🎙 Podcast Episode: AI, Jobs, and the Human Cost of Automation
Summary: Data Analytics, AI in Hiring, and Labor Market Disruption
In Season 2, Episode 15 of the G'Day Mate with Evan and Tom podcast, the hosts sit down with Kevin Bihan-Poudec, founder of the Voice for Change Foundation, to explore the evolving relationship between artificial intelligence and the modern workforce. Kevin shares compelling firsthand experiences from his career as AI technologies became increasingly integrated into professional environments, offering a nuanced look at the ethical challenges and disruptions that followed. The conversation dives into the shifting landscape that many professionals now navigate, from job displacement to the urgent need for responsible innovation.
Key Viewpoints and Themes
1. Early Career and Impact through Data Analytics
Behavioral KPIs and engagement measurement: Kevin began in customer service QA, auditing calls and building Excel trackers for empathy, name usage, and closing questions. He discovered actual customer phone engagement was ~30% (vs. leadership’s 80%), benchmarked a competitor at 45%, and raised engagement into the 70s through training and monitoring.
Analytics as decision support: He framed analytics as storytelling—turning trends across agents, teams, and sites into actionable operational improvements rather than just dashboards.
2. Distinguishing Data Roles and Tools
Engineering vs. analysis functions: Data engineering builds infrastructure and dashboards (Tableau/Power BI, coding), while analysis translates outputs into business decisions. Both are essential; analysts contextualize and communicate insights.
Consulting experience: In fractional accounting, Kevin provided SMBs performance visibility, likening operations without analytics to flying blind—emphasizing targets, historical baselines, and corrective actions.
3. Job Loss and Market Shifts (2023–2024)
AI-driven restructuring: After a November 2023 layoff, Kevin anticipated a normal rebound in early 2024 but saw mass AI-led streamlining, reduced openings, and no interviews through 2024. He referenced reporting that ~60% of displaced white-collar workers didn’t secure roles in 2024.
Hiring climate contrast: Post-COVID over-hiring (interviews after 30–50 applications) has shifted to saturation, with postings drawing hundreds to 1,000+ applicants in 24 hours, lowering individual probabilities.
4. AI-Driven Hiring: Opacity, ATS, and Resume Tailoring
Opaque AI interviewing and scoring: Kevin reported interviewing with an AI bot and receiving an “N/A” grade despite a decade in analytics, with little transparency on rejection reasons. He advocated readable codes or explanations for candidates.
Keyword gaming and degraded experience: Applicants tailor resumes and interview responses to match job-description keywords, suspecting speech-pattern match scoring. This encourages bot-like language (e.g., repeating “Power BI”) over authentic achievement narratives, potentially harming hiring quality.
Recruiter insights on AI resumes: Many resumes appear AI-generated; some employers claim to detect and reject them. Kevin recommends aligning resumes to real achievements and avoiding generic AI phrasing.
Education gap: Evan noted limited training on ATS optimization and AI-assisted resume crafting; Kevin emphasized that leveraging AI is increasingly necessary, creating barriers for new entrants and displaced workers.
5. Upskilling, AI Literacy, and Productivity Multipliers
Certifications and adoption: Kevin is pursuing AI certifications, believing traditional dashboard-building won’t sustain multi-decade careers. AI literacy is becoming a prerequisite; he cited research that ~66% of tech leaders won’t consider candidates without AI knowledge and ~71% prefer one year of AI experience over a decade in other fields.
Economic burden: Individuals self-fund and self-direct upskilling, adding financial strain without structured transition support.
Productivity multipliers: AI enables analysts to scale from 6–8 tasks/day to 25+ tasks/day, shrinking teams (e.g., eight analysts to two). Remaining employable requires upskilling to join reduced core staff.
Verification and judgment: Speed gains demand rigorous validation of AI outputs and domain knowledge. AI is particularly helpful with coding and formulas, but human oversight remains critical.
6. Evolving Analyst Roles and AI Agents
Translator/implementer of AI: Analyst roles are shifting toward managing AI tools and contextualizing outputs for decision-makers. Kevin positions himself as a data storyteller.
Agents and displacement risk: The rise of AI agents in 2025 may automate even the “manager of AI” role. Absent policy guardrails, exponential tech growth could further erode employment.
7. Macroeconomic Signals and Structural Change
Cash positioning as warning: Evan referenced Berkshire Hathaway’s elevated cash position as potential signal of a pending correction, tying rising AI-driven unemployment to broader economic waves.
Structural vs cyclical: Unlike oil and gas boom-bust cycles, AI indicates a structural demand shift, requiring different responses from workers and institutions.
8. Regulation, Safety Nets, and Workforce Transition
EU vs U.S. regulation: The EU AI Act’s principles aim at ethical societal implementation, discouraging mass layoffs. In the U.S., fragmented state-level guardrails (e.g., California’s SB 53) invite regulatory arbitrage and forum shopping. Kevin argues for national frameworks.
Blue-collar automation horizon: Kevin predicts humanoid robots affecting manual labor within 8–10 years, referencing current industrial robots and the potential acceleration via ML on how-to videos. Even dexterity-heavy trades could face pressure.
UBI skepticism and transitional policies: Kevin is skeptical about UBI feasibility without mandates; he suggests caps on annual workforce reductions (e.g., 2–3%) and fines for “overfiring” to fund reskilling, seeking balanced transitional safeguards.
Employment protections abroad vs U.S. at-will: Evan and Kevin contrasted severance and reskilling protections in the EU (e.g., France’s multi-month termination review) with U.S. at-will employment and limited safety nets.
Agency modernization: Kevin recounted faxing unemployment paperwork in 2024, calling systems archaic and unprepared for displacement volumes; modernization is urgently needed.
9. Societal Impacts and Civic Engagement
Housing and relocation: Kevin became unhoused in early 2025 due to prolonged unemployment, relocating from Southern California to Texas, underscoring growing societal strain.
Singapore’s case study: Tom highlighted Singapore’s historical full-employment strategy (staffing elevators, parking) and supportive housing/social systems as a microcosm for managing technological transitions.
Voice for Change Foundation: Kevin founded a 501(c)(3) advocating ethical AI and job preservation, promoting voiceforchangefoundation.org and #ActNowOnAI to collect public experiences to inform policymakers.
10. Guidance for Students and Cultural Risks
Critical thinking first: Kevin advises students to build foundational thinking and avoid over-reliance on ChatGPT as a crutch. Evan warned that pervasive automation may erode responsibility and basic competencies.
Dystopian scenarios: Evan imagined cultural degradation (e.g., reliance on robots for daily cleanup), while Kevin noted that once-sci-fi ideas (AGI, “Black Mirror”) feel closer, intensifying urgency for ethical governance.
Conclusion
Kevin’s journey from customer service QA to data analytics demonstrates tangible operational improvements through measuring behavioral KPIs and translating data into action. The conversation widens to the structural labor market disruption driven by AI: opaque hiring processes, ATS filtering, keyword-centric optimization, and surging demand for AI literacy. Amid personal hardship—including homelessness and relocation—Kevin’s Voice for Change initiative seeks ethical AI adoption and policy guardrails. The rise of AI agents, productivity multipliers, and potential blue-collar automation signal a fundamental demand shift, not a cyclical downturn. Proposed responses include national regulation, transitional layoff caps and reskilling funds, agency modernization, and civic storytelling to guide policy. For individuals, authentic resumes, AI skills, and strong critical thinking are key to navigating an increasingly automated labor market with potential macroeconomic consequences.
Full transcript:
00:00:02 Host
Welcome to season two of the G'day Mate with Evan and Tom podcast. Evan and Tom met in 2011 in Western Australia when Tom was wrapping up a 30-year career as CEO of the largest Australian ROV company. And Evan was just solidifying his own decade-long adventure serving in the offshore energy industry down under. They've kept up their friendship over the years, and now you can listen in to their weekly chats with interesting people and their stories from around the world.
00:00:34 Host
So now, let's join Evan and Tom.
00:00:54 Tom Pado
Glad to be back. And who do you have today.
00:00:58 Evan Zimmerman
I've got a good friend here. We've got Kevin. Kevin from Dallas. Welcome to the show.
00:01:05 Kevin Bihan-Poudec
Kevin. Thank you, Tom. Thank you, Evan. Thanks for having me.
00:01:08 Evan Zimmerman
Yeah. So why don't you tell us what you do for work.
00:01:13 Kevin Bihan-Poudec
What do I do for work? I have been a data analyst for about 10 years now.
00:01:20 Evan Zimmerman
Okay.
00:01:21 Kevin Bihan-Poudec
Yeah. I actually, after a shift of career, I did on an article about 10 years ago. I didn't know what I was going to do, you know, professionally. Yeah. And it said that the data was going to be the new oil. When I read that, I was like, huh, let me jump on this because I will never be out of a job. Right. And, you know, my career started really at the bottom. I worked in customer service for an online fashion retailer.
00:01:54 Kevin Bihan-Poudec
And because I speak French. I'm originally from France, and I had moved to America when I was 16 years old. I could answer the phone in French and English. So it was basically a customer service position. Really quickly, I realized that the company had been out there for about nine years. And from what I was understanding, the leadership team said that they were engaging with their customers at 80%.
00:02:26 Kevin Bihan-Poudec
80% of the time, they were engaging with their customers with a phone. So I was really surprised to hear this, because from all the people in the room that I was hearing phone calls, I was like, I don't think we're any close to 80%. So anyway, three months in, I kind of got out of the mailroom, if you will, and they opened a position of a quality specialist. So that's kind of like how my data analytics career started. I remember listening to hours and hours of phone calls of people interacting with one another from all over the world and measuring different KPIs, key communicators.
00:03:08 Kevin Bihan-Poudec
So, you know, it ranges from, you know, are you reiterating the person's name throughout the phone call to make them feel, you know, they're not just a number? Yeah. Are you asking at the end of the phone call, you know, is there anything else I can help you with? Are you engaging with them? Are you expressing empathy? All these things, right? So all these are different measurable things. And it was literally me creating an Excel spreadsheet with different columns and rows and putting a bunch of ones and zeros.
00:03:36 Evan Zimmerman
Oh, wow. Okay.
00:03:37 Kevin Bihan-Poudec
So basically I started creating. trends of graphs of different things that we were measuring over time for different, customer service agents, teams, by location. And I kind of started building that story, if you will, for that company. And I realized we were engaging about 30% of the time.
00:04:01 Evan Zimmerman
Okay. It still seems kind of good.
00:04:03 Kevin Bihan-Poudec
Yeah. It was, you know, compared to what we want it to be, it was pretty low. Yeah, yeah, yeah. So I remember part of my job of, you know, analyzing this behavioral data, if you will, which was my first experience doing this. I would not only get several customer service agents into a room and go through different exercises. If you have a customer that calls you and feels distressed because their package is lost or they're having an issue that seems important, you have to first thing express empathy and say, I'm so sorry you're going through this. I'll do anything that I can do to make it right and things like that.
00:04:46 Kevin Bihan-Poudec
Yeah. And then I would listen to phone calls of these specific customer service agents over a period of two weeks and I would look at their data two weeks later and I would compare it. Are they doing it or are they not? Right. And then so by doing through that work, I also called one of our competitors that was doing a similar business model a hundred times.
00:05:15 Evan Zimmerman
A hundred, so you did the sampling, okay.
00:05:16 Kevin Bihan-Poudec
Yeah, so I just called them and I wanted to know how engaging they were so I could compare it to the level of customer service we're providing. They were engaging, I believe, at 45%.
00:05:31 Evan Zimmerman
Okay.
00:05:32 Kevin Bihan-Poudec
And the way I was able to also fact check, how we were performing at a company, I also called my own company a hundred times, without them knowing. Oh, okay. Oh, I work there and I know exactly what I wanna measure and we were way below our competitors. But anyway.
00:05:49 Evan Zimmerman
Wow, okay.
00:05:50 Kevin Bihan-Poudec
Long story short, by doing that work of training, and kind of, maybe it's because I'm from Europe and I have lived in different countries in Asia for a long time and I've kind of had that background of different cultures, even though sometimes the language, and there's the cultural barrier, They're just things that, you know, are trainable. Yeah. And I was able to bring the level of the customer service engagement level to, I think, in the 70s.
00:06:26 Tom Pado
Oh, wow.
00:06:28 Kevin Bihan-Poudec
That's excellent. Alongside the team of quality specialists that were located in other parts of the world. But, yeah, that was my first job in data.
00:06:38 Evan Zimmerman
So that's interesting. So, yeah, someone might see the term data analytics and think, OK, well, it is like what you described, like Excel spreadsheets. But they probably, unless they're experienced with that, might not understand that you're really kind of leveraging the data to have a desired impact.
00:06:55 Kevin Bihan-Poudec
Yeah, I mean, the whole point of, I mean, there are different sides of data, right? There's the data engineering part, if you will, where you are, you know, the person behind the computer building, you know, these dashboards, right? Like using data visualization tools, such as Tableau or Microsoft Power BI, where you're really doing the technical aspect, you know, coding and building all these graphs. But there's also the part of the analysis of the data, right? So being able to understand numbers, displaying it in a way that people can understand it and make, you know, their best business decision. So in my prior role, my last role, I worked in consulting.
00:07:44 Kevin Bihan-Poudec
for a fractional accounting firm and this was a service that we were providing to them okay we were providing them um you know the data that they needed to have visibility on so that they could be you know more successful we were servicing uh small to medium sized business owners and you know this kind of like a saying in my field that if you don't look into your data analytics as a business it's like flying a plane blind because you don't know yeah because you know you have no.
00:08:18 Kevin Bihan-Poudec
set goals and targets and you're not really looking you know historically how you've been performing so there's no way you know where you're going no it makes sense it's like almost like an.
00:08:28 Evan Zimmerman
altimeter in a yeah you don't know what your altitude you don't know how close you're risky you are to the ground and if you're doing like what it sounds like tracking your expenses and your income i mean you kind of know where you add altitude wise if you need to correct if you're, or well yeah.
00:08:44 Kevin Bihan-Poudec
And that is true to every industry out there, you know. And so that's why, you know, I got into data analytics because I thought, well, every type of business out there need to, you know, look into their data so they can perform at their best, basically.
00:08:59 Evan Zimmerman
Yeah, that makes sense. So now I understand you have started an organization, right.
00:09:05 Kevin Bihan-Poudec
Yes. So that came later on. So, yeah, back story. I lost my employment working in consulting. It was November 2023, which, you know, was right around the holidays. I think it was a week before Thanksgiving. I had never really lost a job before due to, you know, financial reasons at a company or whatnot. But bottom line, I was thinking. Well, I have this specific skill, right? I have developed my skills in data analytics, you know, whether it's learning tools or kind of, you know, talking to clients in regards to their data. So I know I have a skill set that's valuable. And I was thinking, well, maybe I'm not really getting interviews because, you know, we're at the end of the year.
00:09:55 Kevin Bihan-Poudec
It's, you know, between two holidays, Christmas around the corner, end of Q4, you know, people on holiday, not a lot of decisions are made at companies. So I expect it to be a little slow, right.
00:10:06 Evan Zimmerman
But that's what you're always going to do. Like when you have an unexpected job ending, you're going to overanalyze and assume all sorts of stuff because you're almost in a vacuum, literally, right.
00:10:18 Kevin Bihan-Poudec
Oh, yeah, totally. So, you know, I was applying for eight hours a day on LinkedIn and was not really getting interviews. You know, the... kind of like the the emails or the phone calls back which was very different from the hiring landscape from what i noticed personally two and a half years higher um i was wanting to work in a different field i work in aviation industry at pens like avionics and i remember within two weeks, Uh, I had applied to maybe 30, 50 jobs on LinkedIn and I was able to land, uh, three.
00:10:55 Kevin Bihan-Poudec
rounds of interviews, three different companies. I remember, you know, that was kind of like in the, kind of like in the middle of post COVID time, which, you know, people were over hiring and like the market was great. Um, and yeah, not that time. So I remember at the end of 2023 thinking, well, you know, surely when the world opens back up, if you will, uh, beginning of January of 2024, when people like, you know, going.
00:11:25 Kevin Bihan-Poudec
back to work and I mean, kind of like it's a new year, I was expecting that, uh, in January I would have a few leads. In February, I would be doing my rounds of interviews. And for sure, by March 1st, I would, you know, lend my next day to analysts.
00:11:43 Evan Zimmerman
Right, that's your timeline.
00:11:45 Kevin Bihan-Poudec
Because at the same time, I'm looking at my Google News feed and I'm saying, oh, you know, such and such companies are streamlining the operations, leveraging artificial intelligence. And, you know, this company has let go of 10,000 workers and that company 7,000. And I was just thinking, oh, yeah, I was in the middle of it in the beginning. I love it, if you will, in doing that boom. And I pretty much did not lend a single energy during 2024.
00:12:15 Kevin Bihan-Poudec
And, you know, the research that I was doing, because I was thinking, am I doing something wrong? You know, there was an article from a fortune, that said that 60% of displaced white collar workers in the United States did not lend a single energy during 2024. And I was one of them. So that kind of like to answer your question, why did you start your nonprofit, Void for Change Foundation? It kind of came from a place where, well, surely I'm not alone, you know, going through this.
00:12:49 Kevin Bihan-Poudec
There's got to be a lot of people out there suffering like me, not being able to reenter, you know, the work market. Let me see if I can come up with some kind of platform, you know, that advocates for the ethical implementation of this technology, AI, into, you know, the labor market. And also preserve jobs, because I was saying, you know, people are just getting lingo right and left.
00:13:16 Evan Zimmerman
Yeah.
00:13:16 Kevin Bihan-Poudec
And yeah, that's kind of like how it started. I kind of wanted to be a place for. people to express themselves so I came up with the website I filed you know my organization is a 501c3 it's not really been getting the traction that I've wanted so far but I feel like now two years later it's October 2025 now from the time I was like over my job I feel like now the word.
00:13:49 Evan Zimmerman
is kind of like getting out well yeah I mean it seems to me as if you know if there I mean I'm sure there are many thousands that are in similar situations as you just described and it probably, To look and seek out something like Voice for Change is probably not something that even, you know, it's probably pretty far down the list of what you might go look. So it's almost counterintuitive for that kind of like ground-swelling grassroots kind of building.
00:14:22 Kevin Bihan-Poudec
Yeah, I kind of thought about it. Should I call it Voices for Change? Because it's not my voice, right? It kind of came from a place where I wanted to be a unified, you know, it's Voice for Change, right? And I came up with a hashtag, ActNowOnAI, which has not gotten the traction that I desire. But, you know, I'm hopeful that at some point, and I feel like, you know, when I go on LinkedIn every day, I'm seeing stories of, you know, I am this person I just commented on. You know, he said, well, this is it.
00:14:53 Kevin Bihan-Poudec
I'm going to become unhoused next month because I can't pay my bills and I can't find a job as a software engineer. And I'm like, yeah, well, that was me. Because at the beginning of this year. Two weeks, I became unhoused. because I couldn't afford my living expenses back on the West Coast in Southern California. So I had to relocate to Texas. And it's becoming a broader societal... I mean, there's obviously a shift in the labor market that I don't think we're prepared for. But it's also how this technology is impacting the hiring landscape.
00:15:28 Kevin Bihan-Poudec
You know, I've spent so many hours applying endlessly online and not really getting the responses that I'm expecting. You know, it's a total different ballgame. You know, when I explain to people who may still have, you know, a comfy, regular job, the last three years post-COVID, you know, and they're like, what do you mean you're interviewing with AI? Like, what are you talking about? And I'm like, no, it's a thing now.
00:16:01 Evan Zimmerman
Oh, yeah. So, I think it's eye-opening. And, you know, it's interesting, you know, just to give you a little contrast, you know, Tom and I come from an industry, you know, in oil and gas where we're used to boom and bust, right? So, many companies can go through this hiring swelling and then you like, you lose half your employees, right, in the bust. But it's a cycle. And so, you know, one of the things that, you know, some people might think by listening to this is, oh, it's like a cycle. But it's not a cycle. It's like a total, like… The demand has changed. The market has changed. And then what you just… I want to talk a little bit more about what you just described, like, just artificial intelligence in the hiring process because…
00:16:44 Evan Zimmerman
It seems to me because, yeah, I'm on LinkedIn. I see tons of jobs posted on LinkedIn. And then sometimes I'll even pay attention to how many applicants for some jobs. You know, it's like one job and I'll see like almost twelve hundred applicants sometimes.
00:16:55 Kevin Bihan-Poudec
Yeah. Within the last 24 hours. And you're like, how? You know, this gives you a probability of maybe zero point zero one percent. Right. Because you're just one of them.
00:17:05 Evan Zimmerman
So what is that like? I mean, you're on that pointy end of that. So, I mean, if you're applying to these jobs and like you said, like in 24 hours, there could be hundreds of applicants. Like, what does that even look like.
00:17:17 Kevin Bihan-Poudec
So what it looks like is I have attempted in the last almost two years to master the art of writing a resume, right? So because of the amount of workers that are in the job market now, I've had conversations with recruiter friends of mine, and they're telling me, you know, two years ago, we used to get maybe 40 applicants a day for an open position, but now we're getting 400 a day. So obviously, you know, it came to the point where companies.
00:17:49 Kevin Bihan-Poudec
and HR departments at different companies have had to leverage automation to kind of filter through those applicants, right? I don't believe the way things are done is working because it's not really working for the job seekers, right? And so whether, you know, it's the ATS system, which is the applicant, on a tracking system that it's kind of like filtering out the noise, if you will. I feel like prior, you know, maybe you had a candidate that had 70 percent match of skill set to the job description and could get a shot.
00:18:27 Kevin Bihan-Poudec
But now, because it's an employer's market, they want the person to match, you know, whatever they're looking for at 99 percent, let's say.
00:18:38 Evan Zimmerman
But that seems kind of arbitrary, too, because, I mean, a resume is one thing. A resume is just like I mean, it's like a cheat sheet. It's like 20 percent of the equation. I mean, I've heard people over my career and like a resume is like less than how do you write.
00:18:54 Kevin Bihan-Poudec
How do you get to the 99 percent? Well, it's actually quite difficult. So. So because AI is being leveraged in hiring to go through the filtering of applicants now, on the job seeker end of things, we have had to leverage that same technology, right? So, you know, having the AI trying to kind of tailor your templated main resume, if you will, which you used to use to apply to so many different companies, right?
00:19:25 Kevin Bihan-Poudec
Now you have to have the AI kind of, you know, match your keywords so that the computer, the bots can actually, you know, see that you qualify.
00:19:38 Evan Zimmerman
And score you correctly.
00:19:40 Kevin Bihan-Poudec
Right, score you correctly or attempting to do so. And it's just not really working because it's leaving, you know, I don't really feel like this has been tested enough.
00:19:53 Evan Zimmerman
Well, Kevin, I mean, how do we even? People like, you know, if a kid is graduating from high school, doesn't plan to go to college. when it's going in the workforce and starting to have to apply for stuff. And now he or she is now going to be involved in this kind of a marketplace to find a job. I've never heard of a class that teaches you how to leverage AI to customize your resume to get the right score. And same for college graduates. So it's kind of painting a pretty bleak picture for people who have some maybe initial skills.
00:20:24 Evan Zimmerman
and now they're entering a labor market where... Sounds like 90% of it's automated and your chances just drop after that.
00:20:34 Kevin Bihan-Poudec
Oh, I agree. I mean, I feel like it's, you know, I've read things before where it's not really AI at first that's going to replace your job is the people who know how to leverage it, right? Because there's so much in demand now that you have to do your homework. And for instance, in my case, as a data analyst, I understand that I'm not going to be building dashboards for the next 35 years until I retire, right? I have to get on this AI train. That's why I've taken certifications in it.
00:21:05 Kevin Bihan-Poudec
because you have to, you know, show to your employer that you know how to leverage this technology, not to only be more professional in your job because this is just where our economy is headed.
00:21:17 Evan Zimmerman
So let's analyze that for a second. Me being an engineer, I want to analyze it. So you've learned these AI tools. So now, and you've worked as a more traditional data analyst. So now... Now that you've kind of learned and kind of, I'm sure, explored what you can do in the skill or augment the skills you already had. So with the tools you have today at your fingertips, how many traditional data analysts could you replace.
00:21:43 Kevin Bihan-Poudec
It's hard to put a number to it, but let me give you an example. If you have a team at a pretty large company of, let's say, eight data analysts, right? Right. Before, you know, doing all these manual processes that, you know, building dashboards and cleaning up spreadsheets and connecting different APIs together. All this takes a lot of time, you know, a lot of troubleshooting. So before, maybe two years ago or three years ago, I was able to maybe accomplish, I don't know.
00:22:14 Kevin Bihan-Poudec
Six to eight tasks within an eight-hour workday, right? Okay. Where we're headed now and pretty soon is having this worker who can maybe leverage this technology and be able to do 25-plus tasks in a day. Yeah. So companies are not going to really need the headcount as much. Maybe this team of eight data analysts, they only need two at the end of the day, right? Right. So what's going to make you be able to be in that pool of these two people who are still going to be or still be hireable is to upscale and rescale.
00:22:52 Kevin Bihan-Poudec
don't believe that we're we're there yet where you know this kind of like a system where you know we're getting help i mean you know i when i lost my job i basically you know was suffering financially to be able to you know pay for this knowledge that i needed all these courses there's not really a kind of like a transition plan you know on how to help people learn about this technology so it's really you have to take the matter into your own hands yeah.
00:23:22 Evan Zimmerman
and that's interesting and that ties into another uh episode that that tom and i talked about when we we explored higher education and what that's going to look like in the future and and a lot of what we saw in the data trends was that traditional colleges and universities you know they they're they're at they've they increased and now they're at a plateau and probably are going to drop off and the amount of people that are training through online free and and other type of resources kind of taking education into your own hands, hands.
00:23:52 Evan Zimmerman
to leverage what's out there is a drastic increase in the last five years. And so what it sounds like you're describing to me kind of matches with that, those trends that we saw that you, you got to take the matter into your own hands, exactly as you just said, like you have to figure out how to leverage these things. But then I also, from my experience in my career, you know, I had, I had engineers that would sit there in front of the software program, learn the program, and then just, it was just, you know, data in, data out. And there wasn't a lot of critical thinking on the data out. Whereas if I looked at it, because I had done it so many times and learned and learned from my mistakes, it would take me 30 seconds, and I could realize it was wrong. Well, that engineer didn't realize it was wrong. So and I would get frustrated. I'm like, how did you not know this is wrong?
00:24:38 Evan Zimmerman
You know, you're not a data entry person. You're an engineer. You're supposed to understand the fundamentals and know whether this is right or wrong. I would assume that's similar being a data analyst. Am I wrong that, you know, if you've done it the manual way for a period of time, you've created expertise and knowledge to where if you're leveraging an AI tool, you might be able to spot that it's not doing it correctly or that kind of stuff.
00:25:00 Kevin Bihan-Poudec
Yeah, and right to your point. And now it's more of a because the technology is not 100% accurate yet, right? I mean, as a data person, the data output that you're presenting, it has to be, you know, accurate, right? Yes. So like the work that I'm doing to verify that whatever the AI is spitting out is indeed accurate, you have to go through this whole process of verification, right? Yeah, I am leveraging it more in the sense where, you know, for coding, purposes, because, you know, there's only...
00:25:33 Kevin Bihan-Poudec
how fast my 10 fingers can type on a keyboard. Right, yeah. So if I tell the AI, hey, you know, I want this formula to do a 12-month rolling average of this or that, like, it kind of, like, gives it to me instantly, and then, you know, I see if it works, if it matches the data that I'm expecting it to calculate, and then I know, okay, moving on. But, yeah, it's kind of, like, a different way of working, but back to what you were mentioning, I think it's kind of a different way of functioning. Um, you know, I'm, I'm forcing, obviously, my field as a pure data analyst, because in some way, I am a storyteller, right? I am. Yeah, I'm putting, you know, context to the data for people to understand. I know that this is my, my job as a pure data analyst is slowly shifting towards becoming the implementor, or the user of AI tools and solutions, because.
00:26:32 Kevin Bihan-Poudec
or the translator. Right, exactly. You know, while the AI can analyze the data much more efficiently than I could ever do in my lifetime. And, you know, I, I am now kind of the person who's kind of managing this. And which, you know, will continue for hopefully a few more years, you know, have a job. But now, you know, with AI agents, which, you know, pretty recently kind of started in 2025, that is also changing the game, in my opinion, because now we're headed towards a time where, you know, even as the human person behind the computer managing these tools and solutions.
00:27:14 Kevin Bihan-Poudec
Now, you may have an AI agent that could replicate this work, if you will. So even that job could become obsolete, you know, on some level. So that's why I'm like, well, you know, we've kind of opened Pandora's box here and there's really no going back. And we have a technology that is increasing at exponential rate. And what is it that we have to do as a society to kind of, you know, make it work for everyone? And.
00:27:45 Kevin Bihan-Poudec
Unfortunately, you know, the way things are is there's no really, you know, percentage cap, for example, on how many private companies can replace their workforce with AI and automation.
00:27:59 Evan Zimmerman
Yeah. So I want to talk about that a little too, because, you know, another, this is linking together so many of our previous discussions that Tom and I have had. And one of them, we covered Warren Buffett in one of our podcasts and Tom's like, where are you going with this? But, you know, one of the things that I saw is that, you know, Berkshire Hathaway now is almost in a 50-50 cash position, right? So they've got 50% of their assets in equities, other companies, and they've got 50% waiting in cash, which is like the biggest, loudest signal ever that there's going to be a huge economic correction on the horizon. And they're waiting to deploy hundreds of billions of dollars when that, you know, when that event occurs, right?
00:28:40 Evan Zimmerman
So what you're describing to me is almost like lining up reasons why, where if you just have this massive increase of unemployment, I mean, that's going to... It's going to create an economic wave, right? So that, you know, what you're describing is not... science fiction or far-fetched this is not it's happening now it's not like oh this is happening.
00:29:02 Kevin Bihan-Poudec
in the future it's like it's happening right now yeah so we're in the middle of it so so i want to.
00:29:08 Tom Pado
have this most interesting talk i'm just amazed at what you're saying i'm a dinosaur and i just.
00:29:15 Evan Zimmerman
you know knock on the door what you're really doing tom and you're like man i'm so glad i'm.
00:29:19 Tom Pado
retired that's what you're really i was gonna say that i was that far from saying that i know.
00:29:24 Evan Zimmerman
i know what you're thinking man i know what you're thinking but i want to i want to hear kevin i want to hear kevin what you like okay so ethical ai you know it's it's a term um i want to explore that a little bit and i basically you kind of described with that whole hiring and applying processes so if you were to get to write the playbook for how the united states does ethical ai when it comes to hiring and applying how would you change that landscape like what would that look like, Because what you're describing, I put myself, let's say I'm an HR person, and I'm the person that's in charge to fill these 12 positions at this big company, and I get what you just said, hundreds of applicants a day.
00:30:09 Evan Zimmerman
Okay, so the tools that are available might not get me the right people, but I still have to get the job done. I still have to fill the function with a person capable, hopefully. So how would you change that landscape.
00:30:22 Kevin Bihan-Poudec
That's a lot of shoulder to put. Well, you know, I feel like the hiring landscape and the process in hiring is very opaque, in my opinion. There's not a lot of visibility from a job seeker's perspective as to why you're being rejected. You know, when I interviewed with one of the AIs, you know, her name was Samantha or I forgot what her name was. You know, I'm talking to a bot for 25 minutes on my computer. And, you know, it's asking me questions, you know, in regards to, you know, what it is I have achieved at prior companies and what this helped do for the business and yada yada.
00:31:00 Kevin Bihan-Poudec
So I'm giving all these examples. And at the end of the phone call, I'm getting auto-generated emails saying, by the way, your grade in data analytics is non-applicable. And I'm like, I've been doing this for 10 years. Why do you mean N-A? Like, I feel like there's not enough, you know, I would be given like a reading code. I can look up in a book somewhere as to why I'm not going to the next step.
00:31:26 Evan Zimmerman
So, again, being an engineer, I want to now do my own sampling like you did earlier with your customer service. I now want to sample, I want to put traditional recruiting and going through resumes and doing in-person interviews and placing a person versus the bots. And I want to see who succeeds, the hire from the traditional method or the hire from the bot. Now I want to see that. Have you seen any data on what retention looks like.
00:31:53 Kevin Bihan-Poudec
I feel like there's not really much data in regards to this available because no one seems to – I mean, I have not seen any data on rejection rate compared to a human review process. If you have 100 candidates being interviewed by AI, and then let's say you're having a human recruiter going through the same amount of applicants with those same skill sets, what would be the difference if you can get passed through the AI or through the human?
00:32:31 Kevin Bihan-Poudec
I would want to see that percentage, and I'm sure you're getting rejected more by a technology now that is not really understandable. It's not fully baked. You know, back in the old days, like two years ago, during an interview – would, you know, try to show your personality or see if it's a good cultural fit with your employer and things like that. But now, you know, when I talk to AI, I kind of feel like I'm speaking like a bot because.
00:33:02 Kevin Bihan-Poudec
I'm trying to remember as many keywords from the job description that I can just batch to the AI, understanding that the AI is most likely analyzing my speech pattern to, you know, provide me a percentage match score to the job description.
00:33:18 Evan Zimmerman
That's right. Yeah.
00:33:19 Kevin Bihan-Poudec
So you're kind of stuck in this loophole where I'm like, oh my God, what is happening? It's literally what you have to do now.
00:33:26 Evan Zimmerman
That's insane. And that makes me, again, go back to, I'm thinking of the person that's graduating with a degree, like a general degree, let's just say like business from a university, and they're going to start interviewing with bots. I mean, that just sounds like a recipe for disaster.
00:33:40 Kevin Bihan-Poudec
I mean, in a perfect world, it would be great if there was a standardized resume, which I don't think is really much possible. You know, you can't think.
00:33:49 Evan Zimmerman
But that's. Resumes in general are a problem. I know I hired people. Resume was just kind of a filtering tool, right, for me. Like a filtering tool. But the interview, I mean, you could have somebody that had a great resume, but I start asking questions on even what's on that. If they can't tell me what they did or they can't articulate, and they're not going to be good with my customers, they're not going to be good with our teams.
00:34:10 Kevin Bihan-Poudec
I mean, now as a recruiter, when, you know, I actually have a recruiter friend of mine, and I ask him, like, how do you know, how could you differentiate a good resume from a bad resume? He's like, well, I don't really know because it all seems like it's written by AI now.
00:34:25 Evan Zimmerman
Yeah, exactly. Yeah.
00:34:26 Kevin Bihan-Poudec
And then, you know, some companies are like, well, you know, we have internal tools that can analyze your resume and find out if you did use AI, and we reject those. But at the same time, you kind of have to. Leverage its technology in order to try to bypass the bots. So you're kind of like stuck in this in-between where you don't know what to do anymore.
00:34:47 Evan Zimmerman
Yeah.
00:34:49 Kevin Bihan-Poudec
And then, you know, eventually your resume, I'm assuming, will get pushed through to the hiring manager who will actually, you know, take the time, read through your skill set and your experience and your work history. But yeah, if it's written by AI, you're not taking the time to modify it in a way that's tailored to you. It's just going to sound so generic that you can absolutely tell if it's written by AI or not. You know, no one uses iSpareHeaded. And, you know, you say, I led.
00:35:20 Kevin Bihan-Poudec
I can't believe, like, it all uses that. Or, you know, the AI doesn't really know exactly, you know, what you've accomplished at companies prior. So you do need to spend and take the time to tailor it to what you've accomplished. But at the same time, you kind of have to play that game where, you know, in my case, if I don't have, you know, the word Power BI, which is a data visualization platform, 50 times in my resume, maybe the AI may think that, Well, this candidate had like 70, and so he's better.
00:35:52 Kevin Bihan-Poudec
You see, like, I'm not really understanding how these tools are working for me to optimize, you know, the writing of my resume. And I feel like there needs to be a lot more transparency, both from an employer's perspective, but also a job seeker's perspective.
00:36:07 Evan Zimmerman
Yeah, that makes sense.
00:36:11 Tom Pado
You know, Evan brought up that he reads a resume, and then he interviews the person. And, okay, la. And, yes, I might hire him, or, boy, they don't know anything, like the resume said. Now, you could almost go to school to learn this stuff, and you're competing against maybe thousands of people. And you're just trying to get a view from the guy. And then you could be worth nothing, but you're really good at AI resumes.
00:36:44 Tom Pado
It's amazing. I just, just, just.
00:36:48 Kevin Bihan-Poudec
From the research I've done, it's said that 66% of tech leaders would not consider a candidate without AI knowledge these days. 71% in the tech field would rather have a candidate who has just one year of AI knowledge than any other knowledge in any fields, like of 10 years. So that just tells you, it's kind of like, well, you know, if you have a job right now and you're kind of like sticking to it because you're understanding, you know, things are not really moving out there as far as the white-collar job market, and you're kind of stinging put, I would recommend study AI on the weekend, at night, whenever you have time, because it's going to come to the point where.
00:37:31 Kevin Bihan-Poudec
If you are, in my case, a regular data analyst, let's say, working at a company that is currently not implementing AI, because a lot of companies have kind of over-invested in AI. We had Intel, who announced the biggest massive layoff in tech, I think it was 15,000 workers. They over-invested in AI technologies at first, right? Then, basically, they're not getting their return on investment. They're showing quarterly earning reports that are not favorable.
00:38:02 Kevin Bihan-Poudec
So the response, especially during 2024, was, well, now we have to kind of get rid of salaries to make our money back. That was before even this technology was able to really replace your skill set. Now we're headed towards a whole different ballgame, especially with AI agents, where, you know, I mean, Mark Zuckerberg said himself, like, you know, we have a technology now that has the level of reasoning of a PhD student. the CEO of Salesforce said we're no longer hiring software engineers in 2025.
00:38:32 Kevin Bihan-Poudec
So, you know, we're kind of like getting to the point where you have to really, Put the greatest chances on your side to still, you know, be useful to a company and that is leveraging AI tools because that's what that's that's what we're in now.
00:38:51 Evan Zimmerman
So, Kevin, you have to explain to Tom what an AI agent is.
00:38:57 Kevin Bihan-Poudec
Think of it as an assistant that can assist you at, you know, making some of your manual processes more efficient, I would say.
00:39:11 Evan Zimmerman
Yeah, I would like a virtual assistant kind of.
00:39:14 Kevin Bihan-Poudec
Yeah. Yeah. Let's just hope that the assistant doesn't take over your whole job. Because, you know, right now we have people in tech software engineers. So one of our, you know, developing a technology that's going after their own skill set. But, you know, at the same time, we don't need jobs at the end of the day. So how do we make it work for everyone.
00:39:37 Evan Zimmerman
Yeah, I thought about that the other day. I saw an ad for one of these new AI tools where it'll develop apps, right? So I'm like, so they've developed an app that can write its own app based on like a narrative. And I'm thinking if I'm the guy coding that, I'm basically coding my own demise because now there's going to be a tool that basically does my entire job. And now we'll do it for the masses. You know, and I even thought about maybe getting that tool and playing with it. But then I'm like, you know what? It's not going to do what I want for at least a year.
00:40:08 Evan Zimmerman
I'm going to give it some time, but it'll get there sooner or later. I want to pivot a little bit and talk about – so you've been kind of describing your experience in the U.S. job market. But do you have any awareness of like is it different in the EU or other places.
00:40:22 Kevin Bihan-Poudec
Back in Europe, where things are done a little bit more ethically, you know, there was regulations on AI that were implemented, like about a year ago in Europe, the EU AI Act already, you know, companies and the governments there, I feel like are, most likely or they are having more conversations if you will on you know while we're going through this transition period of this ai boom era if you will yeah you know they're basically saying don't.
00:40:53 Kevin Bihan-Poudec
overfire your workforce um because that's going to put too much stress on the economy right on society too yeah absolutely unemployment services and the health care system kind of thing um but the thing is no the us is you know it's capitalism and and i mean i'm sorry to say this but it's you know it's it's always been to you know the the profit of the the private companies unfortunately and it's kind of like now that we have this technology that's being introduced that's.
00:41:25 Kevin Bihan-Poudec
not really being regulated in the job market i'm afraid that's going to hurt our local economy for for sure unless we come up with a, transition plan where well you know all these software engineers now maybe they can work in the renewables.
00:41:41 Evan Zimmerman
I mean, I see a lot of solar panels that need to be put on rooftops, you know, maybe blue college houses making a comeback. I don't know. But yeah, I want to talk about that, too. But, you know, I want to go back to the EU. So the EU Act you mentioned. So that basically provides some limitations on like percentage of workforce layoffs or how does it actually function to your knowledge.
00:42:02 Kevin Bihan-Poudec
I would actually have to, you know, you can ask the strategy PT to summarize this now, but I don't believe that it provides a percentage per se. It's more of the ethical implementation of this technology and society more broadly.
00:42:21 Evan Zimmerman
Okay, so it's more of like goal oriented or principle.
00:42:24 Kevin Bihan-Poudec
Now, you know, we have states, I mean, when I compare to what's going on there, because, you know, because in the United States, it's kind of like up the states, there's not like a framework on how we can regulate this technology, like they're doing back in Europe, but it's more done at a state level, right? So, for example, most recently, Governor Newsom Gavin in the state of California passed a standard bill 53, which advocates for the safe use of AI, right? So it's basically putting guardrails on larger, large language models that are of a certain size.
00:43:02 Kevin Bihan-Poudec
So now it's kind of like up to the states to do whatever they want to do, which is a good start, in my opinion. But it's just going to make, you know, companies move to other states that would be less regulated.
00:43:15 Evan Zimmerman
You're going to forum shop. It's just like a law legal case. You'll forum shop.
00:43:18 Kevin Bihan-Poudec
Yeah, exactly. So, I mean, yeah, we need to push for a kind of a more... Sweeping. Yeah, for a solution that works, you know, wherever you live in the United States.
00:43:35 Evan Zimmerman
the blue collar comment you made. So you're describing your experience from a white collar, but Tom and I also had an episode where we talked about humanoid robots. And so now leveraging AI and humanoid robots, that's going after blue collar jobs. So you see that on the horizon too.
00:43:53 Kevin Bihan-Poudec
I feel like that's maybe eight, 10 years down the road. But when people say, well, we've gone through the industrialization age before, we've always adapted to new technologies, we'll be fine. And when you look at what happened, especially in Detroit, when automation and machines were introduced in the manufactories, that took over 10 to 15 years. I feel like people had the time to adapt. But never in history have we had a technology that can fully replace you as a person.
00:44:26 Kevin Bihan-Poudec
Because now you have BMW, you have literally robots now that can put car parts together. Yeah. you know, there will be no human workers putting, you know, my cars together pretty soon. So we're kind of like headed towards this place where, okay, well, yes, the blue collar jobs could also be impacted. Actually, I don't know if you saw that. Do you know who Geoffrey Hinton is, one of the godfathers of AI.
00:44:54 Evan Zimmerman
No, tell me.
00:44:56 Kevin Bihan-Poudec
So he was interviewed by the BBC, I think it was last year, yeah, mid-2024. And he was asked by the interviewer, you know, it sounds like this whole AI thing is putting all the jobs, you know, kind of like up in the air. What would be your recommendation for parents to tell their kids what to study in college these days? How can they adapt to this technology? And he said, well, my recommendation would be to become a plumber. Because plumbing requires a lot of dexterity that a humanoid robot will most likely have trouble replicating.
00:45:33 Kevin Bihan-Poudec
And when I saw this, I was thinking, should I become a plumber? I mean, maybe. I mean, I'm sure you'll be in demand. But now, you know, they're uploading, you know, how to do videos from YouTube, I'm sure, to the AI. And they're tagging them. You know, because there's just. so much there are thousands and thousands of hours of footage on how to fix your sync oh yeah i use them all the time if you tag these videos and you upload this to the ai you can pretty much, upload this to the chip of the humanoid robot and and they're doing this right now now and now.
00:46:10 Kevin Bihan-Poudec
it can recognize you know which dishes are dirty and which wants to clean because it has all information and it's machine learning so it really i mean knows how to pretty much do everything yeah.
00:46:20 Evan Zimmerman
it learns machine learning it's gonna it does yeah it's gonna retain to what extent is it gonna.
00:46:26 Kevin Bihan-Poudec
impact us as a human species i don't know so that's another thing so what you kind of described.
00:46:32 Evan Zimmerman
i've heard other podcasts on the topic they say that's why with the continued evolution and adoption of ai and humanoid robots that there will have to come a point where you have a universal minimum income because there just won't be enough jobs, So what do you think about that.
00:46:48 Kevin Bihan-Poudec
I mean, I understand the vision. I would really hope that the one person would, you know, provide you with an income and lay down and do nothing by the beach eight hours a day. In theory, this is great. But, you know, to me, it's like, you know, when you look at a history, when have we gone through a time where in times of transition where the people at the top help the people at the bottom? I can't recall.
00:47:19 Kevin Bihan-Poudec
And, you know, if we can't really even fix universal health care in the United States, you know, I'm not a pessimist person. I'm more of an optimistic person. But realistically, it's going to be very difficult unless, you know. it's it's mandated or you know the laws are being changed or we're pushing towards this because that's obviously somewhere where you know we will be at a point in time where the robots and ai will be taking over all the jobs and what do we do like you know i feel like there are some solutions that.
00:47:55 Kevin Bihan-Poudec
can be done for this transition where you know if like back to what i was saying earlier if you put a percentage cap on how many uh how much companies can get rid of their human workers which you know if you put a cap at two to three percent a year or something yeah yeah i mean you know if you have like i don't know a room of a hundred customer service agents on the floor answering the phone there's no regulation right now that tells this employer hey tomorrow you can't get rid of 90.
00:48:25 Kevin Bihan-Poudec
of them i replace them by an ai agent that can answer the phone you know thousands of.
00:48:32 Evan Zimmerman
them what about the company though that says that well i also, I also can't support because now my competitors in other countries like Asia or something don't have that restriction and it's a global market for my service. So how do you balance that out with the other side of that argument? That if they have to do it from a preserving capital.
00:49:20 Kevin Bihan-Poudec
Tell companies, you know, if you're overfiring, you're going to get fined. This goes towards a pool of money that, you know, we could subsidize rescaling programs for these workers to transition. I feel like we can do things in a way that's helping the workforce help the broader economy at the same time.
00:49:41 Evan Zimmerman
I think that, you know, some countries in the EU, if I remember correctly, I used to do some business over there, like they had certain regulations. Regulations were like if you fired somebody in a skilled labor position, there was a mandatory severance or some sort of period where they would continue to get income, which would give them the opportunity to still be being paid, maybe reskill, but also just find another position. So that's not just a cutoff. There are regulations like that that provide a more soft landing.
00:50:11 Kevin Bihan-Poudec
Yeah, there's a lot more. I mean, back in Europe, I mean, it's really difficult to get let go from a job in France. You know, it's a three month process. You know, the company has to prove basically or show the reasons as to why you're needing to fire this person to the government, you know, because and here it's kind of like, you know, it's right to work. Yeah, you can work tomorrow for whatever it's at will. So, you know, the company can decide to. Get rid of you for X, Y, Z reason the next day and you don't really have the safety net, unfortunately, in this country.
00:50:50 Kevin Bihan-Poudec
the case to kind of like fall back on but now we're getting to the point where we have government agencies you know they're pretty much archaic I mean when I lost my job end of 2023 I've had to fax paperwork to unemployment services I'm like it's 2024 like why are they asking me to fax you know these these agencies are kind of like they've not really evolved with time so I feel like not only they're not ready to meet the current demand of displaced workers.
00:51:21 Kevin Bihan-Poudec
but yet along the upcoming demand yeah and yeah so hopefully um you know things are headed in the right direction to to help the workforce I'm hopeful yeah Kevin your discussion about.
00:51:34 Tom Pado
mandatory mandatory people working I went to Singapore in 77 and it's a small island it's a microcosm of what you're talking about Lee Kuan Yew was a benevolent dictator and he built that country. I get there and I go to park and there's like five people. I get a ticket, push something on a ticket, then I pay. I get in an elevator that's automatic that I can push, can't do it.
00:52:06 Tom Pado
There's a person in there pushing the button for you. And they put everybody to work in that whole country when they were developing. And now they're developed. So he was well aware of what you're saying, that they were trying to get into the modern age and they pulled them all out of campons, which are little villages, gave them all flats or condos, HDB, Housing Development Board, gave them a superannuation or Social Security, and they built that country.
00:52:37 Tom Pado
And, man, they're prosperous. And they have nothing but brainpower over there. And they did that. So with that microcosm. Some of. Making people work, whether they needed it or not, really helped the economy of that country.
00:52:52 Kevin Bihan-Poudec
Yeah, well, I'm hopeful that we can kind of hopefully mimic these models from successful countries that you're describing.
00:53:00 Tom Pado
Yeah, it was interesting going around the world, living in different places. Sweden, Australia, wow. They got a great country there. It's a voice for change. I'm just amazed at what you're saying. I understand. But I didn't know that.
00:53:23 Evan Zimmerman
Yeah, well, that's why we have people on, Tom. So we can learn these things. So, Kevin, tell us.
00:53:28 Kevin Bihan-Poudec
I'm here to talk about my personal experience as an immigrant from France, navigating the first year of the AI silent revolution in 2034.
00:53:38 Host
There you go.
00:53:40 Evan Zimmerman
So Voice for Change as it stands now. So what do you encourage folks to do if they want to learn more or they want to engage with you on this topic? Or what does that look like.
00:53:51 Kevin Bihan-Poudec
Well, I don't really have a presence on social media, but I do have a website. It's voiceforchangefoundation.org. There's a contact form there. I'm hoping I have this campaign. that I'm trying to start, which is the hashtag act now on AI. I've been posting about it on my LinkedIn or on my own social media. It's really trying to give a platform to people to kind of express their own stories and what they're going through, you know, whether AI is working for them.
00:54:25 Kevin Bihan-Poudec
or not. Because I feel like, you know, we kind of have to make ourselves heard, if you will, for the leaders of this country to understand, you know, what we're going through, because I.
00:54:37 Evan Zimmerman
feel like there's maybe a disconnect right now. And what is your advice to that young student coming out and finishing their education and trying to enter the workforce? What are your words of wisdom for them.
00:54:52 Kevin Bihan-Poudec
Develop the critical thinking skills and not use chat GPT for this.
00:54:58 Evan Zimmerman
Yeah, exactly. I like that answer. Yes. I mean, that's scary. Think about it, man.
00:55:05 Kevin Bihan-Poudec
I mean, you're partially thinking about it. I mean, you and I, you know, we're similar generation. We used to have to write essays of pages of pages when we were younger. And now it's kind of like as a Gen Z or someone, you know, who is now coming out of adolescence. You're kind of, you know, you're born in technology and now you have tools that can do all these things for you. And it's like, well, when did this go through my own brain kind of thing, you know? Yeah. You really have to learn and develop that, you know, that skill.
00:55:38 Evan Zimmerman
That's what scares me the most, to be honest, Kevin. It scares me the most not knowing the fundamentals and trust in the machines. And, you know, now that's what really makes the gray hair make sense on me now because, you know, that's the kind of stuff as a young person I would think of these old geezers that say, you know, you can't trust, you got to do it by hand, you know, all that kind of stuff. Like, oh, these people are crazy. You got to adapt and accept technology. But now seeing and having experience through it, you know, it scares me more than anything to think about the next generation and the generation after that will just not have some of the fundamental skills that you and I and Tom, you know, developed growing up in a totally different era.
00:56:17 Evan Zimmerman
And now with everything at their fingertips and potentially, you know, a humanoid robot in their house that will do the stuff that they don't want to do, their chores and all this other stuff. I mean, what does that mean for society? When, you know, evolution of human growth stuff out their windows of their cars because. Glittering is no big deal because a robot comes by every night and picks it up. I mean, just think about it. I mean, you could pull any of these strings and just think about the worst case scenario. And it's all possible in the next decade.
00:56:45 Kevin Bihan-Poudec
Yeah, but you're describing sounds like a dystopian sci-fi future movie. I feel like, you know, you would have asked me, you know, five, three, five years ago, I would have been, I don't know. This sounds so like Black Mirror, you know, but I feel like we're getting closer to, you know, AGI and all of that at the fastest speed that I think people are realizing. So without being said, you know, there are certain things that we have to think as a society to really make this, you know, work for everyone.
00:57:19 Kevin Bihan-Poudec
And this starts with people with, you know, the power at the top. Well, actually, it starts with people at the bottom who voice to people at the top. They need to do something.
00:57:27 Evan Zimmerman
There you go. Well, awesome, Kevin. This has been a very interesting discussion. Tom learned so much. Thanks for having me.
00:57:35 Tom Pado
Well, I learned a lot. I have to put my dinosaurs away.
00:57:39 Evan Zimmerman
Oh, this is great. Thanks so much, Kevin. It's good getting you.
00:57:42 Kevin Bihan-Poudec
Thank you. I appreciate you.
00:57:44 Tom Pado
Have a good one. Bye-bye.
Thank you so much. I enjoyed it thoroughly.
Thanks for joining Evan, Tom, and their guest today. You can find more on Facebook and Instagram, or if you want to be a guest on the show, fill out the form at mermanweb.com under the podcast tab. Cheers, mates with salt in his veins follow that calling through sunshine and rain he packed up his dreams moved to perth with open eyes chasing innovation and work with those who don't say it's a day on the line come rain or shine talking science satellites ai design from all rigs to poetry from rockets to roam their curiosity flies while they're safe at home.
00:58:55 Intro
heaven and tom say day with a touch of draw solving world in a weekly call he started from scratch just grit and a plan in a shed on a block with grease on his hands, Remote operated, they said with a grin, but Tom saw the light in those deep diving fins. Paul came round with a handshake tight, together they launched into day and night.
00:59:31 Intro
They explored the seafloor, where life grows, found treasure in valves and tethered hoes. It is today on the line, come rain or shine, talking science, satellites and AI design. From Oryx to poetry, from rockets to Rome, their curiosity flies while they're safe at home. Evan and Tom say day with a touch of draw, solving the world in a weekly call.
01:00:08 Intro
And just before the sun set west, he crossed paths with a curious guest. Evan asked, what drives a man like you? Tom just smiled. Evan, I always knew. Back to Indiana, where the cornfields sway. But a part of Tom will always stay. In waves off Perth, in dusty breeze. A quiet legend beneath gum trees.
01:00:42 Intro
It's today on the line, come rain or shine. Talking science, satellites, and AI design. From oil rigs to poetry, from rockets to Rome. Their curiosity flies, while they're safe at home. Evan and Tom say hey, with a touch of drawl. Solving the world in a weekly call. Tame the winds off the cyclone sea Made solutions in steel for the industry.
01:01:14 Intro
Map the tides, the anchors for the drill Built a calm where the waves won't kill Friends came fast in the harbor town Where the port lights blink as the sun goes down Woodside to Vryhof, gears in motion Bound by purpose, driven by ocean It is today on the line, Come rain or shine Talking science, satellites, and AI design From oil rigs to poetry Rockets to roam, their curiosity flies.
01:01:45 Intro
While they're safe at home Heaven and Tom say day With a touch of draw, Solving the world in a weak way. They built their names in offshore days Iron and inside an ocean haze Now the sea's behind but the mind don't rest Still chasing the sparks that they know best, Tom's got notes in a leather-bound book Drawing up schematics that still get a look Evan's on Bluetooth pacing the yard Advocating for those that stand guard.
01:02:16 Intro
It is today on the line Come rain or shine Talking science, satellites, and AI design From oil rigs to poetry From rockets to Rome Their curiosity flies While they're safe at home Evan and Tom say day With a touch of draw, Solving the world in a weekly call