We've switched tracks over to Building AI Skills in Cybersecurity Workforce, and you will be talking about the AI impact of redefining jobs and workforce dynamics. So I'm really looking forward to your talk. So please welcome Emilie. Awesome. Awesome.
All right, well, lovely to see you here today. So my name is Emilie, and we are going to be talking about the future of work.
Now, 20 minutes is really, really short to talk about all of the future of work. So this presentation may leave you with more questions than answers, but hopefully it will leave you with the right questions at least.
Right, so let's take like 10 massive steps back and look at what work, what is work exactly? So work is actually merely just a social artifact that is very prone to shift with technological change. So on the screen at the moment, you see a scene from 7071, which is basically only 200 years ago, where the history of like nine to five jobs was simply life itself. Every day was a dance between survival and community, and people just had no idea what the future would look like at that time.
So at that time, the same about five years after this picture was painted, the US Census only tracked about a dozen different kinds of jobs. So what changed, you'd say?
Well, a lot of change came with this man who is James Watt, and James Watt had a lot to do with the wide implementations of the steam engine. And this catapulted us into the Industrial Revolution and the modern age that we know today. And so if you look at the US Census today, we are tracking several hundreds kinds of jobs. Most jobs created were pretty short lived as automation and productive gains continued. Nations after James Watt and the steam engine, that brings us to the 1950s, where technology basically already displaced some eight million farmers.
Seven million factory workers, over one million railroad workers, professions like street lighters, hundreds of thousands of telephone operators, gas pumpers and elevator attendants. And so you may think, OK, so what happened to all of these different people with all these different jobs?
Well, they got new jobs like travel agents, telemarketing workers, forklift drivers, copy editors. But as you know today, spoiler alert, we all know that AI is very likely to automate those away as well. So we may think that we've been here before, you know, with the automation and productive gains and all the job displacement, just continue the way that we did it before. We just create new jobs, right?
Well, it feels like we've been here before, but it is a little bit different today because even though there's still automation and productive gains and job displacements, these are pretty false narratives that kind of missed the point, according to me. I think we need to start thinking bigger and further out of the box, more likely like a complete shift from work life as we know it. And both in its dynamics and the way that we perceive work all together.
So a few weeks ago, both the people you see on the pictures, which are Sam Altman and Dario Amadei, who are the CEOs of the two of the largest AI companies at the moment, are predicting that AGI is coming in the next one to two years. And so just like there's different definitions of AGI. We're not going to go into that. It's a way different topic, but let's just define it as AI systems that surpass human intelligence across various disciplines, across all modalities, control embodied tools and learn and act 10 to 100 times faster than humans.
So, right, if that's coming in the next one to two years, we really need to start thinking about how we're going to change our workforce. And so today we live in a kind of knowledge based economy. And some might say that with the rise of AI, the same way that we invented mechanical muscles that automated the jobs that we saw in the black and white pictures, we are now inventing mechanical minds to take over some other kind of labor. The difference is that right now we've always assumed that there was only one kind of intelligence in the room, which was human intelligence.
Humans for monitoring work, coaching humans. But today we're going to start dealing with a different kind of intelligence. And this comes together, whether you believe in AGI or not. It comes together with prediction of some Altman that bets that intelligence as a service is getting better and cheaper every single year. And so some people are saying that, you know, with these new technologies, we're going to get a cognitive labor revolution and we're going to get a hyper abundance of knowledge work.
And some people say that this is going to lead like to post labor economics where we won't even need to work at all. But yeah, for the skeptics out there, like I'm still I'm still not sure where I sit with this. But the truth is, it doesn't even matter if you think about AGI or not, because some experts say that with the current capabilities, just as it is today. It will take about. With a few adjustments. So.
Leaving aside the conversation of AGI for a little bit, I want to show you this beautiful quote from Nobel Prize winning economist and professor at MIT, Darren Acemoglu, and he's basically saying that even though machine intelligence makes for a better story and business marketing opportunity with all the AGI talk, let's maybe focus on putting a global race towards pursuing machine usefulness instead, resulting in human augmentation. Let's go for the human complementary path where machines complement workers, skills and expertise over. The gold in the box type solution. And so this should lead.
If we do it right, this could lead to collective intelligence, which is basically the shared the. And competition of many individuals. Let's think more in terms of smaller, narrower AIs that are actually really reliable in what they're doing and kind of interlink them in the current mesh of the workforce between human beings.
Right, so machines can be useful in all kinds of ways. Study by Elizabeth Altman, who is a professor at MIT, and she's been doing this for the last four years and like, she's really, really into it. And I really loved it because she basically kind of broke it down to something a little bit more tangible. And she said that there are five different types of work related technology out there. There is the technology that helps us to accomplish our current work, which is very much in the same vein as, like, in all of those AI tools that you see in the meeting.
Harvard, BCG and Warwick University that said that just by giving people access to GPT-4, it increases performance by 40% on real consulting tasks. Moving on to workplace tech, which is the kind of like the Zooms and the Microsoft Teams of this world, which allows us to be working together in the room today. Moving on to workforce tech, which is basically the tech that helps us to manage and supply our workers.
Moving on to verification tech, which, you know, and the cybersecurity conference we're kind of familiar with, which is the credentialization technology that is basically giving people the ability to, in real time, prove who they say they are, what they can do and what they've done before. And then the fifth flavor is basically the one that is very sexy in the media at the moment, where you see technology as a workforce participant and more like a collaborator. And so these different types of technology change can influence work in different ways.
So it influences work in the way that we design it, in the way that we conduct it, in the way that we supply our workers and that we measure them. And so just for a few very practical examples about these are like these really blew my mind, by the way, I'm very excited about what the future can bring. So this is an example of a supplying workers technology. I'm going to tell you about a tool that is being developed by Novartis. And for those of you who don't know, Novartis is a really large pharmaceutical company with about 75,000 plus employees.
And they're basically building this tool that allows them to think a lot more holistically about what kind of resources they need to accomplish their goals. So every time they'll get a new project, because Novartis also may be a side note, is that they have a lot of external workers. I think they've got something like 35 percent external workers, which is actually not even that special in today's workforce.
And so basically they've developed a tool or they're developing it at the moment where when they get a new project in, they basically press on a button and their databases of external workers and internal workers all kind of like merged into one. And they use different kinds of AIs to help them see which match of workers would be best fitted for the task. And what's great is that the AI with enough information knows enough about the workers and what they can do.
And then moving forward, when the project move forward, it can also keep going with its suggestions of what other people should be added on the team and how that can be used. So that's the first example. Moving on to the second example, which is how AI can be used to measure work and workers. So this is such a fun application. There's this AI agent being developed at the moment called Lindy, and there's a CEO of a company of about 10,000, sorry, about a thousand employees who over the weekend just like fiddled around with it.
And the next Monday, it made that agent call simultaneously all of the thousand employees in his own voice and ask questions. It was a very short phone call. Employees were a bit surprised and they were just answering the questions. And later that week, the CEO just got basically, probably not even later that week, it's probably like pretty instantaneous. He got reports of everything, like just the summarized versions of what all these employees said to it.
And so basically, if you look at this from a project management perspective, it kind of replaces a big part of the middle management of a business, because you no longer need a human to distill the information up and down with all of the biases and the politics and the slowness involved in that. It's just individual contributors, a CEO and AI in the middle.
Now, OK, this is very naive to think like I'm pretty sure it's going to be a lot of, yeah, there's a lot to think and a lot to unpack about this. But the fact that this is still in its better stage and is the ugliest it's ever going to look is quite fascinating. Right. And then moving forward to the third example, which is a way that is mainly discussed in the media today of like how you can use vertical agents to conduct work. And so I want to make a little comparison. I want to say that in 1984, Apple and the Mac brought personal computing to the masses.
And I think that 2024, 2025, 2026 are going to be the years that. To be brought to the masses, which if you think about it in the 80s is the first time that people were being referred to as talent, because they had all of they had a computer to to explore different kind of things they could do. And so just a few examples of how AI is used to to conduct work. There is a solution, an agentic solution called Repl.it out there, and it's still very much in the beginning phases, but it helps with software development.
And so there's a startup to spend about eight to build the same app in about 10 minutes using Repl.it, 18 months to 10 minutes. There's another company that spent about a year building an app and they were able to spend it to build it in about an hour, which saves, of course, millions of dollars of human hours. And then another example of an autonomous agent. This was a really funny one. There's Ethan Mollick, who's a professor that I follow, which is another type of agent for all kinds of things. And he basically just on a Friday night, he thought, OK, like, let's go on.
He made Devin go on Reddit and just offer people the service of building websites. And so at some point, you know, he was just going out about his business, drinking a cup of tea, and Devin just like metaphorically tugs him on his sleeve and asks him to fill in a capture. So Ethan goes like, OK, well, why do you want me to fill in this capture? And then basically Devin was already setting up a strike payment, charging people, I think, something between like 50 and 100 dollars an hour to build a website, which is kind of insane. But the fact that it did that is kind of funny already.
And so predictions are going that by the end of this year already, we're most likely going to see a lot of autonomous agents doing, going about their business, which is, you know, taking on marketing leads and all kinds of different tasks. So what does that mean? Does that mean that it's like the end of like work as we know it, that we no longer need large organizations because everyone has the access to to a whole boardroom in their pocket? This is here an image of the very first organizational chart. And this stems from 1855, which is barely 200 years old. And it hasn't changed much.
And basically my message with this is that we seem to think that like organizations the way we know it have always been this way. But they haven't. And basically organizations have never, haven't really been made to last either. Organizations throughout history have come together to accomplish a task and then often dissolved. Which leads me to my next point, which is together with this shift of the way that we work, there's also a bit of a shift in the values and anyone who's worked with Gen Z can testify to this.
Yeah, like we're kind of moving towards a phase where people are valuing flexible work and work-life balance more than the paycheck and the promotion and the job title. So there's on the left about the prediction about the global gig economy. And I think that when when people have the occasion to have the help that they need to fulfill certain tasks, thanks to agents, people will be able to to start their own little ventures and find their own values. And for people who don't even like who you think like, oh, well, they could never be an entrepreneur or like why?
Why that they're not the kind of person to do that kind of thing. For instance, an employee filling the shelves at Tesco. Right. In his 30s, who hasn't really accomplished much, but who loves. He's very good at. To get an agent to suggest what that person could contribute to society and maybe it could start making digital little T-shirt animations for avatars in the future. Like these are all jobs that don't exist yet. But a statistic showed that 80 percent of the people who start gig work quit within their first year just because they don't have the infrastructure to make a living out of it.
Well, agents solve that problem. And so there's a quote by Navarra, the country's a big investor and venture capitalist. And he said, there's almost seven billion people on this planet. And someday, I hope there will be almost seven billion companies, which is very provocative. This is like opening. So moving forward to going back those 200 years, when I saw this picture, it kind of reminded me of where we are today. This picture was at the dawn of the Industrial Revolution. And I feel like right now we're at the dawn of a whole different kind of revolution.
These people could never imagine that there would be software engineers and women on only fans. Right. So what are the jobs in the future and how what is that going to look like? So what now is the question? And I think. It's going to require a lot of joint efforts. To achieve collective intelligence, and this goes through three different phases, you need to think about what the government can do. And I think that, you know, there's all kinds of thoughts right now going about universal basic income.
And I think Sam Altman even stopped taxing income altogether and tax land and technology because technology equals companies, which is a very different system. And of course, there's a whole health care system and social security. But I think that the thing that's really important, at least what I find really important, is that we we also think about economic inclusivity. And we don't just further the the the wage gap where the richer people get richer and the poorer people get poorer. Let's think about that Tesco worker and how that Tesco worker can can also benefit of these technologies.
This brings us to the second part, which is organizational. Yeah, guys, it's coming.
Like, you know, at this point, as they said, even if if everything stops evolving, it's going to take us 10 years to move forward. We don't have 10 years, most likely. So instead of pushing away, let's maybe find or being scared about it. Let's just be be fast about it and and find ways to promote it in responsible ways. There's this one example of this company that basically gives ten thousand dollars every single week to the. Automate the business. And and yes. And then moving to the last bit, which is individual responsibility. How do we make sure that people are enlightened?
AI in the future. How can we make sure that people can find their own little glimmer, their own little independent skills and work, hopefully have a more fulfilling life later and still find a way to to live with with the income that they need? If income is still going to be a thing with this, I would like to thank you for your attention. Thank you very much. So first of all, you need to talk to the electronic agent of your Internet provider because sometimes there was a short hiccup. So but we guessed I think everything that was was swallowed. So it was an additional task for us.
But we I think we managed to do so. Thank you very much for these interesting thoughts. That was really great. And we don't have don't have the time for additional questions because you filled the time up very, very evenly. But talking to you and reaching out to you always is fun. So I think if people have questions, they can just reach out to you and they can find you online and add additional questions. It was a pleasure having you here. It would be even better if you would be here.
But yeah, it's OK. OK, we take that. So thank you very much, Emily. Thanks for the time. Thank you.
