we need to put people in different roles at different stages of time based on whether you might want to, I don’t know. Be moving from mid tier customer to enterprise all of a sudden to go from you know XR to something over. Time. That’s an entirely new business strategy that’s going to fundamentally impact the business on a on a big level. So that will impact existing hires and how you look at retaining them and perhaps skilling them to be able to move to enterprise clients for example. And then fundamentally bring in a whole other set which may impact then you know retention and and what they may need. Those new highs coming in as well. So it’s it’s, it’s super multifaceted. Once you get into the retention piece long term. 

Hessie Jones 

Yeah. Can I ask you a personal question? Have you ever dealt with a company that had to do a major pivot and had that impacted their existing culture and employees. 

Beth Nevins 

Oh well, it pretty much happens at every inflection point. I mean, pretty much after every fundraise, there’s a significant change in, you know, the acceleration of the business, not just in terms of like the rate that needs to keep growing like 10X in every single like year basically. But also as I. Said like how you do that. So like if you’re going to increase your revenue, it could be through like you know product diversification and that. Impacts, like a lot of like how you’re building robustness of the platform technology and uptick, as I said, it could be like different customer segmentation or. A customer base that you start going into and they may need different features or different needs. It could be simply then just using selling the. Same product but. Then into multiple countries. So all of a sudden you’ve got different cultures all come in and that impacts people of how they, you know, work with diverse talent and teams in different times and all the rest of that comes with scale. So this has happened operationally just through like. Organic growth and like success in that way, but then you also have it in the state of play that we have post COVID world, which has been a lot of over hiring as we’ve seen. I don’t need to tell you about  that and there is a a a change and shift of focusing a lot more on performance right now and what we can get and going back to those lean core principles and less. Is more and. So forth. There’s a balance there though, because still the best companies still need to grow and and do so. So we don’t want to be stuck in that cycle. So again, this is all nuance. And context related and. And yeah, I’ve been part of that. I’ve been part recently of a company cannot name but 100 million plus in in funding and came in and did an audit for the board and the complete whole you know. The the the entire strategy didn’t fit the commercial. You know the business model or even the commercial direction. So that needed a whole new strategy and and and right sizing that comes with that to take the business forward in the right direction. That was ultimately the right thing for the right people who stay there and the right thing for the company to to survive. 

Hessie Jones 

OK, that’s I and I. It’s par of the course. I I’d say as as technology ebbs and flows and as technology companies start to get into this fray of uncertainty. Then I guess uncertainty becomes the norm. I would say so let’s how does tell me about culture and and because that’s the thing that ends up permeating an organization from the early onset to now that a company being like. Seven say 100 plus even 200 employees. How does that change and what is the role of HR or slash people management when it comes to infusing and maintaining that culture? 

Beth Nevins 

All right, I’m getting. I’m getting no philosophical here but. I I tend to come back to fundamentals and and arguably this does come back to language actually ironically, but I think like first of all I like to look at roles and then definitions. So like what is the role the founder plays in culture now? For me, the founder is accountable for the culture. 

The CEO, depending on what stage you are and titles and so forth. So the founders accountable for culture in the company, that’s very important. It’s not just or only HR or people, it is the founder. The CEO. So that’s very important to understand that and how does that play out then in, in their responsibilities? Well, fundamentally it’s role modelling and how they make decisions that is very important in terms of their role and responsibility when it comes to to culture, then we look at the role of the people team. I think the people team are critical and. Needlers to you know the culture that they found and the vision they want to set and and people, teams certainly can create people, visions that align with with that. But you know we have to appreciate this very much. Found alleged as founder LED companies for a reason. So I encourage the founder to lead on on people where some people get excited that they’ve got this role at the table. So full but and and I’ve been in that camp years ago and I was younger and didn’t really understand. And then I came to really appreciate no, it’s it’s all about founder like push it onto the founder, help the founder, make sure the founder wants this in terms of their, you know, their vision and their their view on people. And then there’s like two things on the the definitions then that need to be like super clear that aren’t always. So for me like. Values stay the same overtime. And culture evolves is more fluid. So let’s break that down on what I mean. So values are things that we believe are key enablers over a period of time to achieve in our mission and and how we can make decisions. And for me they are more useful in times of conflict. Crisis and how we commute. 

That’s how I think they should be used. I’m less concerned on whether they used day-to-day and we can like, seriously. See them embedded. All over the place, or suck up on. The wall where? Culture is much more around the idiosyncrasies and things you might see organically, day-to-day, and the rituals we have at that stage in time that help us get the work done and how we tend to sort of operate. And there are two things I think need to be explained and then. Where people teams come in to help culture then is on two big enablers. One is like good design. So can we help facilitate some like like touch design principles and how we want to do? Mine operations and infrastructure and people products and things people will use in the company, that sort of standardized and keeps to the sort of philosophy and principles that we want, whether that’s lean, light, whatever, transparency, whatever. And and two is like good people, that’s the people role in in culture as they keep going back to these inputs obsession. That I I seem to have today is, you know, making sure that we are. The gatekeepers or focusing attention there on making sure that we bring in great people that align to then build those systems and and teams that we need to to take us forward and with that then some people argue that culture is defined by the lowest level of behaviour that you will tolerate. Well, that’s when we think about good people and standards. That’s where people teams can really help with standardising what is our definition of high performance and and low performance and so forth and standardize that across the company. 

Hessie Jones 

That’s great. That’s great. I like how, like you’re even talking about how culture develops organically and that that you don’t necessarily sit around the table with your Co founders to define your mission and vision without without infusing an understanding of what is the kind of company. Want to create. I wonder if those are the those are the discussions that are even had at the very early stages. 

Beth Nevins 

Yeah, I I look, I think culture is organic, but it’s again, I I don’t want to contradict myself too much, but it it also has to be a little bit intentional. As I said, you know what, what are what is the target state that we might want to be going through operationally and and again cultures the rituals and changes that might need to happen to to make that happen. And again you know let’s say. Culture is intentional in the sense that I go back to some of that stage fit synergy. You know, we don’t want to hire people that can’t and block themselves early on, and that’s quite important then in the culture that you might want. And how that looks and feels and how we do things early on in in the company too. So I think you need to be a little bit intentional for what you’re looking for and how that might play out at the stage and time. But again, it’s likely to form very early on in that DNA around the founder behaviour in any event and that’s the beauty of early stage performance. It’s just so visible. 

Hessie Jones 

Mm-hmm. That’s that’s a that’s a great answer. OK. So I have one more question for you. That’s OK. So for companies who haven’t invested in developing a lot of the stuff that you’re talking about the the practices, some of the policies, what kind of risk do they run into and how can they? And can they catch up? And I’m specifically looking at what does that look like from the first meeting that you have with the founder who is just exploring like what to do as he starts growing? 

Beth Nevins 

OK, so I think this will comes down to like being very careful on how you position risk and discuss risk with a founder. So what I. Mean by that is, you know, as I said like, you know, write an order for a board or a, you know, a professional COO is very easy to discuss. You know, you go. Into business and you look at what’s going on with people and talent. And fundamentally, there’s three business risks, which is. One execution risk. Two can be financial risk and three is like brand risk and there are definitely certain minutiae risk like legal. Security and all that that. They fall into those 3 buckets ultimately, and those are the things that you can spot very quickly. As a experienced people or talent leader, when you’re looking at the current state of play and certainly when operations are skilled and and things have got a bit out of control. But the conversation we have with the founder then about how you position these things is OK like a. Like how great is that risk and the likelihood of it to actually happen? B. We then analyze the reward versus the risk, and this is where like bit scaling like Reid, Hoffman’s book and the theory of that kind of helps. Like, you know, do we believe that just growing and forgetting about this debt and the risk is actually, you know, a trade off to achieve brick things if it pays off, it’s just better to ignore some of these fires right now and just carry. You know, bulldozing through for growth reasons, then that’s a discussion we have from a commercial perspective and then like, see, you know, this where values does come into play, right, we’ve we’ve got these risks. We’ve got these problems. Uhm, you know how from a values perspective, whatever they may are, do we want to address these? Because that will be indicative and play out then in terms of how people perceive we’ve ignored them or addressed them or whatever. So I think, you know my point is people teams should be able to accept that we can make recommendations for sure and that is our job to do that. But you know, particularly on the outside now my my job is to advise not necessarily influence or I’d be more of an influencer as a as an operator perhaps. So I think you need to be mindful of that with founders and if if founders align and you come to a conclusion. Then that things need to change. Then first of all, you need to agree then what that target state might look like and just come back to that. Why? Why we’ve agreed that this is the case. So you’ve got full buy in and that messaging is consistent across that leadership team. And then look at baking into your road map, how we then that’s going to to play out. Now the one thing I see with people, leaders that I’ve mentioned over time. As an operator and as a consultant is. It if the risk is ignored and then you know it happens, then the people leader needs to be very smart with the fund to make sure that that’s discussed upfront, that if we ignore this and it happens, this is the impact then on our existing road map in terms of delivery because this is where burnout happens where the people leader hasn’t had the foresight. Perhaps of like the the scenario playing and and then they are still expected to deliver on this colossal road map, particularly for quite lean and trying to do all these things and then fighting some of these risks that didn’t pay off there and which could come through disputes or certain. You know different employee relations problems and that leads their burnout of their time. So it’s that’s the way you have that conversation then with the founder. CEO is as I go back and just summarize is you know, play out those scenarios and how that would impact an existing road map that remains or do we create a new one together. 

Hessie Jones 

Thank you. That’s a. That’s a great synopsis on on why this this needs to be done even earlier on. So thank you so much. I think that’s all we have time for today. And my goodness, you’ve opened up my eyes to to the role, the important role of of people management and I think I don’t think. People understand actually at this stage like how important this becomes, especially as the company grows. So thank you so much, Beth. Where where can people reach you if they need to contact you? 

Beth Nevins 

Awesome. Thank you so much for having me. Hessie, by the way, where can you find me? So my website is developer which is DEVELOPA dot IO and I’m on LinkedIn. I do tend to post quite a bit going back to our branding point and I’m Beth Nevins and we also have a company. Page codes to. 

Hessie Jones 

OK, perfect. Thank you. So just for just to to kind. Of wrap up. For startups, HR isn’t just about the paperwork and the policies. It’s really about nurturing the heartbeat of of the organization, which are the people. And thank you for for hitting that home in spades. So for our. Audience if you have any other topics you’d like us to cover, please e-mail us at communication. At altitudeaccelerator.ca Tech, Uncensored is powered and is produced by altitude accelerator and we’re hosted on Spotify. You can get us wherever you get your podcasts until next time. My name is Hessie Jones. Have fun. And stay safe. 

Christina Kosmowski 

Thank you. Thanks so much for having me today. 

Hessie Jones 

OK, so I want to. Pull out a couple of statistics, but before. I do that. I want you. To tell me a little. 

Christina Kosmowski 

Bit about your company. All right, so logic monitor, we’re a hybrid observability platform that’s powered by AI. So what does that actually mean? It means that we collect data natively from thousands of different. Environment. So whether it’s from the network to the infrastructure to the cloud. Containers through the application and then because we collect all that data, we’re able to predict anomalies before they become a problem and either bring their systems completely down or there’s performance issues across your entire IT infrastructure environment. 

Hessie Jones 

1. So here’s some things and I think we just updated some of these metrics. But from a scale perspective, you monitor over a trillion, is that right? Trillion trillion records per day? Three 3 million active devices? Or is that updated as well?

Christina Kosmowski 

Billion records? Yes, as per day. Yes, that’s that’s correct. Around 3 million active devices across 100,000. Users, you know we’re in 30 plus countries, you know, so we’re we’re really excited about our scalability, our extensibility, our depth and breadth of of coverage. 

Hessie Jones 

1. So tell me about. Given the number of metrics that you’re actually supporting, which variables do you specifically prioritize to deliver the kind of service that you do?

Christina Kosmowski 

Yeah. I mean, I think it’s super important that you first and foremost can see everything in your environment. So you need to be able to collect this data from all the different sources. So again, whether it’s your network, whether it’s your database, whether it’s your server, whether it’s a cloud container, you’ve got to be able to see all of it. Collect all of it and then that way you don’t have any blind spots, so once we see and collect it, we then have the context from being able to. Use that that ultimately we can become very predictive and find, you know, with pinpoint accuracy and anomaly before it becomes a problem. And ultimately we can we can, you know, also automate and solve that for for our customers directly as well. 

Hessie Jones 

OK, so you have different companies? Coming in from different verticals. So I would assume that there’s also different thresholds when it comes. To I don’t know, critical infrastructure problems, right? So how do you specifically adapt to each one of those scenarios? 

Christina Kosmowski 

Yeah. So we are a great use case for any type of company across any vertical in any size. So we’ve got a fortune. Healthcare company, you know, big retail name brands, we’ve got financial service institutions, we’ve got your local sports team that all use use logic monitor, but each of that data is collected within their own customer environment data. So it’s their. Data specific to their environment and then we have all the expertise around that type of infrastructure and network data that we collect. And so that data constantly gets smarter because it’s learning from our customers own data and it’s not a bolt on, it’s not a ChatGPT wrapper on top of someone else’s data, it’s actually. Our individual customers data and we don’t share that data across any other customers as well. 

Hessie Jones 

OK so. Let’s talk with some of your top company top clients. You have Coca-Cola, top golf, Airbnb. You said you also had healthcare companies. What are some of the top challenges that that they actually I don’t know try to solve on a day. Day basis. 

Christina Kosmowski 

I mean, first of all, I mean, I think we all see this even in our daily lives. But think about IT environments. They are certainly not getting less complex, they’re getting more complex and their surface areas increasing. So people are adding applications, they’re adding databases, they’re adding infrastructures, they’re moving from the cloud, back from the cloud back on. And so these environments are complicated and so more and more data is coming in as we mentioned just even. On this this podcast we’ve updated. The you know amounts of records that we’re ingesting every single day, and so that’s that’s constantly scaling. So it gets noisy and it’s hard to find the signal between the noise. So if you think about an IT operations. And they’re getting diluted with all these this data, all these alerts and they’re quickly trying to find out what alert actually matters, what one actually can really be a problem for them and make sense of all of it and kind of be able to summarize, you know, all of that together to say, where are these more systemic issues that that I can go proactively? 

Hessie Jones 

So and you mentioned anomalies versus fluctuations and so the systems that you’re using. Are they learning from the? I guess the best practices that the humans have evolved over time, or how much better are they than humans? 

Christina Kosmowski 

 Yeah. I mean, I think, you know, we’ve been using machine learning and stochastic model techniques in our platform since inception. So we were founded in 2007. So for 17 years we’ve been. We’ve been using that, but now with kind of the evolution of the generative AI, we can get even more predictive around where we find these anomalies first and foremost. And then secondly, we can use natural language to summarize this. So now we can start to say, oh, you. 

We had 12,000 alerts. Well now we’re going to bring that down to 100 and be able to say these are the. Specific areas that. Those alerts are happening and now an IT person can actually ask questions. You know, in a in a normal language and say, alright, tell me more about what’s happening here and then we can actually give recommendations on what that root cause analysis is and what they should do. And as they get. Comfortable with that and we can then even start to automate those those recommendations. 

Hessie Jones 

OK, so for your client. Help me understand what’s important for them in order to trust that your system is doing the job that it’s supposed to do. 

Speaker 2 

Doing, yeah, I mean, so we just were working, we just were working with a customer recently and they had an issue that they didn’t know about. Three years and within one hour of giving logic monitor in their environment, they were able to find that. Issue. Yeah, I mentioned the number of alerts that are coming at them. They have 12,500 alerts coming at them. We reduced that by 75% immediately. So I think first and foremost is we’re able to get up and running quickly to show this value early and then they’re seeing these real time results about you know being able to reduce the noise, being able to pinpoint the issue. 

It is impossible to pinpoint and then that’s ultimately making them more efficient so that they can go scale and they can go do more strategic innovative projects instead of being buried in the noise, trying to kind of. Find these these issues. 

Hessie Jones 

Issues. OK, thank you so. So how does your? I guess your generative AI, how is it used? First of all, and how is it helping to adapt and and monitor some of these specific use cases when it comes to the different verticals, but because I’m assuming there’s going to be, there’s going to be. These differences among the different verticals, yeah. 

Christina Kosmowski 

I mean, I think I keep going back to we’re not a bolt on, we don’t have a ChatGPT wrapper, we are actually at the first and foremost, we’re a hybrid observability platform. So we collect this data natively. Therefore we use a combination of rag techniques and kind of small language models that get trained on specific observability data, right? So the large language model such as like open AI or even. You know the open source models, like they’re not trained specifically on observability data and we are. We’re trained on that. That’s our bread and butter. That’s what we do. And so we’re in our own customers environments. We’re already that trusted partner in their environment. We’re getting that data natively and so the models are continually getting smarter. You can approve that budget. 

As the customer’s data is continuing  to kind of grow and evolve with logic monitor. 

Hessie Jones 

So you mentioned Reg. Because I just learned about RAG, probably with everyone else like two months ago. What the heck is that? It’s another not another cleaner. It’s actually a technique called. OK. Remind me what it stands for again, because I just had it. 

Christina Kosmowski 

Runtime. Performance and so this is really where. We we’re we’re not reliant on like a single a single large language model. We’re able to go kind of kind of left and get this data kind of real time and and learn on those models instead of being these dependency on kind of these these larger larger models. 

Hessie jones 

So I knew. I don’t know what the R is, but I know it’s oh runtime augmented processing. Is that what it is? OK. 

So I guess from that perspective, because my next question was going to be how you ensure that it doesn’t inadvertently expose sensitive information from one client to the next by it by training it on only observable data, then using Reg, then you’re actually only ensuring that the information that. As is, is what the. 

Christina Kosmowski 

Customers environment, their environment, their own environment and I think that’s super, super important. And you know we take security very seriously. We’ve been you know, we used across thousands of customers. We’ve been in business for 17 years now. So it’s it’s something that’s very important to. 

Hessie Jones 

So tell me about. What makes you unique? Because you’re considered a hybrid? Nature observability. So tell me about that a little. 

Christina Kosmowski 

Yeah, I mean, nobody else can do that. You know, I think, you know back in the day, you had kind of on premise observability tools that were born in kind of the late 90s, early 2000s, then in, you know, the 2010 with the rise of the hyperscalers, you had folks kind of rush to to monitor the cloud. 

There was nobody that was bridging those two things together and the world is hybrid. So over 86% of companies are hybrid and expect to remain hybrid for the foreseeable future. So you had that plus the fact that you know IT proliferation is continuing to happen at a pace. That we’ve never seen before. We’re really in this unique position where we can see the step and breadth of information in a single unified view that nobody else can. 

Hessie Jones 

1. So one last question, Christina, so we know that it we I think we already ran into an AI height about 1.5 years ago. And now we’re generative. AI is a different it’s a different beach. So how do you ensure that you are developing or you’re delivering tangible value to your clients and not just another round? Of hype that. They have to latch on.

Christina Kosmowski 

Too, yeah, definitely. I mean, we use the term practical AI quite a bit and you know everything we do is we innovate with our customers. So this is something that our customers have been asking us for and we’ve been able to Co innovate with them and get you know, again using their real data, solving real business problems that they have and showing those results. In a quick time to value and that’s really why our customers really love working with us. 

Hessie Jones 

Perfect. Thank you so much. Well, thank you and yes. We will be back. 

Emily Reid 

Absolutely. Thank you Hessie so much for having me. Appreciate it. 

Hessie Jones 

So let’s start off with you. Tell me a little bit about yourself. Like how like your interest in this topic, what you have done and how you end up at AI. 

Emily Reid 

Overall, yeah, absolutely. Thank you. So in terms of my own story, I think one of the fastest ways to describe myself as I’m a computer scientist raised by educators, both of my parents have been educators their whole lives. They grew up in a really working class. Background and really kind of use education as a way to bring themselves into the middle class. My father grew up on a farm and became a professor. My mom grew up in a in a project and became a lifelong elementary school teacher, and I’ve always looked at education as a way to solve problems. Though initially in my career I thought I wouldn’t go into education at all. It ended up being something that I really kind of couldn’t, couldn’t, couldn’t. Away from I studied math and computer science in college and that is around when I really started to develop an interest in and concern about the lack of diversity that I was seeing in the spaces that I was in. I was usually one of the only women you know, maybe one of two or three women in a lot of my computer. Science classes and then eventually when I went into the tech workforce working as an engineer. I I saw the same thing and was also becoming increasingly concerned about what I saw as sort of a lack of ethical frameworks around a lot of the work that we were doing. You know, it felt a bit like kind of the Wild West. There are a lot of other disciplines like law or medicine that have. Then you can, you know, criticize the the structure, but they at least have a structure around and framework around ethical standards, and that’s not something that is standardized yet in the AI space and so. For me it was. I was kind of really becoming concerned by what I saw. I was also experiencing a lot of personal frustration and challenges as one of the only women in this space, and I also knew that I was walking into those rooms with a ton of privilege. And so it just spoke to. To the fact that this was a really deep problem. And so that was really when I became increasingly interested in how we could be using computer science education to address some of these problems, you know, kind of where what’s the root of this issue? I always say to folks, I think computer scientists are problem solvers at their core. And this was the problem I became most interested in. So I ended up going back to Graduate School for AI, and I did research and work in computational linguistics, natural language processing, machine learning. But it was during that time I was also kind of going deeper and deeper into computer science education. I ended up joining what was in a small organization called Girls who code was with girls who code through our kind of hyper scaling period and really learned how to scale high impact education programs. I came to AI for all in 2018 to launch our Open Learning program, which was a program to really take a lot of what we had found as successful in the our early high school programs and take it online and bring it into schools directly. During that time, we were also launching our college programs today, after all, and that was where there was really kind of increasing interest in getting students directly into the workforce. AI was really picking up. And I think what’s been interesting, I became CEO about two years ago and what’s been interesting. During this last two years, has been seeing this real evolution in and and kind of resurgence of AI and generative AI. And of course when. Chachi, BT then Gemini Dolly, like all of these tools, came out. They really they really had such a huge cultural impact. And I think that what’s been interesting about being at AI for all through that period is it’s some, it’s a moment that I think our founders really anticipated when they started that. Original program that you described, they really were. They knew that this moment would come and this society would need to be much more ready than we were. And so I think our, you know, our mission to create the next generation of AI change makers that the world needs. Is really focused on the fact that this is a. This is a train that has left the station. I understand when folks are really concerned about where AI is going, I think those concerns are valid. I also understand when folks get extremely excited about where it’s going and what the opportunities. And I think that’s valid as well. To me, we have a we have a choice about what the future of AI looks like. It’s not something that is already set in stone, but it is going to be written by this next generation of AI technologists. And if we don’t make some changes now around what the diversity of that cohort. Of technologists look. Like and if we don’t make some changes around what the industry standards around, you know, trustworthiness, human centered AI, responsibility, ethics. If we don’t make some changes there, then I think we’re we’re on a really troubling trajectory. So now is the time. 

Hessie Jones 

So like I think even when AI was starting to emerge and you, you and I had this conversation earlier, there was. Is understanding that there was a diversity gap and so industry, not only the tech industry but even outside of tech and even in an investment side, they started making strides to ensure that there there was a lot more representation, a lot more voice. Their diversity of voice, when it came to technology or when it came to actually investing in founders, what have you witnessed recently that that seems to have? I don’t know makes it makes it seem that that we’ve taken a couple of steps back since that recognition. 

Emily Reid 

Yeah. Yeah. No, I love this question has because. I think it’s. Really cuts to the heart of a of a kind of concerning, but also potentially opportune time that we’re in and so. Uh. You. Know. 2022 in particular was an interesting year because when I started out in this role, AI had to actually kind of briefly left the headlines for a little while, and everything was about crypto. And we said, you know, kind of how what’s our kind of position around crypto. It’s like a lot of folks. Like students, funders are asking. About and how do we really make sure that we continue? You know, we know that AI is still going to be pivotal, but it’s sort of not the hot topic right now, which I think people forget about in the wake of the generative AI explosion. But there was really that that period for. For a good part of that year and then we went through towards the end of 2022 tech layoffs, which had an enormous ripple in our industry of computer science education and workforce develop. And because many of these organizations are, if all included, work with corporate partnerships as part of our funding models and it’s something that we’ve really valued because part of what we want to be able to do is to launch these students into their first roles in AI. So we want to understand what’s going on in the workforce. You know what are folks hiring for? That is a space that is changing much more rapidly than the universities that the students are in. And so we see ourselves as really kind of a bridge between that university experience and the workforce that is so rapidly changing and so. You know what we were definitely seeing during that time was there folks that I knew in the tech industry who maybe worked specifically in the DI space or the corporate social responsibility space, that was really kind of getting more constrained at the same time there was, you know, about a month or two. Later, there were these real enormous booms and investment in AI, and those were the teams that were grown. And I would say that folks who were maybe on like responsible AI teams, that was a little bit in the middle, that wasn’t necessarily growing in the same way that like an AI product team was growing, but it was also, you know, AI specific. And so it might have, you know retained. Been retained or prioritized in a way during those tech layoffs, so everything that’s going on in the tech industry ends up having a real impact on the nonprofit organizations that are partnered with tech companies. And so you know, for us it was a it was an interesting period because there were constraints with some of the partners that we were speaking to and there were others who were, you know, excited or coming to us because they wanted to get more involved in AI organizations. I’ve unfortunately seen over the past couple of months, some really wonderful. Organizations like women who code girls in tech global need to shut down and and I don’t know the details around why that is, but I have to imagine that there’s some element of this is sort of a. This is a really critical issue that I I don’t want the tech industry to forget about. I think that there was a lot of progress made for a number of years and the kind of layoff period I think has rolled back some of that progress. So my my feeling is that. We really do have a choice around what the future of AI looks like. We have a choice around what the future of technology looks like. I think I used to really talk about those things separately, but the way that tech has really been developing and AI has been developing is that AI is going to be part of everything. I think junior Rometti from IBM said that AI is going to change 100% of jobs. 100% of industries and 100% of professions, and I I don’t think that that’s hyperbole. There might have been a time where I felt like that was hyperbole and I no longer feel that way, in part because. I’ve and I’ve heard some folks liking this, and I think it’s a reasonable comparison to liking it to the Internet. There were people who said Ohh the Internet. ‘S going to. Be. A fad in the early 90s, right? And we all know that that wasn’t. That didn’t end up being the case. In fact, access to Internet is really critical for students to even have access to learn about. Some of these tools. Never mind. Learn how to. Program them. So that I think is that kind. Of. Really rapidly changing nature. To me means that this moment is very critical. It means that if we don’t make changes right now, then the status quo of a really homogeneous industry that does not have a standard ethical responsible framework is going to be the one that we will move forward with as a society. And it’s going to be increasingly difficult to change as the years go on. At the same time, I think if we are making a lot of changes now, that will become the model of what the future of AI looks like because things are changing so rapidly. So I have both a deep, deep concern about where we could go on one path and a real excitement and sense of hope about where we could go on another. 

Hessie Jones 

It’s it’s the you know I have the same some some days it’s better than others. But like on on some. The areas where where some of these amazing DIY initiatives have failed, like I’ll add, the one that I know for this fund which was specifically started to invest in women of color or women founders in tech, was recently closed down and it had raised over. $25 million the It’s closure set ways through the industry because it was so successful in its race, but through legal issues which they could not afford, they unfortunately were unable to to continue. But I will say from an investment perspective, what I’m seeing is that investors, especially when they’re starting to look at what gender today I can do, they’re increasingly anxious about the technology and not really sure what to trust and what can’t be, what, what to trust and what not. Trust. And so they’re looking to to people, to who who know the tech to get deeper into it, to even provide some elevated form of technical due diligence in order for investors to to be able to say this is good enough for us. So I think in a lot of ways. In advance of legislation, I think it’ll it’ll happen just because you know investors need to make their. Money. And if money talks, then this. Is the way to do it right? 

Emily Reid 

It’s so true. It’s so true, because I think, I mean I I think that is unfortunately a perfect example of, you know, how it affects the the the investment in industry side and I. And it’s interesting. What you what you say mentioned about regulation because I felt like that you know, really a lot of my conversations with partners and advisors in our space in 2023, no question was around generative AI, generative AI. We don’t want to get left behind. We don’t want to kind of miss. This boat, like how do we use? I had. I had friends who work in like relatively non-technical industries like maybe like like a A. You know, might be technical in a different way, but they’re not necessarily using AI in their day-to-day. So I had a friend who’s works in kind of more of a sales position at like a bio Med company called Me to. Be like, well, how do we what? Should our AI strategy be, you know, they were all of a sudden there was kind of so much focus on we don’t and an anxiety as you mentioned around we don’t want to get left behind. And we want to make sure we’re taking advantage of this. And then most of those same conversations this past year or the last six to nine months or so have been around governance. And what does AI governance mean and how do we make sure to manage these risks in the absence of any kind of real legal framework? And that’s something that actually our our founder, doctor Faithy Lee has talked a lot about that we are. You know, looking at what, how that might be something that some of our students are interested in because there is a real lack of. There’s a real lack of overlap in the expertise for folks who are in the kind of policy and legal space. And the AI space. And there really is a need. I think this has always been the issue with technology legislation is that it’s moving at so much more quickly than the legislation and policy. Move and you have so few folks who are really sitting at the intersection of those two areas of expertise. And so I do hope that we will kind of get to a a, a great framework. But I think it’s going to take more folks who have that computer science background working in that space or maybe advising. Not work. 

Hessie Jones 

OK, I want to touch on when we talk about AI advancement and why diversity is so crucial and we’re at a time right now where we’re starting to see some of the impact of why, like, why DI is actually needed. And so I want you to talk to me a little bit about the anthropomorphization. AI and it’s money I actually got through saying that word because it became. A. A word that it is so difficult to say, but it is a mainstream term and so when we talked about this, we’re talking about the attribution of human characteristics, whether or not it’s motion, whether or not it’s it’s, you know, it’s gender related. But we’re starting to see it a lot. More in in some of these AI chat bots, and I know it became a little bit of an issue when when Siri came onto the market as well as Alexa and Google Assistant. But I want you to to speak to me a little bit about. What the implications are from a gender perspective, as these AI chat bots start to roll out in in massive ways. 

Emily Reid 

Yeah, absolutely. I think it’s a, it’s a huge issue and agreed that well, it’s a little bit of a mouthful. It’s good that anthropomorphization of AI has become a little bit more of a household. Concept because I do think that it’s. It it, there’s a lot of complexity to what what the impacts of a really. Of an AI that seems like it could pass the Turing test, so it would be it would be difficult to tell whether this is really an AI or a human. There’s definitely a lot of evidence that users human beings prefer interacting with a an AI that does seem very human. I think you know there’s a lot more awareness of the concept of the uncanny valley, the idea that. That folks are are have a really positive reaction to an AI or a robot until it gets almost too human. And if it’s, if it’s kind of just short of being too human, it’s it’s, it’s really uncanny and that can be, you know, something that then people reject. So there’s there’s some interesting ideas around, like, you know, whether you go kind of further along on the spectrum of recognizing it’s not in AI or it’s not a human being, or whether you really try to make it as human as possible. And part of to me, part of the challenge with that is. It does become confusing. I see my my I have a three-year old daughter. She talks to Siri. I’ve turned my Siri into a male voice just to mix it up, but I think that it’s, you know, she she will chat with Siri and try to ask him questions her questions in ways that. You know, she’s aware that it’s not a real person, I think. But they are kind of growing up around these technologies as though they may be human beings or it might be difficult to. Differentiate which is. Which, especially on the gender side, I think this is a it’s a really big issue because they continue to see the vast, vast majority of voice assistance. Tend to be female coded in some way, whether it be the name and the voice, or both. That, to me is a really, really big concern, in part because it continues to kind of put women in a position of the kind of stereotype of being a helper, being an assistant. Part of the reason why I know that that happens beyond our. Our kind of gender norms and societies that some developers say that folks respond better to a female voice, and that’s part of the the testing, right. And so that might be a legitimate reason, but what are the other impacts of? That and is that the only thing that we should be valuing? To me? This is this is the kind of. This is the sort of issue that we need to be grappling with in an ethical framework for AI. What are the ethics of that choice? I also think that it in addition to it, just really reinforcing gender to gender stereotypes. It can. I think. I really believe that it is something that if we are having a much more inclusive industry, it’s not, it’s certainly not on the shoulders of the one female engineer in the room to make to to kind of raise this as an issue, but. As we have a much more if we are moving towards a more. Inclusive more diverse industry, I think we’re going to have much more nuanced conversations around what this could look like, what our default assistant voice should be, what kind of options are available to change that and being able to properly evaluate what the risks and harms are. We don’t really have developed standard frameworks for that at this point. And again, I think that if we don’t change that soon, we’re going to be in a world where Voice Assistant and female voice seems synonymous. 

Hessie Jones 

Yeah, I wanted to add to that because like. There there are. A lot more emotionally supportive chat bots. I actually tried one. It’s called my pie and it’s supposed to act as a coach and a confidant. And sometimes I ask the questions like how do I deal with this person? Who is highly sensitive or who is an intern? And I really want to convey this message, but I do want I want to do it in a way that’s non confrontational. And it is early effective in its advice. So I I think that so it brings up a couple of questions which which I want you to address is it’s one thing about dependence, but then the the second thing which you kind of covered in the in the last statement you made but. 

Hessie Jones 

But the legalities when it comes to the advice that’s given and and who’s going to be liable if if somebody takes that advice to heart. 

Emily Reid 

Yeah, it’s a really it’s a really, really rich example. I think because I think there’s a number of of issues that it brings up kind of first up point about kind of going backwards here. One of the last things you mentioned around the like what what’s going to be shared, who’s liable, I started out. My my career actually in cyber security and got into machine learning through that process and. So I’m always looking at all of these models with a A-frame around. What? What was what? Should have been the sort of privacy restriction on the data that was used to train this model. And as we’re continuing to feed more data into models, how you know? How much kind of personal information or company information might we be giving up? How is that being used? You know, I think that that’s a that’s a huge issue that companies and individuals are grappling with. But at the same time, you know another example of this type of bot is there was a story recently about one of these kind of emotional support chat bots that are being used with more lonely adults. And we know that loneliness for. Older generations is a really, really huge mental health problem, and so I think to me it’s a. There are some real difficult questions there. You know, one is. I think I think the sort of negative reaction, visceral reaction that folks have to something like that comes from the place of like, we don’t want this to be a Band-Aid, but at the same time the the current. State of affairs is is also not working and so is it going to be the case that maybe having more of those emotional support chat bots available to folks is gonna actually help them, at least in the short term. But then it also runs the risk of being a Band-Aid to a problem. And not really actually treating the the the deeper, the deeper disease, right, like kind of just treating the symptom, not the disease. And so I think it brings up some really challenging questions because I think it is. It’s concerning to folks to to really kind of put these emotional, put some of this emotional labor in, if you will, into the into AI’s, but at the same time it might actually, you know, compared to. The world as it is today. Without it, it might actually help some people. And so I think that’s one of the biggest challenges is really how do we navigate that part. What you mentioned about the pie chart bot I think is a really great example because. You know it. It brings up OK. It is kind of maybe giving advice that you could imagine a world where this is more veering on the could be something that, like therapists are using, right. Like this. There’s one thing that you might you might be using in conversation with your team. And then there’s the kind of another deeper level of what. You know, say a therapist might be using. UM and on one hand, I think that it does bring up a lot of issues around like are we now outsourcing that kind of real human emotional work to an AI that we I think that all of these bring up real questions and anxieties about what it means to be human. And so I think that that’s that’s one part of it. But again at the same time is it knowing how influential these technologies are. Where it would it be better to have an AI chat bot that is not emotionally intelligent at all, and what would that look? What would that look like? Right. And so I think these to me again, these are the really kind of thorny, challenging issues which there’s no real easy answers to my belief and. Then this is really why we do this work at AI. For all is that I have, you know, I always say to folks, I have my own ideas around what I think we should do as an industry and what I think needs to change. But ultimately I really believe that what is going to make the biggest difference and really help us head in the right direction and answer these really thorny issues is when we have a large diverse. Ethically trained generation of AI technologists who are bringing their own areas of expertise, bringing their own life experiences, bringing those perspectives into the conversation, and are feel empowered to do so. Are in positions where people will actually listen to what they say. You know we have we have this, we call the future Forum dinner series. We have these kind of salon style dinners with advisors and partners and and some of our staff as well as our students and we bring up a big issue around. You know what the future of AI is going to look like as we’re having generative AI and Gen. C really intersect? What’s that going to look like? And I’ve been in conversations where folks are talk. Thing about Gen. Z and STEM education and that phrased my hand. And said. This is a great conversation, but there’s no one from Gen. Z in the room, you. Know what do. They think. And so we we we make sure to bring our students into the conversation and keep their their perspectives at the same level as some of our, you know, sea level partners at top AI companies. Because to me, their opinions are just as valid and I’m a really huge believer in that kind of if you will, collective intelligence of our our real human net. Work coming together on these issues because that that that question of, you know, is emotional intelligence being built into a chat bot, positive or negative. It’s a really thorny question without a clear answer right now, but I think that’s the way that we’re going to be able to get to better answers. 

Hessie Jones 

Yeah. No, I agree with you. And I think that the the thing that’s really hard about AI and I think for people that have developed software, they know that you don’t ship something that that doesn’t 100% work. It has a different beast because it it it trains and it gets better, but it has to train on real data in order to get better. So you can’t kind of keep it locked in the box and assume that you keep feeding and other data to get better. It really. It’s it’s the unfortunate part of of being an alter, a different technology than than normal software, right? So I want to get into because you’re you’re talking about students that and I want to switch a little bit to education because there was a recent article. About a company called all here, and they’re an educational platform. That was, I think they’re hired by the Los Angeles Unified School District to actually build. A $6 million AI chat bot called Ed to help both students and parents navigate a lot of the educational resources and supplement some of the classroom instruction. So from all from, I guess, from the optics perspective, it looks like this is a really, really good thing. Not only will it help. Students, it’ll help kind of get them get the teachers moving to to a better level of instruction and help, I guess, reduce their I guess. Being overwhelmed not only within the classroom, which they have been for years, but also be kind of like the catalyst to actually enable a new type of instruction to get their kids moving in the direction that we want them to go. So apparently it failed and so that down the left. As well as the the employees which they furloughed, I want you to to speak to this idea of of education and AI in the classroom and. And situations like this that that have been trying to appeal to bring bringing not only like the technology but also the new wave of of how we do things into into the education system which we know has far been. I guess it’s archaic. 

Emily Reid 

Yeah, I think that’s, I think that’s a fair, fair way to put it because there is a. You know, it’s it’s so difficult for some of these really large institutions to change and there’s certainly some, some reasons why we may be want to be, you know, thoughtful and methodical in that process. But there’s also times when it can just really kind of create a a really challenging. Environment for innovation and and I do think that that you know the way that I think about change and innovation is around opportunity. I think it’s in those moments of change and innovation and when things are kind of shifting, the ground feels like the ground is shifting underneath you. That’s actually when I think we have the most opportunity to. Change systems for the. Better because it’s really hard to change systems when they are really firm and and, you know, immovable objects. And so there is, you know, when I’ve heard about this. Story there’s a couple different things that came up for me. One in terms of the what, what sounded like a. Relatively ambitious project for this for this new organization, you know from from what I from what I knew about the story, it did read to me a bit as something that. Was maybe kind of a victim of the hype cycle of AI. We know that technology goes through these hype cycles. There are periods when we get really hot on a particular technology and has gone through this a number of times and then we go through an AI winter where some of those those ideas. Don’t necessarily bear out and there ends up being a lack of investment in both the private sector and public sector. So I think that that that kind of like both the the business investment and the like scientific investment and kind of R&D in those different technologies, it goes through these cycles. And to me it read a little bit as something that. There probably was a lot of hype and excitement that then really overshot what was possible to do in that period of time and. You know that that I think is going to create a lot of challenges for a lot of these AI companies. Of course, we’re going to go through kind of these, you know, boom and bust cycles. But the thing that worries me the most is kind of who gets lost in that process. I do believe that overtime technological. Action tends to actually create more jobs, and it gets rid of, but it does get rid of jobs and there is a lot of reshuffling in the short term, and it’s the folks who are more marginalized in society that are going to experience the the worst of that, and they may not necessarily be. Trained up or have the social safety net that they need to manage those periods where the jobs are shifting around. So so those are a couple of the the things that come up for me on the industry side when I read that story, but also thinking about the education. Sorry I am a huge believer in the need for AI literacy, and by that I don’t just mean students being able to being competent in using AI tools. I think that that’s something that this generation, as long as they have access to the Internet, which can be its own. Question. But if they have access to the Internet, they’re going to have access to these tools and they’re going to become, you know, more expert on them than than I am. So that part, I believe that they’ll be competent users really quickly as long as they have Internet access. That’s not necessarily the same as being able to to understand what is really underneath the machines and what is really inside of these algorithms, and understanding being able to be real kind of informed citizens or informed users of these technologies, that doesn’t necessarily mean you need to be. Teaching machine learning to kindergarteners. But I think that there is actually a way for us to teach the fundamentals of artificial intelligence in a way that. Is. Not dumbing it down is it is really honest about what the technologies. Are what the risks, risks and benefits are and allow for an educational system that is helping students to, at a minimum, become real, informed users of these technologies and understand a little bit more what’s happening behind the scenes? I think in in sort of pre AI world I compared this. Or kind of pre AI focus and and thinking about computer science more broadly. I had compared this to, you know, the kind of Mavis speaking, typing classes and math blaster that. I did in my computer class in elementary school as opposed to learning how to program right and those are two really different things. We can be users of technology. Great, but how do we actually become the creators of it? Or it be able to be informed enough to influence it? So to me that all starts with AI literacy. You don’t necessarily need everyone to become. A machine learning engineer, but even getting more diverse and more diverse group of machine learning engineers is going to have to start with more AI literacy. Because if you don’t even have that kind of point A for. You end up having more and I, you know, have seen this so much working across the high school to college, to early career space is that if you don’t have really broad AI literacy, really broad computer science education, then what happens is that the. Students who end up studying that in college. Maybe you only get the students who were the the top math and science students, or you get a lot more students whose parents were already in the industry and have been around it at home. And so in order to really make that a much more diverse space, we have to be able to start at more of a level playing. Field and so it brings up a lot of those challenges. When I when I read this story is both, you know, the that that kind of like hype cycle and what that’s going to mean in the industry space, but also what the what the role of AI tools in education is going to look like. 

Hessie Jones 

It’s it’s funny that you’re saying that that’s going to ask you a question about the importance of STEM, but then I’m. I’m starting to realize that the way you’re talking it’s it’s not really forcing people into STEM, but but creating a foundation. At the very beginning where they’re understanding the technology and what are the intricacies of it, you may not necessarily be be good in math. You may not necessarily be good in science, but at least know enough about the fundamentals of the technologies that you’re using to apply more. I guess a critical thinking. Perspective. And so if you think about it and maybe this is something that that you could also discuss like there is an importance of going still going into social sciences still going into the humanities and arts. So how does that all integrate as let’s say if I decided like I like AI, I like to use it as a user. I understand it’s important in how I navigate all my daily stuff. But you know, I want to. Be an artist. You know, I want. I want to help people. In in social science. So from that perspective that you’re saying it’s not, it’s not going to limit you if you if you apply some of the stuff that you were. Talking about. 

Emily Reid 

Yeah, absolutely. I think it’s a, it’s a really, it’s a really important example because. I think the in the computer science space, for example, a lot of the kind of workforce development and CS education work has been focused on. There is like one piece around air literacy, but then we’re talking more about how we’re developing programmers, computer scientists, machine learning engineers. Software engineers, to me, AI is really upending that entire system. And and I’ll give a couple examples of why I think that’s true. And I think folks have talked about STEM education for a long time and I’ve I frankly, I’ve always had trouble with that particular acronym because I think that it actually ends up kind of obscuring what we’re talking about. It was often used kind of interchangeably with computer science. But actually none of those things. Math, science, technology, engineering, or math are exactly the same thing as computer science. So right, so, so they’re they’re it really kind of obscures, I think can can run that risk. UM, and now even seeing kind of computer science based versus AI space, there are things that are really different and unique about AI. That’s one of the things that really brought me into this particular organization. When I first joined, because there was a sense of there are while AI could certainly be viewed as. A sub discipline of computer science. There are some real kind of unique qualities around AI that are that are different and that I think really end up being highlighted when we’re talking about the practical impacts in education and workforce. So one example. I during those tech layoffs we talked about in addition to having, you know, friends who worked in the DI space at tech companies experiencing layoffs, I also had friends who were software engineers who were getting laid off, and not just because the company was potentially shrinking. But also because, as you know, in the sort of later phases of that, as more generative AI tools were coming out, it was actually replacing software engineers. In some cases, you know, our conversation around job replacement and AI. I years ago was more focused on concern around blue collar jobs and what we’re seeing now with generative AI is there is a displacement in white collar work for lawyers, for content creators, for graphic artists and for computer scientists and software engineers. If I think in that particular the sort of asterisk I’ll put there is. If they don’t have a a AI skill set that they can actually continue to apply in their work. So I when when we think about AI, AI education? While the computer science world is is changing and shifting, I think there were the kind of concept for a lot of the early computer science organizations education organizations. Was that OK? Having a degree in computer science will give you kind of a ticket to a great career path. And again, I I think that is true when you put the asterisk on it that it’s including AI skills, but at the same time, computational thinking has actually become more important for everyone in every industry, because every time what AI, what generative AI in particular has done. Is it’s turned all of us into programmers. We’re just programming in natural language. So when you go to, you know, chat TBT or you go to Gemini and you’re creating something, I’ll have friends who describe, OK, maybe they want to, you know, there’s some content. The social media that they’re creating or there’s some sort of image they want. To create they have. To go through a number of iterations and they have to give it feedback and they have to test it and run it again. And I say, Oh yeah, you’re debugging your code, your code is just in English or natural language of choice. Right. And so I think that it has really, you know, in computer science we talk about the kind of layers of abstraction. It’s allowed us to now, you know we were we were once really programming practically in in binary and then we built compilers and then we had things like Python And we kind of went further and further up. And now we have. We can program in our natural languages so that kind of that is really a shift in all roles. And I really I do, I sometimes hesitate around this because I don’t want to slip into to the kind of hype. Side but that quote from General Medi around this is going to change 100% of jobs. I think there’s really really something to that because one of the one of the examples that folks have given and I think it is a pretty valid comparison is is this kind of AI generative. In particular, explosion going to be similar to the the onset of the Internet and broad access. The Internet and there were folks in the early 90s who were saying, oh, this is the Internet thing is a fad, and we’re going to, we’re going to kind of come back to how we used to do business. And obviously that wasn’t true. And I think that it’s possible that AI is, if not to that level, certainly within that order of magnitude. So I to me that creates an environment where, yes, artist. Lawyers, social scientists. I know a lot of folks who work in computational social science and have been the lab that I actually worked on worked out in grad school, was really focused in that space. And you have folks who are social scientists and machine learning engineers coming together and working on some really interesting. Problems. And so I think it’s going to become more of just. A way that we all work. And so that real being able to bring that AI expertise into other spaces is a really different story than than years past, where this was something that, you know, only really lived in the most advanced labs and and some of the top research institutions. It’s going to be something that we all are touching in some way. Regardless of what role we may be in. 

Hessie Jones 

Yeah, it’s interesting that you say that because that, I mean, when we talk about AI and potential displacement now, you didn’t say that Gini Rometti said that people will be displaced. You said that AI will change everything. And I I listen to a lot of. Artists and I listen to a lot of writers who you’re afraid that. AI’s going to get good enough that they don’t need me? But I said, you know, writers and and people who have a specific craft have an advantage over others who don’t, and that they may be able to use their natural language programming to up level their skill. So they may not necessarily, right. In scratch, but they will become super editors and. They will go. Faster than those who haven’t written like a stitch of an article in their whole life. Right. It’s the same with artists. Maybe it’ll elevate everyone. To a new level that they’ve never hit before because because it brings on a new type of efficiency and obviously a new type of adaptability. 

Emily Reid 

Exactly. Yeah, I think that there is a. I guess what one of my real kind of core philosophies around technology is that it it should be. We should be looking at it as a tool and as a means to an end and not the end in and of itself. And I think that’s one of the things. That the philosophy that I think prevail tends to prevail in a lot of Silicon Valley companies and tech world is, you know, the technology has this end goal in advancing this technology and and I think that’s where to me some of the personally my opinion is that like the obsession. With AGI is incredibly displaced. To me, this really should all be around. This is a tool for what we the the goals that we have as a society, as communities, as individuals. These are tools, really wonderful tools that we can use if only we have. Access to them and and if only they are being developed by a relatively representative group of people, I agree. I think there’s a lot of potential for innovation in those in those other spaces. And I will say I have. I have friends who say our graphic artists and are really concerned because some of the work that they would be paid to do previously I can do in Dolly, you know, or maybe not as good a version, but I can do a quick version in Dolly in a couple seconds, right? And that does cause a real issue for them in the short term, but I agree. I think if we are. Because we sometimes get into this mindset of thinking about the technology as the end goal. We missed some opportunities around, well, maybe this is really just something that’s more assistive, there might be some areas where this is this is you know the real end goal. But you know I’ve heard a lot of folks talk about it as it really creates a get getting rid of the sort of blank page problem as. A writer, if you need to write a report of some sort, you know within all of the ethical frameworks of whether you’re doing that in a in a company or a school. But I think it does really remove a little bit of that kind of rough draft, you know, kind of first pass blank page issue. Because it gets us started with something. As you said. You can then be editing. You can then be in kind of a different role. I think that the I I was really interested when you know, SAG went on strike and actors and writers went on strike. And I think that there’s some really interesting stories and lessons there of how. You know human, the human beings at risk right now can really use some of their own collective bargaining power to to really force industry to come up with processes, policies that is are going to be more fair because I do think that there is a real. There is a real benefit to using AI in particular areas, but we don’t want it to be. At the risk of some of that real human creativity, especially when a lot of the AI creativity is is has been trained on those artists, right? It’s a lot of the tech, you know, I can I there’s been a lot of stories around that, but I could go into one of the image generation. And tools and say, you know, develop a, you know, a a new logo for for all in this artist style, right. And and some tools will kind of say, oh, we can’t do that and others will just go ahead and create it and there’s no real kind of strict framework. And so all of those. All of that kind of human creativity, some of which artists kind of either gave up, you know, may have kind of given up for free. Those tools are have been trained on and are charging others for, so I think we really need more more activity and we really need those voices to be heard in order for us to figure out what are the economic frameworks that are going to be fair here, given that there are. There is this the kind of value that artists have already created, that they’re not getting the benefit from and some of for some of these paid. Tools in particular. 

Hessie Jones 

I agree with you, it seems it seems like we’re at an inflection point where you know government, you know, policy artists, everyone. This is an interdisciplinary problem that we. Need to solve. 

Emily Reid 

Yeah. 

Hessie Jones 

Because it it’ll hit all of us at some. Point in time. Somebody told me, you know, maybe AI is the biggest equalizer and that it that it will do everything better than that normal human being. And so we bring in universal basic income. And I said, you know. I do not want that to be a default, even though even though it it may be true at at some point in time, there will be jobs that will be displaced and you cannot replace them if unless they upskill themselves that that has to be done. But I apologize, I actually want to talk to you for another. Hour or two hours, but I can’t. Because we have opinions. 

Thank you. Thank you so much, Emily, for coming and I look forward to more discussions like this as we. As you know, AI develops further. I want I want to see what kind of milestones that a fall is actually making in the next in the coming years. 

Emily Reid 

Thanks. Thank you so much and I’ll just I’ll 11. Last note that I’ll, I’ll, I’ll leave our conversation on this has been wonderful. Thank you. Again, I know we went, I could keep going too. So we’ll we’ll just we’ll have to do it again another time. But what you just mentioned there, you know I I I had a conversation with. A colleague who works in one of the. The major generative AI companies, and he was making a, you know, an argument that there this is kind of potentially a great equalizer, creating A level playing field. And again, I would I think this is a good place to use that argument around the Internet or that comparison to the Internet is we that was a bit of an argument at the time. For the Internet as well, that it was going to be really a great equalizer, it was going. To kind of. Create actions today we still don’t there are still folks who don’t have Internet access. And the other thing that happened is, is that sort of early those early days of the Internet, it was much more of the Wild West. And there were. Those bulletin boards, and there was, you know, a really kind of different kind of environment. And then you have, you know, more companies getting into the space, economic consolidation happening. And now the Internet is really influenced by. Like 90% of it is influenced by four or five companies. So that to me, I think that what technologies like this do is they create the opportunity for that to be the case. But they are ultimately going to end up working within this. The other systems that we have, the other political systems, the social systems, all of those systems that we live in as human beings, it’s going to adapt to that unless we are thoughtful and strategic and. And and and really kind of active and hopeful about what we can change there and how we can use those the opportunity with these technologies to change some of those systems. So again, I think that there is a lot of hope for where we can have AI go, but so much of it is going to be dependent on what we choose to do. So thank you, Jesse. 

Hessie Jones 

I have hope for the next generation, so I I tell my kids this all the time. You guys are going to change the world and I hope and I hope it. It allows us all to live a happier life, right these days, it’s hard to question that. 

Emily Reid 

Thank you. 

Hessie Jones 

Anyway, thank you. Again. So for our audience, thank you for joining us today. If you have topics that you want us to cover, please e-mail us the communications with our accelerator tech uncensored is produced and powered by ultimate Accelerator. You can find it. My name is Hessie Jones, and until next time have fun. And stay safe.