0:03 We know AI adoption is rapidly
0:05 accelerating in the workplace. And now
0:08 new research shows which regions and
0:10 jobs are most at risk. According to the
0:13 Society of Human Resource Management,
0:16 SHRM for short, more than 9 million US
0:18 jobs are at risk. And they're mostly
0:21 white-collar jobs. More than 50% of
0:23 writers, programmers, and designers
0:26 could be displaced. Cities with the high
0:28 concentration of white-collar and tech
0:31 jobs will face the brunt of the impact,
0:33 including San Jose, Washington, D.C.,
0:37 and Durham. Roughly 38% of the workforce
0:39 fall into more AI-resistant roles, but
0:42 they are among the lowest-paid jobs. To
0:43 talk more about ways workers and
0:46 employers can prepare for this AI shift
0:48 is Johnny Taylor, president and CEO of
0:50 SHRM. Johnny, thank you for being with
0:52 us. Your research shows the projected
0:55 job losses are concentrated in about two
0:57 dozen occupations.
0:59 So, what makes these jobs the most
1:01 vulnerable to AI?
1:03 Well, first of all, they can be
1:06 automated. More than 50% of the task
1:08 associated with the jobs
1:10 can be automated. And to the extent that
1:12 the computer, some algorithm, can do the
1:14 work, then it makes the jobs and the
1:17 people in those jobs vulnerable.
1:21 So, research found the AI-resistant jobs
1:23 tend to be some of the lowest pay. So,
1:25 what does this mean for the future of
1:29 the US workforce and salaries? Well,
1:30 it's kind of scary, right? We're feeling
1:32 the pressure.
1:33 The reality is healthcare jobs, for
1:35 example, right now healthcare jobs are
1:38 at the same level as they were in 2022.
1:40 Huge demand, but to your point, many of
1:42 these jobs are CNA jobs. They're
1:46 lower-paid human touch jobs. And that
1:47 does pose a real concern for the US
1:49 economy because at the end of the day,
1:51 paying people or a lot of people less
1:54 money will impact the community of the
1:56 the economy because they'll have less
1:58 money to spend as consumers. Sure. And
2:00 and what can workers and employers do to
2:02 make this transition a little less
2:04 painful? Is there anything we can do?
2:06 Well, yeah, there are jobs on the other
2:08 side of this. Some jobs will go away.
2:09 There's naturally going to be some
2:11 displacement. Our research says as many
2:13 as 9 million jobs. So, what we can do is
2:16 be honest with people now and say this
2:18 is your opportunity to reskill for the
2:20 jobs on the other side of this. There
2:22 will be higher-paying jobs in the
2:24 post-COVID world, but we've got to
2:26 identify what those jobs are. Most
2:28 importantly, we've got to ensure that
2:30 the people have the skills to do those
2:32 jobs when they're made available and
2:35 posted. We've been reporting about some
2:36 gig workers who are actually getting
2:41 paid to train AI to essentially do their
2:42 jobs. And we're seeing for all
2:44 industries, including journalism, by the
2:46 way. I'm curious, are we going to see
2:49 more of that? Yes. Um, you know, this
2:50 the whole that we all talk about AI
2:52 right now, artificial intelligence. The
2:55 holy grail of this is AGI, artificial
2:57 general intelligence. This notion that
3:00 it can do most, if not all, of what
3:03 human beings do as work today. So, AI's
3:05 only getting better and better every
3:07 day. And so, we as human beings have to
3:09 try to figure out how to outpace it in
3:12 terms of what we do really well, which
3:15 is we innovate. We we work in teams. We
3:17 we create new products. That's going to
3:18 be what human beings are going to have
3:20 to be able to focus on. Are you seeing
3:22 some parallels between this moment in
3:25 time to say the Industrial Revolution,
3:27 where we saw machines taking over a lot
3:30 of jobs that previously were held by
3:32 human beings? Is it going to be a
3:34 similar transition, do you think?
3:35 Similar, but it's going to move much
3:37 more quickly. So, to that point, one of
3:39 my favorite movies, I don't know if I'd
3:41 describe it, but it was Hidden Figures.
3:43 The idea that, you know, at that moment,
3:45 if you remember in history, human beings
3:46 were called
3:48 computers. They literally were called
3:50 computers. Not machine computers, but
3:52 human computers, computers because there
3:53 weren't computers. And then all of a
3:56 sudden, the main main stream, get that,
3:58 mainframe computer came about. And the
4:00 people who did that work, the women who
4:04 literally put us on the moon, calculated
4:05 how we arrived safely on the moon and
4:08 left the moon, were now made
4:10 basically without jobs. And so, what
4:11 they did was they retrained and
4:13 reskilled themselves. That's the
4:15 opportunity now. This is our hidden
4:17 figures moment. We've seen this in
4:19 history before. There are jobs on the
4:21 other side of this, but what we've got
4:22 to do is make sure that we have the
4:25 skills, the commitment to to be able to
4:27 provide those jobs, to do those jobs in
4:29 the future. And we all have to be open
4:31 to evolving and learning new skills. I
4:33 love that we're doing that. Yes,
4:35 lifelong learning. It never stops.
4:37 Johnny Taylor, great to see you. Thanks
4:39 so much for being on our show. Thank you.
