Podcasts about b2b saas

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

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

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

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

Send us Fan MailIt's been 130 episodes since Sidecar Sync last did a true deep dive on data—and a lot has changed. In this refreshed 2026 perspective, Amith Nagarajan and Mallory Mejias unpack what “association data” really means today, from structured CRM records to the massive untapped world of unstructured data like emails, community posts, and content libraries. They explore how AI—especially vectors and reasoning models—has flipped the script, making previously unusable data suddenly actionable. The conversation then tackles one of the biggest shifts in the AI era: data ownership. Even if you legally own your data, fragmented systems and vendor restrictions can limit how you actually use it. Finally, they break down what becomes possible once your data is unified and activated—from predictive insights to deeply personalized member experiences—and offer a realistic starting point for associations ready to take action. 

Send us Fan MailThis week on Sidecar Sync, Amith Nagarajan and Mallory Mejias break down a whirlwind week in AI, from Anthropic's rapid-fire Claude releases to OpenAI's tightly controlled GPT 5.6 rollout. They unpack the surprising performance of mid-tier models like Sonnet 5, the implications of government intervention in frontier AI, and what it means when access to the most powerful tools is suddenly restricted. The conversation then shifts to a new study reshaping the AI jobs narrative, revealing that companies investing deeply in AI are actually growing headcount—while others fall behind. From practical model selection strategies to big-picture workforce implications, this episode connects the dots between cutting-edge tech, policy, and the future of work. 

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

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

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

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

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

As B2B technology companies scale, size often comes at the cost of agility. Processes calcify, buying cycles stretch, and marketing teams end up running up and down the stairs faster instead of building the elevator.In this episode of the FINITE Podcast, Jodi Norris sits down with Carol Carpenter, CMO at Cohesity, to unpack what it really takes to market at enterprise scale without slowing down. Fresh from Cohesity's merger with Veritas, Carol shares the realities of integrating two large marketing organisations, building trust across cultures, and holding on to startup-style velocity inside a 5,800‑person business.She explains how her team uses AI to redesign workflows – from translation and brand governance tools, to AI‑powered SDR outreach that doubles lead‑to‑meeting conversion. Along the way, she draws a firm line between what can be automated and what cannot: creativity, taste and strategic judgement.Carol has been in technology marketing for most of her career, starting as a product manager at Apple. She enjoys scaling and transforming companies and has done that in leadership roles at VMware, Google, Apple, Trend Micro and now as CMO of Cohesity. Carol gives back through mentorship programs such as the HBS Women Entrepreneurship program and Monte Jade, an AAPI professional organisation.Inside you'll find…How to merge marketing cultures without losing speed, trust or clarityWhere AI genuinely shortens a six‑month enterprise buying journey – and where humans must stay in the loopA practical framework for lifting teams out of execution and into more strategic, high‑leverage work

Kaleigh Moore is an AI search strategist who helps B2B SaaS content teams show up in AI-generated answers. She is now researching LLM information retrieval at Harvard, she advises content teams on AI search strategy, and publishes the Context Window newsletter.

Send us Fan MailIn this episode of Sidecar Sync, Amith Nagarajan and Mallory Mejias explore one of the most provocative ideas in AI yet: companies owned and operated entirely by artificial intelligence. Sparked by a proposal out of Argentina, they unpack what “non-human corporations” could mean for accountability, governance, and the future of work. From there, they break down Agentic Resource Discovery (ARD), a new standard shaping how AI agents find and evaluate tools, and how it complements (and competes with) Model Context Protocol (MCP). Finally, they tackle a growing concern among associations and enterprises alike—the environmental footprint of AI—and what organizations should be asking as adoption accelerates. 

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

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

George had to wind down his last startup and give investors their money back. He went deep into the valley of despair, certain he'd missed his window to build something big. Then he met a co-founder, decided to start over, and started selling.In this episode, George breaks down how a customer signed a $36K pilot off nothing but a Loom and a one-pager, how cold email took him from zero to $1M ARR with no sales team, and why a "seven out of ten" is the most dangerous hire you can make.Why You Should ListenHow a customer signed a $36K pilot after a single Loom and zero calls.Why he gave the money back on his last startup—and what "follow your energy" really means.How cold outbound email built his first $1M ARR with no sales team.Why a "seven out of ten" is the most dangerous hire you can make.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, fintech, accounts receivable automation, AI agents, cold outbound email, B2B SaaS, Series A fundraising, services as softwareChapters00:00:00 Intro00:01:39 The Moment of True Product Market Fit00:03:33 Shutting Down a Small-Market Startup00:07:44 Picking Fintech From Five Ideas00:17:12 From Black Box to Full App00:24:47 $1M ARR on Cold Email Alone00:36:11 Why a "Seven" Is the Most Dangerous Hire00:42:15 Compressing a $25M Series ASend me a message to let me know what you think!

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

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

In this episode, Nikola reveals his contrarian belief that AI can be better than humans at customer service (not instead of humans, they'll do very different work), why he spent two hours on the phone with Vodafone when he got their confirmation email with someone else's name on it (and why that's not really about AI or humans), why they built a "token leaderboard" internally to track which AI tools they're using most, why junior developers will definitely beat senior ones at learning these tools (plasticity just goes down as you age), how AI gives him superpowers as a CEO (a chief of staff reminding him he promised something 4 days ago), and why their business model is "Rolls Royce for large enterprises, BMW for everyone else." He also shares his journey from a Serbian family to the University of Cambridge, how his PhD supervisor convinced him not to do a PowerPoint job at McKinsey, and why he ended up founding a voice AI company instead of working in finance (he wanted to be "a proper monkey" as well as a PowerPoint monkey, in his words).What you'll learn:

Send us Fan MailWhat if building a million-dollar business no longer required a team, funding rounds, or even technical expertise? In this episode of Sidecar Sync, Mallory Mejias sits down with AI strategist and GenAIPI CEO Jon Cheney to explore how artificial intelligence is radically changing the economics of innovation. Jon shares the story of launching a company in a single weekend for just $400—and scaling it to $1M in six months—using a concept known as “vibe coding.” The conversation dives into what separates organizations that successfully adopt AI from those that stall, why execution matters more than ever, and how AI is unlocking entirely new forms of value creation beyond simple automation. If you've ever felt limited by budget, staff, or technical skills, this episode will challenge everything you thought was possible. 

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

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

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

In this episode, Stijn sits down with Antoine, Kalungi's new CEO, for an energizing conversation about what it really takes to grow as a marketing professional in 2026. The centerpiece is Antoine's upcoming book, Level Up — a practical, story-driven guide to becoming the kind of marketer, manager, and teammate that no AI can replace.Antoine pulls from an unconventional journey — from chasing a professional soccer career to leading one of the top B2B SaaS marketing firms in the world — to show that the principles behind great teams aren't that different whether you're on a pitch or in a pipeline review.You'll hear about the frameworks that actually move the needle: situational leadership, managing up and down, self-awareness, and the Super Communicator model — a concept that bridges how you lead your team with how you get the most out of AI tools. Spoiler: the skill that saves you from a 6am angry message from an ambassador might also be the skill that saves your next campaign.Whether you're an individual contributor trying to stand out, or a marketing leader building a team that punches above its weight, this episode will leave you with new language for old challenges — and a few ideas worth acting on today. 

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

If you're running Google Ads and haven't checked your account-level automated assets recently, there's a good chance you're spending money on site links, callouts, and snippets and other AI-generated assets you never set up yourself.In this quick tutorial, I show you exactly where to find automated extensions in Google Ads and how to switch them off. This will save you a lot of money. This came up during a client account audit, and it was AI that reminded me and flagged where the spend was actually going — which is part of why I keep pushing AI as a core part of any PPC workflow nowadays. If you missed the previous video on how I use AI day-to-day for B2B SaaS marketing, the link is in the resources below.Note: this tutorial will help if followed visually.-------------------------------------------------

Pricing is the most avoided conversation in B2B marketing. It's handed down from finance, rubber-stamped by sales, and marketers are expected to promote whatever number comes out. Matt and Liam make the case that pricing is actually a marketing problem — and that the explosion of "no decision" outcomes in B2B SaaS is largely a symptom of not understanding how buyers think about value relative to cost. Keywords: B2B SaaS pricing, pricing strategy, marketing and finance alignment, go-to-market, buyer psychology

Send us Fan MailIn this episode of Sidecar Sync, Amith and Mallory explore three major developments shaping the future of AI. First, they unpack how an OpenAI model independently solved a decades-old mathematical problem—challenging long-held assumptions and proving AI can generate truly novel insights. Then, they break down Microsoft Build 2026 and the shift toward an “agent-first” world, where AI systems take on real work across your organization. Finally, they dive into Anthropic's powerful new Claude Fable model, discussing its capabilities, real-world applications, and the growing tension between performance and transparency. Along the way, they connect these breakthroughs back to what matters most for associations: data strategy, flexibility, and staying ahead in a rapidly evolving AI landscape. 

Explore how transparency and accountability can transform PPC campaigns and client relationships. Simran Harichand shares lessons from her experiences, including a major underspend mistake that ultimately strengthened client trust.Main Topics:Accountability and transparency in PPCRebuilding client trust after budget overspendFoundational best practices in PPCEthical and effective use of AI toolsCommunication and relationship building in advertisingChapters:00:00 Welcome, introductions, and Simran's journey from Pakistan to UK paid media04:17 A €30,000 underspend on a major B2B SaaS account06:17 How a Target CPA change quietly tanked spend08:42 Owning the mistake and facing the client10:11 Rebuilding trust after a costly PPC error12:30 Why underspending can create serious business problems16:48 Breaking into PPC, internships, and hiring junior talent20:27 Why brilliant basics matter more than shiny tactics25:03 Advice for marketers who discover a major mistake26:27 Building client relationships before things go wrong28:46 The worst GA4 and conversion tracking mistakes in account audits33:20 AI Max, Performance Max, and testing new Google features37:54 Where marketers are getting AI wrong41:41 "Nobody Told Me This Was a Career"42:53 Closing thoughts and where to find SimranSimran Harichand - LinkedInPPC Live The Podcast features weekly conversations with paid search experts sharing their experiences, challenges, and triumphs in the ever-changing digital marketing landscape.Thanks to our sponsor ⁠Adsquire,⁠ a small team of passionate and focused legal marketers that do what it takes to get law firms spectacular results! With the landscape always changing they stay on top of the trends and are first to find and use new strategies to accomplish this for our clients - for example they are the FIRST to serve a lawyer ad on ChatGPT.Join the next ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PPC Live ⁠⁠⁠eventFollow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Slack Group⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter⁠⁠

What does it actually take to build a go-to-market strategy for a category that barely existed 18 months ago?In this episode of The Growth Leaders Series, Charlie Marchant sits down with Nick Lafferty, Founding Marketing Engineer at Profound, the AI Search tracking platform helping major brands understand how they show up in ChatGPT, Gemini, Perplexity, and other LLMs.Nick brings serious growth experience to this role. Before Profound, he drove millions in B2B SaaS pipeline at Loom and Mailgun, then spent two years running a solo consulting agency before joining Profound. In this episode, Nick Lafferty covers:Why velocity is a moat in AI SearchWhat a modern, lean marketing team actually looks like, why Nick hires for a generative marketer mindset, and his advice for showcasing this online The growth strategy behind Profound, centred on sharing data and insightsThe go-to-market motion behind Profound's Zero Click events, scaling from 400 to 800+ attendees across New York and LondonThe layered mentality around AI Search for different business sizesWhy FAQ content and FAQ schema is the lowest-hanging fruit most big brands are leaving on the tableHow to make the internal case for AI Search investment when leadership is still thinking in Google termsThe career advice he'd send back to his first dayRead the full show notes: https://exposureninja.com/podcast/growth-leader-series-nick-lafferty/Follow Nick Lafferty on LinkedIn: https://www.linkedin.com/in/nicklafferty/New episode launches every Wednesday throughout June 2026, so stay tuned to hear from growth leaders from leading brands like McKinsey and Company, Wise, and AirOps! Book a consultation to get a live review of your website and marketing

Send us Fan MailGuest: Ivan Lee, Founder & CEO of DatasaurWe're looking at what happens when AI changes the market faster than the old SaaS playbook can keep up.Ivan Lee, founder and CEO of Datasaur, joins SaaS Backwards to share how his company navigated one of the most dramatic shifts in enterprise AI. Datasaur started as a data annotation platform before ChatGPT changed customer priorities, paused AI roadmaps, and forced the company to rethink its product, GTM strategy, and business model.Ivan explains why out-of-the-box tools like ChatGPT Enterprise and Microsoft Copilot can be useful starting points, but often hit a ceiling for regulated enterprises that need private AI trained on their own data, workflows, and processes.He also shares how Datasaur moved from a traditional SaaS model toward end-to-end AI solutions, what founders can learn from disrupted marketing channels, and why the future of SaaS may depend less on selling software access and more on solving the customer's actual job to be done.Key Takeaways:Why enterprise AI often breaks down when it lacks access to private data and internal workflowsHow ChatGPT disrupted Datasaur's original AI roadmap and customer baseWhy old SaaS GTM channels stopped working in a crowded AI marketHow Datasaur rebuilt around private, secure AI for regulated industriesWhat SaaS founders should measure when marketing “best practices” stop producing results---Stalled pipeline? Lost deals? Diagnose your GTM gaps with a free, actionable checkup. 

#361 | In this episode, Matt Carnevale, Head of Community at Exit Five talks with three marketers doing impactful work in AEO. AI search is changing how buyers find products, and most B2B teams are still figuring out where to start. In this session, each marketer shares what's working and wins they've experienced — from earned media and technical audits to homepage fixes and tracking AI visibility. Whether you call it AEO, GEO, LLMO, or EIEIO – this one's for you. This session features guests Matt Dzugan, VP of Data Intelligence at Muckrack, Brett Bernath, Director of Product at Webflow, and Jess Joyce, Founder of Inbound Scope – an SEO and AI Search consultancy.Timestamps(00:00) - - - Why 80% of CMOs say AEO is a top priority — and most don't know where to start (02:48) - - - How Muckrack used original research to get cited in ChatGPT before their product launch (02:50) - - - Why top-of-funnel content is getting eaten by AI — and where to focus instead (02:53) - - - Quick win #3: authority — how to show up in Reddit and third-party platforms (02:56) - - - The sleeper tip: Bing Webmaster Tools is already giving you first-party AI data (03:07) - - - How to handle competitor comparison content without verifiable claims falling flat (03:23) - - - The four-bucket AEO maturity model: content, technical, authority, measurement (03:24) - - - Why your homepage is your worst-performing page for AI discoverability (03:27) - - - Quick win #1: technical hygiene — schema, meta descriptions, and structured data (03:28) - - - How to identify which journalists get cited most by AI in your niche (03:29) - - - Quick win #2: are you actually answering what your customers are asking? (03:34) - - - Why 1 in 3 B2B SaaS sites have technical blockers killing AI discoverability (03:36) - - - Why original research is the single best content type for earning AI citations Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - A no-code AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Learn how to deploy agents on your marketing team at Agents in the Mix. Learn more at optimizely.com/exitfive. Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn more at vector.co, and get their new MCP server by clicking here. Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Join Angel Horvat, Founder and CEO of AI Readi, for a candid evaluation of why the corporate rush into generative AI is grinding to an unexpected halt. Despite massive infrastructure investments, the enterprise journey is hitting a hard wall: while 88% of organizations have initiated AI pilots, a staggering 94% remain permanently trapped in pilot purgatory. Drawing on his years leading AI and data strategy at Nike (EMEA) and Gartner, Angel reveals that these failures are almost never a failure of the tech—they are structural failures of the organization itself. In this episode, we discover how to bridge the gap between initial demo and scaled business value.

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

"A CRO told us the word our customers cared about most was extensibility. I'm a marketer — I've never used that word in my life."When was the last time marketing had a real say in what got built? In most B2B SaaS companies, engineers and founders own the product, and marketers inherit whatever comes out the other end. Matt Sciannella and Liam Moroney explore what it would look like for marketers to genuinely influence product direction — not by taking over, but by asking the questions nobody else is asking. Keywords: B2B marketing strategy, product marketing, market research, customer discovery, SaaS go-to-market.

Most founders think they have a sales problem. According to Lou Shipley, they usually have a customer understanding problem.Lou is a 3x CEO, Senior Lecturer at Harvard Business School, former CEO of Black Duck Software, and co-author of Unlikely Entrepreneurs.During his time at Black Duck, Lou repositioned the company from open-source compliance to open-source security, quadrupled revenue, and helped lead the company to a $565 million acquisition by Synopsys.In this conversation, we discuss: Why founders should not hand off sales too early  The real purpose of your first 100 customer conversations  How to know if you're solving a painful enough problem  Why competitive markets can be better than new markets  The go-to-market framework that helped scale Black Duck  How to identify product-market fit before building too much  What causes churn and how to spot it before it happens  Why most founders misunderstand scaling a sales team  The reality of AI and what founders should pay attention to  Lessons from six startups, multiple exits, and decades of leadership This is a practical conversation about sales, positioning, product-market fit, scaling teams, and building companies that customers actually want.00:00 Introduction to Lou Shipley and Black Duck Software02:00 The Black Duck acquisition story and repositioning strategy04:30 Why founders should own sales longer than they think09:10 Learning from customers before chasing revenue12:00 Why competitive markets are often better opportunities15:00 The myth of the young founder and why experience matters18:40 Understanding customer pain deeply enough to build a company21:20 Signs you're building a solution nobody truly needs22:45 Building software for yourself vs guessing what customers want25:00 How Lou repositioned Black Duck around security27:30 Managing vs leading as your company scales31:00 Escaping the weeds and thinking like an investor33:10 The sales framework behind Black Duck's growth39:00 Churn, product-market fit, and customer retention43:30 AI, software startups, and what founders should watch51:30 What Lou learned after running multiple companies57:20 The one message every founder needs to hearUnlikely Entrepreneurs: Wins, Losses, and Crucial Lessons on Building Great Companies: https://a.co/d/0fPfhi1D

In this episode, we're joined by George Storm, CRO at N.rich, for a conversation about why traditional B2B SaaS forecasting is no longer good enough in today's market. George shares how N.rich, the European ABM platform, helps sales-led companies influence complex buying committees, warm up priority accounts, and progress accounts before sales ever reaches out.  We spoke with George about why forecasting can't be treated as a static quarterly exercise anymore, why revenue leaders need to account for macro signals like layoffs, budget freezes, acquisitions, interest rates, and market turbulence, and how to move from fixed-number forecasting to ranges, probabilities, and continuous forecast loops. He explains why CROs should think in “regimes” like calm, turbulent, and stormy markets, and how that changes the way you model win rates, sales cycles, ACV, and pipeline coverage. Here are some of the key questions we address: Why is traditional SaaS forecasting broken? Why should forecasts be modeled as ranges instead of fixed numbers? How do macro signals like layoffs, acquisitions, and budget freezes impact pipeline confidence? Why can historical win rates be misleading in today's market? What does it mean to forecast in calm, turbulent, or stormy weather? How can CROs build a continuous forecasting loop instead of relying on quarterly updates? What should revenue leaders monitor weekly to avoid surprise misses?

Salesforce made waves in February 2026 by introducing the Agentic Work Unit, or AWU, as a new way to measure and potentially price AI agent activity. The Metrics Brothers, Ray "Growth" Rike and Dave "CAC" Kellogg dig into whether the AWU is a legitimate step toward outcome-based pricing, a vendor-specific adoption metric, or just another awkward intermediate measure in the long, messy history of software pricing models.Episode Highlights:Tokens are not business metrics. Ray and Dave open by drawing a clear line: tokens measure text processing and compute consumption, not business outcomes. Nobody walks into a board meeting announcing they processed 14 billion word fragments, and enterprise buyers should not be priced on that basis.What Salesforce is actually trying to do with the AWU. Defined as "one discrete task accomplished by an AI agent," the AWU is Salesforce's attempt to bridge the gap between low-level compute metrics and business outcomes. The hosts debate whether it is a pricing metric in waiting, an AI adoption signal, or simply the best available approximation of work performed by agents.A short history of bad pricing units. From CPU counts to MIPS to kilocharacters to gigabytes, Ray and Dave trace the long, humbling history of software vendors searching for a pricing metric that maps to value. The Soviet chandelier analogy makes an appearance, courtesy of Appian CEO Matt Calkins.Activity versus outcome: the core tension. Ray argues the AWU is directionally right but fundamentally an activity metric, not a true economic outcome. Dave is more skeptical that outcome-based pricing can scale broadly, pointing out that customers say they want value-based pricing until a vendor actually tries to take a cut of the upside.Vertical AI applications have the clearest path. Both hosts agree that verticals with well-defined, countable outputs, such as cases resolved in customer support or claims processed in insurance, are best positioned to price on outcomes and may not need the AWU at all.The AWU needs a new name, and probably a new definition. Ray and Dave close with the observation that just as NRR took nearly a decade to emerge as a standard SaaS metric, meaningful AI metrics will take time to mature. The AWU, as currently defined, is a Salesforce-specific construct and unlikely to become an industry standard.If you are a B2B SaaS or AI-Native software operating executive, this conversation on one of the first agentic AI metrics to measure work activity is a great listen.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Aidan Madigan-Curtis's path to becoming a partner at Eclipse hinges on fundamental and generalizable takeaways about the U.S. economy that are coming to the fore again. When she was on the manufacturing team at Apple, helping to launch the first Apple Watch, she realized many of the best minds in the world where whittling away at B2B SaaS even as 85% of global GDP, concentrated on how we make, move, and power things, would require step-changes in both hardware, production processes, and decarbonization and sustainability. To address that gap, Aidan and partners have built Eclipse Capital into a $10 billion venture firm that invests in and support companies combining novel approaches to both atoms and bits-focused businesses to rebuild American manufacturing, energy infrastructure, and industrial capacity.Fast-forward to 2026, and the results validate the insight into the core needs and the opportunity to address it: Eclipse recently raised another $1.3 billion across two new funds to back everything from sodium-ion battery storage (Peak Energy) to next-generation nuclear reactors to radiopharmaceutical manufacturing facilities. The firm's portfolio also spans advanced metal 3D printing (Vulcan Forms), autonomous construction equipment (Bedrock Robotics), and cell therapy manufacturing (Solaris). And the thesis remains largely the same, namely that physical-world companies with durable advantages will define the next market cycle, especially as AI demand collides with infrastructural realities.There's a lot more to Nick and Aidan's convo than this, too. Nick and Aidan also zoomed out to examine topics such as: • How data center build-out could accelerate renewable deployment and other elements of the push to decarbonize and advance sustainability prerogatives• How and why public market narratives are shifting to reward companies with physical assets whereas these were less privileged even a few years ago• The power of manufacturing scale to create geopolitical advantages whether economically or in terms of national security. Tune in for all that and more! To learn more about Eclipse and to explore their portfolio, you can also explore their website here: https://eclipse.capital/Plus, to learn more about their recent fundraising and their theses moving forward, catch up on news articles like this one: https://www.manufacturingdive.com/news/vc-firm-eclipse-raises-1-b-physical-industries-university-endowments/818546/Timestamps:00:02:21 - Eclipse's New Funds and Capital Raising00:03:34 - Eclipse's Focus on Industrial Technologies00:05:47 - Watching the Market Catch Up to Eclipse's Theses00:07:03 - Aidan's Experience at Apple00:08:43 - How COVID and Supply Chain Disruptions Catalyze Change00:09:14 - The Need for Domestic Manufacturing00:11:07 - Company Case Study: Peak Energy and Battery Storage00:12:23 - Company Case Study: The Nuclear Company and "Pre-approved Nuclear"00:15:38 - Geographic Dispersion of Technological Innovation and Impact00:18:23 - Grassroots Resistance to Data Centers00:22:14 - The Opportunity Inherent to Data Centers and Decarbonization00:24:46 - The Industrial Revolution and Rapid Transitions of The Past00:27:09 - The Role of Venture Capital in Sustainability00:28:27 - Shifting Public Market Appetite for Physical Companies00:31:13 - Public Market Dynamics and Narratives00:34:07 - Industrial Innovations and Manufacturing00:38:23 - Advanced Manufacturing in the U.S.00:40:01 - U.S. vs. China in Manufacturing Scale00:43:23 - An Eye Towards the Future of Energy and Climate Tech00:45:01 - Non-linearity in Climate ChangeFinal notes:To keep up with Aidan and her work, you can also follow her on LinkedIn: https://www.linkedin.com/in/aidan-madigan-curtisPlus, you can stay up to date on all things Keep Cool here: https://keepcool.co/ and follow Nick on LinkedIn: https://www.linkedin.com/in/nicholasvanosdol/Thank you so much.

Host: Annik Sobing Guest: Niki McKinnell Published: May 2026 Length: ~22 minutes Presented by: Global Training Center Niki McKinnell on Sales, Marketing, and the Story Behind Supply Chain Growth Annik Sobing welcomes Niki McKinnell to the Simply Trade Roundup for a conversation about what happens when sales and marketing break down in B2B SaaS supply chain companies. Niki shares how her career began in public sector communications and crisis press offices, how she learned to build a story with limited resources, and how that foundation shaped the way she approaches marketing, messaging, and go-to-market strategy today. What You'll Learn in This Episode How Niki built a career around storytelling Niki explains how her path started in government communications, where she worked in press offices and crisis environments. She talks about how those early experiences taught her to think strategically about messaging, audience, and impact. Why sales and marketing break down The episode explores the most common reasons sales and marketing teams lose alignment in supply chain SaaS companies. Niki describes how different definitions, assumptions, and metrics can create friction even when everyone is working toward the same goal. What makes supply chain different Niki breaks down why supply chain has its own flavor when it comes to go-to-market strategy. Buyers are focused on their operations, not your product, which means credibility, timing, and intentional messaging matter more than ever. How to bring teams back into alignment One of the most useful parts of the conversation is Niki's framework for stronger execution: alignment, coordination, and visibility. She explains how teams can work more intentionally before, during, and after GTM activity so they are moving with the same goals in mind. Why long sales cycles need a different approach Niki and Annik discuss how complex buying committees, long sales cycles, and deeply rooted habits make this industry especially challenging. Niki shares how companies need to adapt their strategy to meet buyers where they are. What to do when pipeline stalls Niki offers advice for founders and leaders who are struggling with pipeline. Her recommendation is to focus on the brand, demand, expand framework, with brand awareness, demand generation, and customer growth all working together to support revenue. Who this episode is for This episode is especially valuable for marketing leaders, sales teams, founders, and GTM professionals working in supply chain or B2B SaaS. It is also a great listen for anyone trying to understand how strategy, communication, and alignment shape growth in a complex industry. This podcast is presented by Global Training Center.  Subscribe & Follow Stay connected with the Simply Trade community and never miss an episode that helps you trade smarter.

From years in the SEO trenches, today's guest knows what it takes to run successful strategies. Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B, SaaS and marketing agencies.Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B SaaS and marketing agencies. He began his entrepreneurial journey in 2020 after leaving corporate marketing to launch a startup consultancy, later evolving it into Search to Sale in 2023. Previously, Adrian worked in data science and marketing analytics after earning a Master's in Applied Data Science from NYU, and earlier in his career founded and led sustainability-focused ventures. CONTACT DETAILS:Email: gerardo@searchtosale.io Business: Search to SaleWebsite: https://www.searchtosale.io/ Social Media:LinkedIN: https://www.linkedin.com/in/adriandahlin/ LinkedIN Company: https://www.linkedin.com/company/search-to-sale-seo-revenue-generation-software/ Remember to SUBSCRIBE so you don't miss "Information That You Can Use." Share Just Minding My Business with your family, friends, and colleagues. Engage with us by leaving a review or comment. https://g.page/r/CVKSq-IsFaY9EBM/review Your support keeps this podcast going and growing.Visit Just Minding My Business Media™ LLC at https://jmmbmediallc.com/ to learn how we can help you get more visibility on your products and services. 

Dan Balcauski is the founder of Product Tranquility, where he helps B2B SaaS companies improve pricing, packaging, and monetization strategy.  In this episode, Dan breaks down the uncomfortable reality behind today's AI gold rush: buyers are tired of "AI-powered" hype, SaaS companies are struggling to monetize features nobody uses, and pricing teams are rewriting their strategies in real time. If your company is trying to monetize AI without becoming another forgettable AI feature, this episode will change how you think about pricing, adoption, and customer value.   Why You Have to Listen: Learn why AI features alone don't drive purchases — and how to position AI around customer outcomes people actually value. Discover the biggest AI pricing mistake SaaS companies are making — charging for features before customers build adoption habits. See how smart SaaS companies roll out AI strategically — using adoption-first pricing, early access models, and workflow-driven product design.   "Prove value with your new AI features before you throw a paywall in front of it." — Dan Balcauski    Topics Covered: 02:10 - "Freemium Is a Terrible Idea for Most SaaS Companies". Why most freemium models fail before companies fully understand the real costs behind them. 06:48 - Why AI Can't Automatically Set Your SaaS Prices. Dan explains where AI can help pricing teams and where human judgment still matters most. 09:53 - The Dangerous Truth About AI Pricing Advice. Most LLMs learned pricing strategy from bad SEO content and outdated thinking. 13:35 - The Adoption vs. Monetization Framework. The simple 2x2 model every SaaS company should use before pricing AI features. 17:34 - Margin Percentage vs. Margin Dollars. A smarter way for CFOs and SaaS leaders to think about AI profitability. 18:32 - "Buyers Don't Care That Your Product Uses AI". Why customers care more about outcomes and workflows than your AI technology. 24:31 - Why SaaS Companies Keep Changing AI Pricing. Most AI pricing models don't survive their first 18 months. 26:07 - The "Early Access" AI Pricing Strategy. How smart SaaS companies introduce AI features without hurting adoption. 29:24 - "Earn the Right to Monetize". Why proving customer value should happen before putting up a paywall.   Key Takeaways: "We need to prove our value first before we can monetize it." – Dan Balcauski   People / Resources Mentioned: Steven Forth — Mentioned as a trusted source of pricing expertise and strategic thinking. Anthropic Claude Code — Dan's primary AI workspace for research synthesis and pricing analysis. Readwise — Tool Dan uses to ground AI outputs using trusted expert highlights and notes. Salesforce — Referenced as an example of rapidly evolving AI pricing strategies. Pragmatic Institute — Mentioned during the discussion on product adoption and feature prioritization.   Connect with Dan Balcauski: Website: https://www.producttranquility.com/ LinkedIn: https://www.linkedin.com/in/balcauski/ X: https://x.com/dan_balcauski Podcast: https://podcasts.apple.com/us/podcast/saas-scaling-secrets/id1682338188   Connect with Mark Stiving: LinkedIn: https://www.linkedin.com/in/stiving/ Email: mark@impactpricing.com  

Most founders treat 'scale' like a switch you flip after raising a round: hire 14 reps, 10x the ad spend, and pray. About half scale too early and burn the runway, while the other half scale too late and get caught by a more aggressive competitor. Almost nobody can tell you, in measurable terms, when they're actually ready.In this episode, Yaniv Bernstein is joined by Mark Roberge - founding CRO at HubSpot (where he scaled the company from $0 to $100M ARR), senior lecturer at Harvard Business School, cofounder of Stage 2 Capital, and author of the new book 'The Science of Scaling'. Mark walks Yaniv through his impressive data-driven framework for scaling that he's spent a decade refining, covering how to objectively define product-market fit, why customer retention is the only honest measure of PMF, and how to instrument a Leading Indicator of Retention you can act on in week one.In this episode, you will:Learn why retention is the only honest measure of product-market fit, and why most founders are flying blind without itDiscover Mark's framework for building a Leading Indicator of Retention (LIR) you can measure in week one, using Slack, HubSpot, and Facebook as worked examplesHear Mark coach Yaniv through Vera's LIR in real time, and pick up a repeatable method for designing one for your own businessLearn the 'Stay/Go/Slow' model for pacing hires and spend post-raise, and why startups should reassess monthly or quarterly rather than locking in an annual planGet Mark's take on why 'paranoid optimism' is the trait that correlates most strongly with founder success, and the link between that trait and founder mental healthTimestamps00:00 Coming Up00:26 On Today's Show: The Science of Scaling01:47 Guest Intro: Mark Roberge02:31 Why Scaling Needs Data04:20 Eric Ries and Product Market Fit06:56 Retention as a North Star10:15 What Makes a Good Leading Indicator?15:00 Case Study: Vera (Yaniv's Startup)17:41 Choosing Frequency and Event23:55 Instrumenting and Unique Value31:12 Blitzscaling and Defining PET34:41 ICP Denominator Rules37:28 Segmenting By Product40:40 Go To Market Fit45:25 Dealing with Revenue-Focused Investor Pressure50:33 The Pace of Scaling56:07 About the Book, The Science of Scaling57:45 Founder Mental Health01:02:28 Closing ThoughtsResources in this episode:Mark Roberge on LinkedIn: https://www.linkedin.com/in/markroberge/‘The Science of Scaling: Using Data to Decide When — and How Fast — to Scale Revenue' by Mark Roberge: https://www.amazon.com/Science-Scaling-Revenue-Mark-Roberge/dp/1394319428Stage 2 Capital (Mark's B2B SaaS-focused venture firm): https://www.stage2.capital/Vera (Yaniv's startup): https://vera.guide/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appFollow us on YouTube for full-video episodes: https://www.youtube.com/@startup-podcastGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean and memorable name, go to https://⁠get.tech/tspThis episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠ The Startup Podcast website: https://www.tsp.show/episodes/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

Consumption pricing puts pressure on the forecast in places traditional SaaS models rarely exposed. Total usage may be easier to model from the CFO's seat, but the field still has to answer harder questions: which customer, which channel, which rep, and when. In this replay segment, Devavrat Shah explains how AI can help teams learn across cohorts, spot patterns in uneven data, and create more trust in a forecast that would otherwise depend on isolated judgment calls.  Devavrat Shah is an MIT professor, director of MIT's Statistics and Data Science Center, and co-founder and CEO of Ikigai Labs. He brings a data science and operator's perspective to forecasting, consumption pricing, and enterprise AI. Connect with Devavrat: LinkedIn Listen to the full episode here: Understanding AI Through History and Practical Application with Devavrat Shah Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Today, we're joined by Dan Balcauski, Founder and Chief Pricing Officer of Product Tranquility, a consulting firm that helps high-volume B2B SaaS CEOs define pricing and packaging for new products. We talk about:How adding AI doesn't always increase valueThe growing importance of discerning what you should buildWhy you should expect to get the pricing of AI capabilities wrong out of the gateWhy charging per tokens is pouring sand in the gas tank of your GTM engine How it's also showing your customers your underpantsCommon bad pricing decisionsThis is Dan's second appearance on SaaS Scaled. You can watch his first episode, “Pricing is Simple, But Not Easy. Dan Balcauski Answers Hard Questions.

This week's throwback episode guest studied Sociology, Cultural Relations and Global Politics at University of Montana before taking the jump into B2B Sales and Marketing where she has spent most of her career. She has been a Sales Director, Head of Sales, Employee #1 to CRO all leading up to the work she does now as the Founder of Sales-Led GTM Agency. At Sales-Led GTM Agency, she focuses on building the outbound sales strategy, processes & skill sets your sales-led organization needs to thrive, and provides B2B Sales Training & GTM Consulting for B2B SaaS & Service orgs between 15 - 50 M in annual revenue. Last time we spoke, she just left corporate, but since then has been building in public, and now we are 2 years in and will be talking about her journey today! Please join me in welcoming Leslie Venetz to The 20% Podcast. In this week's episode, we discussed:Trusting Your Gut Why Become An EntrepreneurA New Wave of EntrepreneursGet Clear On WorkGetting Specific With Your AsksMuch MorePlease enjoy this week's episode with Leslie VenetzI am now in the early stages of writing my first book! It will cover my journey into sales, the lessons learned, and include stories and advice from top sales professionals around the world. I'm excited to share these interviews and bring you along on this journey!Like the show? Subscribe to the email: Subscribe HereI want your feedback! Reach out at 20percentpodcastquestions@gmail.com or connect with me on LinkedIn.If you know anyone who would benefit from this show, please share it! If you have suggestions for guests, let me know!Enjoy the show!

Evan Spiegel, the co-founder and CEO of Snap, is one of the very few people in the world who has successfully built and scaled a lasting consumer social product. Snapchat has nearly 1 billion MAUs, and Evan and his team invented some of the most important consumer products and features, including Stories, AR glasses, swipe-based navigation, the camera as the primary UX, and a lot more.In our in-depth conversation, we discuss:1. Why distribution is now the biggest challenge for creating a consumer technology business2. How Snap innovates at scale with a 9-to-12-person design team: no titles, no hierarchy, hundreds of ideas reviewed weekly with the CEO3. Why a pure software business is no longer a moat, and what actually creates durable competitive advantages today4. How AI is changing the way designers work and why they're now shipping code5. Why every major Snap feature was copied and how that forced the company to work differently6. Evan's prediction that humanity's comfort with AI will be a bigger bottleneck than the technology itself7. This year's crucible moment for Snap—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-is—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Evan Spiegel:• X: https://x.com/evanspiegel• Snapchat: https://www.snapchat.com/@evan• LinkedIn: https://www.linkedin.com/in/evan-spiegel• Website: https://www.spiegelfamilyfund.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Evan Spiegel(02:28) Why consumer social products are so hard to build(04:31) How Snapchat cracked distribution with close friends, not network size(05:50) Why distribution is the new moat in the AI era(08:39) Snapchat's innovation track record (and why software isn't a moat)(11:39) Why Snap is betting on two of the hardest businesses: consumer social and hardware(16:00) Specs use cases(17:56) The innovation process(21:34) The velocity of design work at Snapchat(25:07) Why Evan says you must talk to customers(26:06) The origin story of Stories(28:25) How screenshot detection saved early Snapchat(31:03) Why they waited to hire PMs—and what role they play now(34:41) How AI is shifting the designer-PM-engineer triad(36:10) Design as an intentional bottleneck for product cohesion(37:24) Why staying close to customers matters for any leader(39:39) What Evan looks for when hiring designers(41:57) How to develop young design talent(44:16) Designers shipping code with AI—and the guardrails needed at scale(47:20) Using jobs-to-be-done to organize AI transformation(48:50) How the CEO job has changed over 15 years(51:30) Learning to communicate(54:08) Why this year is Snapchat's “crucible moment”(56:22) Being the “middle child” in tech(57:51) Screen-time philosophy with four kids (ages 2 to 15)(1:01:08) AI Corner(1:04:02) Contrarian Corner(1:06:04) Lightning round and final thoughts—References: https://www.lennysnewsletter.com/p/snapchat-ceo-why-distribution-is—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Cat Wu is Head of Product for Claude Code and Cowork at Anthropic, building one of the most important AI products of this generation. Before joining Anthropic, Cat spent years as an engineer and briefly worked in VC. Today, she's interviewing hundreds of product managers who are trying to break into AI—and seeing firsthand what separates those who thrive from those who fall behind.We discuss:1. How Anthropic's shipping cadence went from months to weeks to days2. The emerging skills PMs need to develop right now3. Why you need to build products that don't yet fully work, so you're ready when the next model closes the gap4. Cat's most underrated AI skill: asking the model to introspect on its own mistakes5. Why Claude's personality is core to its success6. Why Anthropic's mission alignment eliminates the friction that slows most large organizations7. Why “just do things” is the most important principle for working at AI-native companies—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Cat Wu:• X: https://x.com/_catwu• LinkedIn: linkedin.com/in/cat-wu• Newsletter: https://catwu.substack.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Cat Wu(01:29) Working with Boris Cherny(04:29) What Anthropic looks for when hiring PMs(06:18) How to help your teams move fast(08:58) How PRDs and roadmaps have evolved at Anthropic(10:28) The Mythos model and Anthropic's shipping velocity(11:54) What happened with the Claude Code source code leak(12:53) Integrating with OpenClaw(14:19) How the PM team is structured at Anthropic(15:42) How engineer and PM roles are merging(17:54) Why product taste is the most valuable skill(20:10) Where human brains will continue to be useful(22:23) How to stay sane in constant chaos(24:16) What gets sacrificed when you ship so fast(27:47) The /powerup command(28:32) Why Anthropic has been so successful(32:28) When to use Claude Code vs. Desktop vs. Cowork(35:58) Tips for getting started with Cowork(38:44) Demo: Using Cowork to build slide decks overnight(41:48) Cat's PM tech stack and internal tools(46:47) Which teams use the most tokens(51:15) The emerging skills PMs need for AI companies(55:00) Why building evals is underappreciated(58:44) Why Claude's character and personality matter so much(1:00:44) How new models force product changes(1:05:11) The vision for Claude Code and Cowork(1:07:22) Advice for thriving in an AI-driven world(1:09:18) Why 95% automation isn't good enough(1:11:58) Build apps you use every day, not prototypes(1:13:41) The divide between AI skeptics and believers(1:15:19) Lightning round—Referenced: https://www.lennysnewsletter.com/p/how-anthropics-product-team-moves—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He's also one of the most honest, unfiltered voices on what's actually happening in product management right now.In our in-depth conversation, we discuss:1. Why the next two years will be the most chaotic period in product management history2. Why half of current product managers are at risk, and what separates those who'll do well3. Why you need to find your “moments of joy” with AI4. The “smiling exhaustion” he's seeing across the product community5. The psychological barriers that prevent people from reinventing themselves6. Why your resume's fancy logos matter less than ever, and what matters now7. His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Nikhyl Singhal:• LinkedIn: https://www.linkedin.com/in/nikhyl• X: https://x.com/nikhyl• Podcast & Newsletter: https://skip.show• Skip Community: https://skip.community• Skip Coach: https://skip.coach• Skip.help: https://skip.help—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Nikhyl Singhal(02:25) The big picture: what's changing for product managers(10:00) Are product leaders doing better than 2-3 years ago?(11:44) What will change in the next couple of years(14:23) How companies are changing the way they build products(15:51) What “judgment” really means for PMs(17:46) Why there won't be any more bad software(20:25) The skills you need to be effective today(23:31) Why there are more PM roles than ever(24:27) The builder versus information-mover divide(30:14) The non-builder problem(30:53) Should PMs code?(34:15) Why experienced leaders still matter(35:44) The diversity setback nobody's talking about(37:21) Why your brand doesn't matter as much anymore(39:54) How valued skills are flipping upside down(40:49) Why change is so hard for humans(43:53) The “equal disappointment” algorithm(46:39) You must cross the threshold(48:37) This chaos will settle(53:19) Finding your moment of joy(58:50) Nikhyl's AI stack and what he's building(1:00:53) The obsolescence mindset(1:05:24) Specific advice for PMs right now(1:08:58) The four jobs that will exist in the future(1:11:59) Why alignment is changing (but not disappearing)(1:15:40) How engineering is changing even more than PM(1:17:04) The surprising design plateau(1:18:49) Finding optimism in the chaos(1:21:12) Lightning round—Referenced:• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career• COBOL: https://en.wikipedia.org/wiki/COBOL• United Airlines: https://www.united.com• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run• Demis Hassabis on X: https://x.com/demishassabis• Sam Altman on X: https://x.com/sama• Dario Amodei on X: https://x.com/DarioAmodei• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A• Claude Code: https://code.claude.com• Codex: https://chatgpt.com/codex• Lovable: https://lovable.dev• Sonos: https://www.sonos.com• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness• Tesla: https://www.tesla.com• Albert Einstein's quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work—Recommended books:• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com