This episode of Get Paid is a little different. Instead of bringing on a guest, Manny sits down with the engineering team at Paid to talk about what it’s actually like to build an AI agent company in year one. No slides, no polished narratives. Just the people building the product and deciding what actually ships. The team talks through what they’re working on day to day: how Paid handles billing and cost management for agents, why the SDK is designed to stay flexible, and how features like credits, Blocks, and self serve have evolved based on how customers are actually using the product. They share examples of where customers pushed the system in ways they didn’t expect, and what that forced them to rethink. They dig into what breaks once agents leave demos and hit production, and how they handle accuracy, evals, and human handoffs. The episode looks ahead to what’s coming next, what the team is excited to build, and what they think matters most as AI agents move from experiments to systems companies rely on. If you’re building with AI agents, thinking about adopting them, or just curious what’s happening behind the code, this episode gives a clear view of how a team is approaching it in real time. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ariel Harmoko went from three-time national racing champion to Formula 3 driver to Cambridge's youngest ML researcher, all before turning 18. On this episode of Get Paid, he tells Manny how he built Artifact AI, an accounting automation platform that's taking on a $600B industry with a two-person team and a radically different approach to vertical AI. Ariel breaks down exactly how he landed his first customers: cold-calling accounting firms he found on the Xero marketplace, armed with nothing but a Figma prototype. He explains why Artifact positions itself against offshore BPO providers rather than software competitors. And why that lets them charge $30K-$200K contracts instead of SaaS prices. The conversation gets tactical on pricing, go-to-market, and why accuracy matters more than speed when you're building AI that handles other people's money. The episode closes with a sharp take on where the industry is headed. Ariel argues the billable hour is dying, the Big Four are vulnerable, and the next generation of accounting giants will be AI-first firms that never hired bookkeepers in the first place. His advice to founders: build your own evaluation infrastructure, don't outsource your accuracy, and don't be afraid to charge what a human employee would cost. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

With over $100 million in recurring revenue, data productivity company Matillion was a thriving, established enterprise. Yet CEO Matthew Scullion spotted a fundamental threat on the horizon. In this episode of the GetPaid podcast, Matthew tells host Manny Medina why he pivoted Matillion’s focus, preemptively, before the rising tide of AI disrupted both their data engineer user base, and the product itself. Matillion’s response is Maia: an AI‑powered “agentic data engineering team.” To build Maia, Scullion assembled the “Maia A‑Team,” a small, multi‑functional startup within the larger organisation. Modelled after a Series A company, the A‑Team favoured agility and short feedback loops over the rhythms of a growth‑stage business. This approach helped quickly prove the concept, learn new go‑to‑market motions, and validate the product through a lighthouse programme with key customers. In this episode, Scullion shares the conviction that came from seeing the technology work. The Maia pivot required rethinking the company’s core assumptions and structure, but ultimately delivered validation at speed. Commercially, Maia shifts Matillion from selling incremental tools to practitioners, to delivering greater enterprise value to executive buyers such as CDOs and CIOs. That unlocks larger budget pools often reserved for BPO, consulting, and human capital. Scullion also explains how zero‑dollar contracts helped Matillion partner with customers early, securing critical validation and public success stories ahead of launch. Matthew’s story is a clear reminder to turn the “fear” of disruption into a focused strategy. As AI and agentic solutions reshape how business gets done, Matillion’s journey offers practical lessons for leaders on the edge of inevitable change. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of Get Paid, we speak to Dave Kellogg about the future of the software industry, with a specific focus on the shift from traditional SaaS models to AI and outcome-based pricing. Kellogg, a seasoned CEO, founder, Executive in Residence at Balderton Capital, and CFO Whisperer, with a background of leadership at Host Analytics (now Planful), MarkLogic, Salesforce.com, and Business Objects, shares his expert insights on the three laws of pricing. He emphasizes that while delivering higher value justifies raising prices, the maximum price is always capped by customer perception of value and the competitive landscape. Together, we explore the inherent challenges and opportunities of implementing outcome-based pricing, particularly the need for predictability, and how creative deal structures, such as including rollover credits and multi-year contracts, can reduce customer friction and increase adoption. We also talk about the operational implications of this transformation, with particular focus on the complex issue of sales commission structures in an outcome-based environment. Kellogg evaluates the balance between incentivizing initial sales and aligning variable compensation with actual value delivery, suggesting a model that pays part upfront and connects the rest to successful customer outcomes over time. We also explore the changing financial metrics for AI-native companies, where rising computation costs might temporarily reduce gross margins. Kellogg challenges the common focus on margin percentage by highlighting that dollar-value gross profit is the key measure of business value and predicting that efficiency improvements in AI models will eventually lead to healthier gross margins. Finally, we discuss the major shift in talent acquisition and go-to-market strategies. We analyze the rise of the "Forward Deployed Engineer" (FDE) as a key role in product development, especially for early-stage, AI-driven companies where the product is still being heavily refined based on real-world use cases - a necessity in a platform-focused world. Kellogg recommends that new AI startups focus on hiring a small, elite team of AI-native engineers and seek go-to-market hires with proven experience in navigating competitive, greenfield markets. He often favors those who excel in aggressive, "uncomfortable" sales environments over those who are comfortable only with established market leadership. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Manny Medina is joined by a guest who is crushing it in the world of sales technology: Nicolas Sharp, Co-founder and CEO of Attio, the AI-Native CRM. Attio's rapid growth with over 5,000 paying customers is a masterclass in strategic pivoting and catching a generational technology wave. Nicolas shares Attio's powerful growth story, which began with the difficult decision to abandon a niche CRM built for investors. They realized this strategy was a "trap" and chose to "completely start again" to capitalize on the massive technology shifts created by No-Code tools and the rise of LLMs—an inflection point that rendered old software playbooks (like the ones used by Salesforce and HubSpot) obsolete. Attio's core thesis is that the legacy CRM industry presented a "false dichotomy" between powerful, complex systems (Salesforce) and simple, limited ones (HubSpot). Attio solves this by arming the "builders" of the modern go-to-market world—the hyper-growth, PLG-focused companies—with a CRM that scales power without sacrificing usability. Their vision is built on the belief that the traditional, linear sales process is dead. Today’s non-linear buyer’s journey requires a new, AI-native approach: Attio captures high-resolution data from every customer interaction to enable deep automation, eliminating the manual data entry that has plagued sales teams for decades. This forward-looking strategy is evident in their new Marketplace where the biggest bet is on extensibility. Instead of getting bogged down in the "death march of long-tail features," Attio is empowering customers to configure and customize their CRM using AI-generated JavaScript code. This allows them to quickly meet highly specific business needs that are beyond any single vendor's roadmap. Nicolas also provides insight into the operational strategy of running a global competitor from London, leveraging a hybrid executive team split between the UK and the US. Their revenue model ensures growth scales with customer success by balancing an accessible seat-based entry price with consumption-based credits that unlock advanced automation. This conversation is an essential listen for anyone who believes the old playbooks are broken and wants to understand the future of go-to-market, underscoring: The death of the rigid, legacy sales pipeline model in the face of non-linear buying journeys. The necessity of high-resolution data and AI-generated code to enable ultimate CRM automation and configuration. Why a hybrid revenue model that combines seat-based and consumption-based pricing is essential for scaling with modern, efficient customers. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In a recent deep dive with Sequoia Capital partner Pat Grady, he shared surprising insights about what separates winning AI companies from the rest, and challenged conventional wisdom about AI moats, pricing models, and what investors truly value.

What happens when productivity shifts from labor to capital but tax systems don’t follow? In this episode, Paid’s Manny Medina and Arnon Shimoni sit down with Spencer Schneier and Sam Suechting from Commenda Technologies to unpack the findings from their landmark study on AI agent taxability. We reveal how the way you structure and price your AI agent can mean the difference between paying sales tax in 22 states or just 4 and how this “hidden arbitrage” could cost U.S. governments $2.7 billion a year starting 2025. This is bigger than it seems. We talk about the rise of the agent builder economy, the policy lag that mirrors e-commerce’s 25-year journey to standardization, and why outcome-based pricing and Private Letter Rulings might be your best defense. Whether you’re building, funding, or regulating AI, this episode offers a front-row seat to the fiscal rewiring of the digital economy and the blueprint for staying ahead of it.

Eric Simons spent seven years building a cloud IDE that millions loved but nobody would pay for. After investing a year into an enterprise product based on 2021 customer enthusiasm, he launched in 2023 to discover all that demand had evaporated. With the company at 18 months of runway and no path forward, Eric made the hardest call of his career: layoffs, followed by one final 90-day bet on a product called Bolt. They shipped it with a single tweet on October 3rd, 2024. What happened next defied everything Eric learned in 15 years of building startups. Bolt added $60,000 of ARR on day one. Then $80,000 on day two. By week one, they hit $1 million. By month two, they went from $4 million to $20 million ARR, and the growth never stopped. The twist? Their customers weren't developers. Product managers at companies discovered they could ship in 60 seconds what used to take six business days through JIRA tickets. Eric breaks down the false demand trap that nearly killed the company, why distribution beats product when competitors have 5X your revenue, how a random tweet turned into a million dollar hackathon, and what it really takes to stay in the game when the odds say fold.

Jonathan Sanders watched two companies suffer through brutal ERP migrations that took 18 months, cost $200K+, and required constant consultant babysitting. At one point, a board literally told leadership not to migrate their ERP because it would be a massive waste of time with zero ROI. That's how broken the category is. So Jonathan built Light, a new type of smart finance platform that turns 18-month implementations into one-day onboardings by letting finance teams configure systems with plain English policies instead of paying consultants to write scripts nobody understands. In this conversation, Jonathan reveals how working with OpenAI months before ChatGPT launched shaped Light's entire product philosophy around "student assistants" that work 24/7, why they deliberately avoided the traditional channel partner model that made legacy ERPs successful, and the brutal truth implementation consultants told him about why their incentives are completely misaligned with customers. He also explains why they almost went full outcome-based pricing but didn't, the new skill emerging in finance teams (hint: it's writing, not coding), and why the ERP category is so meaningless that it needs to die. If you've ever suffered through a NetSuite migration, this episode will feel like therapy.

Manny and Raj sit down over a scotch to celebrate closing Paid's $21M seed round at a valuation between $100-200M, just 10 months after starting the company in an Airbnb with four people. This is the most candid conversation yet about what it actually takes to build a company at breakneck speed in the AI era. They unpack why Manny took a high valuation he's confident he can deliver on, how they closed the round with a 60% working demo, and why Raj moved from India to London despite living like a king. Manny and Raj get honest about the cultural foundation we’ve built between seed and Series A, the arguing that nearly tore cofounders apart, and why they're done optimizing for the next round. They discuss forward deployment tactics borrowed from Palantir, why engineers don't need product managers, and the difference between playing for market share versus wallet share. Cofounder Manoj crashes the recording to share why he left retirement after two days of not being able to stop thinking about Paid's mission. This is the story of building with conviction, abundance mindset, and the belief that winning is the only metric that matters.

Amjad Masad, CEO of Replit, just raised $250M at a $3B valuation and hit $150M ARR. But his most controversial take? The Silicon Valley model of scaling point solutions to IPO is dead. In this episode, Amjad reveals how Agent 3 works under the hood (spoiler: it's actually multiple agents arguing with each other), why their pricing model confuses consumers who think they're buying software when they're actually hiring a $1,000/year employee, and how Replit's own HR person built their org chart software in 3 days because vendors wanted tens of thousands for something that didn't fit. We dive deep into the future Amjad sees: fewer billionaires, way more millionaires building hyper-specific micro-SaaS for niches only they understand. He shares real stories of a VC CFO building software for other VC CFOs, freelancers doing Upwork arbitrage with Agent 3, and why Salesforce closing Slack's API is the exact wrong move. Plus, Amjad gets brutally honest about VCs leaking his numbers twice, why the management fee structure attracts short-term thinkers, and how Replit does seasonal intensity (12/12/7 during launches, sustainable hours the rest of the year) instead of the 996 grind. This is the playbook for surviving and thriving as traditional SaaS dies and the agent era begins.

In this episode, Manny sits down with Nick Holzherr, founder of GitLaw and former contestant on The Apprentice, to dive deep into the legal industry's impending transformation. Nick shares his journey from paying hundreds of thousands in legal fees for what he discovered were essentially template documents, to building a platform that automates 90% of legal work. He breaks down the shocking economics of law firms—where partners charge $2000/hour while paying juniors $100/hour to fill out standardized forms—and explains why this model is about to collapse entirely. The conversation takes a fascinating turn as Nick and Manny explore the broader implications of AI displacing entire professions. From the thousands of qualified junior lawyers who can't find work right now (but politicians don't see it yet), to the mind-bending tax revenue crisis coming when AI agents replace human workers, this episode reveals the systemic changes already happening beneath the surface. Nick also pulls back the curtain on his Apprentice experience, sharing the psychological manipulation tactics used by reality TV producers, and offers his "barbell strategy" for legal spending: automate the basics, hire only the absolute best commercial lawyers for complex deals.

Madhavan Ramanujam stepped into the AI pricing conversation with a contrarian view: everyone is doing it wrong. As author of "Monetizing Innovation" and founding partner of 49 Palms Ventures, he's advised 250+ companies on pricing strategy. Now he's seeing founders leave millions on the table by using outdated SaaS playbooks for AI products. > "Your moat is monetization and GTM. Can you get products in the hands of many people? Can you make it stick? And will they pay for the value?" His message is clear: the two-year coding head start you're banking on is worthless. In the AI era, your competitive advantage isn't technology - it's how you price and distribute it. The $50K to $500K Pricing Revelation Madhavan shares a story that captures everything wrong with AI pricing today. A founder came to him charging $50K for an AI agent that was creating tens of millions in value. The founder thought it was "reasonable" and helped close deals faster. "Put an option on the table. A $50,000 plus 10% of the outcomes that I generate or a $500,000 fixed fee." This simple choice changed everything. The conversation shifted from price to value measurement. Customers chose the $500K option and negotiated down to $400K - a 10X increase with the same sales velocity. "Which investor does not want that, right? That you could actually 10X your price and have the same sales velocity. Why wouldn't you do that?" Why most AI companies are pricing wrong The failing pattern Madhavan sees repeatedly: Companies tie pricing to costs rather than value. Founders add 20% margin to token costs and call it pricing. As costs fall, so does revenue, even though customer value remains constant. > "You just tied yourself to like a destiny that your pricing is going to keep coming down." The labor budget opportunity. AI agents tap into labor budgets that are 10X larger than IT budgets, yet founders still price like SaaS: > "200k to hire a salesperson and you charge $5,000 for a seat for a year. I mean like that doesn't make sense, right?" Underestimating AI's value capture potential. Traditional SaaS captures 10% of value created. AI with high autonomy and clear attribution can capture 25-50%: > "There is increased autonomy and there is increased attribution. And you can justify that." The Human + AI Pricing Formula Madhavan's framework for pricing AI that replaces human labor challenges conventional thinking: > "If your AI can operate as the best salesperson, and is available 24/7, why wouldn't you think about it that way?" His argument: It takes six months to hire someone, six months to train them. By the time they're productive, you've invested two years of salary. Your AI is productive on day one and works 24/7. Price accordingly - at or above human cost, not at 10% of it. The Partnership Imperative For established SaaS companies trying to add AI, Madhavan sees most getting stuck in seat-based pricing with no path to value attribution. His advice for companies like Slack: > "I think it has to be first principles thinking, can I build some agents that actually sit on top of Slack and can do some meaningful work that I can monetize on it separately." The key is finding the equivalent of Intercom's Fin.ai model - an agent that solves end-to-end workflows with clear value attribution. Pricing in the Age of AI Madhavan's framework for AI monetization starts before building: > "Price before product. Period." His approach:Have willingness-to-pay conversations before writing codeBuild only what people value enough to pay forChoose the right pricing archetype based on product characteristicsMove from cost-plus to value-based pricing as quickly as possibleLeadership Lessons from the Pricing Frontier Madhavan is juggling three major initiatives: running his fund (49 Palms), deploying capital, and launching his new book "Scaling Innovation." His thesis ties them together: > "Monetization is the key to winning in AI." His investment philosophy focuses on durable monetization rather than growth at all costs: > "You need to have a clear conviction that you can actually, at the end of the day, build a profitable growth business." Companies Mentioned49 Palms VenturesDelphiSierraIntercom (Fin.ai)ServiceNowSlackWorkdaySuperhumanGmailDocuSignZapierYC (Y Combinator)First Round CapitalNFXStanford See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

John Sabino stepped into LivePerson as CEO facing a perfect storm: financial distress, harsh investor criticism, and a company that had "lost its way" trying to build the world's best LLM. Within weeks, major investors were telling him his strategy wouldn't work and that the company wasn't mentioned in critical industry reports. "You have to have the wherewithal and the courage to say, I'm not doing that. I'm going to try this first." His response was to refocus on LivePerson's core strength - 30 years of orchestration expertise - while pioneering what became an industry standard: Bring Your Own LLM (BYOLLM). "Everybody thought I was nuts when we started with that. And now that is the industry buzzword. I'll be blunt. I think we started that." Why 95% (or maybe 60%?) of AI Pilots Fail The MIT study showing 95% of AI pilot failures validates what John has been saying all along, although we're not sure it's actually 95%. Maybe 60%.... "A use case is not a process. A use case is not a full customer journey. A use case is not a solution. That is what many people miss." The failing pattern:AI companies grab isolated use casesThey wow boardrooms with resolution ratesThey ignore orchestration, multi-channel needs, and escalation pathsCustomers get frustrated when complex issues can't be resolved The Human + AI Formula John's framework for CEOs navigating AI transformation focuses on strategic augmentation rather than wholesale replacement: "Don't fire all your humans. Those are the people that should handle complex cases where the package went to the wrong location, where the payment was wrong." The opportunity lies in creating premium tiers of service. High-net-worth banking customers and serious gamers will pay for white-glove human support. Meanwhile, automate the "high caloric, low value tasks" that make up 45% or more of inquiries. The Partnership Imperative Drawing from his GE Digital experience, John believes survival in the AI era requires acknowledging what you can't build: "Partnerships matter. Unless you're one of the big three, you're just not going to have the resources to build everything for everybody." LivePerson's partnership strategy today includes multiple LLM providers - through a BYOLLM approach. For John, this was validated when DeepSeek emerged "out of left field" and could immediately integrate with LivePerson's platform. Pricing in the Age of AI John's approach to monetizing new AI capabilities rejects the "fail fast and free" mentality: "I don't believe in doing a pilot for nothing because if it's free, it doesn't have value or a basis to establish value." His framework:Start with 3-4 early access customersCharge professional services fees initiallyUnderstand value creation through real usageThen determine pricing model (seats, volume, or outcomes)The goal is outcome-based pricing, but you need data first: "In order to price any metric or unit that you're going to use to establish value, you need to know the outcome that you're driving." Leadership lessons from crisis John's military background (Ranger School, Airborne) shaped his crisis leadership philosophy: "You want people in your organization that have the grit, that know how to take the objective with less. If they fail, they dust themselves off and say 'next one, let's go.'" His framework for CEO decision-making:Always consider shareholder valueAnswer to your customersTake care of employeesMake financially responsible decisionsNever shy away from tough choicesThe Gartner Magic Quadrant Journey Responding to investor criticism about not being in key reports, LivePerson made a concerted effort to improve their position. The achievement wasn't just about targeting the report: "It needs to be your technology speaking for you. It needs to be your customers speaking for you. And that's what that Gartner report represents." Companies mentionedLivePersonAmazon ConnectAvayaGoogle RCSWhatsAppDeepSeekIntercomDecagonMicrosoftSalesforceProcter & Gamble (P&G)VMwareGE Digital See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ethan Ding (TextQL) tells Manny all the AI industry's dirty secrets. Companies bought 500 AI tools in 2024's spending frenzy - now procurement teams have total vendor bans and are cutting 250+ tools because nobody actually uses them. Meanwhile, 90% of people using AI tools like Harvey have no idea what ChatGPT or OpenAI even are, creating massive moats for first movers. The enterprise AI bubble is bursting with 50% of initiatives already dead, but nobody wants to admit their AI strategy failed. OpenAI's aggressive pricing on GPT-5 reveals their endgame: becoming a billion-user ad platform where ChatGPT recommends therapists, books appointments, and charges your credit card. Ethan's contrarian playbook? Don't innovate on UI (every attempt has been "a horrible idea"), just copy ChatGPT's interface and put all innovation into branding and distribution. Meanwhile, everyone fights over $10B exits and Ethan is going after AWS with a "flip coins at 51% odds" strategy because only trillion-dollar markets matter. The punchline: data science is an infinite arms race where Blackstone analyzes weekly, Cerberus counters daily, then someone goes hourly - creating exponential demand that rewards volume over margins.

Join Manny and Rob Litterst, pricing expert and founder of PricingSaaS, as they dive deep into the seismic shift happening in SaaS pricing models. Rob reveals how traditional seat-based pricing is becoming obsolete as AI agents transform software delivery, with companies scrambling to figure out how to price outcomes rather than access. From Intercom's recent fundraise based entirely on their AI agent growth to the emergence of "ergonomic pricing" models that blend licenses with outcomes, this conversation unpacks the most pressing challenge facing every SaaS company today. The discussion ventures into unexpected territory, exploring how companies are using AI to access software through Claude rather than traditional interfaces, the rise of "vibe marketing" in a stagnant marketing landscape, and Rob's provocative prediction of a "revenue bloodbath" in SaaS as teams shrink and AI-native companies capture market share. Rob shares candid insights from his new venture helping companies navigate this transition, including the surprising revelation that even traditional "mom and pop" SaaS companies are now asking about agent pricing - signaling that this transformation is hitting mainstream faster than anyone expected.

When Silicon Valley drama gets juicier than reality TV, you know something is changing in tech. In this episode, Foundation Capital partners Ashu Garg and Jaya Gupta reveal why SaaS is dying, explaining that 9 out of 10 mid-sized SaaS companies are already seeing churn as AI-native startups eat them from below. They share wild stories from the frontlines, including a startup landing a $20M deal with their first customer and the soap opera unfolding at Cursor - where the company collecting 100% of your infrastructure costs is simultaneously building your competitor. As Manny puts it, "This is better than Love Island." The conversation gets spicier as Ashu admits he won't even take meetings with companies that have revenue ("I want big ideas, not messy revenues") and predicts that neither OpenAI nor Anthropic will be the ultimate winners despite their massive valuations. Jaya reveals how young founders are outpacing third-time unicorn builders because in AI, everyone's learning curve started at the same time - experience just became a liability. They close with a radical vision: forget building another feature company. The future belongs to companies deploying 500 AI agents to replace what used to require 500 different SaaS tools. For founders navigating this chaos, their advice is brutal but simple: think bigger, move faster, and remember that most of what worked in SaaS won't work in AI.

Blue AI Labs founder Maruthi Medisetty reveals how AI agents are transforming sales enablement by giving reps 60-75 minutes of practice time they'd never spend with managers. His provocative take: while AI can handle prospecting, no one's signing a $10,000 contract with a bot anytime soon. The real opportunity isn't replacing salespeople—it's turning your best reps into "super sellers" who can handle 5x their current quota. From eliminating 50-hour workweeks for enablement teams to challenging the entire learning management system stack, Maruthi breaks down why capturing just 10% of delivered value is the sweet spot for AI pricing. He shares candid insights on attribution challenges, the psychology of B2B purchasing decisions, and why companies still pay McKinsey millions just to have someone to blame. This episode cuts through the AI hype to reveal what actually works when augmenting human sales teams.

Christopher O'Donnell, founder of Day.ai, joins the show to break down why building a new system of record for the AI era is both brutally hard and absolutely necessary. He reveals the preprocessing magic that makes their AI-powered CRM actually useful (spoiler: it's not just throwing database tables at an LLM), explains why the economics of AI should focus on customer value rather than minimizing costs, and shares his contrarian take on why transparency trumps features every time. From navigating the technical nightmare of Gmail APIs to philosophical insights about the universe conspiring in our favor, Christopher offers a rare glimpse into what it really takes to compete in the infinite CRM market. Whether you're building AI products, rethinking your data strategy, or just trying to survive the entrepreneurial journey, this conversation delivers actionable insights wrapped in refreshingly honest storytelling about the realities of deep tech entrepreneurship.

Amos Bar-Joseph is proving that the traditional startup playbook is broken. As CEO of Swan AI, he's building what he calls an "autonomous business" - targeting $30 million in revenue with just three people by 2025. His company has been approached by over 50 VCs, not because they need funding, but because investors are starting to realize the venture capital model itself is changing when startups no longer need massive capital to scale. The secret isn't replacing humans with AI - it's reimagining how humans and AI collaborate. Amos breaks down his three-function business model: revenue creators, product creators, and agent creators. Each person manages armies of AI agents designed not to automate processes, but to amplify human potential. The result is a fundamental shift from scaling with headcount to scaling with intelligence, creating unfair advantages that traditional enterprises can't replicate without rebuilding from scratch.