Podcasts about b2b saas

A founder can be burned out enough to want an exit and still not beready to let the business go.Juan Ignacio knows that tension from both sides. He built a B2B SaaSlending platform from Madrid, raised tens of millions in venturecapital along with a $100 million debt facility, and exited four yearsafter launching. The deal created relief, but it also exposed aquestion the transition could only postpone: What do I do with my lifenow?In this episode of Your NEXT, Juan joins Jerome Myers to unpack whathis own near distressed exit taught him about preparation, runway, andthe danger of waiting until the company needs a transaction. He alsoexplains how that experience led him to found L40, a sell side M&Aadvisory firm focused largely on B2B SaaS companies.Together, Jerome and Juan explore why founders sometimes sabotage thetransactions they say they want, how valuation expectations and markettiming affect the likelihood of a sale, and why “I just need themoney” may be a signal to examine the real problem before hiring anadvisor.Juan also explains the Rule of 40, what makes an advisor a genuinefit, and why a healthy company is not automatically a desirableacquisition target.The hardest part of an exit is not always finding a buyer.Sometimes it is helping the founder become willing to leave.Learn more about Juan and L40:https://www.l40.comTake the Exit Readiness Assessment:https://www.exittoexcellence.com/eraLearn more about Jerome Myers:https://www.exittoexcellence.comThank you,Jerome Learn more about your ad choices. Visit megaphone.fm/adchoices

Struggling to scale your ads? Spending more won't fix the problem. Let's figure out why your growth strategies are not working. Talk to us at https://www.tiereleven.com/apply You've hired agency after agency, and no matter what they try, you can't scale your Google Ads. You don't have a Google problem. It's a creative strategy problem. Until you fix demand creation, no amount of campaign optimization is going to get you unstuck.In this episode, I break down a real client situation: a 14-year-old B2B SaaS company spending upwards of $200K a month on Google Search with no good answer for why growth has stalled. I'll walk you through why Google is a demand-capture platform, not a demand-creation one, and why 80% of any market lives in what I call the "zone of indifference." We also look at why the real fix lives in creative strategy on Meta, programmatic, and connected TV, not another Google audit. If your cost of acquiring new customers keeps climbing no matter what you spend, this one's for you.In this episode:- Why Google Search has a demand capture ceiling- The difference between demand capture and demand creation channels - Why branded search clicks cost 10-20x less than non-branded keywords - The "zone of indifference" and why it's 80% of your market - Why the agency rotation trap makes Google Ads more expensive- Why hook rate and hold rate matter more than landing page optimization - The B2B creative mistake of writing ads for users instead of buyers - How messaging extraction uncovers what decision-makers care about - Three questions to ask your agency if your Google spend has stalledMentioned in the Episode: Case Study on Optimizing Ad Spend: https://perpetualtraffic.com/podcast/episode-801-from-2-5m-to-4m-a-month-ad-spend-barely-changed-heres-why/ Tier 11's Data Suite: https://www.tiereleven.com/what-we-do/data-suiteJoin Ralph Burns and John Moran every Friday for The Ad Lab Live: https://www.youtube.com/@Tier11/streams Listen to This Episode on Your Favorite Podcast Channel:Follow and listen on Apple: https://podcasts.apple.com/us/podcast/perpetual-traffic/id1022441491 Follow and listen on Spotify:https://open.spotify.com/show/59lhtIWHw1XXsRmT5HBAuK Subscribe and watch on YouTube: https://www.youtube.com/@perpetual_traffic?sub_confirmation=1We Appreciate Your Support!Visit our website: https://perpetualtraffic.com/ Connect with Ralph Burns: LinkedIn - https://www.linkedin.com/in/ralphburns Instagram - https://www.instagram.com/ralphhburns/ Hire Tier11 - https://www.tiereleven.com/apply-now Mentioned in this episode:https://perpetualtraffic.com/advertise-with-us/

In this week's One Vision Podcast, Theodora Lau hosts Kathryn Outlaw, Co-Founder and COO of Spheros, a B2B SaaS platform that aims to unify client data into a single source of truth, enabling accurate, consented data sharing and supporting reliable AI agents. Kathryn talks about her entrepreneurial journey, the inspiration behind the startup, and the goal of making Spheros as essential as DocuSign for onboarding.

In this episode of Strap On Your Boots, I explore why I've stopped treating five-year business plans as predictions of where a company will actually end up. After years of building startups, I've learned that customers, technology, competition, and entire markets can change faster than any spreadsheet can anticipate. I share how Vengo AI evolved from consumer to B2B SaaS to enterprise, why I now plan more specifically for the near term, and how founders can balance having a long-term direction with staying flexible enough to respond when the market tells them something new.

Michael Gants, founder and CEO of Encore, breaks down how apps can turn unused "streak" and celebration screens into a new revenue stream, without a single ad or paywall. He shares how brands like Disney, Uber, and Apple are paying to reach users in their happiest moments, and why founders should be more afraid of a broken CAC/LTV ratio than of running a monetization experiment.

https://youtu.be/_OpJcRFbACk Abhi Jadhav, Founder and CEO of Bay Leaf Digital and Founder of Brazenly, helps B2B SaaS leaders create operational rigor while embracing AI, innovation, and disciplined execution. Driven by the freedom to experiment, anticipate change, and take the road less traveled, Abhi builds businesses that combine human expertise with AI-powered systems to help teams remain competitive and shape their own futures. In this conversation, Abhi introduces The Operational Rigor Framework—Aggregate To-Dos, Import Them into a System, Prioritize Weekly, and Execute. He explains how turning commitments into organized, prioritized tasks prevents work from falling through the cracks and improves execution across client projects, internal initiatives, and personal responsibilities. Abhi also discusses keeping humans in the loop when deploying AI agents, responding to rapid changes in the SaaS market, and driving growth through visibility, exceptional people, and innovation. — Create Operational Rigueur with Abhi Jadhav Good day. Steve Preda here, and today I’m joined by Abhi Jadhav, founder and CEO of Bay Leaf Digital, a B2B SaaS marketing agency he’s run since 2013. And he’s also the founder of Brazenly, an AI agent orchestration platform he’s building for marketing teams. Abhi, welcome to the show.  Thank you for having me, Steve. Appreciate it.  Well, great to have you here. And I’m super interested in how you are evolving your business and your focus on SaaS companies, which is very interesting. But I’d like to start with the question, your personal why. So how are you contributing to human flourishing? What is your personal why, and how do you manifest it in your business, in Bay Leaf Digital?  Yeah. So I gave this some thought. It’s been several years since I’ve been running the agency, so it took me back a few years to figure out, well, why did I even start this? So I’m going to date myself, and this is way back when I was in business school. One of my dear friends and classmates at the time, she pointed to me when she was asked, “Who’s most likely to strike it out on their own and run their own business?” And I thought that was really funny at that time because I had no desire to be my own entrepreneur or guy running your own business, et cetera. I was through and through a corporate guy.  And a few years later, I realized that I don’t really get energized by playing corporate games to climb the ladder. I prefer freedom to do things and to experiment. And I love taking the road less traveled, and it’s not just speak, it’s actually doing. Yeah. There’s a sign here. It’s a Jeep sign that says, “Road ends and fun begins.” And that kind of encapsulates who I am, really. Yeah. So when I saw this sign, I’m like, “This is me. It needs to go on my wall.” Okay. So that’s my personal why, how I ended up doing what I’m doing.  So how does it manifest in Bay Leaf or even Brazenly? What is that road that has not been traveled yet that you are embarking on?  So it is more about being able to anticipate what is coming and being able to be ready for it. I think that is primarily the difference, or to me, that is being my own business owner, it allows me to do that. So moving from an agency and seeing what is coming, AI is going to touch all of us. It already has in so many different ways.  So understanding where we are going and being able to do something about it rather than wait for someone else to come along and tell us, “You guys are now obsolete, and we are now moving on to something else.” So it allows me to chart my own path and be able to control, to a certain extent, I can’t say 100%, to a certain extent, what our business destiny is going to be.  So where are we going? What is your vision of the direction?  AI has tremendous power, right? And we are still very early, in the early stages of where it’s going to take us. But I’m a firm believer that the human in the loop is extremely important, and that we need to shape AI systems, our posture, in a manner that we maximize the use of AI while making sure that it is being run for the benefit of humans. So the way we approach it is, yes, we will adopt AI, but at the same time, we will never compromise on human in the loop in that process.  So our approach is, let’s take things that are easily automated. Let’s take things that we would otherwise not have been able to do. I have a framework on how we deploy AI, and the lowest part of that framework is obvious processes need to be automated. Top part of that framework is things that you could not ever do because it was impossible, impractical to do, automate that.  But in all of that, there is a certain amount of co-piloting or piloting that is needed in order to get valuable outcomes. Where I see us going is we will become pilots or co-pilots with extremely efficient and informed agents that will do the work, but they will not do the work exactly as is needed unless there’s a human in the loop that’s guiding it, right?  Yeah.  And I foresee this for the next several years. At some point, and this evolves all the time, but that’s kind of our posture at the moment.  Very interesting. So basically, AI agents do the work, or much of the repetitive work, a big part of the work, and the humans are directing the agents. Is that your vision?  Humans are directing the agents as well as checking on agent outputs, basically being the guide on making sure that things get done the way they should be done. So more and more, we are leaning on domain expertise, people who know what needs to be done, have that knowledge to be able to man these agents today.  Yeah. That is fascinating. So you mentioned that you have a framework. So what is your framework?  I have multiple frameworks. Let me talk about probably the most fundamental, foundational framework that can be used by anyone and everyone. Again, going back to my days before the agency, when I was in the corporate world, we would do 360 feedbacks. And at one point, one of the feedback I got, obviously it was constructive feedback, but it was that, “Abhi’s got a great team and he’s a great leader, but sometimes the team does not follow through on things that we’re committed to.”  I looked at that feedback and I said, “This is not something that my team needs to fix. This is something that I need to fix. It’s not a team problem.” So we took that weakness and we turned that into a core strength. That is one of my foundational approaches to the way we work, the way we run the business. I’ll break it down for you. Here’s what we really do.  Yeah, please.  So you and I are having a conversation. Something comes out of that conversation and I tell you, “Hey, I’ll follow up. I’ll get back to you.” And great, I’ll make a note of it. We have 100 different conversations like this throughout the day. I talk to my clients, I talk to my team. Everyone has something that they need or they’re going to offer. So there’s a pile of things that need to be done.  And without having operational discipline around it, things fall through the cracks, right? So a very simple manifestation of that is, hey, make a list. Make a list of things. But there’s a lot more to that. Yeah, you can make a list. You can write it in pencil on a pad, and then it gathers dust, and then you look at it after a few months, you’re like, “Oh, I never really got back to this person, didn’t really do it.” So what we’ve done is we’ve evolved that into an operational rigor where nothing, almost nothing, ever falls through the cracks.  We talk, we’ll organize our thoughts. We’ll put it—it doesn’t matter. It’s a framework, right? It’s not a set of rules. So people might take down notes. They might use Zoom meeting transcripts, whatever it might be. But at the end of the day, we are constantly putting together a list of to-dos. And that’s never enough because at the end of the week, you need to make sure that you’re working on the most important things. So we’ll go through a prioritization process.  I go through it, my team goes through it, and then that defines what we do in the following week. It’s very agile-like, but it’s not quite agile. It’s a set of tasks that eventually kind of come together for a greater purpose. So we’re looking and we are prioritizing and going, “Which is important, which is not?” So the conversations that we have with our clients, I have internally with the team, the conversations are never about, “Hey, whatever happened to that thing?” It never happens. The conversations are more about, “We talked about this last week. Here’s where we are.  Here’s why we are going to either change course, not do it anymore, or we have found a different way of doing it.” In terms of just an extremely important skill, whether you’re a business owner, in the corporate world, to me, operational rigor that leads to disciplined execution is absolutely key.  So what are the steps to this operational rigor framework? How do you actually do it? What are three to five steps or elements that will allow us to communicate to our listeners and maybe for them to try it out?  Yeah. Let’s just list them out. So the first thing is just pulling together the to-dos, right? So whether it’s from OneNote or a pad or transcripts, at the end of the day, set aside 30 minutes, an hour max, typically towards the end of the week, to allow you to go through that list of things that you want to do, okay? Put them into a system that allows you to keep track of the status.  We use our systems. We use a tool called ClickUp. We’ve used many, many different tools, but today we use that tool. We are able to change priorities, we are able to increase or reduce the size of those tasks, and we are able to collaborate with people on those tasks. So we’ll organize that, spend an hour or so, put it in ClickUp in the agile framework, if you will, and then we execute throughout the week. Monday morning, I look at—there’s never a question about, “Hey, what am I going to do today?” Right?  There’s never a question of, “Oh my God, there’s such a long list of things to do. How am I ever going to get through?” By the time Monday morning hits, I know exactly what I’m doing. By the time Tuesday morning hits, I know how much I accomplished on Monday and what I’m accomplishing on Tuesday. So it becomes very well-oiled, and it’s execution through and through without letting anything fall through the cracks.  Okay.  And this is not just me. The entire team behaves this way, and that is what is different, right? This was my feedback I got so many years ago, is follow-through is not there. Okay, we turned that around, made it a strength.  Love it. So aggregate the to-dos, import them into the system, prioritize, and execute.  And execute. And throughout the week, if you need to reprioritize, reprioritize. It’s okay.  And this pertains to your client work, or that also pertains to your internal projects that are improving your business, your own business?  It pertains to everything. It’s fundamental to everything we do, right? When we are improving our own business, we break it down to a series of steps, and then we prioritize the steps. For example, Steve, I could have come to your podcast seeing that, oh, I have a meeting at 8:00 AM on Friday. Let me just make sure that I have a shirt on and come to it.  But the way I approached it is the same operational rigor, which is, okay, sometime during this week, I’m going to make sure that this wall behind me is clean. I’m going to make sure that I have my talking points, and I’m going to go through a dress rehearsal myself before I come to you. So this operational rigor offers a much higher quality of output.  So it’s not just client-facing, it’s not internal. It’s in everything we do, and that includes my personal life too. To a fault, I use lists to organize my life, but it’s the same concept. It’s not as rigorous, but it is, I have this big to-do, let me break it down into a smaller to-do, knock it out, move to the next one.  Love it. That’s great. It’s a good framework. We can call it the Operational Rigor Framework, perhaps.  Operational rigor? Yeah, absolutely.  Yeah. Okay. So let’s switch gears here, and I’m really curious what drives growth in your businesses, in Bay Leaf Digital and your new business as well?  Okay, so we’re a marketing agency, but more specifically, we are a SaaS marketing agency. So we are niched down into SaaS, and then we niched down further. We are a B2B SaaS marketing agency, right? So it’s a very specific niche that we operate in. But some of the forces that affect us are the same as other agencies, other marketing agencies. So we live on this edge of constant change. And let me explain what these changes are. Number one, marketing channel change.  You’ve got Google, Meta, LinkedIn, you name it. They’re all making changes to their platforms all the time, right? Some of my marketers tell me they go in one day into the Meta advertising platform, and the next day they see something completely different. A feature has changed, and what they thought was working yesterday no longer works. So we are living on this edge of constant change as far as the marketing channels are concerned. Then it’s very surprising how much the economy actually affects us.  Because we serve a startup audience, B2B SaaS startup audience, whether there is money flowing in the economy or not actually affects us quite a bit. Post-Covid, when suddenly there was contraction, we felt it. So there’s that external force. And then our competitors are not sitting down napping. They’re also on top of these things. So we have these three external forces affecting us all the time. So for us to live and thrive on this edge, we have to be forward-looking, and we have to be extremely methodical. It goes back to that operational rigor.  Imagine you’ve got 20 different things coming at you. You have to figure out, “Hey, what is the most important thing here, and how do I respond to that? How do I respond to the change?” Being able to anticipate, being able to be forward-looking, and responding has allowed us to weather storms. It was post-Covid, obviously Covid too, but post-Covid was more important from a business perspective. And then you may have heard about this in maybe other conversations, when Claude released their latest version of Claude Code, they unleashed what is called the SaaS apocalypse, meaning SaaS companies, right?  SaaS companies are outdated. They no longer are needed because you can create your own app and you don’t need a SaaS company. So companies that help SaaS companies such as ourselves, B2B SaaS marketing agencies, were in that first line of companies to get affected. But we saw that coming. We saw that coming a year before, right? And we were working, we were preparing for it. So six, eight months in, it doesn’t hurt us as much as maybe it has hurt other companies. These are the things that help us grow. Yeah. Go ahead.  No, no. I’m just wondering, I understand that you are very agile. I mean, you use an agile system, but you’re also agile in the way you are responding to the market changes. You adjusted well with the SaaS apocalypse, or whatever it’s called. But just because you are avoiding a major landmine doesn’t mean it’s going to grow your business. So I wonder what drives the growth beyond being agile. So as the market changes, does it automatically help you create more revenue?  That’s a good question. Yes. Yes. So I’m probably talking very big picture. So let me convert that into how this actually drives growth for us. So a change happens, we respond to that change. Sure, it helps us survive. But think about a change in the LLM, the large language models such as Gemini or ChatGPT or Claude, the way they process information and the way they present that information to you.  Because we see that, we respond to it, we are able to make sure that when, Steve, maybe you go into Claude and you ask for, “Hey, I want to work with a SaaS marketing agency,” we make sure that we are on top over there because we are responding to changes that are happening in the back end, right? So it’s not just thriving, it’s making sure that you know what is happening so that you can always be at the forefront. Wherever people go to find you, you need to be there. But I’m simplifying this significantly. So that is just responding, still responding. The thing that really drives growth for us internally is we find and work with really, really good people, great team members. We put in a lot of effort to find these great people, and when we find them, we hold onto them. This did not happen overnight. Finding great people is extremely hard, especially for a small business. So we came up with a methodology to actually finding those people. I looked at various methods, but the one method that appealed to me was this method called Who by Geoff Smart and Randy Street, these two authors.  I started there, but then I evolved that framework significantly over time to where it is now a core strength. So whenever we hire people, we know exactly what are the areas of strength they have, what are the areas that we’ll need to help them improve in. And so we know who is coming in and how we can best leverage them. So once we know that, we’re able to move forward fairly easily. So that’s one of our core things that helps us.  So what drives growth is, if I hear it correctly, what drives growth is staying visible with all the algorithm changes and the platform changes. You’re going towards more AI visibility as opposed to SEO visibility. And then also being able to recruit those people who will be able to do the work that is going to respond to the market demand.  Exactly. And the third part, the third lever to all of this is innovation. I mean, you would think that an agency would not innovate, but there’s innovation happening all the time in various things. Whether it’s marketing approaches, whether it’s putting together a marketing operational platform or whatever else, you have to innovate to stay ahead. So I would say these are the three things that help us grow.  Yeah. Love it. That’s great. So visibility, people attraction, and innovation. That’s the three legs of the stool. Yeah. So what is one thing that you’re actively trying to figure out in your business right now?  One thing we’re trying to actively figure out? That’s such an interesting question. There’s so many things happening all the time, right? So let me pick one that comes right to mind. We’re trying to figure out whether we should raise capital or not. Because we look at some of the greatest recent successes in AI platforms and companies, and they’ve come from one-person to five-person teams. So the question now becomes, when there is no technology moat, there is no reason to hire 100 people to thrive, what is the role of capital?  So that is something that I’m debating internally about what should we do here. Do we need it? Do we not need it? And there are so many other things that we work on all the time. But I would say this is, at the moment, today, June or July 2026, what’s top of mind for me.  Yeah. It’s a great question. If you don’t need more people and it’s a professional service that you’re providing, you’re innovating with your own resources and AI, then what is the capital need of the business?  Right.  So to what extent can a business grow today without adding people? Is it possible to scale a business without adding people?  The popular word on the street is grow a business without people. I don’t think that’s possible, to grow a business without people. If you have domain experts and you have people with the right traits, you can grow with a very small company. And those traits are things like: you need to be a critical thinker. You need to be able to question and analyze things a bit differently. You need to have domain expertise.  Most important for me, you need to have operational rigor. And when you have these kinds of people, yeah, I think you can grow with a very small team, but not a one-person company. Maybe in a few years, maybe in a few decades, that might be possible. But today, you can significantly speed up what you can do using AI, but you definitely need people who can manage those systems, who can squeeze the most out of those systems. You need those people manning those systems.  I also wonder if there is a threshold below which you don’t actually have a business. If you don’t have enough people to reduce your dependency on any single individual, including yourself, then do you actually have a business, or is it more like a professional practice?  Right. Right. Yeah. So it’s like you design a system and you just deploy it and it just runs on its own. So is that the business? I don’t know. It’s not really a business at that point. It’s just a tool that you put out there in the universe and it’s doing its thing. It’s a very philosophical question about where we end up as a result.  Yeah. And then things are changing all the time, so can you just rely on the system rejuvenating itself, or do you still need human energy to keep improving and keeping that business competitive in a fast-changing environment?  Right. It’s a question of the autonomous agents, right? How autonomous can autonomous agents really be today, and where does that lead us eventually? Does it lead us into AGI? We’ll see. But today, no. I would say strongly that autonomous agents are good within certain guardrails.  You can provide a lot more freedom for them to do it as long as they don’t break certain rules, and that’s where humans come in to make sure that those guardrails are maintained and the guiding happens. In today’s world, I don’t think that business exists without any employees. In tomorrow’s world, it might. Yeah. And that changes the economy, that changes everything.  Yeah. That’s an even bigger question.  Yeah.  Well, if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  Learning curves. I would love to skip past learning curves. And by that I mean, even when we find the best people with all the best traits, frequently what we see is we end up having to train people on what we know and what we know works, and that takes several months. It takes several months to get there. So it’d be amazing if I had a magic wand and day one they’re ready to go.  Yeah. So you’re deploying these agents for SaaS companies. Who is the ideal client that you would like to contact you when they hear this podcast?  We serve B2B SaaS companies, right? And so we work typically with companies who have what is called product-market fit, meaning their value has been articulated and they’re looking to grow. Those are the companies that we normally work with. And as we deploy Brazenly, our marketing operations orchestration tool, we’re looking to help marketing teams. And those teams could be a three-person team.  It could be a 20-person team. It doesn’t matter. It is built to scale. But that part of the business is definitely focused more on team productivity or automation of marketing teams as opposed to an individual. That is what ChatGPT and others are focused on, individual productivity. We focus on the team productivity aspect of it.  So you need companies that have figured out their product and they already have multiple people in marketing that need the support?  We normally work with companies that have figured out their value proposition and are looking to grow, so they don’t have, this is on the marketing agency side, right? So they don’t have all the people in place in order for them to grow, and that’s where we come in. We come in as a marketing department, and we will help take over all those marketing functions. That is on the agency side.  On the AI platform side, it’s a slightly different focus, which is we are helping ourselves. We are helping ourselves become more efficient as we help our clients. That’s one. But for someone else to use a marketing platform, they need to have a marketing team that can actually leverage the marketing operations platform. So there’s the agency focus, and then there’s the Brazenly marketing platform focus.  So you have the done-for-you version, which is the professional service as an agency, and then you have the DIY version, which is them using your platform to do it themselves, basically.  You’re exactly right. So there’s the done-for-you, there’s the done-with-you, and then there’s the DIY, right? Okay. Done-for-you is the agency. The done-with-you is, as much as there’s promise with AI platforms, you still need to build the agents. You still need to deploy them. That is the done-with-you, which is we’ll consult with marketing teams to help them deploy these agents. And the DIY is, here’s the AI operations platform. Go and use it yourself. That is the DIY. Okay. So you’re exactly right.  Yeah. Love it. Love it. That’s a great slate of services. So if people would like to learn more, I get it, they should not go to LinkedIn, but where should they go? Where can they find you?  Yeah. So you can always contact me on various platforms, Twitter, email, Instagram, and such. But if you want to get in touch, I would say the best way is to just drop me an email. My email is aj@bayleafdigital.com.  So Abhi Jadhav, the CEO and Founder of Bayleaf Digital and Brazenly. So if you’d like to accelerate your marketing, if you’re a B2B SaaS company and you want to accelerate your marketing, whether it’s complete done-for-you, done-with-you, or DIY, Bayleaf Digital and Brazenly can take care of you. So reach out to Abhi Jadhav on Twitter, email, or through the website, bayleafdigital.com.  And if you enjoyed this episode, then make sure you subscribe and you follow us because we come out with a couple of interviews every week with exciting entrepreneurs who are building great businesses. So thanks for coming, Abhi, and sharing your wisdom, and thanks for listening.  Thank you very much for having me, Steve. It was a pleasure.  Important Links: Abhi's LinkedIn Abhi's website Abhi's email: aj@bayleafdigital.com

Send us Fan MailMeta and NVIDIA are turning up the heat in the open-weight AI race, while Anthropic is taking a very different approach by watermarking Claude-generated content. Amith Nagarajan and Mallory Mejias unpack what these moves mean for associations, from choosing models and inference providers to avoiding lock-in at the agent harness layer. They also explore why AI models are becoming increasingly commoditized, where proprietary models may still have an edge, and why Claude's new watermarking strategy probably isn't a foolproof solution for detecting AI-generated work. Plus, Amith shares lessons from Blue Cypress's latest AI hackathon, including how associations can use focused offsites to accelerate adoption and generate new ideas, and the hosts discuss a more productive approach to AI-generated submissions: using AI to evaluate quality, relevance, and originality rather than trying to ban it outright. 

Today, we're joined by Alex Yakubovich, co-founder and CEO of Levelpath, the AI-native procurement platform transforming how global enterprises manage indirect spend. We talk about:If software apps must become autonomous to be successful in the futureUsing AI to help build softwareDoes SaaS have a future, or strictly AI as a service?Why the question, “Is SaaS dead” is mootHow some SaaS pricing models can feel icky

Brook Schaaf, co-founder of FMTC and author of the new book Affiliate Hypotheses, joins Tye to make the case that affiliate marketing might be the internet's best — and sometimes only — way to monetize the open web. They dig into why walled gardens like Google and Meta get outsized credit for conversions, and why AI search is putting the open web's survival at risk.

In this episode of Strap On Your Boots, I explore one of the biggest advantages startups have over much larger competitors: speed. Drawing from my experience building companies and the evolution of Vengo AI from a consumer product to B2B SaaS and eventually enterprise, I explain how small teams can listen to customers, test ideas, pivot, and respond to the market incredibly quickly. Large companies may have more money, people, and resources, but all of that size comes with organizational weight. Sometimes the startup that can adapt in days has an advantage that money simply can't buy.

Amanda Dyson is the Chief Marketing Officer at DemandTec, a Commercial Trade Intelligence platform connecting retailers and CPG partners through one shared system. With more than 20 years of B2B software marketing experience, she specializes in enterprise technology, supply chain, pipeline generation, and go-to-market strategy. She leads DemandTec's marketing organization and shapes how the market understands its retail trade intelligence platform. Before joining DemandTec, Amanda held marketing leadership roles at FourKites, Blue Yonder, and e2open. In this episode… B2B buyers are surrounded by polished content and increasingly similar brand voices. As AI makes publishing easier, how can a brand remain recognizable and trustworthy? Amanda Dyson, an enterprise B2B SaaS marketing leader with deep experience in pipeline generation, says the answer is an authentic voice supported by a clear point of view. Instead of using AI to manufacture finished thought leadership, she recommends using it for ideation, then bringing people back in to add judgment, originality, and trust. Amanda encourages marketers to focus on the audiences they can serve best, take bolder positions, and connect creative work to measurable business outcomes. Together, these practices give buyers something specific to believe in rather than simply more content to consume. In this episode of the Revenue Engine Podcast, Alex Gluz talks with Amanda Dyson, Chief Marketing Officer at DemandTec, about how B2B brands can stand out in a crowded market. Amanda explains how to build a distinct point of view, use AI without losing originality, and connect marketing to pipeline. She also touches on event strategy, buying committees, and customer advocacy.

Vikram Chalana, CEO and founder of Pictory, joins the show to unpack how his AI video platform went from 50 customers to 5,000 in just 60 days — and why the breakthrough came from changing the market, not the product. He digs into Pictory's State of Video report (AI adoption has no Silicon Valley bias — Florida and Indiana are leading users), the design principles that keep Pictory out of the "timeline" trap of tools like Premiere, and what's next: code-generated, interactive video that could reinvent how content gets made and consumed.

Send us Fan MailIn this interview episode, Mallory Mejias and Amith Nagarajan sit down with Torey Carter-Conneen, CEO of the American Society of Landscape Architects (ASLA), to explore what it really looks like when AI becomes embedded in everyday work. Torey shares how ASLA moved beyond experimentation to fully operationalize AI across the organization—from internal, tiered training programs to AI-powered member service workflows that generate reports and respond to inquiries in minutes. They dive into how automation is freeing up staff time to focus on higher-value work, enabling deeper member understanding and more meaningful engagement. The conversation also tackles leadership mindset, the importance of starting before you feel ready, and how associations can rethink their role by building tools that directly impact members' day-to-day work. 

There's an instinct every marketing leader is trained to have: fight for credit on pipeline. The best CMOs have realized it's exactly what's holding them back.In this episode, Carolyn breaks down the behavioral traits that separate the best CMOs from everyone else — the patterns she sees every day working with marketing leaders across B2B SaaS. Rather than another set of tactics or a borrowed playbook, she unpacks why the highest-performing CMOs let go of credit, obsess over data integrity, stop waiting on RevOps, and build a culture of making decisions from evidence instead of assumptions.In this episode:(01:45) Why the best CMOs let go of marketing credit and what they focus on instead(07:33) Why a relentless commitment to data integrity is the foundation every good decision rests on(09:55) How elite CMOs stop waiting on RevOps to hand them the numbers that matter(14:45) Why the strongest leaders play offense and defense with their board at the same time(19:55) How ruthless experimentation drives real breakout results(27:12) Why nobody understands your buyer better than your own team(28:56) What it actually takes to survive the mental grind of modern marketing leadershipIf you're trying to prove marketing's impact, make sharper decisions with your data, or build the resilience to lead through constant change, this episode will challenge how you think about what it takes to be a great CMO today.-----------------------------------------------------

Customer retention isn't glamorous, but it's the engine that funds everything else a business does. Andrea Bumstead, Founder & CEO of CS Impact, helps B2B SaaS companies transform post-sales customer experiences into a strategic growth driver. She unpacks why customer success functions are so often under-resourced, under-trained, and overlooked at the boardroom level, even though retaining customers is far more profitable that acquiring new ones. Discovery is foundational and recognizing how AI is reshaping the customer success function is non-negotiable. Andrea's leadership has been redefined by applying a "70% rule" which has strengthened her decision-making and she never forgets that growth and rest can coexist.

What if the person who shaped your entire career was someone you met on a soccer pitch at university? In this episode, Kieran Corbett, venture capital professional at StudioVC in New York, shares how a 13-year career in futures trading, a soccer club chairmanship at university, and one chance encounter at an event in New York led him to the second chapter of his career in early-stage venture capital. Kieran grew up watching his brother build Wayflyer, an Irish unicorn, from the inside. He watched a mentor named Kieran Nestor walk into any room and connect with anyone. Those two relationships didn't just shape who Kieran became. They pointed him directly toward the career he is building today.   [00:04:00] What He Does and Who He Serves Works at StudioVC, a seed-stage VC fund based in New York City Invests in B2B SaaS, enterprise AI, and fintech founders post-product and post-revenue Serves both founders looking for capital and investors looking to deploy it [00:06:00] How He Got Here: From Futures Trading to Venture Capital Started as a futures trader at Positive Equity in Dublin at 23 Traded a wide variety of products for 13 years; moved to New York in 2016 Started angel investing on the side while still trading Made the full move to venture capital in September 2024 [00:08:00] Finding StudioVC Joined the Venture Institute accelerator to build knowledge of the VC industry Got a residency placement at Geek Ventures, a fund investing in immigrant founders Met StudioVC principal Toby McCoy at an event; discovered they shared an investment in Wayflyer After several meetings over the following year, joined the team [00:10:00] What Inspires Him Loves being on the journey with founders as they grow and face challenges Finds it deeply satisfying to make introductions that unlock new doors for companies Sees AI as one of the most exciting spaces in early-stage investing right now [00:11:00] Client Impact: Helping Founders Raise Their First Capital Has introduced founders to investors who were the right fit for what they were building Raising capital at the seed stage is one of the hardest things a first-time founder can do Getting the right people in the room can be the single biggest unlock for an early company [00:13:00] The First Relationship That Changed Everything: Kieran Nestor Met Kieran Nestor through the UCC soccer club in Cork when he started university in 2006 Nestor was the chairman of the alumni and the first real entrepreneur Kieran had ever met Took Kieran under his wing, gave him enormous amounts of his time, and taught him how to build relationships Twenty years later they still talk; Kieran credits him with sparking the desire to own his own destiny [00:16:00] The Second Relationship: His Brother His brother founded Wayflyer, an Irish unicorn doing revenue-based financing for e-commerce businesses Gave Kieran an inside look at what building a successful company actually looks like That relationship also created a natural alignment with StudioVC, who were investors in Wayflyer His brother's journey pointed him directly toward the private markets career he is now in [00:18:00] The Impact That Shaped Him: Chairman of the Soccer Club Nestor made him secretary of the soccer club in his second year at university The following year he became chairman; a big responsibility at a very young age The role forced him to get comfortable with public speaking and building relationships That experience put him on the path toward wanting to be in rooms where he could make an impact [00:20:00] Vision Going Forward StudioVC has two performing funds and a talented small team in New York Wants to grow the fund to invest larger amounts of capital with more great companies Excited to build relationships with the next generation of founders in AI and technology [00:22:00] Final Word: Say Yes to Everything Met Joel Strauss in Madrid through his cousin; that relationship eventually led to this podcast Encourages people starting something new to say yes to every coffee, every event, every connection Not all of them will be great, but the ones that are can send you in a direction you never expected   KEY QUOTES "Say yes to everything. Not all of them will be fantastic, but the ones that are can really send you in a good direction." - Kieran Corbett "Someone who had that hunger, that drive, and was also very generous with their time for the next crop of people coming through. That gave me a lot of inspiration from a very early age." - Kieran Corbett CONNECT WITH KIERAN CORBETT Website: https://www.studio.vc LinkedIn: https://www.linkedin.com/in/kieran-corbett-a45b5a4 Email: kcorbett@studio.vc   Thanks for tuning in! If you liked my show, please LEAVE A 5-STAR REVIEW, like, and subscribe! Find me on: Apple Podcasts | Spotify | iHeart Radio | Stitcher

The Local Enterprise Awards for 2026 taok place recently in the Mansion House in Dublin. The awards are a celebration of the very best small businesses from across the country and serve to highlight the work that Local Enterprise Offices are doing with up-and-coming enterprises in every local authority area in the country.The 31 finalists have each been chosen by their Local Enterprise Office to represent them and their area at the awards, which are in their 26th year. The finalists feature a diverse mix of sectors and founders all competing for a share of the €80,000 prize fund and the chance to be named National Enterprise of the Year. There are also regional awards, and categories for Best Start-Up, Best Export Business, the Innovation Award, Digital Award, Green Award and the Female Entrepreneur award.I caught up with three of the finalists, and the second finalist I spoke to was Stephen O'Sullivan the founder of I left you a note and the Tipperary finalist. Stephen talks to me about his background, what I left you a note does, digital concierge, AI, National Enterprise Awards, LEO and more More about  I left you a note:I left you a note was founded by Stephen O'Sullivan, a former Deputy Chief Actuary at Met Life, Stephen wanted to address the problem modern hotels are having in the fact that customers ‘no longer complain to the front desk' but rather are quick to write a bad review online. Stephen estimates that a bad review can cost hotels up to €15,000 in lost bookings per month. I Left You A Note aims to address that gap in communication to an online B2B SaaS platform which allows guests to connect with the front desk of hotels instantly through scanning QR codes located across the hotel. The portal opens instantly on any phone and allows guests to submit feedback, raise service requests, interact with an AI concierge, and access hotel facilities and room service with one click. When an issue is raised, AI triages it, creates a service ticket and routes it automatically to the correct department.

#377 | 80% of B2B brands are completely forgettable - and it's not because their product is weak. Dave sits down with Louis Grenier, founder of Stand The F*ck Out and author of the book by the same name, to unpack a study he ran scoring 100 B2B companies on how distinctive their brands actually are. Louis breaks down the real difference between "distinctive" and "different," why almost no B2B SaaS company has a mascot (and why that's a missed opportunity), what Wiz and Mutiny are doing that most brands aren't, and why sound might be the most underused brand asset in marketing. They also get into how Louis built the entire 100-company study himself using Claude Code and parallel AI agents, and why picking a lane and committing to it beats chasing the "perfect" idea.Timestamps (00:00) - - What makes a brand distinctive vs. differentiated (03:35) - - Why brand distinctiveness matters more in the age of AI (05:54) - - Inside the study: scoring 100 B2B brands on 8 assets (07:49) - - Wiz and Mutiny: two brands that stood out (12:12) - - Why almost no B2B SaaS company has a mascot (15:39) - - How to get buy-in for a risky brand idea internally (17:29) - - The research behind how brand memories are actually built (20:35) - - Linear: picking a lane and going all in (24:01) - - Why sound is a brand asset most companies ignore (30:58) - - How Louis built the study using Claude Code and AI agents Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Zoom Webinars & Events – The virtual event platform built to help B2B marketers run webinars that actually drive pipeline, with branded registration pages, live engagement features, and built-in tools to repurpose sessions into clips and content. Learn more at zoom.com/exitfive.Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn how to build an ABM program that scales at vector.co/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.Walker Sands - An integrated B2B marketing and growth services agency that helps marketing leaders turn strategy into measurable business impact through their Outcome-based Marketing model. Learn more at walkersands.com/exitfive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

How do the best marketers actually integrate brand and performance – not just in theory, but in org design, planning and measurement? Prophet's Kate Price and Monica El Hassan join WARC's Ann Marie Kerwin to discuss strategies for a more integrated approach, based on WARC's marketing effectiveness research The Multiplier Playbook. Guests: Kate Price, Partner - Marketing Transformation, Prophet Monica El-Hassan, Partner - Media, Prophet Chapters: 00:00 – Intro: The Multiplier Playbook on The WARC Podcast 01:49 – Why brand and performance marketing teams are siloed 03:18 – Cultural divides: traditional brand vs digital performance 04:28 – Specialist skills, loss of generalists and pressure on CMOs 07:15 – Shared language: bridging brand, demand and media teams 09:58 – Customer-centric marketing: changing behaviour to drive growth 14:30 – Integrated planning: how overperforming marketers organise 19:45 – Budget rebalancing: brand investment vs performance metrics 24:19 – B2B SaaS case: US Open sponsorship as a brand–demand engine 31:23 – Shared KPIs: proving commercial impact of brand marketing 38:34 – Org design: generalists, team structures and planning rhythms 40:21 – Key takeaways for marketing leaders For 40 years, WARC has been providing the marketing industry with rigorous, unbiased evidence and expert effectiveness guidance. The WARC Podcast publishes a new episode every Tuesday and Thursday. Subscribe to The WARC Podcast here: https://www.warc.com/en/warc-podcastsSign up to daily WARC News for free: https://www.warc.com/en/latestBook a demo: https://www.warc.com/en/subscribe

Send us Fan MailThis week, Amith Nagarajan and Mallory Mejias unpack a whirlwind of AI developments shaping the future of associations. From Anthropic's release of a more efficient Claude Opus 5 with dynamic “effort dialing,” to a shocking autonomous AI cyberattack that breached Hugging Face, the conversation dives deep into what these moments mean for trust, safety, and strategy. They explore the growing divide over open vs. closed AI models, why major tech players are rallying around open weights, and how AI-powered cybersecurity is quickly becoming essential. Along the way, they break down practical use cases for choosing the right model and share what association leaders must do now to stay secure, informed, and ready for what's next. 

In this episode, Sai breaks down why he deliberately chose a services business over pure SaaS, how a human-in-the-loop model creates a defensible moat in a world increasingly disrupted by Claude and ChatGPT, and why acquiring books of business from retiring CPAs is one of the most underrated go-to-market strategies nobody is talking about. He also shares the three categories of SaaS he believes will survive the AI disruption — and why everything else is in serious trouble.If you're building in a high-stakes industry or trying to compete where trust is currency, this episode is essential listening.Key Takeaways4:12 – Guest intro: Sai Dhanak — two exits, four patents, from Caribou to Latch to Deduction4:33 – What shipping early and obsessing over design taught Sai about building products people want5:35 – The connecting thread across cybersecurity, IoT, and service design patents7:00 – Seven years at Latch: What going from seed to IPO really teaches you about scale7:45 – "More money, more problems" — why lean is a feature, not a constraint8:28 – The compounding risk of bad hires at scale8:57 – What Deduction actually does: AI-native H&R Block for a fraction of the price9:33 – Real-time example: How Sai's wife emailed a charitable donation to the AI agent mid-year12:38 – The deliberate bet on services over pure SaaS — and why it was the right call14:07 – The AI SaaSpocalypse: Three types of SaaS that will survive disruption16:27 – How the human-in-the-loop model works operationally (Deduction OS)20:04 – Why Sai left Latch right after the IPO — the mental playbook he was building22:00 – The co-founder advantage: Moving faster because you already trust each other23:33 – The fractional-to-full-time hiring model that built the team efficiently25:17 – The critical fork in the road: Full-stack tax firm vs. selling software to accountants28:00 – Why the B2B SaaS tax market is flooded and the personal accountant market is fragmented29:20 – The personal accountant market: 18B, fragmented, no dominant player except H&R Block32:00 – Why TurboTax and DIY tax software are getting eaten by ChatGPT and Claude30:00 – Email as the primary channel: The internal debate and why async won31:24 – Why email is more enduring than it looks — even for Gen Z32:23 – The trust premium of human touch in an increasingly AI world36:15 – The onboarding call insight: 15 minutes with a human = customers happily working with AI37:57 – Acquiring books of business from retiring CPAs as a go-to-market engine40:57 – The referral flywheel: Emailing taylor@deduction.com directly, no app required41:25 – What Sai would do differently: Start acquiring firms sooner; build partnerships earlier43:40 – Flat architecture, "everyone is a builder," and why the 1-person company is the wrong aspiration45:36 – The most fulfilling part of building a company is always the people46:10 – The question every founder should be asking: Do you love this problem enough to work on it for 10 years?Tweetable Quotes"The most interesting opportunity in the AI era isn't building tools that replace humans. It's building businesses that use AI to make humans dramatically better — while keeping the one thing AI can't provide: trust." — Jeff Mains"AI can process the data. But it can't sign its name to it. Can't sit across from a client and take the blame when things go wrong. That's still you." — Jeff Mains"The intelligence of AI with the trust of a human. That's Deduction." — Sai Dhanak"In an ever-increasingly AI world, the human touch will have an ever-increasing premium." — Sai Dhanak"People don't want to sit in front of a chat box doing their taxes. The whole point of having an accountant is so you can go do something else." — Sai Dhanak"More money, more problems. When you're lean and scrappy, you stay focused. Raise too much capital and focus becomes exponentially harder." — Sai Dhanak"Do you love this problem enough to still be working on it in 10 years? Not the trend — the problem." — Sai Dhanak"Every founder, when asked what the highlight was, says the same thing: bringing on amazing people who are now my friends." — Sai Dhanak"Trends fade. Trust doesn't." — Jeff MainsSaaS Leadership Lessons1. The three types of SaaS that survive AI disruption Sai identified a clear framework early: the only SaaS that holds value long-term are (1) businesses with hardcore integration moats you can't vibe-code (like Stripe), (2) ledgers and systems of record that are structurally difficult to disrupt, and (3) anything that requires a human liability backstop. If your SaaS doesn't fit one of those three, it's at risk.2. The human in the loop is a competitive moat, not a limitation Rather than chasing full automation, Deduction deliberately built a model where licensed tax professionals review, verify, and sign off on AI-generated work. That signature requirement — mandated by the IRS — is baked-in defensibility. In high-stakes industries, the human backstop isn't a workaround. It's the whole product.3. Ship early, obsess over design Going back to his first company, Caribou (sold to Mattel), Sai learned two lessons that still guide him: launch before you're ready to get real feedback fast, and invest heavily in design and experience. In an AI world where anyone can build anything, experience is what differentiates.4. Lean is a feature, not a constraint After watching Latch raise hundreds of millions of dollars and experience the chaos that came with it, Sai deliberately built Deduction as a lean, flat organization. The goal isn't a one-person billion-dollar company — it's high margins with a small team that can move fast, maintain quality, and stay culturally tight. Every hire matters exponentially more in a small company.5. Choose your channel based on operational reality, not trend The decision to lead with email over chat or SMS wasn't a legacy move — it was a strategic one. Email's async nature gave Deduction manageable response windows as an early-stage company while also matching the customer expectation: "I hired an accountant so I don't have to sit and do this myself." Build for your operational reality first, then open faster channels as you can guarantee the experience.6. Acquire instead of just acquiring customers One of Deduction's most powerful go-to-market moves is buying books of business from retiring CPAs. The market for small accounting firms is fragmented and surprisingly liquid — entire websites are dedicated to the sale of these practices. Instead of competing cold for customers, Deduction inherits trusted relationships already built. It's an asymmetric growth lever that most founders never consider.Guest Resourcessai@deduction.comhttps://deduction.com/www.linkedin.com/in/saayujhttps://x.com/SaiDhanakEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Remy's Danielle Sakher explains how her team balances speed with observability and security as AI coding tools move from novelty to enterprise infrastructure. She shares what it's really like selling an AI builder to security-conscious industries like financial services, plus a wild stat: alpha users have already spent $300K in AI compute.

Adam Wexler played fantasy football since seventh grade — and turned that habit into a $4.15 billion exit. He built his first company out of a dorm room at the University of Georgia, launched a music discovery platform called Go Rankem in Athens, then pivoted into B2B SaaS with clients like Holiday Inn, IHG, Home Depot, UPS, and Coca-Cola before he ever touched sports. In 2015, he incorporated Side Prize. When the fantasy sports market collapsed under regulatory pressure that same year, he rebuilt from a shoestring budget and relaunched in 2017 as PrizePicks — built on a "stupid simple" premise: get sports fans in and out of the app in under 60 seconds. He ran it as CEO for a decade, scaled it into the largest daily fantasy sports platform in North America, and in 2025 sold a majority stake to Allwyn International in a deal valued at up to $4.15 billion — without ever raising venture capital. Rather than ride off into retirement, Wexler went back to where he started: music. He's now CEO of The Hidden Jams, a nonprofit platform funded by $5 million of his own money that crowdsources deep cuts and hidden gems from artist catalogs Make sure to check out Prize Picks at: https://www.prizepicks.com/ And The Hidden Jams at: https://thehiddenjams.org/   Check out my new book on Amazon: https://amzn.to/4kRKGTX Watch our mini-doc - Starting Small: The Raw Truth Behind Entrepreneurship and the American Dream: https://youtu.be/eHuq93wIxs0?si=eDB-ycngvWNapRLO Visit Starting Small Media: https://startingsmallmedia.org/ Subscribe to exclusive Starting Small emails: https://startingsmallmedia.org/newsletter-signup   Follow Starting Small: Instagram: https://www.instagram.com/startingsmallpod/ Facebook: https://www.facebook.com/Startingsmallpod/?modal=admin_todo_tour LinkedIn: http://linkedin.com/in/cameronnagle   Sound is one of those things that makes or breaks a hosting experience — and we finally got it right.  We went with KICKER KB6 speakers mounted above the deck. Full-range, weather-resistant, built to throw sound across an open space without losing quality. Kicker's been in the game since 1973 — car audio, marine, powersports — and they've expanded into Home & Personal audio, with headphones, earbuds and a complete line-up of outdoor audio speakers & subs. Engineered for performance, wherever you need sound. If you're setting up an outdoor space for your home or business, and the audio isn't dialed in yet, start at Kicker.com.

What happens when a technical B2B SaaS company decides it doesn't need a twelve person marketing team? In this episode of Content Amplified, Jerome Stewart, a marketing leader who has spent the past decade building AI powered B2B SaaS brands in the analytics and observability space, explains how he helped shrink one marketing org from twelve people to four without breaking the work. Jerome walks through his "cover your bases" framework for deciding which functions still need a human at the helm (creative, communications, marketing ops, demand gen, product marketing), and how he builds "skills," reusable brand, PowerPoint, and social media instructions built from existing blogs, transcripts, and websites, so AI carries the last mile of production. He also digs into where AI quietly breaks down: content saturation, the em dash as an AI "tell," and a widening skills gap between AI fluent early career marketers and the business writing fundamentals that came before AI. If you're trying to figure out where to trust AI and where to still put a human, this conversation gives you a real framework, not just a hot take.About JeromeJerome Stewart began his career as a Peace Corps volunteer before going back to business school and starting out as a product manager at Microsoft, where he later moved into marketing. He lived in the Seattle area and overseas while at Microsoft, then relocated to the San Francisco Bay Area about ten years ago, where he has spent the past decade marketing AI powered B2B SaaS companies in the analytics and observability space. Jerome now spends much of his professional energy mentoring earlier career marketers, helping them pair their natural AI fluency with the business fundamentals it can't replace.Show NotesConnect with Jerome on LinkedIn: https://www.linkedin.com/in/jeromestewart/Text us what you think about this episode!

Send us Fan MailIn this episode, Amith Nagarajan and Mallory Mejias unpack a rapidly shifting AI landscape where powerful, low-cost open-weight models from Chinese labs are challenging the dominance of major U.S. players. They explore Moonshot AI's Kimi K3 and Alibaba's latest model announcements, breaking down what “open” AI really means and why it's driving costs down while increasing competition. The conversation also dives into the growing geopolitical tension around AI, including potential U.S. government intervention and the risks and realities of using models developed abroad. Finally, they discuss a proposal from DeepMind's Demis Hassabis to introduce an industry “referee” for AI safety—and what it could mean for innovation. The big takeaway: intelligence is becoming abundant and affordable, and association leaders must rethink strategy now to take advantage of what's coming next. 

Darryl Yearwood, who leads partnerships at Apollo, makes the case that a smaller, higher-quality partner roster beats chasing volume every time. He and Tye get into what "not plug-and-play" really means in affiliate marketing and how to build partnerships that actually move revenue.

What if the biggest risk in marketing isn't failing, it's being too afraid to?In this episode of the FINITE Podcast, Jodi Norris sits down with Amanda Cole, CMO at Bloomreach, to unpack why building a culture that tolerates, and even expects, failure has never mattered more. As AI reshapes how marketing teams operate, Amanda argues the old playbook no longer applies. Instead, the marketers who thrive will be the ones comfortable saying "that didn't work" and moving on fast.The conversation explores what happens when decision-making shifts from humans to AI agents, why scale changes the stakes of every mistake, and why Amanda believes the real risk isn't moving too fast, but forgetting to bring your whole organisation along with you.Amanda Cole has spent over 20 years building B2B marketing functions from the ground up, starting in direct mail and rising to CMO at Bloomreach, where she leads global marketing, technology partnerships and pipeline generation. Known for her candour and appetite for risk, she's built a reputation for pairing bold experimentation with hard-won lessons on leadership and change.Inside you'll find…Why Amanda believes 80% of marketing doesn't work as intended, and why that's exactly the pointHow AI agents change the scale and speed of failure, and what "human in the loop" really means in practiceThe sunk cost trap every marketing leader falls into, and the discipline it takes to walk back before the finish line

Send us Fan MailIn this interview episode, Mallory Mejias and Amith Nagarajan sit down with Vicki Deal-Williams, CEO of the American Speech-Language-Hearing Association (ASHA), to explore how a century-old organization is thoughtfully embracing AI. Vicki shares how ASHA's AI Innovation Day sparked a cultural shift across staff—from skeptics to the “suspenders group” eager to experiment—while reinforcing a people-first philosophy. The conversation dives into balancing governance with creativity, designing impactful AI experiences even for small teams, and building tools like an evidence-based AI navigator to better serve members. Along the way, they unpack leadership lessons on change management, capacity building, and why adopting AI is less about perfection and more like jumping into a game of double Dutch—messy, continuous, and worth it. 

Avishag Daniely built One Layer's entire marketing and partnerships function from scratch — coming from a background in IDF cyber intelligence. She shares how her military analytical training shaped her approach to B2B GTM, what it takes to build pipeline when no one's heard of your company yet, and why she treats every marketing decision like an intelligence problem.

Affiliate fraud investigator Ben Edelman joins Tye for an emergency episode breaking down the "Phia fiasco" that's rattling the affiliate marketing industry. Edelman, who's spent over 20 years catching affiliate fraud and has collaborated with Tye on forensic cases throughout his career, walks through what makes this case different from the usual stand-down violations that have hit shopping plugins like Honey and Microsoft Edge in the past.

Send us Fan MailPricing AI is the hardest pricing problem in software right now, and most SaaS leaders are getting it wrong on the first try. In this episode of Navigating the Customer Experience, host Yanique Grant sits down with Dan Balcauski, founder of Product Tranquility, to unpack why the first AI price a company sets is always wrong, how to set usage caps when you have no historical data, and what smart B2B SaaS leaders do differently to turn pricing from a liability into a strategic advantage.Dan has spent more than 20 years in software, starting as an engineer before moving into product management and discovering that how a company captures value matters far more than how it builds the product. Today he advises B2B SaaS CEOs on the AI pricing and packaging decisions that keep them up at night, and in this conversation he shares the frameworks, the mistakes to avoid, and the practical playbook he uses with real companies.WHAT YOU WILL LEARN IN THIS EPISODEWhy the first AI price is always wrong, and why that has nothing to do with how smart your team or your consultants are. Dan explains the fundamental economic shift underway in software, where both sides of the pricing equation are moving at once. On the cost side, he points to a benchmark showing the cost per task for a top model dropping by roughly 390 times in a single year, a change no normal business ever absorbs in its cost of goods sold. On the value side, models keep getting more capable, handling this month what they could not handle last month. His research shows that every application layer software company he studied that released AI capabilities revised its pricing and packaging within 18 months. The lesson is not to price perfectly on day one. It is to build for change.How to set usage caps and pricing tiers with zero historical data. Dan frames the real problem plainly. You know what a token costs, but you have no idea what customers will actually do with a new AI feature. Products are full of features that barely got adopted, and AI features do not get to skip that step of the innovation cycle. On top of that, a small group of power users, often around 5 to 10 percent, can drive the overwhelming majority of usage and cost. That makes the tempting shortcuts unreliable. Using dashboard views as a proxy breaks down because good AI gets used far more than the dashboards it replaces, and a beta group rarely matches the usage profile of the full market.The early access playbook that sits between beta and general availability. Dan recommends a stage where companies announce their limits, put a price on the feature, and communicate it clearly, but do not enforce or meter it yet for a defined window that can run anywhere from six weeks to 18 months. This eases customer anxiety about surprise bills, encourages real adoption, and lets the company gather genuine usage patterns instead of guessing from proxies that break down.Why you should separate ordinary plan limits from fair use limits. Even when you are not metering usage, Dan explains, you can reserve the right to throttle or downgrade the rare customer using a capability a hundred or a thousand times more than the average, much like companies already do with API request limits. Those levers let teams keep experimenting during early access without the finance team panicking when the bill arrives.Why communication is where pricing changes succeed or fail. As Dan puts it, most pricing blowups come not from the change itself but from the fact that it was communicated poorly or not at all. Agility beats certainty, and reviewing pricing on a quarterly cadence beats the old annual or five year rhythm.This episode is essential listening for SaaS founders, product leaders, pricing strategists, and customer experience professionals who want to understand how AI is reshaping the economics of software and what to do about it before the market forces the decision for them.ABOUT DAN BALCAUSKIDan Balcauski is the founder of Product Tranquility, where he helps B2B SaaS CEOs turn pricing from a confusing liability into a strategic advantage. With more than 20 years in software, Dan began his career as an engineer before moving into product management and discovering that how companies capture value matters far more than how they build it. His work now centers on one of the most pressing questions in software today: how to price AI. Before founding Product Tranquility, Dan was a principal product strategist at SolarWinds and head of product at LawnStarter. He holds a BSc in computer engineering from Iowa State University and an MBA from the Kellogg School of Management at Northwestern, where he also helps teach executive education courses on product strategy. He is the host of the SaaS Scaling Secrets podcast.QUESTIONS YANIQUE ASKEDCould you share a little about your journey and how you got from where you were to where you are today? You have said the first AI price is always wrong. Why is that, and what should a SaaS company do differently knowing they are going to get it wrong the first time? So many companies are trying to set usage caps and pricing tiers for AI with zero historical data. How would you advise a CEO to make that decision when they are essentially flying blind? What is the one online resource, tool, website, or application that you absolutely cannot live without in your business? Can you share one or two books that have had a positive impact on you, professionally or personally? What is one thing going on in your life right now that you are really excited about? Do you have a quote or saying that keeps you on track during times of adversity? Where can listeners find and connect with you online?KEY TAKEAWAYSThe first AI price is always wrong, and that is not a failure of intelligence. It reflects a fundamental economic shift where both cost and value are moving fast. Every application layer company Dan studied revised its AI pricing and packaging within 18 months. Plan for revision, not perfection. Agility beats certainty. Review pricing on a quarterly cadence rather than annually or every five years. Communication is where pricing changes succeed or fail. Most blowups come from poor communication, not the change itself. Usage proxies break down. Dashboard views and beta groups rarely predict how customers will actually use an AI feature. A small group of power users can drive the majority of usage and cost, so average user assumptions are dangerous. Early access is the smart middle stage. Announce and price the limits, communicate them, but do not meter yet while you gather real data. Separate plan limits from fair use limits. Reserve the right to throttle extreme usage even when you are not metering everyone. Do not borrow problems from the future. Anxiety about what has not happened yet only adds problems to the present. AI is making custom, personal business software economically viable for the first time, opening the door to tools built exactly the way you work.CHAPTERS 00:00 Introduction and Guest Bio 01:51 Dan's Journey: From Engineer to Pricing Strategist 04:03 Learning That Pricing Is Different in Every Industry 04:49 Why the First AI Price Is Always Wrong 05:36 The 390x Cost Shift and the Moving Value Equation 06:49 Agility, Faster Pricing Reviews, and Communication 09:28 Setting Usage Caps With No Historical Data 11:32 Why Dashboard Proxies and Beta Groups Break Down 12:59 The Early Access Playbook Between Beta and GA 13:20 Plan Limits vs. Fair Use Limits 17:04 The One Tool Dan Cannot Live Without: Claude Code 17:38 Book Recommendation: Monetizing Innovation 18:31 Building Custom Business Software With AI 19:55 How to Connect With Dan Online 20:27 Dan's Guiding Quote: Don't Borrow Problems From the FutureFEATURED RESOURCESBook mentioned: Monetizing Innovation by Madhavan Ramanujam and Georg TackeTool mentioned: Claude Code, Dan's work surface and the engine behind his custom business softwareCONNECT WITH DANLinkedIn: Search Dan Balcauski on LinkedIn, and mention that you heard him on the podcast so he can separate you from the spamWebsite: producttranquility.comPodcast: SaaS Scaling Secrets, wherever podcasts are foundDAN'S GUIDING QUOTE"Don't borrow problems from the future." Dan BalcauskiDan explains that most of our anxiety is about things that have not happened yet. Worrying about a future scenario pulls that problem into the present before it ever arrives, giving you more to carry now for no reason. Like debt, it is borrowing against your future self. His practice is to stay focused on what is real and in front of him, which keeps him grounded when challenges or uncertainty threaten to pull him off track.ABOUT N

In this episode, Carolyn sits down with Liam MacCormack, a fractional head of growth who's spent years in the weeds of B2B paid search, to unpack why the channel keeps burning B2B budgets and what to do about it.They get into the market forces making paid search harder than ever: rising CPCs, zero-click search, and agencies still optimizing like it's 2015. Then they go tactical on a real client scenario — a high-ACV company that's switched agencies multiple times and still can't make it work — and what that reveals about when a channel is worth fixing versus when it's just draining spend.Topics covered:Why "you need to spend more on impression share" is the most seductive upsell in paid search, and the one metric that actually tells you if it's trueThe profile of a company paid search works for, and the profile where it's a waste of moneyWhy paid search is a demand-capture channel, and what that means for how you budget itHow to know when to cut the channel, cut the budget in half, or let it run as a tertiary sourceWhy branded search is propping up most agency reporting, and the one question to ask to find outIf your paid search has been underperforming for quarters and the answer is always "give it more time," this one's worth your attention.-----------------------------------------------------

In this Kitchen Side episode, Alex Birkett, Allie Decker, and David Ly Khim unpack how brands should actually measure success in AI search, and why traditional attribution models are breaking down as buyer behavior shifts from search links to AI assistants and workflows. They discuss why self-reported attribution is becoming the most reliable signal available, the different tiers of AI visibility from simple citations to category-level consensus, and why AI visibility functions more like a brand recall survey than a channel you can optimize in isolation. The conversation also covers the idea of a “post-channel marketer” who coordinates across product, customer education, and PR, and why chasing visibility tactics with no underlying business purpose rarely pays off. Key Takeaways Self-reported attribution is more reliable than clickstream data for AI search because most AI-driven visits never result in a tracked click. AI visibility behaves more like a brand recall survey than a channel, reflecting how a brand compares to competitors rather than something to optimize in isolation. Picking up a citation on a narrow, low-competition prompt is easy, but shifting broader category-level consensus, like being named among the best CRMs, is far harder and requires changing an entire conversation. Correcting outdated third-party pricing information moved AI search outputs within about a month in one client engagement, though this was a sentiment fix rather than a visibility gain. Some AI answers now include negative or qualifying recommendations, meaning a mention doesn't always translate into a positive outcome for a brand. Different AI tools serve different roles, with ChatGPT and AI Overviews functioning as quick answer engines while agentic tools like Claude Code perform deeper multi-step research, and visibility strategies should account for that difference. Whether a company should publish a markdown version of its site for easier AI retrieval depends on its audience, mattering more for technical, developer-facing products than for typical B2B SaaS buyers. Sustainable AI search performance comes from investing in product experience, customer education, and reviews, since genuine third-party validation is difficult to fake once a brand has earned it. Marketing teams need a “post-channel” role that coordinates across product, customer success, and PR to influence sentiment and visibility, rather than treating this as an SEO-only or PR-only problem. Tactics pursued only to move an AI visibility score, with no underlying business purpose, are usually not worth the effort unless they're addressing existing negative sentiment. Show Links Connect with David Khim on LinkedIn and Twitter Connect with Alex Birkett on LinkedIn and Twitter Connect with Allie Decker on LinkedIn and Twitter Connect with Omniscient Digital on LinkedIn or Twitter What is Kitchen Side? One big benefit of running an agency or working at one is you get to see the "kitchen side" of many different businesses; their revenue, their operations, their automations, and their culture. You understand how things look from the inside and how that differs from the outside. You understand how the sausage is made. As an agency ourselves, we're working both on growing our clients' businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business. We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship. Past guests on The Long Game podcast include: Morgan Brown (Shopify), Ryan Law (Animalz), Dan Shure (Evolving SEO), Kaleigh Moore (freelancer), Eric Siu (Clickflow), Peep Laja (CXL), Chelsea Castle (Chili Piper), Tracey Wallace (Klaviyo), Tim Soulo (Ahrefs), Ryan McReady (Reforge), and many more. Some interviews you might enjoy and learn from: Actionable Tips and Secrets to SEO Strategy with Dan Shure (Evolving SEO) Building Competitive Marketing Content with Sam Chapman (Aprimo) How to Build the Right Data Workflow with Blake Burch (Shipyard) Data-Driven Thought Leadership with Alicia Johnston (Sprout Social) Purpose-Driven Leadership & Building a Content Team with Ty Magnin (UiPath) Also, check out our Kitchen Side series where we take you behind the scenes to see how the sausage is made at our agency: Blue Ocean vs Red Ocean SEO Should You Hire Writers or Subject Matter Experts? How Do Growth and Content Overlap? Connect with Omniscient Digital on social: Twitter: @beomniscient LinkedIn: Be Omniscient Listen to more episodes of The Long Game podcast here: https://beomniscient.com/podcast/

The MQL is marketing's worst open secret. Everyone in the room knows the number is gamed. The leads are low-intent, the scoring is guesswork, and the pipeline isn't growing. Yet marketing keeps getting graded on the one metric nobody actually trusts.In this workshop, Carolyn and Amber sit down with Jon Miller to take apart the metric the entire B2B playbook still runs on and walk through anonymized customer data showing exactly what it costs. Hand raisers in this account converted 83X better than MQLs and qualified faster. The MQLs that didn't convert got worked for two months before anyone disqualified them. That's the drain nobody puts on a slide.What this workshop covers:Why the MQL became gospel and the moment a sound idea turned into a volume game sales learned to ignoreThe gumball machine fallacy: why "more budget in, more pipeline out" assumes a linear process that buying stopped being years agoNonlinear buying, the dark funnel, and why one overwritten lead record erases the history you actually needReal customer data: MQLs accounted for under 6% of pipeline while hand raisers drove 39% of closed-won revenueJon's three-tier lead model and why waiting for hand raisers alone forfeits first-mover advantage and your future pipelineThe KPI cheat sheet: pipeline velocity, brand-question surveys, post-sale revenue metrics, and the numbers a board actually speaksWhat the shift means for your MarTech stack as AI moves orchestration from rules to reasoningThe leaders who get out from under the MQL don't kill it overnight. They layer in metrics their CFO and head of sales already recognize and stop defending volume that was never converting.-----------------------------------------------------

One Big Idea 3 - Driving Enterprise Value: From Funding Architectures and Radical Letting Go to Systems-Driven RevenueIn this episode of One Big Idea, host Josh Elledge connects with Anthony Rose, Latif Hamilton, Dan Rochon, Ronald Robinson, and Mark Osborne to dissect the foundational operational strategies required to elevate enterprise value, optimize leadership psychology, and construct predictable growth engines. Anthony Rose, Founder and CEO of SeedLegals, kicks off the discussion by introducing a fairer, more transparent fundraising mechanism designed to protect early-stage founders. Latif Hamilton, Founder of SpiritHoods, then shifts focus to executive psychology, mapping out structural frameworks to help founders overcome cognitive biases and master the art of letting go. Next, CPI Community Founder Dan Rochon outlines a guide to replacing high-pressure sales with consultative, guidance-based relationship building. Ronald Robinson, Founder of Expanded Learning Academy, dives deep into the profound link between childhood social-emotional competencies and adult executive leadership. Finally, Mark Osborne, Fractional Revenue Leader for Professional Services & B2B SaaS at Modern Revenue Strategies, closes the episode by delivering a blueprint on transitioning from hustle-centric business development to completely automated, system-driven revenue architecture.Smarter Fundraising for Startups Using SAFERs Instead of Traditional SAFEs with SeedLegals' Anthony RoseEarly-stage fundraising has long relied on Simple Agreements for Future Equity (SAFEs) to bypass the slow, expensive legal hurdles of traditional priced funding rounds. However, legal tech pioneer Anthony Rose argues that his "one big idea" exposes how traditional SAFEs routinely blindside founders with massive, compounded dilution once conversion math kicks in at the next priced round. Because SAFEs don't update the cap table in real time, founders frequently underestimate their stacked equity obligations, sometimes waking up to find they have accidentally surrendered a majority stake in their own company. Furthermore, SAFEs present critical tax ambiguities for savvy investors regarding when the five-year Qualified Small Business Stock (QSBS) holding clock officially begins.To solve these hidden structural hazards, Anthony introduces the SAFER (Simple Agreement for Future Equity and Regular Shares). This framework retains the rapid, low-cost execution speed of a traditional SAFE but requires that investors receive their stock immediately upon investment. This instantaneous cap table visibility ensures founders see the exact equity impact of every dollar raised in real time, preventing unexpected minority status down the line. By utilizing automated legal modeling tools, early-stage companies raising between $500K and $2M can establish flawless financial transparency, kickstart the investor's QSBS tax clock on day one, and secure institutional-grade corporate clarity without the bloated fees of legacy law firms.Breaking Free by Outsmarting Your Brain and Letting Go Like a Pro with SpiritHoods' Latif HamiltonOne of the greatest operational barriers to scaling an enterprise is the founder's own psychological attachment to underperforming elements of the business. Latif Hamilton explains that his core thesis addresses why entrepreneurs struggle to cut ties with failing product lines, toxic corporate cultures, or stagnant business models. This operational paralysis is driven by two hardwired cognitive biases: the endowment effect, which causes leaders to artificially overvalue an asset simply because they own it, and loss aversion, where the psychological pain of losing an asset is twice as powerful as the pleasure of gaining an equivalent win. Left unchecked, these biases trap executives in an expensive cycle of protecting sunk costs instead of pursuing high-yield commercial opportunities.To bypass these emotional roadblocks, Latif provides a tactical toolkit designed to decouple human emotion from strategic analysis. Founders must routinely challenge their operations by asking the "starting fresh" question: If I didn't already own this product or employ this person, would I choose to buy or hire them today? If the answer is no, immediate divestment is required. By mapping out a physical grid to calculate the true cost of inaction—including opportunity cost and team morale drain—leaders can clearly see the numbers in black and white. Transitioning into authentic thought leadership through platforms like Substack and high-level podcast guesting allows founders to pivot their energy toward market authority, turning perceived organizational losses into scalable future gains.Building Client Trust While Breaking Through Internal Resistance with CPI Community's Dan RochonIn a transparent and highly competitive marketplace, traditional, aggressive sales closing tactics create immediate buyer friction and erosion of brand trust. Sales consultant Dan Rochon outlines his "one big idea" that modern sales must pivot completely away from psychological manipulation and transition into an act of collaborative leadership and client guidance. The primary obstacle in a commercial transaction is rarely external market competition; rather, it is the prospect's internal resistance, driven by unvoiced fears, self-doubt, and structural uncertainty. By stepping into the role of a guide rather than an aggressive closing hero, the sales professional shifts from an administrative solicitor to a trusted advisor.To execute this consultative framework consistently, Dan structures his methodology across three actionable operational behaviors: connecting authentically to build immediate rapport, asking deep questions that target the prospect's root motivation, and actively listening to emotional hesitation rather than just verbal compliance. This client-centric approach forms the bedrock of consistent and predictable revenue, allowing founders to easily transition away from founder-led sales. By thoroughly documenting these conversational processes into corporate playbooks, leveraging CRM data tracking, and utilizing podcasts for high-level ecosystem networking, organizations can seamlessly scale their business development teams beyond the personal bandwidth of the company founder.The Hidden Link Between Childhood SEL and Adult Workplace Success with Expanded Learning Academy's Ronald RobinsonTechnical expertise and operational software systems are useless if an organization lacks the foundational soft skills required to execute effectively under high-pressure conditions. Education strategist Ronald Robinson shares his core thesis that Social Emotional Learning (SEL) competencies are not merely childhood development concepts, but the primary drivers of modern workplace productivity and elite corporate culture. High-performing business units separate themselves not by raw technical capabilities, but by their team leaders' capacity to operate with high levels of self-awareness, self-management, social awareness, relationship management, and responsible decision-making.To bridge the gap between abstract emotional intelligence and rigid corporate KPIs, Ronald introduces the advanced concept of SELF (Social Emotional Learning Fundamentals), which mandates that executives systematically prioritize self-care, self-confidence, and self-assurance. When corporate leaders fail to manage their internal emotional triggers, they inadvertently project impulsivity onto their direct reports, destroying psychological safety and driving up employee turnover. By embedding regular 360-degree feedback loops, active listening training, and strict emotional regulation boundaries directly into adult workforce development programs, companies can build inclusive, highly resilient environments. Ultimately, designing a culture where personnel thrive emotionally serves as a primary macro competitive advantage.Enhancing Revenue Systems Through AI and Strategic Leadership with Modern Revenue Strategies's Mark OsborneMany growing companies fall victim to the hazardous trap of "hero mode" growth, where top-line revenue numbers are driven purely by the ad-hoc charisma, brute-force hustle, and personal networks of the founding team. Fractional revenue expert Mark Osborne demonstrates that his core framework addresses why this personality-driven revenue is actually a severe structural liability that drastically tanks a company's enterprise valuation during an M&A or investment round. If a business cannot mathematically prove that its customer acquisition engine is entirely predictable, repeatable, transferable, and independent of any single rainmaker, buyers will view that income stream as high-risk phantom equity.To convert volatile cash generation into a verified corporate asset, Mark details a systemized architecture built upon three interlocking workflows: attraction systems (leveraging hyper-targeted client profiles), acceleration systems (streamlining sales pipeline velocity via automated proposals), and activation systems (maximizing client onboarding and referral loops). When integrating artificial intelligence into this revenue strategy, executives must strictly avoid the mistake of chasing popular software tools before defining their core processes; AI must be deployed exclusively as a force multiplier layered onto pre-existing, human-mapped customer journeys. By visually whiteboarding the entire critical client flow, assigning absolute ownership to each conversion metric, and conducting rigorous quarterly quality-of-earnings audits, business leaders successfully build an institutionalized revenue engine that functions flawlessly without founder...

Kako izgleda kada umesto sedenja ispod masline završiš u marketingu, prkosiš pravilima najvećih svetskih sistema i biraš uverenje umesto prećutkivanja istine?

Logan Dunn, Head of E-commerce at Wyze, ran 100 ads at once, cut traffic, and watched conversions go up. In this episode, he breaks down the counterintuitive paid media playbook behind Wyze's D2C growth — including why narrow targeting beats broad reach, how in-house creative unlocks faster iteration, and what most brands get wrong about scaling paid spend.

Pavel drove AI customer support adoption as head of support...and then he got laid off. Mat chats with Pavel about his 13 years in B2B SaaS customer experience about the way execs are thinking about support today.Plus: why customers still want a human to confirm an answer, even when the AI got it exactly right.For a full transcript, notes, and links, see https://www.helpscout.com/blog/inside-ai-customer-supportFind Pavel at: https://www.linkedin.com/in/pmalyshev/

Join Marc-Antoine Lacroix, Co-founder and CEO of Pivot, for a crucial evaluation of the fatal design flaw stalling modern enterprise AI. Across the corporate landscape, millions are spent bolting generative chat wrappers onto outdated back-office databases, only for the applications to hallucinate, cross wires, and fail. Drawing from his time as CTO and CPO scaling the French fintech unicorn Qonto, Marc-Antoine realized that enterprise software fails when its data layer is broken. In this episode—following Pivot's massive $40M Series B funding round—we discuss why sequence matters infinitely more than speed, and why true agentic AI requires building a rock-solid, real-time system of record before writing a single prompt.

Today, we're joined by Mahesh Rajasekharan, President and CEO of Cleo, the global leader in supply chain orchestration (SCO) solutions. We talk about:How SaaS companies can win in the AI ageThe top three challenges to focus on when adding AI to your productClarifying the misconception that software development becomes trivial in the AI worldThe best domains in which to start a new companyTransitioning from building software for human users to human-supervised, tech-driven operations

Bill Widmer is an SEO veteran with over a decade of experience, including building and selling a high-traffic travel blog and writing content for B2B SaaS like Ahrefs, Semrush, Monday, and more. These days, he's obsessed with using AI to augment SEO so growing SaaS companies can show up in AI results without the giant agency pricetag. Bill is also the founder of Momentum Lab, where he coaches ADHD solopreneurs out of overwhelm and into actually shipping their work. Links https://billwidmer.com/ https://theblogwhisperer.com/ https://www.foundersguildhq.com/ Key Moments 04:08 Selling customized glass frames 09:00 Freelance writing rate progression 11:15 Partner split and Momentum Lab launch 14:36 Embracing AI and automation If you're enjoying Entrepreneur's Enigma, please give me a review on the podcast directory of your choice. The show is on all of them and these reviews really help others find the show. iTunes: https://gmwd.us/itunes Podchaser: https://gmwd.us/podchaser TrueFans: https://gmwd.us/truefans Also, if you're getting value from the show and want to buy me a coffee, go to the show notes to get the link to get me a coffee to keep me awake, while I work on bringing you more great episodes to your ears. →  https://ko-fi.com/entrepreneursenigma Support me on TrueFans.fm → https://gmwd.us/truefans. Support The Show & Get Merch: https://shop.entrepreneursenigma.com Want to learn from a 15 year veteran? Check out the Podcast Mastery Community:https://www.skool.com/podcasting Follow Seth Online: Instagram: https://instagram.com/s3th.me LinkedIn: https://www.linkedin.com/in/sethmgoldstein/ Seth On Mastodon: https://indieweb.social/@phillycodehound The Marketing Junto Newsletter: https://MarketingJunto.com Leave The Show A Voicemail: https://podcastfeedback.com/entrepreneursenigma Learn more about your ad choices. Visit megaphone.fm/adchoices

Many B2B marketing leaders still evaluate website performance by total traffic, engagement, or surface-level conversion metrics. What they should do is view the website as a core revenue engine, where everything from messaging, page layouts, and UX decisions directly accelerate sales-qualified leads, demo booking quality, and pipeline velocity. So, how can B2B SaaS companies design high-converting websites that capture high-intent demand and generate predictable, long-term revenue?That's why we're talking to Sahil Patel (CEO, Spiralyze) to unlock data-backed strategies on how to transform B2B SaaS website traffic into predictable revenue. During our conversation, Sahil reveals why most B2B SaaS website fail as revenue engines based on large-scale conversion rate optimization (CRO) testing data. He discussed why displaying your actual product immediately can drive a 19% lift in conversion rates. Sahil also introduced actionable diagnostic frameworks like the “one-second test” to see if your homepage actually works, and provided tips on how to conduct the competitor homepage test. He provided a tactical roadmap for focusing exclusively on high-intent buyer traffic, deploying friction-free CTAs, and using credible, customer-validated proof points while stripping away overcomplication with too much information.

On this episode of People Solve Problems, host Jamie Flinchbaugh welcomes Brittany Irwin, Applications and AI Engineering Manager at NFI Industries. With an industrial engineering degree from the University of Pittsburgh's Swanson School of Engineering and a career spanning large-scale third-party logistics and fast-moving B2B SaaS startups, Brittany offers a grounded view of how people, processes, and technology fit together. The conversation centers on a theme she has presented publicly, including at the Lehigh CSCRL Spring Symposium: how organizations move AI from hype to habit. Brittany makes a case that runs counter to a common assumption. Most AI and automation efforts in logistics do not stall because the technology falls short, she explains, but because the business was never ready for it. The industrial engineering instinct to find waste and standardize it is the same discipline AI demands, since a tool can only return a reliable output when it is given a reliable input. So before moving any process toward automation, Brittany asks a pointed set of questions: where the process begins and ends, what the top exceptions are and why they happen, and whether any of it is actually written down. That last question leads to what Brittany finds most underestimated, which is language itself. Being on time, she notes, can mean leaving the dock to one team and reaching the customer to another, and AI cannot reconcile a definition that people have never agreed on. This is why she puts such weight on a single source of truth. Consistent data definitions, documented exceptions, and shared context are what let a team build trust, reduce rework, and prepare the ground before automation is switched on. Just as important, and far less often discussed, is the politics of change. Brittany asks two questions of every process: who gains power if it is standardized or automated, and who loses it. The first reveals a natural champion. The second reveals the person most likely to slow things down, and she reads withheld information and dragging feet as signs of exactly that. When the resistance outweighs what a project can overcome, she takes it as a cue to redirect her energy elsewhere. Brittany is also reassuring about the fear underneath that resistance. AI, she insists, belongs in the second chair, not the first. It will suggest a course confidently and sometimes be confidently wrong, which is why a human must stay accountable for every decision, approval, and exception. The real risk she sees is not the technology but the person who trusts it more than their own judgment and waves work through without reading it. When colleagues feel exposed, she reframes the change as something that lets them handle more, not something that erases their value. Asked how she balances speed with thoroughness, Brittany describes a patient crawl, walk, run approach, one bite of the elephant at a time. Her team automates a single painful workflow first, works to earn a positive reaction to it, and only then scales to the next phase. Adoption cannot be forced, she stresses; a technically flawless project still fails if people push back. It is a fitting throughline for someone who counts standardizing her own role until she was replaceable as a point of pride, then carrying the same playbook somewhere new. Throughout, Jamie keeps the spotlight on Brittany and the human ingredients that decide whether AI succeeds, which reach well beyond prompt engineering. To learn more about Brittany's work, visit NFI Industries at nfiindustries.com and connect with her on LinkedIn.

Ara Ohanian is the CEO of Netstock, a global provider of AI-powered inventory optimization and supply chain planning software for mid-market businesses. An experienced B2B SaaS and enterprise software leader, he brings deep insight into the challenges companies face when managing inventory, forecasting demand, and scaling operations. Ara leads Netstock's mission to help businesses reduce stockouts, lower excess inventory, and unlock working capital. He has held executive roles at Systech, Unite Us, Infor, and Dubilier & Co., bringing broad expertise across supply chain, ERP, compliance, and growth strategy. In this episode… Inventory can either fuel growth or quietly drain cash from a business. When companies rely on spreadsheets or outdated planning systems, they risk tying up working capital in the wrong products while missing demand for the right ones. So how can growing businesses forecast smarter, reduce stockouts, and keep cash moving? Ara Ohanian, a seasoned B2B SaaS and enterprise software leader, says businesses need better visibility into what inventory they should have in the future, not just what they have today. He highlights the importance of using predictive planning tools to help companies make faster decisions when demand shifts, supplier costs change, or disruptions hit the supply chain. The main impact is more efficient inventory management, fewer missed sales, and less working capital tied up in excess stock. Instead of relying on manual spreadsheets, businesses can use AI-powered insights to anticipate demand across warehouses, markets, and product categories. This gives mid-market companies a stronger chance to compete with larger enterprises that have historically had access to more sophisticated planning resources. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz speaks with Ara Ohanian, CEO of Netstock, to discuss smarter forecasting for inventory and cash flow. Ara explains predictive ERP overlays, demand planning across warehouses, and retail forecasting challenges like pricing, promotions, and shelf life. He also shares leadership lessons on culture and curiosity.

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

What if your next big business breakthrough started with owning your own blind spots?This Fan Favorite episode throws you in the trenches with Cameron Herold and SaaS Academy's former COO and current CEO Matt Verlachi, as they go far beyond surface-level business banter. From surviving firefighting chaos to building, selling, and now scaling SaaS Academy, Matt Verlachi exposes the real skills that make or break Second in Commands. They unpack why customer obsession cures more growth headaches than any software, how to weaponize one-on-ones for radical team development, and what most COOs get dead wrong about CEO dynamics.Miss this? You risk coasting on old habits while others engineer unfair advantages. Listen now for hard-won tactics you won't find in any business course from the world's largest SaaS coaching engine. Timestamped Highlights00:45 – The firefighting mindset that built decisive business instincts05:56 – The overlooked power of “small unit” teams to unlock real growth09:44 – Are you a COO trapped in a CEO's title? The unexpected identity test13:43 – Brutal truths about customer obsession and why most leaders fail here16:18 – The lesson no founder learns soon enough when selling their company18:22 – The surprising reason joining SaaS Academy changed his life21:31 – The founder's hidden block: How self-worth destroys pricing27:31 – The counterintuitive leadership split that 10x'd their decision speed41:07 – How his “full-person” one-on-ones rip open performance breakthroughs About the GuestMatt Verlaque was the former COO of SaaS Academy (now Precision), steering operational strategy for the largest coaching platform serving B2B SaaS entrepreneurs. With first-hand experience ranging from firefighting to founding and selling a SaaS startup, he delivers operating wisdom forged under real pressure. He is currently serving as the CEO at Precision, where they help growth-minded founders understand how their business actually works so they can scale with clarity, not chaos.

“It's stressful to work for an employer and it's stressful to work for yourself. It's just like ‘choose your stress.'” – Anna Burgess YangIn this episode of the Sunlight Tax Podcast, I sit down with Anna Burgess Yang to discuss her journey from corporate banking to solopreneurship. We explore the skills that helped her make the leap, how she approaches business decisions and financial management, and what it really means to navigate risk as a self-employed business owner. Anna also shares practical insights on freelancing and building a sustainable solo business while having effective financial planningAlso mentioned in today's episode:00:10 Introduction to Solopreneurship06:01 Transitioning from Corporate to Freelance10:25 Common Mistakes Solopreneurs Make12:26 The Risks of Employment vs. Self-Employment17:10 Current Economic Landscape for Freelancers22:20 Navigating Career Pivots28:04 Financial Management for SolopreneursIf you enjoyed this episode, please rate, review and share it! Every review makes a difference by telling Apple or Spotify to show the Sunlight Tax podcast to new audiences.About Anna Burgess Yang:Anna Burgess Yang is a freelance content marketer and journalist specializing in B2B SaaS and fintech. She is also a solopreneur educator focused on back-end business operations. A former corporate executive, she now writes long-form content for clients. She also maintains her own blog, newsletter, and a tutorials site. Within her content, she teaches other solopreneurs and marketers how to use AI and automation to work more efficiently.Check Out Anna's Work:FREE RESOURCE: Budget Health CheckAnna's InstagramAnna's LinkedInAnna's YouTube ChannelEpisode Links:Join the Workshop: Save Like a Millionaire: Using Tax-Smart AccountsGet your FREE visual guide to tax deductionsOrder my book: Taxes for Humans: Simplify Your Taxes and Change the World When You're Self-Employed Get full access to Taxes For Humans at sunlighttax.substack.com/subscribe

Place in B2B used to mean partnerships, system integrators, and analyst relations. Now everyone's adding AEO and GEO to the list. But Matt and Liam question whether being mentioned by an LLM actually changes buying behavior — or whether it's just a new version of the same old "just get in front of people" fallacy. A grounded, skeptical conversation about what distribution really means when your product lives in the cloud. Keywords: GEO, AEO, B2B distribution strategy, B2B SaaS go-to-market, AI search marketing, brand consideration

In this today's segment, Dan Sperring, founder and CEO of Align ICP, breaks down a mistake most revenue leaders make when defining their ideal customer profile. The instinct is to chase the highest lifetime value customers, but those segments are often the hardest to win, the slowest to close, and the first to break when the market shifts. This clip focuses on how to balance three critical factors inside your ICP: lifetime value, ease of acquisition, and market health. Dan explains why ignoring any one of these creates pipeline risk, and how leaders can avoid over-rotating into segments that look great on paper but fail in execution. For leaders responsible for predictable growth, this is about making smarter tradeoffs, not just better targeting. Dan Sperring is the founder and CEO of AlignICP, a company focused on helping revenue teams align around high-value customer segments to drive predictable growth. He brings experience across customer success, revenue leadership, and scaling SaaS businesses through product-market and go-to-market alignment. Connect with Dan: AlignICP LinkedIn Books mentioned: The Innovator's Dilemma by Clayton M. Christensen The Innovator's Solution by Clayton M. Christensen and Michael E. Raynor Predictable Revenue by Aaron Ross and ​​Marylou Tyler  Amp It Up by Frank Slootman Tools and podcasts mentioned: clay.com zoominfo.com The Science of Scaling Podcast Listen to the full episode: Aligning Pipeline to Ideal Customer Profile with Dan Sperring Get the Force Management framework for aligning your ICP, sales motion, and customer lifecycle around high-value use cases and measurable business outcomes: The Predictable Revenue Framework: Guide for Leaders Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

In this episode, recorded out in the New Mexico desert at ChiliPalooza, Jordan Crawford makes a blunt case to B2B SaaS: the methodologies you built your career on are about to age out, and the only way through is to get your hands on Claude Code.Jordan's spent his whole job lately doing one thing: teaching clients to work with AI. And what he's found cuts against almost everything sales and marketing teams currently do.What this episode covers:Why the constraint on building things isn't budget or headcount anymore, it's imaginationThe SDR question every revenue leader is asking today: we went all-in, we see the volume, and we don't know what's working...so now what?How Jordan rebuilds prospecting strategies from what customers actually did, not what a rep thinks they wantWhy being wrong fast and cheap beats being right slowly: "you can beat any grandmaster if you get two moves to their one"The truth about a sloppier world, and why polish is no longer the pointWhy the gap between people who are great at this and people who are bad at it comes down to how you think, not skillWhy the "graybeards" built on ten-year-old playbooks are going away, and what replaces themThe people who get in the tool will build things the graybeards can't imagine. The ones who don't will spend the next few years explaining a methodology nobody's buying.-----------------------------------------------------