Bill Gates on the Turbulent AI Era | [Sidecar Sync Episode 158]

Summary:

Bill Gates believes the move into the AI era will be one of the most turbulent stretches in human history, and that there's still no real plan for it. Flying solo, Mallory Mejias breaks down Gates's recent essay for association leaders trying to make sense of where AI is taking their professions. She traces why Gates says this shift moves faster than the PC era and reaches human thinking itself, unpacks his three big risks—vanishing entry-level jobs, misuse by bad actors, and always-agreeable AI companions—and weighs them against new McKinsey and Gartner data. She also explains the upsides Gates sees in science, healthcare, agriculture, and education, sharing how Sidecar uses AI to keep courses current and power an AI tutor. Along the way, Mallory makes the case that associations are best placed to deliver the retraining Gates calls for but never assigns, and explores his idea of a "human reserved" domain of work.

Timestamps:

00:00 - Mallory Takes On the Gates Essay
02:46 - Why AI Isn't Like the PC Era
10:03 - Jobs, Misuse, and AI Companions
19:59 - Where Gates Sees AI Helping Most
28:46 - Making the Case for Human Reserved
35:30 - Why Associations Should Lead Retraining

👥Provide comprehensive AI education for your team

https://learn.sidecar.ai/teams

🤖 Join the AI Mastermind:

https://sidecar.ai/association-ai-mas...

🎀 Use code AIPOD50 for $50 off your Association AI Professional (AAiP) certification

https://learn.sidecar.ai/

📕 Download ‘Ascend 3rd Edition: Unlocking the Power of AI for Associations’ for FREE

https://sidecar.ai/ai

🛠 AI Tools and Resources Mentioned in This Episode:

 'The Turbulent AI Era Is Here' (Bill Gates' Essay) ➔ https://shorturl.at/p63m8

'Scaling AI for Social Good with Wadhwani AI' (Sidecar Sync Ep. 101) ➔ https://shorturl.at/p63m8

'An FBI Operative’s Guide to Cybersecurity, Deepfakes, and Modern Spycraft with Eric O’Neill' (Sidecar Sync Ep. 129) ➔ https://shorturl.at/iO7up

👍Please Like & Subscribe!

https://www.linkedin.com/company/sidecar-global

https://twitter.com/sidecarglobal

https://www.youtube.com/@SidecarSync

Follow Sidecar on LinkedIn

⚙️ Other Resources from Sidecar: 

• Sidecar Blog
• Sidecar Community
• digitalNow Conference
• Upcoming Webinars and Events
• Association AI Mastermind Group

More about Your Hosts:

Amith Nagarajan is the Chairman of Blue Cypress 🔗 https://BlueCypress.io, a family of purpose-driven companies and proud practitioners of Conscious Capitalism. The Blue Cypress companies focus on helping associations, non-profits, and other purpose-driven organizations achieve long-term success. Amith is also an active early-stage investor in B2B SaaS companies. He’s had the good fortune of nearly three decades of success as an entrepreneur and enjoys helping others in their journey.

📣 Follow Amith on LinkedIn:
https://linkedin.com/amithnagarajan

Mallory Mejias is passionate about creating opportunities for association professionals to learn, grow, and better serve their members using artificial intelligence. She enjoys blending creativity and innovation to produce fresh, meaningful content for the association space.

📣 Follow Mallory on Linkedin:
https://linkedin.com/mallorymejias

Read the Transcript

🤖 Please note this transcript was generated using (you guessed it) AI, so please excuse any errors 🤖

[00:00:01:18 - 00:00:10:01]
Amith
 Welcome to the Sidecar Sync Podcast, your home for all things innovation, artificial intelligence and associations.

[00:00:14:15 - 00:02:45:05]
Mallory
 Hello everyone and welcome to the Sidecar Sync Podcast. My name is Mallory Mejias and I'm one of your co-hosts along with Amit Nagarajan. But today I am flying solo for this episode. Right now I am recording this episode before digital now. I leave for the event in just two days or so, but you will be listening to this episode after digital now. I'm so excited for the event. We all are here at Sidecar. And if you were listening to this episode and you attended the event, thank you so much for coming. Amitha and I will be sure to share everything that we learned from the event in the coming weeks. And I'm so excited for it. As I said, today I'm flying solo. To break down an essay called "The Turbulent AI Era Is Here, The Choices We Make Now Are Critical." So in late August of this year, 2026, Bill Gates published a long essay about artificial intelligence. And I want to be specific about which Bill Gates is writing here. This is not really the Microsoft founder talking about software. Gates says he has had two jobs in his life. The first was building software The second, which he started full time in 2008 and says he will have for the rest of his life, is giving that wealth away through the Gates Foundation with the goal of making the world healthier, better educated, and more equitable. So this piece is written by someone who has spent almost 20 years working on global poverty, disease and education and who is now saying that the thing he is most worried about is the transition to AI. His core claim is blunt. He writes that the transition to the AI era will be one of the most turbulent times in human history and that right now we are not preparing adequately for it and that there's no plan to ease the entry into that era. One note before we get into it, Gates discloses his own potential bias in the piece. He still has financial ties to the technology industry and is working with Microsoft and other AI companies in his role at the foundation. He does say any profits from those investments go to the foundation and that readers will have to decide for themselves whether that clouds his view or not. I think that's worth knowing as you listen to this episode. Here is where we are going today. First, why Gates thinks this technology transition is different from the ones that came before. Then the three risks he lays out. Then his idea of a human reserve domain, which I think is one of the most interesting things to come out of this piece. And along the way, of course, I want to stop and talk about what any and all of this means for those of us working in and around associations. So here we go.

[00:02:46:22 - 00:04:40:05]
Mallory
 In the first part of this episode, let's explore the first argument that Gates makes. And honestly, it's kind of the one everything else is resting on. And it's that comparisons to past technology shifts are misleading. The comparison people usually reach for is the move from farm work to office work in the United States. Gates says that analogy does not hold and for two reasons. The first is speed. When the personal computer arrived, he says it took roughly 20 years to significantly change how people worked. The software had to be built. The price had to come down. People had to learn the tools and figure out how to fit them into their business processes. AI, by contrast, runs on the devices we already own and it uses natural language, meaning you talk to it in plain English rather than learning a programming language. His line is that we don't have to adapt to it because it can adapt to us. He points out it can watch the same training video used to train a human worker and learn from that too. The second reason is more fundamental. Past transitions move people out of physical labor and into jobs that required human thinking. Gates argues this one is different because the technology substitutes for human cognition itself. It can see, listen, speak, and reason. At least theoretically it can reason. So it does not hit one sector. He expects it to reach law, customer service, medicine, software, and manufacturing and to do that over the course of a decade rather than several generations. He also addresses the most common reason people wave this off, which is that these models still make mistakes. He brings up the examples everyone has seen, like models that could not count the letters in a simple word. His argument is that the reliability problem is being fixed quickly as researchers build models that check their own work and that soon they will be substantially better than humans at many tasks.

[00:04:41:16 - 00:05:17:11]
Mallory
 I have a couple of points to comment on here. One thing that I want to talk about is the speed element, which I feel like we can all agree on, but just a couple of points to drive that home. This made me think of the story of desktop computers, which I had to do a quick search on, became available in people's homes in the late 70s, kind of early 80s. That's the first time people were putting computers into their homes. Me and my household, we actually did not have a computer until I think the year 2000, so we were a little bit late there. Comparing to that, we know ChatGPT blew up, was released in late 2022.

[00:05:18:15 - 00:11:19:20]
Mallory
 My mom is now an avid ChatGPT user. I would not say she is particularly technically savvy, as you can see by the first computers coming out into people's households. We didn't have them for a little under 20 years compared to ChatGPT being released in 2022. My mom, probably by the end of 2023, was telling me all the time, "Oh, Mario, ChatGPT, just throw that into ChatGPT. Just search that on ChatGPT." I think the speed of adoption point is accurate, how Gates is describing it. I also want to talk about ChatGPT struggling with math problems. I found this report that in early 2023, it was reported, "ChatGPT failed Singapore's sixth grade math test miserably. It scored a 16% and couldn't do basic arithmetic." In 2026, three years later, AI is claiming to have solved one of the Millennium Prize problems, which is a set of seven math problems that have never been solved whose solutions are valued at $1 million each. I just want us all to take a second and realize that in three years, this technology has advanced from a point where it failed a sixth grade math exam and really failed it, right? Not like a 60%, a 16, one sixth percent on that math exam, to solving one of the biggest math problems that we have or had in the current day. This stuff is moving exponentially quickly. And then I also want to talk about a topic that he touches on very briefly, which is the idea that these models can improve themselves. And this is something called RSI or Recursive Self-Improvement. It's a process where an artificial intelligence system autonomously designs codes or trains the next generation of itself, creating a continuous loop of capability enhancements. So whereas in the past we were limited in terms of technology advancements to basically human innovation, we're actually seeing models improve themselves. So the speed at which AI is progressing is unlike anything we've seen before. I think we can all agree with Bill Gates on that point. In terms of replacing human cognition, I think if I asked you to consider the wildest dreams for your organization, products or services that you'd like to launch, unprecedented levels of value or service for your members, if I were to ask you to daydream with that for a moment and then ask you, what is the thing that is stopping you from getting there? At least in the past, it would be labor. It would be the number of people on our team. Malorie, we don't have enough people that could work toward that goal. The crazy thing about AI is that it is scary and exciting, but substituting for human cognition in a way. It's giving us access to more brains, more intelligence on our teams. So having a small team in theory is no longer stopping you from chasing your wildest dreams as an organization. The abundance of human cognition and labor and intelligence essentially makes anything possible. I do have one pushback on Bill Gates, a little bit of a pushback that despite the speed and abundance of intelligence that I think we can all agree on, we're definitely still seeing organizations struggle to move from individual gains to organizational gains. And this is something we've talked about on the podcast. We've seen many major headlines on this too. Technology diffusion still seems to be slow with respect to how quickly the industry is moving. We often say on the podcast, if AI had stopped improving even in 2023, 2024, we would all still be figuring out new ways to use it, new value to mine out of it. So I think that the technology is advancing very quickly, but being able to translate that into actual business impact is a little bit slower. And there was a recent Gartner press release that said, and I'm going to, this is a direct line from the press release, only 13% of CIOs reported their enterprise has realized significant or transformational value from AI so far, according to Gartner, a business and technology insights company. While AI use cases have accelerated over the past two years, the likelihood of use case success remains one in five. So I just think that's maybe not a disagreement to what Bill Gates is saying, but more of a bit of a counter perspective. Yes, this technology is moving very quickly. Yes, it's adapting to us and the way that we work. However, we as a society, as humans are struggling to transform those individual productivity gains into meaningful impact for our business. Now I want to get into the biggest three risks with AI, according to Bill Gates. The first is jobs, and this is where he spends probably the most time in the essay. He says the jobs most at risk are entry and mid level, while the new jobs being created will mostly require skills that take many years to learn. He points to evidence that this has already started, saying that after generative AI was widely adopted, employment fell significantly among young workers in the roles most exposed to replacement, but not among their older colleagues. He expects it to spread well beyond the obvious candidates. He named sales, customer support, software engineering and paralegal work as likely first, and then says it will reach tasks that still require trained workers today. Things like assessing loan applications, doing data analysis and even triaging patients. He also thinks blue collar work is coming and predicts that capable robots will begin competing with people on physical tasks in construction and hospitality by the end of this decade. The mechanism he describes is competitive rather than voluntary. Once one company adopts these tools and uses the savings to lower prices, its competitors face enormous pressure to do the same. If existing companies do not, startups will.

[00:11:20:23 - 00:13:04:15]
Mallory
 The second risk he covers is that AI hands real capability to people who want to cause harm. Fraud, disinformation, deepfakes and surveillance are the ones he says people will feel most in daily life. On cybersecurity, he makes a point, which is that the same model that finds a flaw in software so a company can patch it can also help a criminal exploit it. We have not figured out how to separate those two capabilities. He says the cybersecurity experts he knows are scared about the next few years because attackers are gaining new capability faster than defenders can close the gaps. And he names the infrastructure at stake, hospitals, financial institutions, water systems, power grids and the systems that manage government benefits. The third risk is about people and especially young people. Gates writes about AI companions and his description is pretty memorable. They talk to you in ways you are already comfortable with. They do not push you outside your comfort zone. They are always available and they never get mad at you. He says that combination makes them potentially very addictive and that it can rob us of the things we learn from dealing with actual people. The evidence here is still early and he does admit that. But he cites a study from researchers at Stanford and Carnegie Mellon of more than 1100 people using AI companions, which found that people with smaller social networks were the most likely to turn to a chatbot for company. And that the heavier and more emotionally personal that use got, the worse they felt. He pairs it with preliminary survey evidence suggesting that heavier AI use was associated with less critical thinking with a stronger effect among younger people.

[00:13:05:17 - 00:13:18:20]
Mallory
 And he makes the point that this would be the worst possible moment for people to lose critical thinking skills because in a world of deep fakes and tailored misinformation, telling what's true from what is not becomes a basic life skill.

[00:13:19:22 - 00:15:43:01]
Mallory
 The first point I want to touch on here is the gap that he mentions in the job section. Gates describes the problem pretty precisely, entry level roles disappearing and new roles appearing that require skills that take years to build. So essentially workers needing retraining and support. I actually just saw a headline about some new jobs research that was done by McKinsey last week. And so I want to share that because I think it's relevant for the context of what we're talking about in this episode. But McKinsey's October 2026 research says the U.S. economy will have more jobs in 2035 than today. But the mix changes. Roughly 11 million Americans may need to leave a shrinking occupation entirely with an average of about 770,000 workers a year crossing occupational lines. That's about 3.6 times the historical rate and it would be sustained for at least a decade. Credentials are the most common hurdle required in 85 percent of growing jobs. But only 38 percent of those are mandated by law. The other 47 percent are employer preference for a degree certificate or job title, which means nearly half the credential problem is hiring convention rather than a safety rule. So overall, exciting. You know, this is just one report, but exciting that it looks like we would see a net positive of job creation in 2035. But it sounds like there will be a lot of change that has to happen for us to get there with roughly 11 million Americans leaving a shrinking occupation entirely. This gets me thinking a lot about you all as associations because you all are often the bodies that define what competence looks like in a profession. You run the credential. You set the continuing education requirements. So I feel like as this next decade progresses and we see these gaps starting to emerge in professions and we see the shrinking entry level or mid level roles in association has a really important job there, which is to help fill those gaps by educating people that are currently in the workforce or coming into the workforce. You have the infrastructure for teaching a specific profession skills to working adults. And I think that's really powerful given what Bill Gates is talking about.

[00:15:44:02 - 00:16:09:22]
Mallory
 Also on the credential, if I take some meaningful share of entry level work in your profession, something to think about is that the path people climb is going to change shape. The junior roles may not exist in the same number. So the typical on ramp for your profession that you might have always relied on the way people go from school to competence might need to be rebuilt or rethought deliberately. I think that's something to consider over the next decade as well.

[00:16:11:08 - 00:20:15:04]
Mallory
 A little bit of a counterpoint here is, of course, you know, I can't argue with data saying entry level jobs are being impacted. But on a recent episode of the podcast, Hanh Nguyen shared an important quote, and that was you can't save your way to prosperity. And so I think if you're only concerned as a leader, as an organization is save, save, save, reduce headcount, reduce expenses to make your business run more efficiently, you will likely just be doing the same thing that you've always done, but with less resources. And if you shift your mindset to growth, to doing things that you've never been able to do and offering your members value they cannot get anywhere else, you might find a situation where you were actually growing your team, where you do need more entry or mid level minds to contribute to the work that you're doing. This is certainly an anecdotal example, but one that I think is important to share with you all and bring into the conversation about hiring entry level or mid level folks. And that is a program that we launched at Blue Cypress within the past few years called the BC Tech Fellows Program, Blue Cypress Tech Fellows Program. Amith has been really instrumental in making this happen, but essentially the more technical our organization has gotten, the more innovative work that we've been doing. We've actually found a big need to bring fresh minds and top talent into our sphere. So the Blue Cypress Tech Fellows Program is a program that exists for us to identify and hire the top talent people coming pretty much directly out of universities that might have a background in computer science or be more technical and really helping to shape them in this world of AI, bringing them onto our team and giving them kind of unparalleled experience with AI and specifically AI assisted software development. So I definitely think there is opportunity here, even though the conversation around job displacement with AI is a very real one, understanding that if you shift your mindset from just save, save and reduce, reduce and bring our costs down, down, you realize if you kind of shift your mindset into a framework of growth and of doing more than you could before, that you actually might be able to grow your team and bring on more entry or mid-level folks onto your team to do work like that. I want to be careful not to make this sound like a doom piece with all the risks because it's actually not that. Gates is explicit that he thinks two opposite mistakes are being made. Some people see only the upside, others focus exclusively on the dangers, which yes are real, and in doing so, miss what this could actually do. His argument is that maximizing the benefits is not a separate project from managing the harms. It's actually part of managing them. His reasoning is that if people see AI making their lives easier, that builds the public trust you need to handle the hard parts of the transition. But if the first thing AI does in most people's lives is take away their job, the people who were already skeptical will reject it outright, and that makes it harder to ever deliver the benefits. On what those benefits look like, he points to a few areas. Science, where AI can pull across every field at once, search enormous bodies of literature, and help researchers decide which experiments are worth running. His point there is that when intelligence stops being the limiting factor, smaller organizations can compete with the ones that have far bigger research budgets. Healthcare, where he gives the example of small American hospitals that do not have specialists on site to diagnose a patient during an emergency, and points to tools already analyzing scans to catch strokes. Agriculture, which he says surprises people, but which he believes is where AI will have the fastest impact in low-income countries. Farmers there often do not get reliable weather forecasts or good advice on what to plant or how to protect crops, and he thinks AI could soon give them better guidance than the richest farmers get today.

[00:20:16:06 - 00:21:17:03]
Mallory
 And government services, where he describes families overwhelmed by the process of applying for health insurance, student aid, or food assistance, and giving up in the face of the paperwork. He also comes back to education, which he had listed as a risk. He thinks AI can free teachers to spend more time with students individually, and he describes a specific design idea worth holding on to. He talks about preserving what researchers call "productive struggle," which is the cognitive work that actually builds understanding. When a student first meets a new idea, the AI explains it fully. Later, when it is checking whether they understood, it holds the answer back and helps them get there themselves. His summary of all of it is that the operative word is "can." AI can improve life for people at every income level, but he says it will not do that automatically, and we have to be deliberate about making sure the benefits reach more than a wealthy few.

[00:21:18:05 - 00:25:03:11]
Mallory
 A lot to unpack here. You know we love talking about the upsides of AI, but we also talk about some of the darker sides too here on the podcast. In terms of education, I obviously feel like that is directly applicable and relevant for you all as associations. I know at Sidecar we've seen some tangible benefits from utilizing AI, one of those being course creation. So we've talked about this on the podcast before, but in our initial iteration of the AI Learning Hub that we have at Sidecar, it was Ameth and I creating all the course content, maybe with a few others on our team, and then we sat down and actually recorded ourselves with our computers teaching all the content. And that was great, but the problem was as soon as we posted the content, it was pretty much out of date. Why? Because at the beginning of this episode we're talking about how fast this stuff is moving. Remember three years in between not being able to do basic arithmetic to solving some of math's greatest challenges. So this stuff is moving so fast that as soon as we recorded something basically it was out of date. So the team has built something called a learning content agent that actually strings together AI tools, it creates AI avatars, uses AI audio, and essentially generates our courses based on design principles that we've assigned and that our instructional designer on our team has utilized for the courses. But basically helps us keep our content up to date and helps us produce content much faster. So we went in our first iteration of the AI Learning Hub from having, I think it was seven or eight courses, to over 70 now, and that's just and probably a two-year turnaround. So that's one way that we've seen AI impact the education we offer. Another way, and Bill Gates kind of touched on this, is the idea of really personalized education. In the example he was talking about it was allowing freeing up teachers time so they can spend more time one-on-one with students, which is incredibly powerful, but for the situations that's not possible. Having AI act as a personalized one-on-one tutor for educational content that you offer, I think could be a huge asset in making sure that the person taking your educational content is getting out of it what you want them to get out of it. And we have actually put an AI tutor, it's a version of Betty that lives in our AI Learning Hub. If you all have been in there, you've probably interacted with this tutor, and she's basically trained, I'm saying she, it is basically trained on our repository of content and courses. So if you're going through some of our educational content and feel like you are stuck, you can actually interact with Betty and ask questions and kind of have the AI simulate this cognitive struggle, which I think is a very useful term in thinking of how we can still learn in the face of AI. But interacting with this AI tutor in a way to make sure that any points that are unclear to you, or even just kind of some creative brainstorming, you have that ability. Whereas before, that is simply something the sidecar team never could have offered, with thousands of learners in our AI Learning Hub and not a huge team, it just would have been impossible for us to offer that level of personalized service. So I feel like in both ways for us at Sidecar, AI has enabled us to do our jobs better as the entity that's creating this educational content. But I also believe that AI itself is helping to improve our learner outcomes too, by giving them more relevant content, more timely content, more content in general to consume, and then also having that kind of AI tutor built in.

[00:25:04:15 - 00:27:46:01]
Mallory
 In terms of the other upsides, this is something, as I mentioned, we like to talk about a lot on the podcast. We have some great episodes on material science advancements. We've talked about drug design advancements on the podcast. In general, better research on diseases. We actually do also have an episode, as I was reading through this essay, I realized, oh, we've talked about AI and agriculture too. It's the Vadvani Institute for AI episode. We will link that in the show notes, but we talked about how AI is helping farmers in India. And in general, maybe you all feel like this too, but there are some professionals that I have interacted with and that I know who I respect very much and who are pretty openly and outwardly anti AI, and they post about it and they say how they'll never use it and that they can't believe people who are using it. But when I see posts like that, while I can certainly hold space for it and understand the nuance of the downsides of AI, my mind always goes to this part of the conversation, which is the potential upside, which is curing diseases that we thought could never be cured, which is that feeding populations that struggle with having food for the people who live within them. And so I think there's a lot of potential upside and good that we have to consider in this conversation. But as a society, I think we just have trouble adapting to such massive amounts of change. We do. And I think associations do have a unique opportunity here, not just in the sense of educating your members or filling those gaps in the workforce, but really just being that beacon of guidance as we see things like science change and evolve or health care change and evolve. I think associations could play a really unique role over the next couple of decades, even less than that, of being a place where people within these industries and professions can go when they're feeling confused, when they need to be inspired, when they want to understand the potential upsides of AI in the work that they do and not just the downsides. I do think associations are in a really important place in those people's lives to create those kinds of conversations. The part of this piece I've thought about the most is an idea Gates gives a name to. And I haven't seen it framed quite this way before. He starts with something personal. His father died of Alzheimer's back in 2020. In the later stages, he was cared for around the clock by paid caregivers. Gates writes that they understood his father even when his father struggled to express himself. He could not always tell them when he was hungry, but they always knew.

[00:27:47:03 - 00:27:55:01]
Mallory
 His conclusion is that something in that care was irreplaceably human and that no robot could or should have done it.

[00:27:56:06 - 00:28:37:14]
Mallory
 So he proposes that as AI and robots improve, we deliberately set aside certain work for people only. He calls that domain human reserved. He says he likes the phrase because it makes him think of nature reserves, places where we could build roads and buildings, but choose not to, because the loss would be too great. His reasons for reserving something are not all the same. Sometimes it would be economic because letting machines take a role would displace a large number of people who cannot easily move into other work. His example is that you cannot tell a 55-year-old who has spent their whole career in construction that they should go work in elder care and expect them to find it fulfilling.

[00:28:38:15 - 00:28:50:08]
Mallory
 And sometimes the reason is not economic at all. He asks you to imagine a robot telling you that you have an incurable disease. He says there is no technical reason. It could not. It just should not.

[00:28:51:08 - 00:29:24:11]
Mallory
 He's honest that this raises questions he can't answer. Who decides what gets reserved? What criteria you use? How you stop companies from quietly using machines anyway? What happens to trade when one country reserves something and another does not? He says those have to get worked out in public. He also notes the lines will differ by place. Some countries might insist that people care for the elderly, but he points out that a country like Japan with a shrinking workforce and not enough young people to do that work may welcome a caregiving robot instead.

[00:29:24:11 - 00:29:26:02]
 (End Playback)

[00:29:26:02 - 00:30:02:21]
Mallory
 I love the idea of this reserve, of a human reserved. I think most of our AI conversations are about capability and what this technology can do, what can we hand off. And that is yes, the right question when we're talking about freeing up our staff time or trying to figure out what better forms of value we might be able to create for our members. We've spent a lot of episodes there. Human reserved is asking something else, right? Not what AI cannot do, but what you would choose not to give it, even though it could, and I feel like that's a different question.

[00:30:04:04 - 00:30:52:18]
Mallory
 In the realm of associations, I think of an example where maybe a member calls you because something has gone deeply wrong in their career or they're having a big issue with your organization. And you could have an AI there, maybe to solve it. And in fact, AI voice is getting so good and generative AI is so good. You maybe could have the first line of defense being an AI. But the fact is, should it? Or is that better suited for a human? And of course, we've talked about on the podcast, you could have AI triaging calls like this and saying, identifying yes, this is something that needs to be sent to a human, but it makes you think a bit deeper. Because eventually, AI in theory could maybe handle some of these tough conversations, but that doesn't mean it should.

[00:30:53:23 - 00:31:12:05]
Mallory
 Personally, I am very aligned with the idea of humans reserved. And I think it's just an interesting way of framing it, especially comparing it to nature reserves. And how yes, we could in theory put buildings or streets or homes there, but we don't because there is an element of beauty that we should preserve.

[00:31:13:09 - 00:32:11:00]
Mallory
 You all know I'm an actor, I'm a creative, and 100% believe there will be a dedicated segment of creative work that will be human reserved. I hope that from my perspective as the actor, the artist, the creator, we want something like that. But I also like to think the market will demand it as well. I'm not naive enough to think that there will not be a market for AI generated shows and movies. I certainly think there will, especially when you talk about the concept of having a super fan of a TV show or a movie, be able to generate episodes that they wanna see on the fly with all the same characters. Yes, I do think there will be a market for that. But I also like to think there will be some sort of designation or stamp or something like that that's applied to creative work that lets audiences know this was 100% generated by humans. And I like to think audiences, some audiences will respect that and wanna see that.

[00:32:12:07 - 00:36:02:21]
Mallory
 How do you enforce it? I don't know, just like Bill Gates, I don't have the answers there. I think this is something that we will see play out in the future. I also think maybe the market will decide, maybe we will see AI, a fully AI run doctor's office competing with a doctor's office that has humans plus AI, and then we'll see how patients feel when they go to both. If they feel like they're getting better quality of service from one versus maybe we eventually see this play out in the market. And then once we see it play out, we have a good idea of, okay, well, then this should be human reserved and this should not be. I also think there's a strategic version of this too. Bill Gates is describing something people will value more because it's scarce. If everything becomes automated and instant, the unmistakably human parts of your association may end up being the most differentiated thing that you have, not the least efficient. And I think that's just an important frame of mind to keep in mind as you're thinking through conversations around this technology. I also think this is just a helpful exercise. Of course, we can talk about it on a societal level, but maybe it's an important exercise to have within your organization. If your team or your staff is really concerned about AI in general, this is a helpful conversation to have. What do you designate within your organization as human reserved? What scenarios must always be handled or signed off on by a human? I don't know, I feel like having conversations like that alongside the conversations of what can we do with this technology and what levels of service or new products or new levels of value can we create for our members. You could have those conversations while also holding space for the conversations around what is innately human about what we do and where are we gonna keep it innately human? And everyone, that is the piece. Bill Gates argues that this transition is different from past technology shifts because it moves faster and because it substitutes for human thinking rather than human labor. He lays out three risks, jobs disappearing at the entry and mid-levels faster than new ones appear, AI giving real capability to people who want to cause harm, and the effect of always agreeable AI on how people, especially young people, develop. He makes a case that the benefits could be substantial in science, healthcare, culture, government, services, and education, but only if we are deliberate about who they reach. And he proposes the idea of a human reserve domain, work we set aside for people by choice. He does also lay out policy proposals, including new national and international institutions for managing the transition and attacks on AI and robots, and I've skipped those here partly because they sit well outside of what any one organization controls. And partly because I think the more useful parts of this piece for the audience are the ones you can actually act on. I will say the honest thing about this essay, which is that it is much stronger on diagnosis than on solution. Gates says so himself in a way, he writes that he rarely stops thinking about AI, not because he has all the answers, but because the questions are too consequential to leave to a small group of technologists. Something I keep coming back to though is that gap in the job section. He says workers will need retraining and support in professions being reshaped, and he doesn't really say who provides that. For a lot of professions, I would say the answer is an association. And that's not a small thing. And I do not think enough people in our sector have fully registered that that role is theirs to claim. I would love to hear what you think. If you've read the piece or if you read it after this, let me know where you land. And if you want to go deeper on any of this, check out the links in our show notes. Everyone, thank you for tuning in to today's episode. We will see you all next week.

[00:36:02:21 - 00:36:04:14]
 (Music)

[00:36:04:14 - 00:36:22:04]
Amith
 Thanks for tuning into the Sidecar Sync podcast. If you want to dive deeper into anything mentioned in this episode, please check out the links in our show notes. And if you're looking for more in-depth AI education for you, your entire team, or your members, head to sidecar.ai.

[00:36:22:04 - 00:36:24:16]
 (Music)