AI and Exponential Growth, Protein Folding, and Predicting the Future of AGI [Sidecar Sync Episode 39]

Timestamps:
00:00 - Introduction to Sidecar Sync
05:06 - Ray Kurzweil's AI History and Predictions
08:06 - Amith's Keynote on Exponential Growth
12:47 - Future AI Capabilities
17:19 - Multi-Agent Systems and Real-Time Applications
20:01 - AI's Impact on Healthcare and Other Industries
25:57 - Balancing Current Operations and Future Planning
28:55 - Removing Constraints with AI
34:17 - Future Predictions: AGI and Singularity
39:34 - Societal Changes and the Exponential Economy
42:04 - Opportunities for Associations

Summary:

Join us on this week's episode of Sidecar Sync as hosts Amith and Mallory dive into the fascinating world of exponential growth and artificial intelligence. From the historical context of computing power to the latest advancements in AI, we explore how these technologies are revolutionizing various industries, including healthcare and associations. Amith shares his insights on the future of AI, the concept of artificial general intelligence (AGI), and how associations can stay ahead in this rapidly evolving landscape. Whether you're curious about AI's impact on society or looking for strategies to future-proof your association, this episode is packed with valuable information and forward-thinking perspectives.

Let us know what you think about the podcast! Drop your questions or comments in the Sidecar community.

This episode is brought to you by Sidecar's AI Learning Hub. The AI Learning Hub blends self-paced learning with live expert interaction. It's designed for the busy association or nonprofit professional.

Follow Sidecar on LinkedIn

📅 digitalNow Conference: October 27th-30th in Washington, D.C. For more information, visit:  https://www.digitalnowconference.com

🛠 AI Tools and Resources Mentioned in This Episode:
Claude 3.5 ➡ https://www.anthropic.com/
ChatGPT 4.0 ➡ https://www.openai.com/
AlphaFold2 ➡ https://www.deepmind.com/research/case-studies/alphafold
GroqCloud ➡ https://console.groq.com
Skip AI ➡ https://helloskip.com

⚙️ Other Resources from Sidecar: 

• Ascend: Unlocking the Power of AI for Associations E-book 
• 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.

Mallory Mejias is the Manager at Sidecar, and she's 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.

Read the Transcript

Amith Nagarajan: Welcome back listeners to the Sidecar Sync. We're super excited to have you back. We have another really interesting discussion for today, all about exponentials and AI, a lot of fun things that we're going to dig into.

Amith Nagarajan: Um, my name is Amith Nagarajan and I'm one of your hosts.

Mallory Mejias: And my name is Mallory Mejias. I'm one of your co hosts and I run Sidecar.

Amith Nagarajan: And before we dive into the wild world of exponential technology and exponential growth, let's take a moment to hear a quick word from our sponsor.

Mallory Mejias: Amith, how are you doing on this lovely morning?

Amith Nagarajan: I am doing great. I was going to say it's pretty hot up here in Utah, but that's not probably true compared to what it's like in the South where you're at.

Mallory Mejias: I will say it's kind of a nice, a nice morning in Atlanta. I don't even know the temperature. It's 83. I feel like I'll take that, an 83 degree morning. What about in Utah?

Amith Nagarajan: I got up to 107 down in Salt Lake yesterday. Um, up here in the mountains where I'm at, it was I think 94, 95, but it is dry. So that helps a little bit. And the nice thing about the low humidity is that you end up in the evenings. It cools down really quick. So by the time the sun was setting, Took my dogs out for a walk.

Amith Nagarajan: It was in the seventies this morning when I got up, it was in the sixties. So there are times of the day that are nice, even when it gets hot out here. But, uh, yeah, comparatively speaking, normally we really enjoy maybe the high temperatures are in the upper eighties in the mountains. So a little bit on the hot side.

Mallory Mejias: Yeah, I feel like it's been, dare I say, maybe a slower few days, weeks, with AI. I don't know, I feel like there hasn't been any major drops, um, in maybe like the last two weeks. Do you agree with that?

Amith Nagarajan: I mean, there's, yeah, I think there's been a lot of technical stuff going on, as there always are. Like there's some interesting projects that are being discussed from a research paper perspective, nothing that we'd really surfaced in the podcast. I think that, you know, after the cloud three, five sonnet, uh, drop a couple of weeks back, a lot of people have been talking about that.

Amith Nagarajan: Um, I personally haven't been like super excited to come to the pod and say, Hey, there's this new tool that everyone in the association community should check out. Um, Um, I will say actually just one last thing on the weather. I don't think this is because of AI, because I don't believe that the AI weather models we've talked about, both the Microsoft research one, and then the one from DeepMind, I don't think either of those have like gotten into actual commercial use, but I'll just say that like, you know, The weather forecast seemed to be getting a lot better in terms of predicting like what things are gonna look like.

Amith Nagarajan: Like I knew a week and a half ago that it was going to be hot for three days and it's hot for these three days. So, um, I think technology in general is getting better. I can't wait for those AI models to tell me exactly what the weather is going to be in my little dot on the map.

Mallory Mejias: Exactly. I think that's, that's a really exciting time. You know, my mom would probably hate me saying this. I don't pay that much attention to the weather. Um, she's normally the one back when I was living in Louisiana who would tell me, you know, Mallory, be on the lookout. There's a big storm coming. So now that I don't have her here in Georgia, uh, I'm realizing I need to pay a little bit closer attention to the weather.

Amith Nagarajan: Yeah, I think in this in certainly in New Orleans, you have to pay attention to it from just a safety perspective when I'm in New Orleans. Honestly, I don't do a great job of paying attention because I just wait for like my phone to start freaking out because there's some kind of tornado alert or

Mallory Mejias: As we do.

Amith Nagarajan: But up here in Utah, the reason I come out here is I just love the outdoors and the mountains and the hiking and the mountain biking and the skiing in the winter. And so So I'm always checking the weather. Cause I want to know when storms are coming in and plan like mountain bike rides and plan when I'm going to go in the lake and all this kind of stuff.

Amith Nagarajan: So I'm super into the weather when I'm up here, when I'm in New Orleans, I don't really pay as much attention.

Mallory Mejias: You just wait for those, uh, storm alerts, flood alerts to, to hit the

Amith Nagarajan: I just try to keep the family safe. That's pretty much because in New Orleans, the way, I mean, there are a few months of the year where it's nice, but there's a good bit of the year where it's just like, just stay indoors.

Mallory Mejias: Well, today we have an exciting episode lined up. Our episode is inspired by a fascinating TED talk that Amith shared with me called The Last Six Decades of AI and What Comes Next by Ray Kurzweil. Kurzweil, a pioneering computer scientist and futurist, has been involved with AI for an impressive 61 years.

Mallory Mejias: He's written several books, including The Singularity is Near in 2005, and The Singularity is Nearer this year. I thought that was a joke from the TED Talk, but he actually did just release a book, uh, with basically the same name. Uh, in this talk, he takes us on a journey through the history of AI, from its inception to the present day, and offers some predictions for the future.

Mallory Mejias: Amithh, have you, I think you've read The Singularity is Near, right?

Amith Nagarajan: That's right. Yeah.

Mallory Mejias: Yep. Is it a good book?

Amith Nagarajan: Yeah. I'd recommend it. I think when you, when you read something from somebody like Ray Kurzweil, but specifically him, um, you're going to learn something. So whether you buy into all of his predictions or not, it doesn't really matter, actually, you can learn a lot just by kind of studying the thinking that goes into a book like that.

Mallory Mejias: Yep, so topic one today we're going to be talking about exponential growth and kind of how we got to this moment. And then topic two we're going to be talking more about AI today and in recent times. And then for topic three we're going to talk about some of those future AI predictions and societal changes.

Mallory Mejias: Starting with exponential growth. This is something Amith you've talked about at length that we've talked about on the podcast. Um, AI has been a concept around on in the world since the 1950s, but recent advancements and compute power have made it feel almost like a new phenomenon. The ideas behind AI have existed for decades, but only now that we have the computation computational resources to make many of these ideas a reality, it seems that we're living in an AI boom.

Mallory Mejias: Since 1939, computing power has increased by a factor of 75 quadrillion when adjusted for inflation. I had to Google that as well because I thought perhaps it was a mistake, but that's real. By a factor of 75 quadrillion. This means that for the same amount of money, we can now buy 75 quadrillion times more computing power than we could in 1939.

Mallory Mejias: This trend has remained pretty consistent over the past 85 years, doubling approximately every two years, a phenomenon known as Moore's Law, which we've also talked about on the podcast. The surge in compute power is what's fueled AI's recent breakthroughs. And the key here is that the growth is exponential, meaning that the rate of advancement continually accelerates, Bringing exponential leaps rather than incremental improvements.

Mallory Mejias: So, Amithh, this got me thinking a lot about your keynote from last year at Digital Now. You have been giving lessons, I mean, or sessions on this idea of exponential growth on Moore's Law for a while. I'm wondering what initially sparked your interest in that? Like, how many years are we talking that you've been really tied into

Amith Nagarajan: mean, it's hard for me to actually answer that question because it's been most of my life. I've been a computer geek since I was a little kid, and, um, just kind of witnessing what had happened through the 80s and 90s and last couple decades in this century, um, it's just kind of been a Mind boggling, you know, and you think about how far we've come in such a short period of time in the grand scheme of things.

Amith Nagarajan: If you think about the course of human history, and you think about the last, you know, 80 ish years that you described, and if you think about even the last, like, 40 years, it's truly remarkable. Uh, to me, I think that a key insight that needs to be extracted from all this is how to think about the future.

Amith Nagarajan: When you think about where we are today, You know, we as a species have evolved to think in linear terms. So we're not thinking about exponential growth. We're thinking about, well, that which will come tomorrow is likely to be similar to that which has come today. But in reality with exponentials, it's so different.

Amith Nagarajan: So you have to think you have to force yourself to use a different frame of reference when it comes to planning, whether it's personal and certainly for business. And we're going to dig into that in this episode, I know, but to me, that's the big thing that I'm interested in is not even so much the technology because I love technology.

Amith Nagarajan: I find that interesting. But, um, my interest was just like looking at it and saying, what can we build with this stuff? You know, when I first started getting into computer software, uh, even before professionally, I was just amazed by it. And then once I Started my first software company, just experiencing what we could do year over year, things that were not possible one year became possible a year or two later.

Amith Nagarajan: And that, that was pretty exciting. So that's how I got into it. I

Mallory Mejias: I'm wondering, and we'll, like you said, we'll get into this in terms of business later in this episode, but the, this factor of 75 quadrillion, I mean, you're more of a math person that probably packs more of a punch to you, but for me, the difference between 75 quadrillion and one quadrillion, I can't even wrap my brain around that.

Mallory Mejias: So my question to you, and not that you may have the perfect answer is what is an average person supposed to do with this information? How could you possibly. 1939 for a factor of 75 quadrillion,

Amith Nagarajan: it's essentially a, it's almost like a number like infinity versus zero. So if you say back in 1939, computing power was close to zero and now computing power is dramatically greater. You know, that's why the factor of growth is so high, because when you divide something by a number, that's a lot.

Amith Nagarajan: close to zero, then the number ends up being very, very, very large. And so you kind of like with limits, you'd say, okay, well, that's basically approaching infinity. Um, in reality, of course, if you change, if you shift the time horizon a little bit and say, well, let's look at like what's happened in the last 10 years, the last 40 years, the number is not quite as big, but it's still super impressive.

Amith Nagarajan: Uh, I think one of the stats from the executive briefing I give on AI talks about how. Since the late nineties, we've had about an 8 million time increase, uh, in, in computing power for the same amount of money. Um, and so that might be a little bit more conceptually, you know, useful, but it's still a massive number, right?

Amith Nagarajan: I mean, 8 million times, like the computer I used to start my first software company, um, you know, 8 million times more power is packed into the, uh, actually now an older iPhone that my kids walk around with. Right. So, um, That's a hard concept to get your head around. But what you have to do is say, okay, what are my assumptions with my business?

Amith Nagarajan: What am I assuming I can do? What am I assuming I cannot do? And based on what's happening with this arc of growth with computing, with AI, with everything, um, what is likely to actually be true and to retest your failed experiments, right? So we might say, oh, okay, well, a year ago we tried to do something with chat GPT and it didn't work.

Amith Nagarajan: Therefore chat GPT doesn't do what I want it to do. And in maybe in the 1990s, if you tried to use Microsoft word for something and you said, oh, well. You know, I tried to get Microsoft word to do X and Microsoft word. Couldn't do it. It would be reasonable for you to say you're later. Well, no, like Microsoft word doesn't do that because I still have the same version of Microsoft word, right?

Amith Nagarajan: I might've had word version, you know, 10 or whatever it was back then. Uh, and word version 11 didn't come out for years. Uh, but things are moving so much faster now and software and computers are getting so much more capable. You have to reassess these. Uh, these results and your assumptions and that I think perhaps is the most important lesson about exponentials is that because you can't look around the corner that well, you have to retest your assumptions more frequently.

Mallory Mejias: Given the trajectory of compute power growth, what capabilities, AI capabilities do you anticipate becoming feasible soon that could impact associations? I know you've talked a lot about autonomous agents, so that's where my mind goes, but is there anything else that's on your horizon?

Amith Nagarajan: I think that if you were to say the current AI capabilities that we have right now in the frontier models, like I know we've talked about Sonnet 3. 5, the cloud Sonnet 3. 5 from Anthropic and obviously ChatGPT 4. 0 or 4. 0 Omni are the latest models from a couple of leading vendors. Uh, they're both amazing, right?

Amith Nagarajan: And, but they're both expensive. And they're both slow. And I say they're expensive and slow in a relative sense. They're basically super cheap and really fast compared to what they, what you would have done two years ago to do the same output, but our expectations continually go up. Humans are insatiable in terms of their demand for things.

Amith Nagarajan: If you use something and it takes 10 seconds, you want it in one second. If it takes one second, You want it instantly. It's like web page load times. If a web page takes longer than a second to load, I think it's something like 50 percent of the people on that page leave, you know, back in the nineties and two thousands, right?

Amith Nagarajan: If you loaded a web page under five seconds, it was considered phenomenally fast. So the reason I raise all that is, um, you know, What I think of is how do you go about doing more real time applications with, um, the capabilities we currently have capabilities are going to grow. So we can talk, come back and talk more about like advanced reasoning capabilities with multi agent systems that we can't do right now.

Amith Nagarajan: That that's exciting. What I think is super exciting though, is that every time a new generation of smaller models comes out, they tend to have performance similar. To the last generation of big models. So put another way, um, if you compare GPT four turbo, uh, which was released, I believe in November of 23, not that long ago, and you compare that to llama three.

Amith Nagarajan: The 70 billion parameter model from llama three, which is about, you know, I think 18 times the size of the original GPT 4 and that's not the original GPT 4, but the GPT 4 turbo model, it's comparable in terms of its benchmarks, yet it inferences or runs about 10 times faster because it's so much smaller.

Amith Nagarajan: And it's free. Um, and, and it also, by the way, it runs on the Grok cloud. We're big fans of Grok, G R O Q. Uh, you check out G R O Q cloud. com. These guys have a proprietary different style of hardware we've talked about in a prior episode, which is literally 10 times as fast for inferencing any LLM. And I share all this because it answered your question.

Amith Nagarajan: I get excited about these kinds of instantaneous real time applications, whether it's for audio, um, and video or, or even for text. A part of the reason I get excited about it is that people are missing a key ingredient and understand what AI systems can do, even if they don't get any smarter, um, everyone's trying to solve for, does the model by itself with zero shot, meaning just like send it a prompt and hope for a good result, does it produce the right outcome?

Amith Nagarajan: You know, does, does chat GPT 4 0 give you the perfect answer on the first attempt? Um, and we want the models to get better at that, right? We want them to get a lot better. But, um, with multiple agents or multiple iterations with the same model even, you can get unbelievably great responses even with current state technology.

Amith Nagarajan: You don't need better AI models to solve a lot of current issues. People look, for example, at some of the things that one of our AI products, Skip, which is this AI, Business analyst and AI data scientist. People look at what skip can do. They're like, how in the world did you make GPT 4. 0 do that? How did you make, and we use GPT 4.

Amith Nagarajan: 0. We also use Sonic. We use some other things in there. Well, the reason is, is because skip is a multi agent solution. Um, the downside to skip is every time skip creates a report for you, it takes between 20 and 40 seconds. Well, what if that was instant, right? So, and skip could also be way smarter if right now, I think skip on average does two or three passes, right?

Amith Nagarajan: When, uh, skip is formulating a response, um, using the intelligence from the models in combination, essentially. Um, but if these things were instant, then skip could do 10 or 15 or 20 passes and produce a way better answer, even if the models got. 0 percent better. So I think that's a key point is that speed is actually a key ingredient in improving capability as well.

Mallory Mejias: Can you clarify to what you mean on multi agentic solution when you mention skip? Mm.

Amith Nagarajan: basic way to understand it is multi agent systems are basically AIs talking to other AIs to produce an outcome. So I might say I want a member retention forecast. So what I want is in my AMS, I have a bunch of data and I have data in other systems. I want an AI to gather all that data.

Amith Nagarajan: I want that AI to run machine learning models against that data and give me a really good forecast of, you know, which of my members are likely to renew and which ones are not right. So it's a classical machine learning type problem. It's a data science issue requires like data gathering. It requires some data analysis, requires running models, and then it requires actually taking the output of running those models.

Amith Nagarajan: and analyzing it and producing report. So there's five or six or seven steps, right? Depending on how you break it down, a multi agent system would say, Hey, each of those steps or each of those different pieces is a quote unquote agent. It's basically an A. I. That's been prompted or trained in a certain way.

Amith Nagarajan: And that A. I. Works with other A. I. S. And then there's a supervisor. A. I. Agent. Much like you'd have a supervisor in a company asking the different A. I. Components or agents to do particular small tasks. So it's the idea of decomposing a larger problem into smaller problems, right? We've been doing that in the world forever.

Amith Nagarajan: We take a big problem like Okay. Well, how do you build a bridge? Well, there's lots of steps to building a bridge. You don't just build a bridge. How do you build a complex computer application? You break it down into small chunks, same idea. Uh, that's all a multi agent system is. And it's not really that novel of a concept.

Amith Nagarajan: Um, but it is something people are like realizing they can combine traditional software engineering techniques with AI and get incredible results. And, and in the context of this discussion, the reason I'm pointing at it is. Um, multi agent systems are slow and expensive right now. That's going to change.

Amith Nagarajan: It's going to become super, super cheap and basically real time.

Mallory Mejias: hmm. So what you're on the lookout for in the next few months to few years is speed of these systems going up, costs going down, and more real time applications.

Amith Nagarajan: Totally. And then at the same time, obviously, the progression of both scaling laws plus algorithmic advancements are going to yield fundamentally smarter models too. I just like to make the point that like, Even if you don't believe that the models are going to get smarter, which I don't think there's good data to support that belief.

Amith Nagarajan: But even if you don't believe that the AI models are going to get better, there's probably 10 years worth of engineering we can do with the current models, maybe longer to like extract every ounce of opportunity out of them. There's so much more. We barely scratched the surface with the stuff that we have in our hands today.

Mallory Mejias: Mm. That's a great point. Moving on to topic two, focusing more on AI today and recently, we know that AI is currently transforming various aspects of business from chatbots to predictive analytics, personalized recommendations, and process automation. AI is reshaping how companies operate and interact with customers and in the case of many of our listeners, members.

Mallory Mejias: However, AI's impact extends far beyond business. It's driving innovation and breakthroughs across numerous industries, Often in ways that aren't immediately visible to the public if they're not looking for them. One area where AI is making profound changes is healthcare and medicine. A striking example of that being vaccine development.

Mallory Mejias: So traditionally, researchers would select a few promising mRNA sequences out of billions of possibilities and then conduct time consuming clinical trials to find the best one. The process, as you can imagine, would take months or even years. But in contrast, Moderna used AI simulations to design their COVID 19 vaccine in just two days by simulating the reaction of billions of mRNA sequences and identifying the most effective one.

Mallory Mejias: Another example is protein folding research, which we've covered in previous episodes of this pod. In 2023, an AI system called AlphaFold2 mapped 200 million proteins in just a few months. To put this into perspective, humans had only mapped 190, 000 proteins in in the entire year of 2022 and that was AlphaFold 2.

Mallory Mejias: We should mention that we just talked about the release of AlphaFold 3 recently on the podcast as well. It's important to break out of the business bubble sometimes and see how AI is accelerating drug discovery, enhancing personalized medicine, and potentially leading to cures for diseases that have long eluded us.

Mallory Mejias: Amithh, I feel like this topic particularly when we talk about how AI is impacting specific professions or industries, it makes me think that we're looking at AI in two ways and it's not so black and white but this is what I think. One that it has the power to transform an association's business operations and offerings and then two, it has the power to completely alter their members professions or industries.

Mallory Mejias: Do you see one of those as more urgent than the other? So, yeah, I

Amith Nagarajan: one is necessarily more urgent than the other in terms of getting awareness. I think people need to look at those externalities, understand what's happening in the broader sense of AI, some of the topics you just touched on, and then how will that affect their, their world? How does it affect their profession, their industry?

Amith Nagarajan: Um, the, Associations have got to get on top of understanding that impact because some industries are going to be radically affected, others more subtly. Um, and I think the learning curve to figure that out is actually not that different than the learning curve to figure out how you can apply it to your own business.

Amith Nagarajan: Um, when we do roadmap work, helping associations and non profits, you know, plan out what the next couple years look like from an AI adoption perspective, um, we spend a lot of time helping people think through how The externalities, taking a fresh look at their industry, taking a fresh look at how AI may affect the key value components of what their industry does.

Amith Nagarajan: So what is it that the industry is doing? Where does it provide value? How will it be disrupted? Not. If, but how will it be disrupted by AI and over what timescale? And then the reason we do that is more than just, there's an exercise there. That's useful doing by itself, which is associations should in turn, take those insights and use them for education and work with our members to help their members realize these things as well as to learn from their members, obviously.

Amith Nagarajan: Their side of it is, is that you have to build what will be relevant in that future, your current educational offerings, your current conferences, whatever it is that you're investing in, um, maybe irrelevant, right? It may need, it may be completely irrelevant. It might need major updates. So working on optimizing something that isn't going to be useful in two years is something you should question.

Amith Nagarajan: It doesn't mean you don't do it. In some cases, the opportunities for AI optimizing existing processes when you know those processes will be obsolete is still worth doing because both it trains you a little bit on AI, and it also means that for the next couple of years, those processes will be radically more efficient.

Amith Nagarajan: So that's great, but don't create a situation where you've hyper optimized and made efficient. a process that doesn't make sense anymore, right? Building something like if I had the most efficient factory for producing, you know, uh, let's say saddles for horses before cars came out and demand for saddles for horses dramatically declined.

Amith Nagarajan: Um, I might be the best in the world at making them at scale, but my business is not going to do too well because the demand for that particular category of product has been displaced by, you know, by technology essentially. So you have to look at it from both the externalities and the internal side. So I don't know that I can answer the urgency question, Mallory, that, The one is more than the other, but I think people have to think about both in parallel and they have to be pragmatic when people get too married to the idea of like, Oh, this is the process I'm going to put in place and it's going to be beautiful and it's going to last for 10 years.

Amith Nagarajan: The reality is, is that, you know, most of your processes probably won't last 24 months. I

Mallory Mejias: And with innovation happening so quickly and at the scale that it's occurring, how do you recommend for associations to kind of keep an eye on what's happening in their sectors and also to discern what's here to stay, what might change? I guess my question is, and maybe you know this more with the roadmap, A lot of this is a guess.

Mallory Mejias: I mean, we're, we're seeing these advancements come out. That's not a guess, but in terms of where we'll be in two to three to five years, that feels like a guess. And so how can you prepare for that without, you know, completely throwing out something that's working for you right now?

Amith Nagarajan: think it's important to. Have a really keen sense of finding the constraints in a system So a constraint would be like a choke point based on a limited resource often. It's labor, right? So if I say how can I deliver better health care if doctors can only spend an average of 5, 000 five minutes or 10 minutes per patient visit in large health care systems.

Amith Nagarajan: How do I improve health care and improve personalization of health care and ultimately patient outcomes if I have such a rare limited resource in doctors? Um, and so, you know, everyone wants better health care. Everyone wants better patient outcomes, yet we have this constraint. So one way to look at it is to say, okay, well, if you.

Amith Nagarajan: A. I. Is really good at answering a lot of basic questions, uh, is able to even potentially do a preliminary diagnosis on whatever it is that's ailing an individual or perhaps even look ahead and and think about how can I help this person improve what's already reasonable health? Um, if an A. I can compliment a human doctor, that's interesting.

Amith Nagarajan: And then you can say, okay, well, if we imagine that that like what if we had unlimited doctors, right? How would we deliver health care? And that's the way of like removing a constraint from a system and saying the value delivery is better healthcare, better patient outcomes. The constraint I'm talking about is number of hours of available, you know, qualified care, number of doctor hours, basically a number of nurse hours.

Amith Nagarajan: What if we imagine that we had an unlimited number of those hours? What would we do? That is what I'd ask people to think about is In it, basically brainstorm what happens if the constraints somehow magically got removed. Um, AI doesn't magically remove the constraint, of course, at least not current generation AI and probably for the next 10 years, but that's not even the point.

Amith Nagarajan: What if we could automate so much of the steps that the human doctors could really spend more quality time with patients, right? Because if you break it down, and this is true for educators as well, the amount of time educators spend educating, Working one on one or working with a small group as an educator or the number of the amount of time a doctor spends the patient is not the substantial majority of their day.

Amith Nagarajan: A lot of times they spend a lot of time doing other things, right? So how can we get rid of those and how can we make them more effective? So to me, that's the opportunity for any businesses to look for those constraints and to imagine a world without those constraints or of those constraints were significantly lessened.

Amith Nagarajan: Mhm. And then that gives you the opportunity, the creative license, if you will, uh, to just envision how you're going to use these technologies differently. Right? That's the new process as opposed to like, Oh, I want to optimize the scheduling of getting people in for their 10 minute meeting. You know, visits with their doctor.

Amith Nagarajan: How can I use AI to reduce the number of no shows? How can I reduce, how can I use AI to get people in and out faster? Right. In terms of getting, and those are, those are not bad things, but if we can solve for the bigger issue, which is the actual scarce resource constraint, which is what's exciting about AI to me, that changes the world,

Mallory Mejias: Um, that's a great system of thinking, uh, in terms of approaching how AI is going to impact a lot of industries and professions with the AI roadmaps. How far ahead do you look with those? What do you suggest is like the cap of how far ahead you should be planning right now?

Amith Nagarajan: generally speaking. We tell people to not. Formally do anything beyond 24 months. It doesn't mean you don't think about what's going to happen beyond a 24 month time horizon, but two years is a really tough period of time to plan for in any significant way, uh, beyond, beyond two years, even honestly, beyond 12 months is tough because remember AI is on a doubling curve that makes Moore's law seem quite modest because Moore's law had a doubling of compute every two years, roughly for a long time.

Amith Nagarajan: And so, you know, that concept is is amazing. But A. I. Is on a doubling of training data, which is roughly equivalent to how powerful these A. I. Systems are. Of six months. So over 24 months, we're going to have four doublings. So that's an extraordinary increase in, in power. It's very difficult to forecast, you know, what we're going to do with our business and envision that, right?

Amith Nagarajan: Even those of us that spend all of our time thinking about this stuff, I think intellectually, honestly must say, we don't know how to forecast in any level of granularity beyond two years. So two years is the answer to your question. That's what we like to focus on for roadmaps.

Mallory Mejias: Okay.

Amith Nagarajan: way, it's a rolling two year road map.

Amith Nagarajan: So you don't just say, Hey, here's the two year road map. Cool, let's go do this. And what you do is you say, Hey, every quarter we're going to incrementally update it because every quarter we learn more every quarter, you know, the curtain opens a little bit wider, right? In terms of seeing what's going to happen next.

Mallory Mejias: And as someone deep in this space and who has their hands in various AI products, Amithh, How far are you realistically looking ahead? Are you actually looking two years ahead or are you more looking like in the next six months to 12 months? This is what I expect we can do and kind of leaving that next year up to I don't know

Amith Nagarajan: when it comes to building software, we are looking a couple of years ahead in terms of thinking about what will likely be possible. And we're kind of working on how to build the software now to anticipate those smarter, more capable, cheaper, and faster models, right? And then they just snap right in and skip again, going back to that example is a perfect example.

Amith Nagarajan: We started working on that. Skip is now on version two. It's going to be released at the end of July. Okay. Uh, based on some really, really cutting edge multi agent concepts and things that would not have been possible even six months ago, certainly 12 months ago, we started working on skip originally about 18 months ago, and we knew very well we wouldn't be able to do a whole lot with the first beta version and even the first version one, which released last summer was super, super limited.

Amith Nagarajan: But we designed the architecture so that we could plug in future capabilities. There's a limit to what you can do with that, because when something totally radically different comes out, you have to rethink that. So that's In my role, I look kind of broadly across these things. Um, I do look at the narrow, you know, lens that you need for specific products, but I'm trying to think about like, how do we build a product portfolio that will solve, you know, the systemic problems and opportunities for associations even five years from now, um, but make investments now in products and services that will go to market, be profitable, be successful, create tremendous value and, and pave the way to those future things that we can't really fully visualize.

Mallory Mejias: hmm. And this makes me think of a point too, that you've brought up several times, the importance of having kind of a layer in between the thing that you're building and the model that you're using. So you can kind of plug and play, uh, based on whatever releases we see in the future. That is a good segue to our third topic of the day, which is future AI predictions and societal changes. Based on what we've seen thus far, we can say AI is expected to continue its rapid advancement, with some experts predicting we'll achieve Artificial General Intelligence, or AGI, AI that matches or exceeds human intelligence across a wide range of tasks soon.

Mallory Mejias: In 1999, Ray predicted we would reach AGI by 2029, and he is still standing by that. So I guess we've got a few years to see how that rolls out. Now one intriguing future concept that he brought up in this tech talk Ted Talk, that's the inspiration of this episode, is the idea of longevity escape velocity.

Mallory Mejias: This suggests that scientific progress, largely driven by AI, will advance fast enough to extend human lifespans by more than a year for each year that passes, potentially leading to increases in human longevity. There are also predictions about brain computer interfaces becoming commonplace by the 2030s, potentially allowing direct connection between our brains and AI systems, and then some say by 2045 we might experience an event called singularity, Where AI could lead to an explosion and human intelligence dramatically enhancing our cognitive abilities, creativity, and problem solving skills.

Mallory Mejias: Uh, Amithhh, this is another one for me that's just, it's just hard to grasp. You think AGI, you think singularity, um, I feel like these are bold predictions, but none of them really surprise me. I want to hear your take on the, the AGI one. Well, first, can you kind of explain to our listeners what AGI is? I feel like it's been a minute since we've done that.

Mallory Mejias: Okay. Okay. Uh, and then I want to hear your thoughts on 2029.

Amith Nagarajan: in pretty much all domains. So rather than being narrowly focused and really, really good at a particular skill, um, AGI is capable across all these domains and critically AGI also has the ability to reason and plan.

Amith Nagarajan: and execute actions. So an A. G. I. System is basically like a multi agentic system with significantly greater reasoning skill. Um, the tools we have today are facsimiles of reasoning. You think you're getting reasoning out of Claude or out of chat GPT. We have to remember that these are very simple systems still that are next token or next word predictors.

Amith Nagarajan: Um, they essentially simulate reasoning because they're so good at predicting, but they're not necessarily checking their work and looping back and saying, Hey, did that make sense? Let me think about that. You know, or like when you write an outline for a blog, you might say, well, let me think about what might make sense there.

Amith Nagarajan: And then you read it again. You go, well, maybe I'll change this. Then you start working on it. Then maybe you change the outline because. Once you start working on it, you realize the outline wasn't great, um, and you go back and forth. It's much more of an iterative kind of thing. And these systems don't do that right now.

Amith Nagarajan: So an AGI system has to have kind of an iterative, uh, multi step planning reasoning capability. Um, you know, how long it'll take to get there. First of all, I'll say this thing about Ray Kurzweil and like why I think he is someone who's been accurate so often is that he's looking at the exponential curve.

Amith Nagarajan: So when, in 1999. I don't think he just like, you know, drank a bunch of beers and said, Hey, it's going to be 30 years from now. And, and he, I think the guy was looking at like, what's happening with the doublings of compute. And, um, that was, uh, you know, I think, uh, you know, what is it five years from now?

Amith Nagarajan: Right. So. Quite a bit of time to get there. I think that we will have systems that whether people will say, you know, we've crossed the finish line and we have AGI or whether they'll say, well, it's not quite AGI, the systems we'll have in 2029 are going to be ridiculously smart compared to what we have now, whether it's through multi agentic kind of approaches, that will be definitely part of the solution or just dramatically smarter models, faster models.

Amith Nagarajan: So, um, That's the basic concept of A. G. I. And I think that we talk about something like escape velocity for longevity. You know, the I think it's an interesting concept. We say, okay, you can add a year of life more than a year of life for each year that you live, and therefore you can infinitely extend your life.

Amith Nagarajan: Right? And there's all sorts of interesting conversations that can come from that. The more important thing to me is that I think it's it's likely in our lifetimes that we're going to see significantly longer lifespans, whether it's to the extent of this so called escape velocity or not, that affects everything.

Amith Nagarajan: That affects resources, that affects healthcare, that affects education, that affects our models of economy, that affects things like what do you do with retirement and social security, uh, that affects also the question of healthspan, not just lifespan. Who wants to live to 120 if you're miserable for the last 40 years of your life?

Amith Nagarajan: You know, you're going to want to have high quality of life. Uh, what are people going to do, especially as AGI systems come online and become smarter and smarter and can replace more of the basics? Um, what happens? Um, one of the things that I spend a lot of time thinking about and talking about in executive briefings and keynotes is the idea of the exponential economy.

Amith Nagarajan: Um, and the exponential economy, Actually, very closely parallels technological progress. If you go back over the course of the last 1000 years and say what's happened in terms of total economic output globally, you will see that the inflection points are when major technological shifts have occurred.

Amith Nagarajan: The printing press, the steam engine, obviously digital technology. We're seeing a further acceleration of that, you know, we've gone from a 1 trillion to a 10 trillion to 100 plus trillion global economy and Really really crazy speed. So what we're gonna see now as we go forward is further growth. That's driven by demand We have to remember especially in the developed rich world That most of the world doesn't live the way we do most of the world has Incredible resource constraints.

Amith Nagarajan: And even in the rich world, we have a lot of inequality. We also have a lot of opportunity. Uh, there's a lot of untapped potential intellectually and the eight plus billion humans that are out there, um, education, healthcare, it's going to change all of that. And so, um, there's a lot of opportunity, whether it has to do with like an individual human lifespan being 150 years or whatever, but what about like how this stuff will affect.

Amith Nagarajan: The billions of people who have horrible and short lifespans and how that changes because of access to healthcare access to this technology. That's a much more interesting conversation in my mind. And what's the net impact of all of those people having, uh, the basics met coming online and contributing their intellectual capability and their creativity, which I think is going to be a domain that humans will continue to excel in compared to machines over that time span.

Amith Nagarajan: So that's what I get excited about. Um, you know, this area where you start getting into like sci fi ish realms like brain computer interface and singularity possibilities. I think these are all things that are going to happen over time. I don't know when they're going to happen. Um, I think what matters a lot more right now is how we think about like the world and how we think about society and how we plan for even the next five to ten years.

Amith Nagarajan: Um, and I think there's a lot, very strong likelihood AGI like system in the next five years or certainly ten years.

Mallory Mejias: Mm hmm. Will an AGI system be, um, multiple agents in one? Well, kind of like the concept you explained with skip, or will it just be a single system that, I don't know if those two, two things are even different from one another. Mm

Amith Nagarajan: you're going to think of it as, uh, a singular thing is how you're going to interact with these things. Because, like, when you think about your computer, you think about, you have a laptop. You don't think that you have an operating system, and you have a web browser, and it's got hardware, and it's got electrical components, and it's got a screen.

Amith Nagarajan: It's just a singular entity to you, and it does stuff, right? So, the utility that it creates is It's really through that singular, you know, uh, concept. I think AI systems are going to be like that to a large extent. And then what's going to happen is people are going to stop talking about AI. Um, because AI is going to blend into the background.

Amith Nagarajan: It's a general purpose technology that's just assumed, just like we assume the internet, just like we assume mobile phones, just like we assume, you know, digital technology or things like language, right? We don't talk about how cool is it. That we've come up with language as a species. It's pretty bad ass actually, but we don't talk about that anymore.

Amith Nagarajan: So we're not going to talk about AI forever. The way we are now. Um, I think it's going to be a big deal because for, for quite some time to talk about, but it's going to blend into the background as an assumed general purpose technology, uh, it's going to change everything. But, um, the biggest problems we have to go solve are how do you drive.

Amith Nagarajan: Equitable access to this stuff throughout the world. How do you deal with the impact of society when jobs are displaced? Cause you can't retrain everyone fast enough. Um, so what do you do with these people? Right? What do you, and what, what happens to the associations that represent sectors that no longer have a workforce?

Amith Nagarajan: Um, those are the big questions with AGI type systems. I think we're in kind of. You know, we're in the early innings right now, so it's important to get out there and learn this stuff because it's gonna affect your space in some way. Um, I will say this just to conclude those remarks. I am super optimistic about, um, the future of associations, not just the defensive mindset of can they exist, but in terms of the opportunistic mindset of what do they do.

Amith Nagarajan: Associations are fundamentally about connecting us, right? It's about how do you bring people together? who are like minded or have a common goal. How do you help them? How do you help them do that better? How do you help them have a bigger impact? That's what associations are about and more than ever in a world of AGI, we will need to connect in deeper and more meaningful ways.

Amith Nagarajan: And that's where I get excited because we'll have more time to do that on. We'll have better opportunities to do that in more meaningful ways, because I will help us do that. So I think associations could really be actually some of the greatest beneficiaries of all these changes.

Mallory Mejias: Hmm, that's a great point. Even reading through this and researching the topics for today, I mean, this one made me a little scared. Just thinking of AGI and singularity and like, how can we prepare for that? And how can we envision what things will look like in 10 years? We just can't, but I think you've got it.

Mallory Mejias: Hit the nail on the head that we will continue to seek out things that make us feel more human and more connected Especially in this ai landscape and associations have an excellent platform to do that and to bolster that um I'm wondering what you think of you've mentioned this before to me this idea of kind of Reinventing what associations are right now thinking exponentially.

Mallory Mejias: I think you might be speaking about that at digital now this year Which is october 27th through 30th in dc You've mentioned things like maybe taking down geographic barriers maybe opening up membership to like adjacent professions Can you talk a little bit about that like what it might mean to reinvent an association moving forward?

Amith Nagarajan: Yeah, I think the, the barriers, whether it's through, you know, these artificial constructs we have of someone is in profession A or profession B, but they're really similar, they're really close to each other. I think you have to zoom out and then the geographical ones, of course, which are, you know, historically, it's hard to distribute value.

Amith Nagarajan: In a distance, right? Um, well, I think that associations, you know, should think more broadly about fundamentally what they're good at and where they can deliver value, ultimately aligning with some deeper purpose. Um, in the book that I wrote back in 2018, the open garden organization, I talk a lot about purpose and how it's the rooting of the culture that you need to get right to really describe your impact on the world, your, your purpose statement, right?

Amith Nagarajan: Your core purpose. And, you know, This idea is so critical now because it's, it's, it's this navigational beacon in a sense where you are saying, Hey, I'm always going to be heading towards that goal. How can I do more of that? How can I improve patient outcomes? How can I improve the reliability and health of our financial systems?

Amith Nagarajan: How can I make construction safer? How can I improve drug discovery? Right? Like these are things that might be at that fundamental lower level. Purpose statement that act as a way of foundationally aligning your thinking, even as everything's changing around you. So to your point, Um, is the best conduit through which you deliver that value your traditional membership base, or should you include adjacencies?

Amith Nagarajan: If your goal is to improve, Patient outcomes. And all you've ever done is focused on a super narrow medical specialty. Could you improve patient outcomes even more by making your content more inclusive by using AI tools to reach other audiences that, uh, might be able to help improve those patient outcomes, whether it's the patients or the families or.

Amith Nagarajan: adjacent medical professions, or even people doing back office roles. So I think associations do need to think in much broader senses, but I think revisiting that purpose, if you don't have a clearly defined core purpose statement, I really encourage you to, to consider, uh, exploring that a lot of associations say, Oh, we have a mission statement, but the mission statement usually is this like super long thing.

Amith Nagarajan: No one remembers. It usually kind of generically says our mission is to improve the success or advance the profession by doing X, Y, and Z. Uh, and those mission statements tend to be forgotten because they're long and ultimately when you read them, they're not inspirational and they tend to be Not only boring, but they tend not to actually convey the point So if you're really successful at making the profession better at what they do Uh, if I do that with doctors lawyers or accountants like who cares?

Amith Nagarajan: Um, ultimately what matters is someone who's not in your profession. Why would they care about the impact you're having? There's some great resources out there. I mean as I mentioned the book that I wrote in 2018 the open garden organization contextualizes Purpose in the context of associations. Um, and of course there's Jim Collins body of work, which is some of my favorite business writing, uh, starting off with built to last and then good to great that talk deeply about core purpose, core values and BHAG, which I'd recommend people check those out.

Amith Nagarajan: I think that stuff is timeless and will serve you well to revisit, even if you've studied it extensively in the past.

Mallory Mejias: Do you think there would be power in the future in, uh, several associations kind of joining forces if they serve similar sectors?

Amith Nagarajan: Yeah, I think consolidation is a natural function of a maturing market of any type, whether it's a nonprofit space or not. I know that, you know, folks that are in our CEO mastermind on AI are talking about this from time to time and how does that affect their big picture strategy? I've heard a lot of people talk about the possibility of merging with adjacent associations.

Amith Nagarajan: I also think there's opportunities for associations to acquire for profit businesses that are complementary to what they do. So an association might say, Oh, we're sitting on. X millions of dollars of reserves. And typically you're doing basically nothing with that. It's your rainy day fund, but you might have, you know, eight years of runway with your reserves.

Amith Nagarajan: You might think about strategically investing some of that, uh, in startups that are in your space that you could build a portfolio to drive innovation, or maybe acquiring companies that have, you know, Lines of business services that are complimentary to your own. There's a lot of creative ways that, um, M and a might be helpful in the association space because, um, you know, scale can drive more opportunity, not always, but, uh, if you have more resources available and you're growing faster, you might be able to get ahead of that curve or at least keep up with it.

Mallory Mejias: Hmm. That's really interesting. I'm hoping you dive more into all of this at, uh, at Digital Now this year. I think it's a great conversation to be had.

Amith Nagarajan: I'm definitely planning on touching on a lot of this. Of course, you know, that's three months and 10 days, I guess, or three months and 18 days away from now, something like that, I need an AI to help me calculate that, but, uh, I probably will be fine tuning that slide presentation up until the pretty much the last minute, because things are changing so fast.

Mallory Mejias: Absolutely. Well, Amith, thank you so much for the conversation today. And we mentioned a few times Digital Now. If you want to get more information on that conference, you can go to www.digitalnowconference.com. We'll also include that in the show notes, and we will see all of you next week.