In this episode of the Get Paid AI Podcast, Jacco Van Der Kooij joins Manny Medina to break down how AI-native companies are redefining SaaS growth strategy. Instead of selling seats, the most successful AI companies with users first, allowing adoption and usage to drive expansion across the organization.

In part 2 of this episode, Mark Roberge, Co-founder of Stage 2 Capital, former CRO of HubSpot, Harvard Business School professor, investor, and author of The Science of Scaling, joins Paid’s Manny Medina to break down the real mechanics of scaling in an AI-fueled market. They delve into execution, exploring how to decide when to accelerate, when to hold, and when to slam on the brakes. Mark also shares the Stay / Go / Slow model, founder versus VC misalignment, AI bubble dynamics, business model innovation, and why retention should be slide one in every board deck. “I have a beautiful hack for you called the stay or go or slow model.” The Anti-Annual Plan Mark challenges one of the most sacred startup rituals: the annual plan. “It’s so stupid that we build these annual plans when you're a $2,000,000 business and we abide by them like they’re scripture.” Instead of blindly chasing a 2-to-20 growth promise, Mark proposes a quarterly decision framework agreed upon in advance with the board. After each quarter, you evaluate three signals:Demand generation for healthConversion performanceLeading indicator of retention If all three are green, you accelerate. If any are yellow, you hold the pace. If any are red, you stop pouring gas and fix the system. Most Founders Are Scaling at the Wrong Pace According to Mark, roughly:45% are going too slow45% are going too fastOnly 10% are at the right pace.Going too slow means the window closes. Going too fast means burn outpaces signal. “If you burned a billion dollars this year and you’re not OpenAI, that’s probably too much.” But burning a dollar is too little. The real skill is calibrating scale risk to context. A high-moat airport software company does not scale like Cursor or Day AI. Winner-take-most markets require aggression. Most-driven markets reward discipline. New Logos Should Not Be Slide One In today’s AI wave, Mark sees a dangerous pattern:Pilot revenue labeled as ARR.Experimental deployments treated as durable revenue.Boards are obsessed with new logos.“The first slide in your board deck should be your leading indicator of retention.” Customer success must be a first-class citizen metric. Not logo count. Not headline ARR. Retention-leading indicators signal real value creation. Everything else is noise. Are We in an AI Bubble? Mark’s answer: yes. Signs of a classic bubble include:Extreme valuation multiplesExtraordinary burn ratiosOvercapitalized first movers‘Vibe revenue’ that looks sticky until renewals hitOn first mover advantage, Mark cites the broader pattern: fast followers win more often than first movers. The first mover wins roughly 35% of the time. The fast follower wins closer to 65%. “I think the last two-year cohort will see the highest failure rate in startup history.” At the same time, the breakout winners could define a generation. Founder vs. VC Incentives VCs have 20 bets. Founders have one. Investors optimize for outliers. Founders optimize for life-changing outcomes. Some investors would rather see a company fail fast than grow steadily at 60% for six years and sell for $700M. That tension fuels overscaling and unnecessary risk. Business Model Risk Is the Startup’s Advantage AI is forcing a rethink of monetization. Per-seat pricing made sense in traditional SaaS. AI automates work. It compresses seats. The safe play is per-module. The bold play may be consumption or outcomes-based pricing. Startups have an advantage: they can take business model risk. Incumbents can’t. Sales compensation plans, revenue expectations, and public market pressures trap incumbents in legacy structures. Today’s Value Prop Won’t Win Tomorrow One of the most strategic insights of the episode: the product printing money today will likely not be the long-term moat. Mark references Amazon’s early focus on books as a wedge. Design big. Start small. Print money in phase one while building infrastructure for phase two. If you build the future too early, the market isn’t ready. If you only optimize for what sells today, you lose the long game. Companies MentionedHubSpotOpenAIAmazonSlackNotionCursorDay AISiebelServiceNowHarvard Business School 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, Paid’s Manny Medina sits down with Mark Roberge, Co-founder of Stage 2 Capital, former CRO of HubSpot, Harvard Business School professor, investor, and author of The Science of Scaling, to break down what actually changes in go-to-market during an AI boom, and what absolutely does not. Mark argues that while AI is accelerating workflows, it has not rewritten the laws of scaling. Human decision-making hasn’t changed. Retention still matters. Unit economics still matter. Hiring 17 reps overnight is still dangerous. They go deep on product-market fit and why it’s not a feeling, the Stay / Go / Slow scaling model, founder vs. VC misalignment, systems of action vs. systems of record, and why the current AI cohort may see the highest failure rate in startup history. “If you burned a billion dollars this year and you’re not OpenAI, that’s probably too much.” First Principles Still Win One of Mark’s central points is simple: AI does not change behavioral science. “AI is not going to change the behavioral science of how humans make decisions.” Much of AI 1.0 has been workflow streamlining, not workflow reinvention. The fundamentals of buyer psychology, sales process design, and value creation still apply. What does change is leverage. Mark believes the first major unlock in go-to-market AI is increasing selling time. “If you increase selling time from 25% to 75%, you triple X productivity right there.” Product-Market Fit Is Not a Feeling Most founders say they’re ready to scale when they ‘feel’ product-market fit. Mark rejects that entirely. “Product-market fit is when you create customer value consistently.” The metric? Retention. Specifically, net dollar retention is north of 100%. In early stages, you can’t wait a year to measure retention, so Mark pushes founders to define a leading indicator: What usage behavior in month one predicts long-term retention?Slack used 2,000 team messages.HubSpot used three features adopted.Notion used weekly engagement.If 80% of new customers hit that indicator, you have product market fit. If not, scaling is premature. The Stay / Go / Slow Model Instead of locking into rigid annual plans, Mark proposes a quarterly decision framework. After each quarter, you evaluate:Demand generation for healthConversion and quota attainmentLeading indicator of retentionIf all are green, go faster. If some are yellow, stay the course. If any are red, slow down and fit it. “It's so stupid that we build these annual plans when you're a $2,000,000 business and we abide by them like they're scripture.” New Logos Should Not Be Slide One In today’s AI cohort, Mark sees a dangerous pattern. Boards ask for new logos, founders report ARR growth, then pilot revenue gets labeled as ARR. But value creation lags. “The first slide in your board deck should be your leading indicator of retention.” Customer success should be a first-class citizen metric. Founder vs. VC Incentives VCs have 20 bets. Founders have one. Some investors would rather see a company fail fast than ‘skimp along’ at 6-% growth. But a founder who builds a durable $700M exit instead of chasing a trillion-dollar dream may protect life-changing outcomes. This tension fuels overscaling. Are We in an AI Bubble? Mark’s answer: yes. Signs of a classic bubble:Extreme valuation multiplesExtraordinary burn ratiosExperimental deployments counted as durable revenueFirst movers overcapitalized “I think the last two-year cohort will see the highest failure rate in startup history.” At the same time, the winners may be generational. Today’s Value Prop Won’t Win Tomorrow The most strategic insight of the episode: The product printing money today is unlikely to be the long-term moat. Mark references Amazon’s early book focus as a wedge. You design big, start small, and build the infrastructure for what the market will want in five years. If you build the future too early, the market isn’t ready. If you only optimize for what sells today, you lose the long game. Companies MentionedHubSpotSalesforceWorkdayZoomInfoOpenAIMicrosoftAmazonSlackNotionCursorDay AIHarvard Business SchoolBoston Consulting Group 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, Paid’s Manny Medina sits down with Luke Harries, Head of Growth at ElevenLabs, to break down how one of the fastest-growing AI companies in the world thinks about growth when everything is crowded, noisy, and competitive. Luke joined ElevenLabs when there were about 20 people. Today, it’s approaching 500, and pushing hard from best-in-class model to full-stack product company. Luke and Manny go deep on why traditional marketing channels are the wrong mental model, how ElevenLabs treats launches as systems, and why video quietly became their highest-leverage growth input. They also unpack hiring mistakes, why onboarding should feel like a video game, and which classic startup roles are being squeezed hardest by AI-native teams. “The overall approach we take at ElevenAds is to try and build growth systems. And so these are, like, treating each channel as its own system and really optimizing it.” Growth Is a System, Not a Channel At the core of Luke’s thinking is a framework that replaces vague channels with concrete systems. “When we break down what actually is a growth system, there’s three main parts. There’s the actual system itself, so that’s the people, the checklist, the automations, the code. There’s your model for the system and the analytics, and then there’s the goal.” Instead of asking how to incrementally improve performance, ElevenLabs sets aggressive output targets and works backward. “Let’s say we’re only getting two meetings booked per month from webinars. We’re like, how do we go from two to 200?” The work then becomes identifying and scaling the inputs that make that output possible. “How do we max out every single one of those inputs?” Why B2B Growth Feels Slower Than Self-Serve Luke contrasts his background in self-serve growth with the realities of B2B. “I come from much more of a self-serve high-volume game where everything can be an AB test. Everything now with B2B is much more like, Okay, over a quarter, we spend this amount of money, we do a shot this way, how did it work?” In B2B, learning cycles are longer, and bets are bigger. “Use your intuition, learn from what worked in the past, put your foot down hard, and give it your best shot.” Why Video Became the Highest-Leverage Growth Hire One of Luke's earliest and most unconventional growth hires at ElevenLabs was a motion designer. The reason? Leverage. “We realized the biggest lever for these launches is just really good engaging videos.” As an audio-first company, ElevenLabs couldn’t rely on text alone. “You really need to show audio through video. You can’t just rely on text.” After experimenting with agencies and contractors, the overhead became obvious. “Contractors and agencies, there’s so much overhead. They need to learn the style, the brand.” Bringing motion design in-house turned launches into a repeatable system instead of a scramble. Treat Case Studies As Launches Luke explains why most companies underutilize their strongest proof. “Lots of companies, maybe you’d like to create a case study, but you don’t do that push in the launch.” When ElevenLabs published a case study with Revolut, they treated it like a full launch moment, and it showed. Hiring, Onboarding, and When It Doesn’t Work Out The biggest lever for improving hiring outcomes is onboarding. At ElevenLabs, onboarding is designed to build momentum fast. “We try to do onboarding where it’s kind of like a video game where you, like, start small tasks which build up, which have an impact.” Early, direct feedback is non-negotiable. “I try early on to give concrete feedback because then not only do you help shape the person to the company and get the best output, but also you’re building that muscle together if I'm going to give you feedback.” From Model Company to Product Ecosystem Luke describes ElevenLabs’ evolution in two phases. “The first is the zero to one billion ElevenLabs.” The next phase is depth and surface area: orchestration, integrations, and enterprise readiness. “Then there’s the whole orchestration, then you do integrations with Salesforce, with HubSpot.” Voice orchestration itself is technically complex. “There’s speech to text, text to speech, and the hand taking, and that has to happen in microseconds.” Final Advice Luke’s advice for marketers and operators trying to stay relevant? You can just do things. Proof of work matters more than credentials. And for anyone hesitating: “Jump headfirst. Don’t think too much about it.” Companies MentionedElevenLabsRevolutPostHogSalesforceHubSpotGoogle GeminiAnthropicLovableRetoolSemrushDeutsche TelekomPagBankMetaResendSupabaseStripeAnthropicY CombinatorOpenAI See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Nikola has been building voice AI since it was basically a lab experiment. Cambridge → a startup that got acquired by Apple → then straight into the deep end with PolyAI: enterprise voice agents for customer support, CX, and the brutal reality of contact centers. In this episode, Nikola and Manny go right at the parts people avoid: why voice is still the dominant interface, why contact centers are structurally broken (attrition, no-shows, “hiring as strategy”), and why the BPO model collapses the second you try to automate it. Then the real fight: monetisation. They unpack why CCaaS incumbents move slowly, why “outcome-based” is trickier than it sounds in enterprise, and how PolyAI actually prices today — consumption vs outcome vs license, plus what happens when buyers demand predictability. If you’re building agentic CX, selling into enterprise, or trying to price AI without nuking your margins — this one is for you. 🟢 Links & resources Get Paid.ai → Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn - → Paid.ai on LinkedIn → Subscribe to AgentTalk (Substack) 🟢 Listen on other platforms: → Substack → Apple Podcasts → Spotify See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

💵Monetise your AI software with credits — start free on Paid! SaaS pricing has lived in a fantasy world. For 20 years, software got to charge per seat, print margins, and ignore what the real economy has dealt with forever: variable cost, capacity, and brutal competition. This week on Get Paid, Manny Medina brings on Dimi, a pricing strategist from Simon-Kucher, to drag AI pricing back to reality. Hotels, airlines, retail, transportation… industries where pricing is a weapon, not a spreadsheet. They break down what SaaS leaders are missing as agents show up: your cost is now variable, your “seat” metric can actually decline, and cost-plus pricing is a trap that forces you into a race to be the cheapest. Dimi explains why usage pricing often incentives the wrong behaviour, why per-minute pricing is fundamentally broken for voice agents, and why outcome pricing is the right north star but way harder than people admit. He also gets into the uncomfortable truth founders avoid: the best B2B revenue is already “personalised pricing” and we just call it discounting. If you’re building agents, trying to protect margins, or figuring out what to charge when your product has real variable cost, this episode will change how you think. 🟢 Links & resources Get Paid.ai → Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn - → Paid.ai on LinkedIn → Subscribe to AgentTalk (Substack) 🟢 Listen on other platforms: → Substack → Apple Podcasts → Spotify See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

💵Monetise your AI software with credits — start free on Paid.ai. Dharmesh Shah didn’t just build a SaaS company — he helped shape the modern SaaS era. As co-founder of HubSpot, he took the company from zero to public and played a central role in defining how software is built, sold, and priced. Now his focus is on what breaks when AI agents stop assisting humans and start doing the work themselves. In this episode of Get Paid, Dharmesh joins Manny Medina for a direct conversation about the next platform shift — and why it’s closer to the dawn of the internet than another product cycle. Manny is the host of Get Paid and the founder/CEO of Paid.ai, focused on how AI companies price, package, and monetise agent-driven products. They unpack why reasoning models changed everything, why “AI will kill SaaS” is the wrong question, and why the real disruption is the abstraction layer moving up. From the limits of vibe coding to why focus still beats building everything yourself, the conversation goes straight at the uncomfortable decisions founders are avoiding. Dharmesh also shares how HubSpot is making the transition for real: throwing out the roadmap, resetting parts of the culture, and running HubSpot Next like a startup inside the company to build agent-native businesses that don’t fit neatly into the core org. On monetisation, he’s blunt: why seats still matter, why credits are inevitable, why outcome-based pricing isn’t always the right answer — and what happens to software economics when agents replace work instead of enabling it. If you’re building AI products, rethinking pricing, or wondering whether your SaaS model survives an agent-first world, this episode will challenge your assumptions. 🟢 Links & resources Try Paid.ai Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn. Paid.ai on LinkedIn. Subscribe to AgentTalk (Substack) 🟢 Listen on other platforms: Apple Podcasts Spotify Youtube See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dan Griggs has seen multiple platform shifts up close. From taking Sitecore through a private equity transformation to leading finance at Intercom, he’s spent his career navigating what happens when business models break and how to rebuild them.

Maria Colacurcio built Smartsheet into a Pacific Northwest giant. Now, as CEO of Syndio, she's betting the company on a pivot from SaaS to agentic AI. And she's not looking back. In this episode, Maria walks Manny through the launch of Syndi, an AI pay expert that transforms how companies make compensation decisions. Instead of annual pay equity audits that send CFOs scrambling for remediation budgets, Syndi embeds fairness as a constraint in every offer, every promotion, every pay decision. The result: companies save millions in payroll waste while actually improving equity outcomes. Maria is candid about what "burning the boats" actually looks like. She discusses the internal communication challenges of asking employees to commoditize their own product, why some attrition is inevitable during a pivot, and how she keeps her board aligned when engagement scores are dropping and Blind is lighting up. Her solution to board prep? A custom GPT loaded with her deck, messaging, and each board member's particular interests, so meetings become strategic conversations rather than rabbit holes. The episode also covers TD SYNNEX's early adoption of agent-powered HR, why the EU Pay Transparency Directive will force multinationals to explain their pay decisions, and how Syndi tracks the network effect of compensation choices over time. Revealing, for example, that 28% of "ex-OpenAI premium" hires leave as non-regrettable attrition. For any CEO wondering whether to bolt AI onto existing systems or rebuild from the ground up, Maria's answer is clear: your faster, more early-stage competitors will quickly overcome you if you don't go native. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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.

When enterprise deals die at the finish line because of a 200-page security questionnaire, you know there's a billion-dollar problem hiding in plain sight. SecurityPal founder Pukar Hamal turned that pain into a service-as-software business that hit $2 million in revenue before building any actual software. The Pain That Creates Billion-Dollar Markets Picture this: you're about to close the deal that changes your company's trajectory. The champagne is ready. Then boom - instead of DocuSign, you get 200 pages of security questionnaires: "We were like ready to pop champagne bottles. And so, you know, we got hit with this, what is this? It's like hieroglyphics, you know, like, do I have barbed wire around my data center?" Pukar's insight was if startups can't afford armies of lawyers to fill out paperwork, only big companies with resources will win enterprise deals: "My like fundamental realization was if companies have to fill out a bunch of paperwork before they close a deal and they don't have the resources to do that, then all the big companies are gonna win, because they do have the resources." Service-as-Software: The $2M Validation Hack Instead of building software first, Pukar started with pure service. A prospect asked if he could just fill out their security questionnaire: "And I hadn't even incorporated the company. And the person was like, send me an invoice. I'm like, what is an invoice? I went on like Stripe Atlas and incorporated the company. So I had a company that wanted an invoice before I even incorporated it." While working a consulting day job, he'd stay up all night filling out compliance forms: "So at night I'd fill out these questionnaires and I had a customer send me a question. I'll be like, I need this back tomorrow morning, East coast time. Stay up all night. I fill it out. I send it at 3 a.m. They got the deal done." The magic was in the positioning - customers didn't want software, they wanted outcomes: "Yeah, so in the beginning, we didn't even have software. I was building this off of Google Sheets and Airtable dashboards. People would be like, when are we going to get a login? I was like, why do you need to log in? You have a form that needs to be filled out, and you want it filled out." This approach generated nearly $2 million before any real software: "We were well over a million, almost a two million before we even like built any software." Complexity-Based Pricing Beats Per-Seat Models SecurityPal charges based on complexity rather than user seats - questionnaire difficulty, product lines, regions, and SLA requirements: "I fundamentally anchored our pricing based on complexity. It's like how complex is your problem as it relates to security reviews and security questionnaires?" Want same-day turnaround? That costs more: "So I would say like the vectors for pricing for us are quantity of work. And then the SLA, right? So do you want it done same day? Or are you okay with like three, five days?" His pricing philosophy is refreshingly practical: "Biggest piece of advice from pricing standpoint that I can give anybody is like your pricing is going to change, forget about perfecting it today." AI Agents Won't Replace Premium Service While the industry rushes toward AI automation, Pukar sees the premium market wanting hyper-personalized service: "My friends that have it told me, you call them, you get a real person and like they're hell bent on figuring out how to help you solve your problem. And like that premium experience, it's like hard to really like get that through like AI agents." The Bootstrap Mentality That Scales Pukar's advice cuts through Silicon Valley hype: "If you really believe in something, you're really passionate about it, and you want a certain version of the world to be created, and you will literally eat ramen and work out of the corner of your apartment, you don't need to raise capital. You just need one customer to believe in you." The key insight: "You don't need to deliver for that customer, the perfect version of the product, you just need to deliver value. And you need to get that customer to say that you've delivered them value. You got to get paid." His brutal reality check: "But by the way, if you're not getting paid, it doesn't exist. It's just basically feel good conversations that are happening." Companies Mentioned: SecurityPalTalentBinTeamableDriftAirtableFigmaVantaCraft VenturesStripe Atlas See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this candid conversation, Jack Altman (Alt Capital) drops his guard and shares what it's really like being a VC - and Sam Altman's brother. From comparing investor-founder relationships to dating ("board members outlast executives") to revealing who the family IT guy was growing up, Jack brings refreshing honesty to startup investing. He breaks down why he started his podcast simply because "it would be fun," admits he's "lower disciplined," and explains his people-first investment philosophy: "I'm not waiting for the pitch, I'm waiting for people." Jack also tackles the big AI questions everyone's asking. He predicts agents will be everywhere but argues the space won't be winner-take-all, using a brilliant payroll industry analogy to explain why multiple AI companies can thrive simultaneously. Plus his brutally honest take on market sizing ("we all suck at trying to guess how big a market's gonna actually be") and why he's deliberately keeping his fund small while others chase billions. Whether you're building a startup or just curious about Silicon Valley family dynamics, this episode delivers insights you won't hear anywhere else.

In this episode, Aaron Levie, CEO of Box, delivers a masterclass on how AI is reshaping the enterprise software landscape. Drawing from his experience building Box through the cloud revolution, Levie provides a pragmatic take on whether we're in the "2005 or 2008 moment" of AI adoption. He argues that most companies are now past the naive ChatGPT-interface phase and have evolved to understand that the real value lies in agentic workflows running in the background. The conversation tackles the hottest debates in SaaS: Is the seat-based pricing model dead? Will AI agents replace traditional software platforms? Levie's contrarian take - that agents will enhance rather than replace existing systems - challenges the prevailing wisdom about AI disruption. The discussion gets spicy when Levie warns that tech leaders using AI as a scapegoat for layoffs could trigger regulatory backlash from politicians like Bernie Sanders, potentially stifling innovation. He advocates for companies to focus on output expansion rather than cost-cutting, sharing real examples from Box where AI implementation led to hiring more engineers, not fewer. The episode explores complex architectural decisions around AI pricing models, the future of systems of record, and why deterministic data should never be owned by non-deterministic AI systems. Levie's bold prediction that legacy giants like Salesforce and Oracle will coexist with AI rather than be disrupted makes for compelling listening, especially when he admits he might be "laughably wrong" in 10 years.

We sit with Bryce Maddock, CEO of TaskUs, a $1B+ revenue BPO managing 60,000 employees across 13 countries. In this brutally honest conversation, Maddock exposes the gap between AI automation claims and reality, revealing how companies publicly boast "80% automation" while privately maintaining the same headcount. He shares TaskUs's controversial strategy of intentionally losing money—cutting prices from $2 to $1 per contact—to accelerate AI deployment and transition from hourly billing to outcome-based pricing. Maddock doesn't sugarcoat the future: "I don't know that all 60,000 are going to make it through the journey. Anyone who says differently isn't being truthful." He explains how AI is creating unexpected challenges, like angry customers escalating to humans after AI failures, and reveals the board-level pressure driving rushed AI decisions based on misleading headlines. From his bold prediction that he'll either "look like a genius or complete fool" to his insights on how humans become "premium features," this episode delivers unprecedented transparency about leading through the AI revolution.

FUSE VC General Partner Kellan Carter reveals why the 2010s SaaS playbook is dead and outcome-based pricing is the future. From a 24-year-old building AI for 911 dispatching to the secret board-level panic over revenue quality, Kellan shares unfiltered insights on what's actually working in AI investments. Topics include voice AI competition, BPO disruption, professional services transformation, and why founder quality now matters more than growth metrics. Recorded at the Paid studio.

In this episode, Alexander Matthey, CTO and co-founder of Parloa, shares his vision for how AI agents will transform customer service. Drawing from his experience at Adyen and previous startups, Matthey explains why he believes people will actually want to talk to customer service in the future. He discusses how AI agents will enable 24/7 support without language barriers or wait times, while human agents will shift to handling more complex, specialized cases that require nuanced problem-solving. Matthey also addresses critical industry questions about implementation challenges, enterprise adoption, and the fate of Business Process Outsourcers (BPOs). He reveals why building modular AI systems from day one is essential for enterprise success, how engineering productivity will change with AI coding tools, and why the industry needs to shift from time-based to value-based pricing models. The conversation covers practical insights on US market expansion, the balance between hype and reality in AI deployment, and why execution remains the key differentiator in a fast-moving market where everyone claims to have the same capabilities.

Max Altschuler, founder of Sales Hacker and GTMfund, shares his unfiltered perspective on how AI is fundamentally reshaping go-to-market strategies. Drawing from his experience building and selling Sales Hacker, he explains why the traditional sales playbook is dead and what's replacing it. His new fund, backed by 300 software executives, is betting on vertical software, global infrastructure plays, and founders who understand that in today's world, "hard is a moat." From his insights on why entry-level sales and marketing roles are disappearing to his bullish take on community-driven growth strategies, Max provides a masterclass in adapting to the AI revolution. He shares specific tactics like poaching top reps from companies like Outreach, supporting them with growth engineers, and building micro-campaigns that cut through the noise. This episode is essential listening for anyone trying to understand where sales, marketing, and venture capital are headed in the AI era.

In this episode of Get Paid, Manny talks with Adam Schoenfeld, CEO of Keyplay, about how AI agents are revolutionizing B2B account selection and targeting. While most companies are using AI to increase volume, Keyplay's agents help teams identify high-quality accounts by analyzing nuanced factors like buying behavior, team dynamics, and technology preferences - things a smart AE would research manually. Adam shares insights on their journey from flat pricing to credits, the challenges of explaining AI value to customers, and why RevOps leaders are surprisingly eager to embrace tools that let them focus on strategic work instead of manual drudgery. Adam also reveals his playbook for building Keyplay: he spent months creating content and building an audience through his Pure Signal newsletter before ever launching a product, allowing him to deeply understand the problem space. Looking forward, he envisions a future where Keyplay could orchestrate entire go-to-market motions, from account selection to ad placement, fundamentally changing how B2B companies find and win customers.

In this episode of Get Paid, Manny talks with Sahil Mansuri, founder of Bravado, about how AI is transforming sales and recruiting. While many SaaS companies see AI as a threat to their business models, Bravado has seen their margins increase from 40% to 90% by using AI to handle repetitive recruiting tasks. What makes this fascinating is that even as AI eliminates jobs across industries, great salespeople are becoming more valuable, not less. Sahil shares counterintuitive insights about hiring top performers (look for people overperforming at lesser companies), the return of prospecting skills, and why technical expertise is non-negotiable for today's AEs. Most provocatively, he argues that AI won't replace enterprise sales roles, pointing to companies like Slack and Square that initially avoided building sales teams only to later realize their necessity. The future belongs to companies that use AI to handle mundane tasks while empowering their human salespeople to focus on high-value relationship building.

In this episode of Get Paid, Manny sits down with Jim Cutillo, founder of Alpha7x, who's revolutionizing the mortgage industry with AI agents. Despite technological advances over the last 30 years, the cost of processing a mortgage is at an all-time high, about $12,000 per loan. The mortgage process takes longer than it did a decade ago. Jim explains how Alpha7x's AI agents are slashing these costs by automating manual tasks that currently make up about $2,200 of that total. With outcome-based pricing, Alpha7x only makes money when they save their clients money, creating perfect alignment. Jim shares how one large bank needed 15 people to perform manual OFAC checks, but with Alpha7x's agent, they'll only need 3. Beyond mortgages, Jim sees potential for this technology across any industry that's document and data-heavy. Most impressively, Jim is building Alpha7x as a lean operation, with a goal of reaching $10M ARR with fewer than 20 employees, practicing what he preaches about AI transformation. The company is experiencing so much inbound interest they haven't even activated their sales strategy yet, allowing them to be selective about fundraising.

Elias Torres (Drift, HubSpot co-founder) joins us to share why he considers his $1.2B exit a "failure" and his radical new approach to company building with Agency. Hear why he's imposing a 100-employee constraint to reach $1B, why he believes seat-based pricing is "stupid" in the AI era, and his controversial stance on revenue-share models. Elias drops knowledge bombs on the psychology of AI adoption, the underrated power of pricing as a growth lever, and why his first hire was a lawyer (not a recruiter). A must-listen for founders navigating the shifting landscape of AI-first companies.

Jasper has built one of the most talked-about AI companies in San Francisco, creating AI employees called "artisans" that handle outbound sales - starting with Ava, their AI BDR. We'll get into how he's navigating the AI boom, his views on outcome-based pricing, and what he's learned from running one of the most polarizing marketing campaigns in tech.

Nick Mehta, Gainsight’s CEO, didn't mince words in our latest conversation. He says the entire enterprise software model we've built over the past 20 years is fundamentally broken. What doesn’t work about SaaS anymore? The model that made Salesforce, Workday, and countless others into tech giants? According to Nick, it doesn't work anymore: "We bring in data from disparate systems, we use some kind of ETL to combine it, we have rules that say if this, then that, we have workflow for humans to take action, and then reports for managers. I argue that model's totally broken." Why? Because: * Users have to take manual action * Ops teams define rigid rules that can't adapt * Leaders review reports on low-quality data that users entered * Nothing happens autonomously Nick framed it in a way that we love, because we feel the same way. "The whole SaaS industry, and maybe all of humanity, is going from 0 to 1 again." The Three Phases of AI Transformation What's replacing the old model? Nick outlined three evolutionary phases for how AI transforms SaaS: * AI as an Assistant: Like an executive assistant for employees – helping write emails, summarizing meetings, prompting within existing workflows * AI as an Analyst: A layer that analyzes all communication in real-time, reveals patterns, and gives leaders visibility they never had before * AI as an Agent: Fully automated workflows that take action without human intervention Most companies are stuck at Phase 1 because they're afraid to cannibalize their existing seat-based business models. Classic innovator’s dilemma! The vibe revenue problem few are talking about Perhaps the most eye-opening part of our conversation was when Nick revealed what he's seeing in the market: "If you're an AI company, you think you have customers. They think they're doing a trial." He explained the disconnect happening across the industry: AI startups claim big enterprise deployments, but when you talk to those same customers, they describe it as "just a proof of concept" or "playing around with it." Even more shocking: "Some of them have massive churn... like 70% churn!" We call this vibe revenue! The innovator's dilemma in action We asked Nick how established companies like Gainsight navigate this transition, and his answer referenced Clayton Christensen's famous book: "Almost every time there's a discontinuous change in business, most companies don't make it because they're not willing to say, 'our old stuff doesn't make sense anymore.'" He's taking his own medicine too. Rather than spreading his new CTO across all of Gainsight, he's focused him exclusively on their new agentic initiatives - acknowledging they need separate focus to avoid being hampered by legacy thinking. Will Your Headcount Survive? When we pushed Nick on what happens to customer success teams as these agents take over, he was surprisingly candid: "Short-term, you're replacing atomic tasks, which is easier to digest... Long-term, yeah, you probably end up replacing seats." "This is going to be awesome for the customer." Nick believes we'll see dramatic consolidation of customer-facing roles. No more need for separate CSMs, TAMs, support people, and account managers - the agentic layer will allow fewer humans to cover more ground. The surprising answer to AI deployment failures Nick's final insight shocked us. When asked why so many AI deployments fail despite the amazing technology, his answer had nothing to do with models or features: "Human change is really hard. We're so inertial." His prediction: companies deploying AI in enterprises will need consulting practices to help re-engineer workflows: "If you're an enterprise company deploying one of these AI tools, if you don't have somebody, some advice and consulting resources, you're going to buy the AI tool and you're going to churn it - guaranteed." Your takeaway from this episode should be that most SaaS companies won't make this transition. They'll keep trying to fit AI into their existing paradigms rather than fundamentally rethinking what software even is. If you want to succeed, do what Nick says: Understand that stuff don’t make sense anymore, and be honest with your employees and customers about it. What do you think? Is your SaaS company ready to make this jump? Leave a comment below 👇 Watch the full podcast here or wherever you listen to podcasts: 👉 https://podcasts.apple.com/us/podcast/agent-talk-podcast/id1792748956?i=1000705561812 👉 https://open.spotify.com/episode/2hd0evHQIByeAbIAT5sIYN?si=MCL2qytsT1GExCCX-oIb5g 👉 https://www.youtube.com/watch?v=HgePQ9sgSXg This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In our latest episode, we talk with Anshul Gupta, who recently raised $22.5M from Bain Capital and First Round Capital for Actively. Our favorite takeaways: * Personalization at scale is actually "laziness at scale": "The amount of emails I got saying 'Anshul, congrats on your recent funding round' followed by a totally unrelated value proposition... if you're looking at thin logic like 'someone raised funding, throw them in a sequence,' that's not working anymore." * Top performers do less, not more: "When you sit down with the best reps, they're not the ones doing the most volume. If you look at the leaderboard, they may even be in the bottom half from an activity perspective. What we see is they're incredibly targeted and laser-focused." * Relevance beats volume: The alternative to personalization is "relevance at scale" – going incredibly deep with research, building proper hypotheses based on customer needs, and focusing on quality over quantity. * AI flips the paradigm: "The revenue orgs in two years will be flipped on their head. Systems of intelligence will, instead of humans asking them what to do, be processing all information and guiding humans where to spend their time." * AISDRs have limited applications: "If you're a company just starting up with no sales reps and want to get meetings by hook or by crook, AISDRs could work. But fundamentally upmarket, in complex ecosystems with multi-product motions, that model doesn't work." * The revenue frontier concept: "Rather than constraining headcount and increasing productivity, increase your ambition. Companies are nowhere near their revenue frontier, and imperfect go-to-market execution is a huge contributor to that gap." * Custom reasoning models: "Ramp has their own Actively reasoning model, Ironclad has their own – trained on the hyper-specific nuances of their products, go-to-market motions, and as those continue to evolve." What makes Actively different: * Their focus is increasing rep productivity and the quality of pipeline each human can generate * They train custom reasoning models specific to each client's business * They deploy a "forward deployed engineering model" similar to Palantir to customize for enterprise needs * The system continues to improve through active learning (hence the name!) What's not working in the market: "All these volume-based solutions purport to help you send more emails or do more calls. It's all about chasing volume when the top performers are actually doing less but with more focus. That thin logic of 'if someone raises funding, throw them into a sequence' is what's corrupted the concept of personalization at scale." What do you think about Anshul's take on personalization vs. relevance? Are you seeing "laziness at scale" in your inbox? Have you identified the revenue frontier for your business? 👇 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In a candid conversation at Paid’s launch party, EQT Ventures investor Doreen Huber shared her razor-sharp perspective on what’s working in AI agent investing, why traditional SaaS is losing ground, and what founders need to bring to the table to secure funding in this new era. Our favorite takeaways: * SaaS is no longer the focus to the B2B Software team at EQT Ventures:"My team is not investing in traditional SaaS anymore. Our strategy is to go for agentic, AI-native companies, and we tend to disqualify what doesn’t fit that bucket." * True agents only:"We only want to support companies doing something end-to-end—not just enhancing customer care with AI-drafted emails. We’re looking for agents that do the actual work from start to finish." * Commercial DNA matters:"I definitely have a thing for founders with commercial DNA. If someone comes from an engineering side, they absolutely need to learn this... the best CEO is also the best product person." * Founder qualities:"I personally love the outliers, the underdogs, or someone with a crazy CV. I'm not into the typical business school, textbook founder. I love it when someone shows up with an edge." * Legacy SaaS is under pressure:"Many legacy SaaS companies will lose market share to agentic players. A lot of them are struggling—they don’t have the AI talent, and they’re stuck in outdated stacks." * On industry hype:"Some of the big players are slapping AI labels onto old products. That’s not agentic innovation. That’s legacy software trying to catch up." What Doreen is looking for now: * Enterprise-ready agentic sales and marketing solutions – not just slim use cases, but holistic systems * Agentic cybersecurity – solving modern threats with AI-native architecture * Vertical AI applications – especially where AI is applied to labor, not just software budgets What’s working: “Most companies moving faster than others have that AI-native mindset. They want lean teams and ask: ‘Can we do this with agents instead?’” What’s not working: “BDR email sequencing or scheduling tools... they look impressive at first, but in reality, these problems won’t exist in a year or two. That’s just a GPT anyone can build.” What are your thoughts on Doreen's take that traditional SaaS is finished? Do you agree that legacy companies can't catch up with AI-native startups? 👇 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In our recent conversation with Amos Bar-Joseph, CEO and co-founder of Swan AI, he shared his radical vision for the future of business: autonomous companies achieving $10M ARR per employee through strategic AI implementation. Beyond the typical AI hype, Amos details a practical roadmap for companies looking to scale with minimal headcount. Here are the key insights from our discussion: The autonomous business revolution * The next 5 years belong to SMBs: "We believe that the next five years are the year of the SMB, the year of the small business, small lean teams that are showing the world that using AI agents, you can reach massive scale, like never seen before." * Rethinking business fundamentals: "It's about reimagining how humans and AI collaborate together, rethinking fundamentally the operating system of a business." * The Swan Metric: Targeting $1M+ ARR per employee (with Swan pushing toward $10M) through strategic AI implementation. Breaking the "throw bodies at problems" mindset Amos said something that we think many founders have thought about: "The first instinct that the old playbook got you to do was throw bodies at the problem, right?". But Amos does things a bit differently, and we like that a lot: * Self-imposed constraints drive innovation: At Swan, they created the constraint that they "can't throw bodies at a problem" - forcing them to find more creative and intelligent solutions. * “Ops Wizards”, not just more headcount: "Every team should have that ops wizard" who can bridge technical understanding with business orientation. * Human-in-the-loop design: Always start with humans supervising AI and providing feedback to create "a self-learning system that takes your knowledge in a collaborative way." The future of sales is human + AI, not AI replacing humans When I asked about which parts of the sales cycle will be replaced by AI, Amos offered nuanced insight: * It depends on ACV: "The higher the ACV, the tougher it is to replace any part of the sales cycle... When you look at $20 million deals, then you want a human in the loop." * Low ACV should be marketing-driven: For $19/seat products, "from a unit economics standpoint, it doesn't make sense to do outbound." * Sellers love winning, not prospecting: "Sellers love winning. And for that 1% that they are winning, they love that notion... It's the best moment of their day when they get that yes on the screen." * The 100X seller: "The future of sales is first of all reimagining how sellers work with human and AI collaboration at the core. And it's more about finding the path to the 100X seller." The three types of businesses in the AI revolution According to Amos, businesses will fall into three categories across a spectrum: * Biggest losers: "Those trying to automate their workforce, replace their workforce with digital workers. And they will be left behind." * Partial winners: "Implementing AI agent tooling across their entire stack... but disparate solutions for different functions." * True unicorns: "Lean teams that are building AI agents as their product, able to rally their entire agentic workforce around that specific agent." Practical advice for outbound sales: Amos had some pretty practical advice too: Move from “digital workers" to storytelling engines. How? * Goodbye sequences, hello relationships: "We never pitch with a hard CTA. Never... It's only about, look, something happened in your business. There's an event that is relevant to your day-to-day as a VP of sales." * Non-deterministic outreach: "Swan looks at a timeline always, looks at what happened before, what is happening now, and will create a recommendation how to engage that lead." * Monitoring relationships at scale: "What we're envisioning is a future where SWAN can monitor relationships, top of the funnel relationships with thousands of accounts in parallel." What's your take? Are you seeing companies in your industry successfully implementing AI to scale without proportional headcount growth? 👇 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com 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. Our favorite takeaways: * Building AI companies is just building a company. It’s 95% the same and people problems still dominate * Trust is the critical design pattern most AI companies miss. Users need to see how you arrive at your results * Most AI products achieve 80% functionality quickly, but the final 20% takes 5-10x longer and is what builds actual trust. * The greatest moat in AI isn't data or tech - it's founders with relentless execution. Pat also added some extra wisdom that we appreciate: * The "data flywheel" appears in 100% of AI pitches but only 1% of companies actually demonstrate it works - Pat demands evidence, not theory * AI pricing will standardize around outcome-based models with huge variation - the most successful companies think about both "input" (work done) and "output" (value created) * For investors, negative gross margins are acceptable in early AI companies because token costs are dropping 99% and multi-tenancy is becoming more accepted * Domain-specific AI products that build real trust can carve out defensible positions against foundation model providers in vertical markets * The most successful AI companies avoid "vibe revenue" (temporary excitement) by focusing on engagement and retention using consumer internet metrics even for B2B products What's your experience with AI products and pricing models? Have you found user trust to be the limiting factor? Share your thoughts below 👇 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Wade Foster, co-founder and CEO of Zapier, shares how they completely revamped their pricing model - and actually REDUCED revenue in the short term - to drive explosive long-term growth. Since launching in 2011, Zapier has grown to help millions connect their apps without code, reaching a valuation over $5B with minimal VC funding. Their recent pricing change offers fascinating insights for SaaS founders. Key takeaways: * Zapier eliminated their dual-metric pricing (Zaps + Tasks) to simplify the customer experience * They made unlimited Zaps available on all plans - removing a major friction point * Every plan now includes pay-as-you-go options beyond the base subscription * They stopped counting utility features as billable tasks - providing more value The result? Short-term revenue dropped significantly, but task consumption and customer happiness soared. This bet on long-term growth would have been impossible for most VC-backed companies. More insights: * Being profitable and bootstrap-focused gave them freedom to make radical customer-first decisions that sacrificed short-term revenue * Pricing "debt" accumulates over time when you experiment with different models - eventually requiring a reset to first principles * The best pricing aims for customer "love" - Wade literally used this word - not just reluctant payment * Competition constantly forces pricing innovation - especially with direct competitors who counter-position against market leaders * AI is rapidly democratizing entrepreneurship - Wade sees teams of 10-20 people reaching millions in revenue with minimal engineering, driven by domain expertise paired with no-code tools What pricing changes have transformed your business? Share your experience below 👇 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Manny hosts Mickey Haslavsky of Enso, who's flipping the script on AI agents in the most refreshing way. Mickey says their mission at Enso is to reduce the prices of services, just in general, for small businesses. While lots of AI companies chase enterprise dollars or build complex multi-agent systems, Mickey's team is printing money with a simple contrarian approach: deliver "good enough" AI agents to small businesses at a flat $49/month. Some key insights from our conversation: * "SaaS is pretty much dead... some of the agents that we're building, you would have to raise like a seed round like three years ago, like raise like $5 million and have a team of like 10 engineers to build them." * "I don't buy this story of losing jobs. What's really important is reaching those who couldn't afford an agency before, who couldn't pay $3,000 for SEO or $5,000 for social media design." * "SMBs don't churn because they go out of business - they churn because they pivot constantly. That's why our unlimited access model works." Mickey's belief that LLMs will become completely commoditized, allowing companies like his to use cheaper models from anywhere (including Deepseek) to deliver services that previously required expensive agencies. The future might not belong to the fanciest AI, but to those who make it accessible, affordable, and actually useful for the millions of SMBs who just need to get things done. What do you think? Is this the true democratization of AI, or are there limits to the "good enough" approach? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rob Litterst (HubSpot exec & SaaS pricing guru) just dropped some mind-blowing insights about how AI is completely disrupting traditional software business models. Whether you're building AI products or just trying to stay ahead of the curve, this episode is packed with spicy takes on where the industry is headed. Three explosive themes that'll blow your mind: * The Death of Per-Seat Pricing: The old way of charging per user is getting absolutely demolished by AI agents. Rob breaks down how companies are being forced to evolve or die as AI native startups come gunning for their lunch. Traditional SaaS companies are scrambling to figure out how to price their products when algorithms are replacing humans left and right. * The Human Premium Paradox: Humans are becoming a premium feature in software. While AI handles the grunt work, companies are starting to position human expertise as the ultra-premium tier. But this creates an insane challenge: how do people gain expertise if AI is handling all the entry-level work? Rob dives deep into this existential crisis facing professional services. * The Revenue Bloodbath is Coming: Companies that don't adapt their pricing models are about to get absolutely wrecked. Rob shares war stories about how AI native startups are growing to $100M+ ARR with tiny teams, while traditional players are stuck in old pricing models that don't match how value is created anymore. The race is on to figure out outcome-based pricing before it's too late. This conversation gets into the nitty-gritty of how companies are navigating this transition - from baby steps toward outcome-based pricing to full-blown business model transformation. If you want to understand how AI is reshaping the fundamentals of software business models, this episode is a must-listen. Whether you're a founder, product leader, or just fascinated by how AI is changing the game, Rob brings deep insights from both the trenches at HubSpot and his broad view across the industry. Don't miss this one - the future of software pricing is being written right now. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

"35% of projects fail, and the numbers haven't budged in 20 years." That's what kept Shawn up at night before founding Coworked. After two decades in project management, he'd seen every methodology, tool, and framework come and go - yet the fundamental problems remained stubbornly unchanged. Shawn is building an AI project manager that could transform how work gets done. But not by replacing humans - by unleashing them. Harmony, Coworked's AI agent is rewriting the rules of project management. While most AI companies are focused on chatbots and automation, Coworked is tackling something far more ambitious: creating an AI teammate that handles everything from resource allocation to risk analysis. What's fascinating is how they've navigated enterprise adoption. When they first pitched to project managers, they hit a wall of resistance. But then they had an insight - they were talking to the wrong people. PMO leaders immediately got it. Not as a way to reduce headcount, but as a solution to a persistent problem: how to handle 2x the projects without 2x the team. Thanks for reading Agent Talk! This post is public so feel free to share it. Project managers spend up to 80% of their time on process tasks rather than strategic work. As Shawn puts it: "There's a subtle but crucial difference between replacing a job and replacing a role." Harmony handles the routine while enabling humans to focus on what matters. Their early results are turning heads. Fresh out of Techstars, they're already landing enterprise clients who see the potential to transform their project delivery. Perhaps the most interesting part is their insight about AI adoption: success comes not from elimination, but elevation. It's not about replacing project managers - it's about making them superhuman. AI transformation isn't just about automation - it's about reimagining how work gets done. And sometimes, the biggest breakthroughs come not from replacing humans, but from freeing them to do what they do best. Just as we saw with Zendesk revolutionizing customer support, Coworked is showing us what's possible when we think bigger about AI's role in enterprise transformation. The question isn't whether AI will transform project management - it's how fast organizations will adapt to this new reality. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Zendesk's journey isn't just another AI adoption story - it's a masterclass in how even enterprise companies can reinvent themselves in the age of AI. While others rushed to add AI features, Zendesk took the bold step of completely reimagining their business model. What's fascinating is how they navigated the classic innovator's dilemma: "It's one thing to understand it, it's another thing to be in it and live it," as Kelly puts it. They didn't just add AI - they fundamentally changed how they deliver and capture value. Think about their evolution: * Started investing in AI years before ChatGPT * Built global AI teams across three continents * Developed models trained on 18 billion support tickets * Shifted from seat-based to outcome-based pricing While most SaaS companies are stuck in the "more seats = more revenue" mindset, Zendesk recognized a fundamental truth: "You've got these dueling factors... seat contraction and AI arriving on the scene." Their response? Align their success directly with customer outcomes. It’s interesting to note that even their private equity owners, typically viewed as cost-cutters, became their biggest champions for transformation. As Kelly says "We've got investors that are actually pushing us to go faster and to spend a lot of time on this very topic versus playing defensively." Zendesk’s approach shows that successful AI transformation isn't about following trends - it's about solving real business problems at scale - just like with last week’s Synthesia episode. Instead of charging for tokens or interactions, they focused on what customers actually care about: resolved support errands and tickets. Notice how Zendesk is being a pro at handling the transition: * starting with clear success metrics * offering predictable pricing through commits * and finally building trust through incremental value delivery. This story isn't just about AI - it's about how enterprise software evolves when technology enables fundamentally new business models. The question isn't whether to transform, but how to do it while keeping your customers' trust. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Synthesia's success wasn't about chasing the latest AI trends, but about solving real business problems with a relentless focus on enterprise-grade features and workflows. In this first episode, Manny talks to Victor Riparbelli, founder and CEO of Synthesia that raised $180M in Series D. What's fascinating is how Synthesia navigated the classic enterprise software challenge: balancing the "wow factor" with sustainable business value. While most AI companies get caught in the "sugar high" of viral demos, which is why we’re seeing massive churn problems right now. Synthesia on the other hand focused on the unsexy but crucial elements: ISO certification, enterprise security, and seamless workflows. As Victor puts it, "These boring things unlock bigger and bigger deal sizes." Think about their evolution: * Started with avatars that grabbed attention * Built out comprehensive video editing capabilities * Added enterprise-grade security and collaboration * Developed deep integration with business workflows The results speak volumes: Their customers aren't just trying AI - they're fundamentally transforming their video production. As Victor says, "For the same budget of producing two traditional videos, we can now make 400 AI-powered ones." Watch how they're shifting from charging per video creation to focusing on video consumption and engagement. It's a masterclass in aligning pricing with actual business value. Synthesia's journey shows that building for lasting impact beats chasing the latest AI trends. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.