Summary:
Domain-specific AI is having its ‘main character’ moment—and Amith Nagarajan and Mallory Mejias break down three sharp examples. They unpack Three Points Law, an AI-first UK firm using tools like Legora and Qanooni to move beyond billable hours into value-based pricing; dive into Project Prometheus, Jeff Bezos’ big bet on ‘physical AI’ that learns from real-world experiments; and explore OpenEvidence as evidence-grounded clinical copilots surge in adoption alongside the American Hospital Association’s push for practical implementation. The throughline for associations: your domain expertise can become your biggest AI advantage—if you pick the right path between partnering, aggregating, or building.
Timestamps:
00:00 - Welcome to Sidecar Sync
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🛠 AI Tools and Resources Mentioned in This Episode:
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Qanooni ➔ https://qanooni.ai/
Project Prometheus ➔ https://shorturl.at/w9ZNZ
OpenEvidence ➔ https://www.openevidence.com/
Gemini 3 Pro ➔ https://deepmind.google/models/gemini/pro/
AHA November Market Scan ➔ https://shorturl.at/ySt96
AI-First Law Firm ➔ https://threepointslaw.com/
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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.
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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
🤖 Please note this transcript was generated using (you guessed it) AI, so please excuse any errors 🤖
[00:00:00:14 - 00:00:09:17]
Amith
Welcome to the Sidecar Sync Podcast, your home for all things innovation, artificial intelligence and associations.
[00:00:09:17 - 00:00:24:05]
Amith
My name is Amith Nagarajan.
[00:00:24:05 - 00:00:25:24]
Mallory
And my name is Mallory Mejias.
[00:00:25:24 - 00:00:43:05]
Amith
And we are your hosts. And as usual, we have an exciting episode, Action Packed, with content all about AI, of course. And we're going to apply it to associations, which will not be hard today because these topics are super relevant to specific professional domains. Mallory, how are you doing today?
[00:00:43:05 - 00:01:04:04]
Mallory
I am doing pretty well, Amith. Down here in Atlanta, it's been pretty chilly, like 30-ish degrees every day. Got to take the two dogs on the walks. But it's good. It's good to get outside. I'm enjoying the winter time. We're in the holiday season, which is kind of hard to imagine. We talk about this a lot, but the time feels like it's flying always.
[00:01:05:06 - 00:01:10:10]
Mallory
But yeah, happy to be in the holiday season. We'll be seeing my family soon, which will be great. How are you doing, Amith?
[00:01:10:10 - 00:02:04:10]
Amith
Doing great. It is that time of year. And it's a good time to reflect and enjoy time with family. It's also a good time to think about all the craziness that's going around us. And AI companies haven't slowed down for the holidays at all. And I don't know if Sam Altman decided he wanted to put on his Christmas sweater, but he declared a code red the other day. And so I don't think it was the sweater he was wearing. I think he was thinking about his good friend, Sundar Pichai, and Demis Asabas over at Google, who released Gemini 3 Pro. And that kind of reframed the conversation for a lot of people. So a lot of crazy happening in the Frontier Labs. I'm sure we will hear from the folks at Anthropic because they don't sit still, and we know they have a lot of good things cooking as well. So it's quite fun. I think we're going to open a lot of interesting early holiday presents over the next couple of weeks before the year wraps up from these AI labs. So I'm excited about that.
[00:02:04:10 - 00:02:19:11]
Mallory
I'm excited about that. But then it's very difficult for us to record the pod during the holiday season with so much going on. But of course, it's like clockwork. Every time there's a holiday, there's all these new model releases, all this drama across all the AI companies. So we'll just have to make it work in me.
[00:02:19:11 - 00:03:03:23]
Amith
Yeah, and since the beginning of this pod, our focus-- and our listeners know this well-- it's not so much to be up to the second with respect to what's the latest in AI. There's plenty of shows that do a fantastic job of keeping you very much up to the minute on what is going on this moment in AI. Our perspective is, how do we apply the trend line of what's going on with AI to the world of associations and nonprofits? How do we help this community think about how to best adopt and embrace and really thrive in this world of AI? And so the trend line certainly is influenced by these incremental events that are occurring, and sometimes step change events that are seemingly on the horizon every week. But that helps a tiny bit. But then again, our listeners do expect us to be talking about the newer models as they come out. So here we go.
[00:03:05:10 - 00:03:10:11]
Mallory
And I'm wearing red in honor of the code red at OpenAI. Amith, you'll have to put on your red for next time.
[00:03:11:11 - 00:03:21:12]
Amith
I just like wearing the hoodie. You know, in New Orleans here, it's 61 degrees. Everyone here thinks that the world is ending, and it's freezing. It's no longer 100 degrees and 100% humid. But I'm loving it, and so I've got my hoodie on.
[00:03:22:14 - 00:03:44:23]
Mallory
Well, today we are looking at domain-specific AI, so not general purpose tools being adapted for industries, but some AI-native companies fundamentally re-imagining how work actually gets done. We're covering three domains today-- legal, engineering, and medical. In each case, AI isn't just assisting with existing workflows. It's enabling new ways of practicing.
[00:03:45:23 - 00:04:47:24]
Mallory
First, we want to touch on legal. We're talking about three points law, the AI-first law firm. Two lawyers from a prominent UK law firm that's represented high-profile clients like TikTok launched three points law in October of this year, 2025, built from day one on AI platforms that handle document analysis, contract drafting, and workflow automation. Here's what makes three points law fundamentally different from traditional law firms. One, it's that AI-first architecture. So it's built on legal AI platforms, one called Ligora, one called Cununi, from day one. And this isn't just a law firm adding AI tools after the fact. It's a law firm built entirely around AI capabilities. Another feature of this company is that they don't have billable hours. They use values-based, flexible pricing model instead of traditional hourly rates. So AI automates the routine work that traditionally got billed by the hour, allowing lawyers to price based on outcomes rather than time spent.
[00:04:48:24 - 00:05:10:17]
Mallory
They're also committing 5% of annual billing or profits to charity and community initiatives. The firm launched with clients already secured over a dozen, including established tech suppliers, rapidly growing media and real estate businesses, and high profile athletes. This suggests the market was ready for an AI-first law firm, not just experimenting with the concept.
[00:05:11:17 - 00:05:18:20]
Mallory
So, Amith, I want to hear your take on a three points law as a model for services, professional services disruption.
[00:05:20:01 - 00:07:10:13]
Amith
Well, I think professional services organizations, for a long time, they bill you based on the ingredients, not based on the meal, so to speak. So if you go to a restaurant and they don't tell you how much the dinner you're ordering is going to cost, but then they start billing you for all the different pieces that go into it, that's kind of the way law firms work and accounting firms work, where you're paying for the hours, right? You're paying for the time materials, as they call it. And for a long time, that's been just kind of the established way of doing things in engineering firms and architecture firms and certainly law firms. Nobody seems to like that a whole lot. Clients certainly don't because there's no degree of certainty and I think it's ultimately going to be seen as a relic of the past. Professional services firms that we operate have been fixed cost, fixed outcome organizations for a long time, well before AI, because the mindset was around how much value were we creating for the client and then of course, how much profit can you generate for that if you do a good job with it, then hopefully you can generate a reasonable profit. But with A, of course, that changes the game because your ability to separate the inputs from the outputs is far greater. What I like about this conversation too, is when you think about the context of associations, many professional societies actually charge memberships based on the number of members in a given firm or just the number of professionals. And that may itself be an interesting problem to think about because if the number of lawyers in a firm isn't as high as it is today, then the total revenue to the association through the number of members they charge for potentially could get impacted. So does that mean that value-based pricing has to apply to the association as well? So that's a little sidebar, but I think that all of these industries as they adapt and change and perhaps the ratio of people relative to the output is shifting, it could cause the association world to have to rethink their models as well.
[00:07:12:02 - 00:07:26:02]
Mallory
I imagine it's pretty abstract for someone listening though to imagine what the pricing would be for their membership without a system like that, without kind of counting in the ingredients. Do you have a suggestion to me for how to think about value-based pricing, how to approach it?
[00:07:26:02 - 00:07:44:04]
Amith
Yeah, I mean, the number of members in a firm, whether you're an accounting association or law association, it's kind of a proxy to the value you're creating. So in theory, if you're saying, listen, if you have 50 lawyers in your firm and we roll up to those 50 memberships to firm-based billing and it's X for 50 people,
[00:07:45:06 - 00:08:02:24]
Amith
is there a value equation from that where if there were 25 attorneys instead of 50, but there was a lot of AI, you could still provide comparable or greater value in that context and charge as much or perhaps a lot more. So I think it just applies, the shift applies to everyone.
[00:08:04:07 - 00:08:49:02]
Amith
Some organizations, particularly trade associations in my experience have models that are actually more similar to this already where they will look at an organization's revenue level and they'll have tiered membership based upon the firm or the company revenue level, or in some cases, if they're dealing with public companies based on their market cap, things like that. So those might be worth exploring. It might be worth looking at different models like that. But I think the idea of what Three Point Slot is doing in the law industry absolutely is going to ripple across the rest of the legal profession, certainly, because I still haven't met a single person who likes the billable hour, the concept of it, or certainly getting bills where they don't know what it's gonna be. So, there's going to be a shift here without a doubt in my mind.
[00:08:50:16 - 00:08:57:02]
Mallory
Do you think this is the future across the board for all professional services businesses? Is this where everything's heading, in your opinion?
[00:08:58:07 - 00:09:33:14]
Amith
Well, if you think about what is the variable cost of delivering the product, the end product that you're requesting. So if you're the customer or the client of a professional services firm, traditionally, the variable costs associated with the delivery of that product or service is labor. It's the professionals, it's the lawyers, the accountants, the doctors, the engineers, the architects that the firm has on payroll or contractors that are the main cost. And so because that's the main cost, they have to kind of, in their minds, they've historically had to say, well, we're gonna build our model around those inputs.
[00:09:34:17 - 00:09:40:23]
Amith
And so in a commodity driven marketplace where firm A and firm B are comparable from the customer's perspective,
[00:09:41:23 - 00:10:31:09]
Amith
ultimately, it all boils down to cost plus in that model, meaning everyone has a comparable cost. They all can achieve similar margins. There's margin compression over time, and ultimately the profits get arbitraged out if it's truly commodity. Now, what I would point out is in the context of AI, there's many different flavors of AI and some people will use it really well. Some people will use it poorly, but still say they use it. You can't really hide because if you use AI really well, you're gonna produce a far superior outcome while at the same time having lower costs, therefore being more competitive, delivering better value to your customer and also enjoying better profit margins. So I think that this of course is one of the reasons that people are looking at AI as not just the technology adoption shift, but a fundamental transformation in their business models. So to answer your question, I don't think there's anywhere to hide. That's the way I'd describe it. It's happening everywhere.
[00:10:32:13 - 00:10:52:15]
Amith
I do think that there might be some relics that are still out there in certain highly specialized industries where domain knowledge is super narrow and super specialized. And so therefore people can or will charge by the hour for years to come and that's fine, but I don't think it's gonna be the mainstream for trillions of dollars of economic output as it is now.
[00:10:54:01 - 00:11:09:23]
Mallory
And for the associations that serve professional services industries, legal accounting or consulting, aside from perhaps how they think about their own membership and business model, how should they be preparing to perhaps have tons of AI native firms in their membership?
[00:11:11:08 - 00:11:21:19]
Amith
I mean, the first thing I would do is say, you need to be at the center of that conversation. So if you are a bar association or some other form of CLE, education for the law sector,
[00:11:22:20 - 00:12:44:01]
Amith
you better be thinking about how AI affects the practices of these individuals who are taking your courses or members of your organization because they're impacted by this. So first and foremost, you have to become quite knowledgeable in AI in the context of your field, your profession, and of course, knowledgeable in AI. Most of the time on this pod, we talk about AI in the context of running your association. But here we're talking about AI deployed in the field by the professionals in your sector. You have to understand how AI applies to them. And in my opinion, you have to train them. It is your job, it's gonna be somebody's job to help the lawyers of the world and the accountants of the world learn how to use AI in the context of their work. It might as well be the association. So that's why we're partnering with associations to stand up AI training for their sector. We do that all the time. We're not in the sidecar. But at the same time, the broader message is, you better get into this conversation because if you're not in the conversation, you're out of the conversation and that's not a good place to be when the entire world is shifting around. So I do think it's super important. And I think associations have to embrace both of those challenges, the external in their field AI impact and also the internal operational impact of AI. The good news is if you become expert in AI, you'll be able to apply it in both ways.
[00:12:45:01 - 00:13:28:20]
Mallory
And bar associations, I don't want you to think we're picking on you. I'm just realizing our last episode was with Ernie Svensson, who is an individual who's out there providing that AI education to lawyers and kind of had some commentary about bar associations. But all this to say, if you're an association leader listening to this, you've gotta be a part of the conversation. You've gotta lead the conversation. First way to do that is educate yourself and then bring that education to your members too. Moving on to our next topic, we're talking Project Prometheus. So this is gonna fall into our kind of engineering and manufacturing domain specific AI section. Jeff Bezos launched Project Prometheus in November of this year with $6 billion in funding, making it one of the most heavily funded AI startups at inception.
[00:13:29:21 - 00:14:10:11]
Mallory
Bezos returned to an operational CEO role co-leading with scientist Vic Bajaj for the first time since leaving Amazon in 2021. So what do they do? The company builds physical AI, systems that learn from conducting actual experiments in the real world, rather than just processing text or running simulations. The focus is on automating engineering workflows, optimizing manufacturing, and accelerating R&D through trial and error in the physical world. So think, AI proposes a new rocket material, robots mix and test it physically, scientific equipment measures the results, and then the AI learns from what actually happened in nature.
[00:14:11:16 - 00:14:31:03]
Mallory
Project Prometheus targets aerospace, clear synergy with Bezos' blue origin, automotive and computing hardware, industries where traditional design, test, iterate cycles take years. The promise is compressing development timelines by having AI systems rapidly prototype, test physically and learn from actual outcomes.
[00:14:32:05 - 00:14:43:14]
Mallory
So Ameth, one of the most heavily funded AI startups at inception, I guess with Bezos behind it, not super surprising, but what does that scale of investment tell us about physical AI and how close we need to be paying attention?
[00:14:43:14 - 00:15:57:12]
Amith
Yeah, I guess Jeff had some spare change in his pocket. He needed to do something with, so he decided to throw it at this project. But it is seriously though really exciting. I think there's a lot of people pursuing this both in embedded situations where existing companies that are deep in industrial automation are looking at ways of leveraging AI, and then there's companies attacking this in various ways. So we've covered robotics on this pod a number of times and the advancements happening there are quite exciting. Here we're talking about kind of a full loop of a system, which would involve robotics certainly for the things that you described, but a systematic approach where they have kind of the brain of AI proposing ideas and then having the industrial complex and the steps involved and actually completing the full loop to get the feedback loop to execute the test and pull back the results, which is a wide array of different insights that are pulled. And there's clearly a partnership here, at least for the foreseeable future with us, where these robots that are working in these Prometheus factories of some sort will have human partners that assist with these processes. And so I think what's exciting about this is that in the domain, certainly for aerospace engineering or any advanced manufacturing,
[00:15:58:19 - 00:16:10:07]
Amith
every single percent of speed and every single percent of materials is both cost and time. There's also opportunities for risk reduction, safety, all those things, and ultimately better quality outputs.
[00:16:11:09 - 00:16:53:13]
Amith
So AI is perfect for that because AI doesn't have the kinds of biases we do. And the bias that I'm referring to most notably here is our emotional bias that goes towards our experiments, right? When you spend 100 hours, 1,000 hours, 10,000 hours in a particular idea, a hypothesis, if you will, even though you know that you have to objectively evaluate the results, you want it to work, you really do. I don't know any scientist who hasn't had a project that they've worked hard on that they've said, "No, I don't really care if this works or doesn't work." Well, Project Prometheus is essentially a system that, of course, has that dispassionate computer side to it, which I think will help improve the results in a way as well.
[00:16:54:13 - 00:17:51:22]
Amith
But ultimately, I see this as a systems concept, right? There's no particularly notable AI breakthrough individually in each of these parts, but it's like pulling it all together. And if you think about Bezos' past in building Amazon, Jeff Bezos did not invent the internet. We all know Al Gore did that. Jeff Bezos did not invent e-commerce. There were plenty of other people doing e-commerce. He didn't invent the browser. He did not invent UPS. Although he's trying to replace it now, or Amazon is, but he didn't invent any of those things. But he had the foresight back in that early 90s timeframe to drive across the country, quit his high paying finance job and start Amazon and integrate all these things together in a beautiful way. And that's what results in not just next day shipping, but two hours shipping to your door in many cities around the country. It's a systems integration problem. And so that's how I see what he's doing here. So certainly he's one of these handful of people who has the intellect, the experience, and also the capital to try to go solve this problem quickly. So I find this very exciting.
[00:17:53:13 - 00:18:34:00]
Mallory
I'm thinking of what we often say on the podcast, which is don't try to boil the ocean. And I'm also thinking about engineering manufacturing associations who are working internally perhaps on just-- I don't even know what you would call it in me. Not physical AI, everything but physical AI. And then also having to sprinkle in, OK, we've got to understand physical AI too, because that has a direct impact on the industries and the professionals that we serve. So I guess my question is how, if you are an engineering manufacturing association leader listening to this, do you see this all as part of the same conversation? I don't know. Physical AI just seems so different from what we normally talk about in my mind.
[00:18:35:09 - 00:18:53:03]
Amith
I think it is in a way in that obviously it's got the physical form to it. But in concept, I think the disruptive impact is very similar. Because processes that are purely digital already, where it's essentially happening in bits and there are no physical elements,
[00:18:54:09 - 00:19:53:08]
Amith
those are processes we can kind of relate to more, but those associations tend to be very information and digitally centric, at least at this point in the mid 2020s. But we're talking about a scenario where you do have certain products and services or value delivery mechanisms in the economy that are very physical in nature. And so it's harder to relate to them in some ways. But I do think that the societies you're referring to that are in various disciplines of engineering or manufacturing or transportation, logistics, groups that are in those fields are very used to the physical world because that is where they live. And so for them, it's almost the inverse, I think, where we're thinking about how a truck operates or how a forklift operates or how a warehouse works and how does AI fit into that context. It's almost like an inversion of that, I believe, for folks who are in those fields. The people that I know that are in associations like that tend to have a lot of domain expertise themselves. A lot of them have come from the field into the association.
[00:19:54:08 - 00:20:22:05]
Amith
But I think the opportunity is just as large, if not more so. The physical world is super messy, it's super hard, it's super challenging in lots of ways. Obviously, there's different kinds of safety issues. So I think you have to approach it differently. But to me, there's definitely parallels. But I also feel differently about it because my expertise is certainly not in that domain. I'm much more of a digital guy. So if it's in the world of bits, I can understand it. Once it gets into the world of atoms, I live in that. But otherwise, I don't really understand it too well from a business perspective.
[00:20:22:05 - 00:20:31:21]
Mallory
That's what I was trying to say. The world of bits, not atoms. I just had a moment there where I was like, not physical AI, the digital AI. Close enough.
[00:20:33:12 - 00:20:38:08]
Mallory
All right, Amith, I want to move to our last example of domain-specific AI. We're talking medical.
[00:20:39:14 - 00:22:27:13]
Mallory
Open evidence is now used by 25% of US doctors, one-fourth, serving 15 million monthly consultations. It functions as a conversational AI trained exclusively on peer-reviewed medical journals. Physicians ask clinical questions and receive evidence-based answers with citations in real time. The innovation here is domain-specific training. The AI only learns from vetted sources like the Journal of the American Medical Association and the New England Journal of Medicine, not general internet content. The adoption pattern demonstrates viral growth rarely seen in healthcare. The platform is free for verified medical professionals showing how domain-specific AI can achieve consumer internet scale adoption when it solves real professional pain points. Beyond open evidence though, I was doing some digging and I wanted to highlight the American Hospital Association and how they're actively helping hospitals implement AI prevention programs at scale. They're pushing implementation guides, governance frameworks and measurement resources as technology moves from pilots to practice. So they posted their November 2025 market scan to show what's working. And I wanted to highlight a few of those items here. Johns Hopkins AI powered diabetes prevention program achieved 93% patient engagement versus 83% in human led programs. The Cleveland Clinic's AI enabled precision health coaching got 71% of patients to achieve A1C targets and Cedar Sinai 24 seven AI virtual care platform has managed 42,000 patients remotely preventing low acuity ER visits. The American Hospital Association's role shows what association should be doing, moving members from does this work to where does it work best and how do we implement it safely?
[00:22:28:15 - 00:22:31:11]
Mallory
So Amith, I don't know, are you familiar with open evidence, have you heard of this?
[00:22:32:11 - 00:23:26:06]
Amith
Yeah, I have. I've talked to actually a handful of association leaders in the healthcare sector who have thought about partnering with them and have evaluated it. So the ones that you mentioned are examples where journals from leading medical groups have been consumed by open evidence, which is part of the reason they're able to do what they're doing. And so that's a whole interesting conversation we can have in a sec, but I am familiar with it. And I think those kinds of deeply domain specialized knowledge resources are fundamentally a different animal. And when you have that number of doctors or lawyers or engineers or other highly specialized professionals using an AI tool, it tells you an awful lot. And it's not about ease or convenience, although it certainly is those things. It's about better advice. It's about better outcomes for their patients in this case. And that's exciting. What you mentioned about Johns Hopkins, I hadn't heard actually the level of engagement you're describing, that's truly stunning, isn't 93%. That's amazing.
[00:23:26:06 - 00:23:55:02]
Mallory
Yep, absolutely amazing. I had prepped this episode a little bit before the timeframe that I normally do. And funny enough, Bailey, my husband who works in the medical field pulled up open evidence unrelated and was like, oh, let me look up something. And I realized like, oh wait, that this is what I'm talking about in the podcast and this is you using it. And he said he loves it. And I just think the adoption of it is really impressive. And as you said, having this deeply domain specific AI that can answer questions with citations,
[00:23:56:04 - 00:23:59:02]
Mallory
it just feels very relevant for professionals.
[00:23:59:02 - 00:27:17:20]
Amith
For sure. And I think there's interesting models around that. I think the key is if you can provide a connection between the professionals' needs in their work and with the right answers and the right conversational guidance, it's pretty magical. And we've seen that happen a lot with knowledge agents like Betty across hundreds of associations. And I think there's an opportunity here. And I think associations in general, including healthcare and medical associations can look at several options. Partnering with an aggregator like an open evidence is certainly one. And there's companies like this in other fields as well that are trying to do the same thing. Essentially what they're doing is they're raising a boatload of venture capital and they're going to the primary providers of content in these different verticals and saying, "Hey, we'll pay you a bunch of money, both upfront and licensing over time to give us your content. We will suck in your content and train our AI on it. And then we will share in the proceeds or share in the benefits and what that means varies between organizations." When you have an anchor client like the ones you mentioned for something like open evidence, probably very favorable terms. Whereas if you're a smaller fish, then probably less so. But I think there in lies an opportunity because it's a monetization path. If you have content that's of value to someone like that, that you can very quickly turn on a tap where it's like, I can take this previously latent asset as we like to say and turn it into an active revenue producing asset. So ease is certainly the advantage there from the association mindset. From the consumer's mindset, the real benefit of this course is multidisciplinary insights because the content then can span and an open evidence platform can span many different specialties. The flip side of that is of course, if you're an association thinking about something like this, if you hand over the keys to the kingdom, so to speak, or really the crown jewels is a better way to describe it, it's hard to get them back. Your content is changing at a certain pace, but once it's gone, it's gone. And yes, by contract, you might be able to claw it back, but as practice might show in the AI field, particularly with heavily funded companies, sometimes organizations are a little bit more willing than perhaps you'd want them to be to not necessarily care about that. And I'm not suggesting anything about open evidence to be abundantly clear. I know nothing about the company, but I'm speaking about heavily funded AI companies in general are often a bit cavalier about how they'll use your data and how much they will respect termination clauses and agreements. And we know that the data, this is not a new thing, but we know that the data, your content in particular, is a particularly key resource for the next phase of AI, which is why these companies want access to it. So if you choose to do this, just make sure that you fully understand the pros and cons and evaluate it compared to the alternative of standing up your own knowledge resource for your members in your domain and owning the space and then owning the complete stack, so to speak, where you never lose control of your content, never lose control of your IP, takes a little bit more effort, perhaps a couple of dollars comparatively, but it turns it into a path that you have more control over. So like everything in life, there's a spectrum of pros and cons with each of these alternatives and there's things in between perhaps, but it's definitely worth considering because these marketplaces or aggregation sites, they've existed in the past and other contexts, but now with AI, they're far more powerful and far more useful than ever before.
[00:27:17:20 - 00:27:59:00]
Mallory
Hmm, this is very interesting. I'm wondering Amith, with your background on the open garden, your first book, or at least your book before Ascend, and the idea of kind of maybe removing some of those boundaries and barriers we've set as an association and opening up our content to more individuals in adjacent fields and professions. So with that open garden concept in mind, versus the idea of, so maybe that's like going the open evidence route, licensing your content and exposing it to all these others, or the idea of building something for your membership, using a knowledge assistant, full disclosure, we have something called Betty and the Blue Cypress family, so that's what I'm thinking of right now.
[00:28:00:09 - 00:28:05:05]
Mallory
Can you kind of give us the pros cons of both of those options? Maybe debate yourself even.
[00:28:05:05 - 00:28:28:14]
Amith
Well, I'm happy to, and I often do actually. It's interesting, at least to me. So back in 2018, the book Malia referred to, it's called the Open Garden Organization. If you comment on this pod wherever you're listening and you want a free copy, ping us there and we'll send it to you. We have copies in the office, but it is a book that in my opinion is still as relevant as when I published it back in 18. I actually started working on that book in 2014,
[00:28:29:18 - 00:29:50:01]
Amith
and that was at the same time as I started working on my very first AI company, which has now turned into Rasa.io. And so 11 years ago, I saw on the horizon that AI was gonna be transformative and that it would be really a sea change for everyone in the world, but particularly for associations. And so what I argued about in the Open Garden Organization was that associations tend to be too walled off to the world. They tended to be too insular and closed. They weren't open communities. They were too narrow-minded with respect to who they defined as their audience when their expertise and their content could potentially be of value to a more diverse potential set of audience members than they realized. And so what essentially we were saying in the book was you should start off with the mindset of delivering value in alignment with your mission to the broadest possible audience. And then most certainly have mechanisms to convert people into a funnel, we're able to monetize them, whether into membership or other kinds of premium products that are behind the paywall. So it was not in any way suggesting that you just throw your content out into the open, but rather to be more open with certain layers of content that would establish your expertise, make you the number one player in the world in your domain, and then cast a wide net and allow really anyone interested in your topic to come to you as opposed to defining yourself strictly around a particular professional designation. A classic example of this is accounting.
[00:29:51:05 - 00:31:42:00]
Amith
Associations of CPAs tend to only think about people who are certified professional accountants. And these are people who are state licensed in their states or in other countries, they're chartered by their province and they have slightly different terms. And as a ratio of the total number of professional accountants in the world, the number of CPAs that are out there are equivalents is actually a fairly small fraction, it's not the majority. And then if you broaden that further and say, well, how many people are in the accounting realm beyond even professional accountants, there's a lot of people who are not degreed and would be considered accountants, but they're not degreed accountants, it's an even larger number. And so my point of view would be that, well, if I'm the association of CPAs, I want to open my doors to anyone in the accounting field, anyone even in the more broad finance discipline, and I wanna share my expertise with them. Now, not all of them are gonna become members and maybe my membership is actually in fact only available if you're a state licensed CPA. However, I can still provide you value with my knowledge, I can still monetize that relationship at certain levels with some subset of the audience. That is what the Open Garden Organization essentially argued for. I think it's more relevant than ever in the context of generative AI and solutions like open evidence, where you're talking about an aggregator combining your content with many other organizations and then selling it, maybe you get a rev share, or maybe you're just getting a license fee. The alternative Mallory that you mentioned would be, I think standing up your own proprietary knowledge assistant, it could be Betty, as Mallory mentioned, that's one of our companies in the Blue Cypress family, it could be something you custom homebrew. There are other products obviously out there that help you with things like this, but essentially your own solution, something you have proprietary control over. I would say that really the main advantage to your own solution is complete control over your IP. So if you are concerned about your content being in the wild or being under the control of somebody else,
[00:31:43:03 - 00:32:29:11]
Amith
you might wanna think really deeply about this and think about what would it take to establish your own solution. If that for whatever reason is a lesser concern for you and you're comfortable with your content in various domains, maybe you're already in that kind of regime, perhaps you use someone like an Elsevier for your publishing and you don't really actually truly own your IP, even if on paper you own some rights to it, you may be more comfortable with that, you may be accustomed to that kind of a scenario. And so maybe something like an open evidence would be a really fast way of monetizing your content. There's nothing wrong with that either. I just suggest that whenever people are making big decisions like this, they try to establish a framework for thinking so that they're looking at it as objectively as possible and weighing the pros and cons, rather than just jumping on the first thing that they see saying, "Hey, we don't do this at all right now, "let's just go do that." Because sometimes these are one-way doors.
[00:32:31:01 - 00:32:41:04]
Mallory
Amith, as we wrap up the episode, do you have any parting words, advice, guidance for our listeners when it comes to domain specific AI as associations?
[00:32:41:04 - 00:34:13:17]
Amith
We recently published an article on Sidecar, I think it was one of your's, Mallory's, and it was about how associations could be the leaders in AI compared to for-profit companies. And part of the rationale behind the article was essentially saying, "Listen, you're in domain, "you're in a domain that you are the world's expert in, "or at least the expert in your region, "or one of the strongest experts." And so it's fairly common for associations to hold that brand position, to have a cornered resource to use the Seven Powers framework a little bit here, the cornered resource being something that you and only you have, and that's something that you can leverage for a route to power. Associations often find themselves with both a brand power and a cornered resource. And so in this context, my takeaway is that domain strength, the narrowness of your domain, may in fact be the ultimate superpower in the world of AI, because people do not want generic, almost good enough advice. They want the right answer. Lawyers are getting literally actively sanctioned by the courts today for incorrectly citing case law. And this is happening more often than you would think, because there was back when chat.GPT first came out, a big article that talked about a lawyer who, I think got disbarred or somehow seriously reprimanded for having false case law in a pleading that was filed with a court. That's obviously really bad. And so if you use AI, you should understand that you need to check its work and all this other stuff. And granted, that was a couple of years ago, and AI has gotten a lot better.
[00:34:14:19 - 00:35:04:18]
Amith
The point I'm making though, is that that's why people in the legal profession are so excited about law specific tools. That's why the firm in the UK that we started the episode with, that is an AI first law firm, is using AI tools specifically built for the legal profession, because those tools probably have access to proprietary knowledge bases, or in partnership with people like West or Lexus, that have access to databases that can do verification and guarantee the results are in fact grounded. There's tremendous value in that. And in fact, that's exactly my point about you as an association leader, you yourself probably are sitting on top of a knowledge base that is of that level of value to people in your domain. So let's go out there and crush it and use this resource to provide immense value to your community and also generate a new stream of revenue for the association.
[00:35:04:18 - 00:35:28:15]
Mallory
These three stories have shown domain specific AI requires deep expertise as Amits just talked about, combined with AI native thinking, legal work restructured around automated document workflows, engineering AI, moving into physical experimentation and manufacturing and then medical AI achieving mass adoption through domain specific training. For you, our association, someone is likely building an
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[00:35:44:24 - 00:36:01:23]
Mallory
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.
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