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Manufacturing is getting faster, messier, and more expensive when quality slips. Daniel First, Founder and CEO at Axion, joins Amir to break down how AI is changing the way manufacturers detect issues in the field, trace root causes across messy data, and shorten the time from “customers are hurting” to “we fixed it.” Episode Summary Daniel First, Founder and CEO at Axion, explains why modern manufacturing is living in the bottom of the quality curve longer than ever, and how AI can help companies spot issues early, investigate faster, and actually close the loop before warranty costs and customer trust spiral. If you work anywhere near hardware, infrastructure, or complex systems, this is a sharp look at what “AI first” means when real products fail in the real world. You will hear why quality is becoming a competitive weapon, how unstructured signals hide the truth, and what changes when AI agents start doing the detection, investigation, and coordination work humans have been drowning in. What you will take away Quality is not just a defect problem, it is a speed and trust problem, especially when product cycles keep compressing. AI creates leverage by pulling together signals across the full product life cycle, not by sprinkling a chatbot on one system. The fastest teams win by finding issues earlier, scoping impact correctly, and fixing what matters before customers notice the pattern. A clear ROI often lives in warranty cost avoidance and downtime reduction, not just “efficiency” metrics. “AI first” gets real when strategy becomes operational, and contradictions in how teams prioritize issues get exposed. Timestamped highlights 00:00 Why manufacturing is a different kind of problem, and why speed is harder than it looks 01:10 What Axion does, and how it detects, investigates, and resolves customer impacting issues 05:10 The new reality, faster product cycles mean living in the bottom of the quality curve 10:05 Why it can take hundreds of days to truly solve an issue, and where the time disappears 16:20 How to evaluate AI vendors in manufacturing, specialization, integrations, and cross system workflows 22:40 The shift coming to quality teams, from reading data all day to making higher level decisions 28:10 What “AI first” looks like in practice, and how AI exposes misalignment across teams A line worth repeating “Humans are not that great at investigating tens of millions of unstructured data points, but AI can detect, scope, root cause, and confirm the fix.” Pro tips you can apply When evaluating an AI solution, ask three questions up front: how specialized the AI must be, whether you need a full workflow solution or just an API, and whether the use case spans multiple systems and teams. Treat early detection as a first class objective, the longer the accumulation phase, the more cost and customer damage you silently absorb. Align issue prioritization to strategy, not just frequency, cost, or the loudest internal voice. Follow: If this episode helped you think differently about quality, speed, and AI in the real world, follow the show on Apple Podcasts or Spotify so you do not miss the next one. If you want more conversations like this, subscribe to the newsletter and connect with Amir on LinkedIn.

Synthetic data is moving from a niche concept to a practical tool for shipping AI in the real world. In this episode, Amit Shivpuja, Director of Data Product and AI Enablement at Walmart, breaks down where synthetic data actually helps, where it can quietly hurt you, and how to think about it like a data leader, not a demo builder. We dig into what blocks AI from reaching production, how regulated industries end up with an unfair advantage, and the simple test that tells you whether synthetic data belongs anywhere near a decision making system. Key Takeaways • AI success still lives or dies on data quality, trust, and traceability, not model hype. • Synthetic data is best for exploration, stress testing, and prototyping, but it should not be the backbone of high stakes decisions. • If you cannot explain how an output was produced, synthetic only pipelines become a risk multiplier fast. • Regulated industries often move faster with AI because their data standards, definitions, and documentation are already disciplined. • The smartest teams plan data early in the product requirements phase, including whether they need synthetic data, third party data, or better metadata. Timestamped Highlights 00:01 The real blockers to getting AI into production, data, culture, and unrealistic scale assumptions 03:40 The satellite launch pad analogy, why data is the enabling infrastructure for every serious AI effort 07:52 Regulated vs unregulated industries, why structure and standards can become a hidden advantage 10:47 A clean definition of synthetic data, what it is, and what it is not 16:56 The “explainability” yardstick, when synthetic data is reasonable and when it is a red flag 19:57 When to think about data in stakeholder conversations, why data literacy matters before the build starts A line worth sharing “AI is like launching satellites. Data is the launch pad.” Pro Tips for tech leaders shipping AI • Start data discovery at the same time you write product requirements, not after the prototype works • Use synthetic data early, then set milestones to shift weight toward real world data as you approach production • Sanity check the solution, sometimes a report, an email, or a deterministic workflow beats an AI system Call to Action If this episode helped you think more clearly about data strategy and AI delivery, follow the show on Apple Podcasts and Spotify, and share it with a builder or leader who is trying to get AI out of pilot mode. You can also follow me on LinkedIn for more episodes and clips.

Tom Pethtel, VP of Engineering at Flock Safety, breaks down the real learning curve of moving from builder to manager, and how to keep your technical edge while scaling your impact through people. You will hear how Tom’s path from rural Ohio to leading high stakes engineering teams shaped his approach to leadership, hiring, and staying close to the customer. Key Takeaways ​ Promotions usually come from doing your current job well, plus stepping into the work above you that is not getting done​ Great leaders do not fully detach from the craft, they stay close enough to the work to make good calls and keep context​ Put yourself where the real learning is happening, watch customers, go to the failure point, get proximity to the source of truth​ Hiring is not only pedigree, it is fundamentals plus grit, the willingness to solve what looks hard because it is “just software”​ As you scale to teams of teams, your job becomes time allocation, jump on the biggest business fire while still making rounds everywhere Timestamped Highlights 00:32 What Flock Safety actually builds, from AI enabled devices to Drone as a First Responder 02:04 Dropping out of Georgia Tech, switching disciplines, and choosing software for speed and impact 03:30 A life threatening detour, learning you owe 18,000 dollars, and teaching yourself to build an iPhone app to survive 06:33 Why Tom values grit and non traditional backgrounds in hiring, and the “it is just software” mindset 08:46 Proximity and learning, go to the problem, plus the lessons he borrows from Toyota Production System 09:55 A practical story of chasing expertise, from Kodak to Nokia, and hiring the right leader by going where the knowledge lives 14:27 The truth about becoming a manager, you rarely feel ready, you take the seat and learn fast 19:18 Leading teams of teams, you cannot be everywhere, so you go where the biggest fire is, without neglecting the rest 22:08 The promotion playbook, stop only doing your job, start solving the next job A line worth stealing “Do your job really well, plus go do the work above you that is not getting done, that’s how you rise.” Pro Tips for engineers stepping into leadership ​ Stay technical enough to keep your judgment sharp, even if it is only five or ten percent of your week​ If you want to grow, chase proximity, sit with the customer, sit with the failure, sit with the best people in the space​ Measure your impact as leverage, if a team of ten is producing ten times, your role is not less valuable, it is multiplied​ When you lead multiple disciplines, rotate your attention intentionally, do not camp on one fire for a full year Call to Action If this episode helped you rethink leadership, share it with one builder who is about to step into management. Subscribe on Apple Podcasts, Spotify, and YouTube, and follow Amir on LinkedIn for more conversations with operators building real teams in the real world.

Phil Freo, VP of Product and Engineering at Close, has lived the rare arc from founding engineer to executive leader. In this conversation, he breaks down why he stayed nearly 12 years, and what it takes to build a team that people actually want to grow with. We get into retention that is earned, not hoped for, the culture choices that compound over time, and the practical systems that make remote work and knowledge sharing hold up at scale. Key takeaways • Staying for a decade is not about loyalty, it is about the job evolving and your scope evolving with it • Strong retention is often a downstream effect of clear values, internal growth opportunities, and leaders who trust people to level up • Remote can work long term when you design for it, hire for communication, and invest in real relationship building • Documentation is not optional in remote, and short lived chat history can force healthier knowledge capture • Bootstrapped, customer funded growth can create stability and control that makes teams feel safer during chaotic markets Timestamped highlights 00:02:13 The founders, the pivots, and why Phil joined before Close was even Close 00:06:17 Why he stayed so long, the role keeps changing, and the work gets more interesting as the team grows 00:10:54 “Build a house you want to live in”, how valuing tenure shapes culture, code quality, and decision making 00:14:14 Remote as a retention advantage, moving life forward without leaving the company behind 00:20:23 Over documenting on purpose, plus the Slack retention window that forces real knowledge capture 00:22:48 Bootstrapped versus VC backed, why steady growth can be a competitive advantage when markets tighten 00:28:18 The career accelerant most people underuse, initiative, and championing ideas before you are asked One line worth stealing “Inertia is really powerful. One person championing an idea can really make a difference.” Practical ideas you can apply • If you want growth where you are, do not wait for permission, propose the problem, the plan, and the first step • If you lead a team, create parallel growth paths, management is not the only promotion ladder • If you are remote, hire for writing, decision clarity, and follow through, not just technical depth • If Slack is your company memory, it is not memory, move durable knowledge into docs, issues, and specs Stay connected: If this episode sparked an idea, follow or subscribe so you do not miss the next one. And if you want more conversations on building durable product and engineering teams, check out my LinkedIn and newsletter.

Pete Hunt, CEO of Dagster Labs, joins Amir Bormand to break down why modern data teams are moving past task based orchestration, and what it really takes to run reliable pipelines at scale. If you have ever wrestled with Apache Airflow pain, multi team deployments, or unclear data lineage, this conversation will give you a clearer mental model and a practical way to think about the next generation of data infrastructure. Key Takeaways • Data orchestration is not just scheduling, it is the control layer that keeps data assets reliable, observable, and usable • Asset based thinking makes debugging easier because the system maps code directly to the data artifacts your business depends on • Multi team data platforms need isolation by default, without it, shared dependencies and shared failures become a tax on every team • Good software engineering practices reduce data chaos, and the tools can get simpler over time as best practices harden • Open source makes sense for core infrastructure, with commercial layers reserved for features larger teams actually need Timestamped Highlights 00:00:50 What Dagster is, and why orchestration matters for every data driven team 00:04:18 The origin story, why critical institutions still cannot answer basic questions about their data 00:07:02 The architectural shift, moving from task based workflows to asset based pipelines 00:08:25 The multi tenancy problem, why shared environments break down across teams, and what to do instead 00:11:21 The path out of complexity, why software engineering best practices are the unlock for data teams 00:17:53 Open source as a strategy, what belongs in the open core, and what belongs in the paid layer A Line Worth Repeating Data orchestration is infrastructure, and most teams want their core infrastructure to be open source. Pro Tips for Data and Platform Teams • If debugging feels impossible, you may be modeling your system around tasks instead of the data assets the business actually consumes • If multiple teams share one codebase, isolate dependencies and runtime early, shared Python environments become a silent reliability risk • Reduce cognitive load by tightening concepts, fewer new nouns usually means a smoother developer experience Call to Action If this episode helped you rethink data orchestration, follow the show on Apple Podcasts and Spotify, and subscribe so you do not miss future conversations on data, AI, and the infrastructure choices that shape real outcomes.

Sandesh Patnam, Managing Partner at Premji Invest, breaks down how long duration capital changes the way you evaluate companies, founders, and moats. We talk about what most growth investors miss, why product strength still matters, and how to separate real AI businesses from thin wrappers in a noisy market. Premji Invest is a captive, evergreen fund built to grow an endowment that supports major education work, which gives the team flexibility on time horizon and partnership style. Sandesh shares how that shows up in diligence, how they think about backing contrarian founders, and why the best companies in this AI era may still be ahead of us. Key Takeaways Focus on the long arc, not quarter by quarter optics, founders make better decisions when they are not trapped in short term metrics In growth investing, TAM models and KPI spreadsheets can distract from the core question, does the product have real strength and an expanding roadmap Enduring outcomes often come from backing a contrarian view early, then helping it move from contrarian to consensus over time Evergreen capital changes behavior, you can slow down, build relationships, and partner across private and public markets instead of treating IPO as the finish line In AI, separate the stack into data center, foundation models, and applications, then look for defensibility like vertical depth, data moats, and compounding usage value Timestamped highlights 00:38 Premji Invest explained, evergreen structure, one LP, and why public markets can be part of the journey, not the exit 04:47 Two common growth investor lenses and what gets missed when product and roadmap do not lead the thesis 08:48 Partnership mindset, building trust, and being the first call when things get hard 12:48 The contrarian to consensus path, what creates alpha, and how to support founders through the lonely middle 19:54 Why rushing decisions is a trap, and how flexibility changes when and how you can partner with a company 20:55 AI investing framework, three layers, what looks frothy, what can endure, and where moats still exist 26:48 The cost of intelligence is collapsing, why this may still be the early internet moment, and what that implies for the next wave A line that stuck with me “We want to be the first port of call when the seas are turbulent.” Practical moves you can steal Pressure test the roadmap, ask when product two ships, what adjacency comes next, and what tradeoffs change at scale When evaluating AI apps, demand a defensibility story beyond the model, look for proprietary data, vertical workflow depth, and value that improves with usage Treat speed as a risk factor, if you cannot complete your churn cycle of doubt and validation, step back rather than force certainty Call to Action If you liked this one, follow the show and share it with a founder, operator, or investor who is building in AI right now. For more conversations at the intersection of tech, business, and execution, subscribe and connect with me on LinkedIn.

Software engineering is changing fast, but not in the way most hot takes claim. Robert Brennan, Co founder and CEO at OpenHands, breaks down what happens when you outsource the typing to the LLM and let software agents handle the repetitive grind, without giving up the judgment that keeps a codebase healthy. This is a practical conversation about agentic development, the real productivity gains teams are seeing, and which skills will matter most as the SDLC keeps evolving. Key Takeaways AI in the IDE is now table stakes for most engineers, the bigger jump is learning when to delegate work to an agent The best early wins are the unglamorous tasks, fixing tests, resolving merge conflicts, dependency updates, and other maintenance work that burns time and attention Bigger output creates new bottlenecks, QA and code review can become the limiting factor if your workflow does not adapt Senior engineering judgment becomes more valuable, good architecture and clean abstractions make it easier to delegate safely and avoid turning the codebase into a mess The most durable human edge is empathy, for users, for teammates, and for your future self maintaining the system Timestamped Highlights 00:40 What OpenHands actually is, a development agent that writes code, runs it, debugs, and iterates toward completion 02:38 The adoption curve, why most teams start with IDE help, and what “agent engineers” do differently to get outsized gains 06:00 If an engineer becomes 10x faster, where does the time go, more creative problem solving, less toil 15:01 A real example of the SDLC shifting, a designer shipping working prototypes and even small UI changes directly 16:51 The messy middle, why many teams see only moderate gains until they redraw the lines between signal and noise 20:42 Skills that last, empathy, critical thinking, and designing systems other people can understand 22:35 Why this is still early, even if models stopped improving today, most orgs have not learned how to use them well yet A line worth sharing “The durable competitive advantage that humans have over AI is empathy.” Pro Tips for Tech Teams Start by delegating low creativity tasks, CI failures, dependency bumps, and coverage improvements are great training wheels Define “safe zones” for non engineers contributing, like UI tweaks, while keeping application logic behind clearer guardrails Invest in abstractions and conventions, you want a codebase an agent can work with, and a human can trust Track where throughput stalls, if PR review and QA are the bottleneck, productivity gains will not show up where you expect Call to Action If you got value from this one, follow the show and share it with an engineer or product leader who is sorting out what “agentic development” actually means in practice.

Deborah Hanus, Co-founder and CEO at Sparrow, joins Amir to unpack the founder journey from academia to building a scaled company. They dig into why leave management is still a messy, high stakes problem, and how Sparrow is turning it into a clean, guided experience for both HR and employees. Sparrow helps companies provide employee leave across the United States and Canada, and Deborah shares what it really takes to scale a compliance driven business without slowing down. From founder resilience and early stage emotional swings to hiring, onboarding, and culture design, this one is packed with lessons for operators and builders. Key takeaways • Academia can be real founder training, especially for building resilience and hearing “no” without losing your edge • Early stage startups feel brutal because you have too few data points, it is easy to overreact to every win or setback • Compliance and leave are fundamentally data problems, the right info to the right person at the right time changes everything • Scaling leadership is mostly communication and alignment, five people and 250 people require totally different systems • Culture does not stay stable by accident, values must drive hiring, training, rewards, and performance management Timestamped highlights 00:37 What Sparrow does, and the 300 million dollars in payroll cost savings milestone 01:37 Why academia can prepare you for founding, and how customer pain beats outside skepticism 03:40 The leave compliance mess, and why state by state rules made the problem explode 08:25 The two real ways startups die, and why morale matters as much as cash 12:55 Leading at scale, onboarding, clarity, and the feedback questions that keep teams aligned 19:54 “Scale intentionally” as a culture principle for a company that cannot afford to break things 25:48 Keeping values stable while everything else evolves as the team grows A line worth sharing “Companies end when you run out of cash or you run out of morale.” Pro tips you can steal • Treat the employee journey like a product journey, from recruiting through promotions and hard moments • Before a big change, collect questions early so the message lands where people actually are • After a meeting, ask “What were the main points?” to see what people heard, then tighten your messaging • Invest in onboarding and goal clarity to prevent teams from drifting into competing priorities Call to action If you enjoyed this conversation, follow and subscribe so you do not miss what is next.

Max Bruner, Founder and CEO of Anzen, joins Amir Bormand to break down why insurance is quietly one of the biggest data and workflow opportunities in tech right now. They dig into Max’s unconventional path from foreign policy to building an executive liability marketplace, and what it really takes to modernize a slow moving industry with AI. If you care about building in real world markets, scaling with discipline, and using AI for more than content, this one will sharpen your thinking fast. Key Takeaways • Insurance is not flashy, but it is foundational, massive, profitable, and packed with repeatable workflows that software can improve • The best tech opportunities are often in slow moving industries with lots of data and outdated systems • Better decision making comes from predicting outcome impact and pressure testing your thinking with a strong community around you • AI value is clearest when it drives real operations, faster transactions, lower costs, and better service • Fundraising is a pipeline game now, treat it like sales, build the plan, hit the numbers, run a tight process Timestamped Highlights 00:42 What Anzen actually does, a one stop marketplace for executive liability quotes across the US 02:29 From Arabic studies and foreign policy to discovering insurance through political risk 08:12 The curiosity engine, how deep research habits shaped his ability to build in new domains 11:23 Decision guardrails, learning from outcomes and using trusted people to keep you efficient 13:12 Why choose insurance, building in industries that make the world work, plus the profit reality 17:29 The startup advantage, modern infrastructure vs incumbent legacy systems, and why catching up takes time 20:36 Raising in today’s market, what changed, what worked, and why the pitch volume matters A line worth stealing “Sometimes in tech we miss the application, there are massive industries to go change if we apply technology in the right way.” Max Bruner Pro Tips for builders • Pick markets with repeatable workflows, you can ship measurable value faster • Spend your time where the outcome impact is high, skip low ROI rabbit holes • Build a real financial plan before fundraising, then operate close to it • Run fundraising like a sales process, pipeline, volume, and discipline win Call to Action If you enjoyed this conversation, follow the show and leave a quick review, it helps more builders find it.

Stu Solomon, CEO of HUMAN, joins Amir to unpack a blind spot most teams underestimate: a huge share of online activity is not people at all, it is automated traffic. They break down how verification really works at internet scale, why agentic workflows change the rules, and what it will take to build trust when bots transact with bots. If you have ever wondered how fraud, fake clicks, account abuse, and synthetic behavior get caught in real time, this episode is a clear, practical look behind the curtain. Key takeaways • Most of the internet is machine traffic now, the goal is no longer spotting bots, it is separating good machines from bad ones • Trust is built by combining behavior, infrastructure signals, and identity or credential history into fast decisions at scale • Agentic systems lower the barrier to entry for attackers, less skilled actors can now create outsized impact • The hard part is accountability, when a machine acts with your authority, who owns the outcome • Adoption follows convenience, but visibility matters, if it feels like a black box, people will not trust it Timestamped highlights 00:33 HUMAN in plain English, making split second decisions about who is human, and whether they are safe 03:59 The trust stack, behavior signals, infrastructure clues, and identity or credential history 10:19 The real shift with AI, lower barriers for attackers, plus the rise of agentic autonomy 14:37 The cake story, an agent completes the task, then surprises you with a 750 dollar bill 17:22 Bots talking to bots, where accountability and liability get messy fast 24:18 Security builds trust, trust unlocks adoption, and society is already closer than it thinks A line you will remember “We have always operated on the notion that if you are human, you are good, and if you are a machine, you are bad. That is simply not the case anymore.” Practical ideas you can use • Add guardrails when you delegate to tools, especially budgets, limits, and approval steps • Watch for trust signals, not just identity checks, behavior plus infrastructure plus history beats any single data point • Design for visibility, show users what the system did and why, so trust can compound over time Follow: If this episode helped you think more clearly about trust, fraud, and agentic systems, follow the show, subscribe for more conversations like this, and share it with a teammate who is building in ads, ecommerce, identity, security, or AI.

Mek Stittri, CTO at Stuut, breaks down a leadership skill that sounds simple but gets messy fast, trust, then verify. You will learn how to delegate without losing control, how to stay close to the work without becoming a micromanager, and how AI is changing what it means to review and own technical outcomes. Key takeaways • Trust and verify starts with alignment, define success clearly, then keep a real line of sight to outcomes • Verification is not micromanagement, it is accountability, your team’s results are your responsibility as a leader • Use lightweight mechanisms like weekly reports, and stay ready to answer questions three levels deep when speed matters • AI is pushing engineers toward system design and management skills, you will manage agents and outputs, not just code • Fast feedback prevents slow damage, address issues early, praise in public, give direct feedback in private Timestamped highlights 00:41 Stuut in one minute, agented AI for finance ops, starting with collections and faster cash outcomes 01:54 Trust without verification becomes disconnect, why leaders still need to get close to the details 03:42 The three levels deep idea, how to keep situational awareness without hovering 06:33 The next five years, engineers managing teams of agents, system design as the differentiator 11:40 Feedback as a gift, why speed and privacy matter when coaching 16:54 The timing art, when to wait, when to jump in, using time and impact as your signal 19:43 Two leaders who shaped Mek’s leadership style, letting people struggle, learn, and then win 23:29 Curiosity as the engine behind trust and verification A line worth repeating “Feedback is a blessing.” Practical coaching moves you can borrow • Set the bar up front, define the end goal and what good looks like • Build a steady cadence, short weekly updates beat occasional deep dives • Calibrate your involvement, give space early, step in when time passes or impact expands • Make feedback faster, smaller course corrections beat late big confrontations • Use AI as a reviewer, get quick context on unfamiliar code and decisions so you can ask better questions Call to action If you found this useful, follow the show and share it with a leader who is leveling up from IC to manager. For more leadership and hiring insights in tech, subscribe and connect with Amir on LinkedIn.

Michael Topol, Co-founder and Co-CEO at MGT Insurance, explains why insurance is quietly becoming one of the most interesting data and AI problems in tech. We get practical about turning messy legacy data into usable signals, how agentic tools change decision making, and why culture and team design matter as much as the models. MGT Insurance is building a fully verticalized AI and agentic native insurance company for small businesses, pairing experienced insurance operators with top tier technologists. Michael breaks down what changed in the last few years that makes real disruption possible now, and what modern product delivery looks like when prototyping is cheap and iteration is fast. Key takeaways • Insurance is a data business at its core, but most incumbents cannot use their data fast enough because it lives across silos, mainframes, and old systems. • Modern AI lets teams combine internal data with public signals to speed up underwriting and improve consistency, without losing human judgement. • Vibe coding and rapid prototyping collapse the gap between idea and implementation, bringing product, engineering, and the business closer together. • Senior talent gets more leverage in an AI driven workflow, and small teams can ship faster by focusing on problem solving, not just building. • Pod based teams, fixed outcome planning, and strong culture help regulated companies move quickly while staying inside the rules. Timestamped highlights 00:44 What MGT Insurance is, and what “AI and agentic native” means in practice 02:09 Why small business insurance matters more than most people realize 06:06 The real blocker for incumbents, data exists but it is not usable 08:55 Vibe coding in a regulated industry, where it helps first 12:54 Requirements are shifting, prototypes bring teams closer to the real problem 17:26 The pod structure, plus the Basecamp inspired approach to scoping and shipping 20:52 Better, faster, cheaper, why AI finally makes all three possible 22:11 Where to connect, and who they are hiring A line you will remember “Insurance is really just a big data problem.” Pro tips you can steal • Build cross functional pods early, include a domain expert, a technical product lead, and a senior engineer from day one. • Scope for outcomes, not perfect specs, then let the team decide the depth as they build. • Use AI to automate collection and synthesis, then keep humans focused on the decisions and trade offs. Call to action If you enjoyed this one, follow the show and share it with a builder who is trying to ship faster with a smaller team.

Marco DeMeireles, co founder and managing partner at ANSA, breaks down how a modern VC firm wins by being focused, data driven, and allergic to hype. If you want a clearer view of how investors evaluate open source, mission critical industries, and AI categories, this is a practical, operator minded look behind the curtain. Marco explains ANSA’s focus on what they call undercover markets, from open source and open core businesses to defense, intelligence, cybersecurity, healthcare IT, and infrastructure companies that become deeply embedded and rarely lose customers. We also get into how they raised their first fund, why portfolio concentration changes everything, and how they push founders toward efficiency and profitability without killing ambition. Key Takeaways • In open source, two things matter more than most people admit: founder DNA tied to the project, and what you put behind the paywall that enterprises will pay for • Concentration forces rigor, fewer bets means deeper diligence, clearer underwriting, and more hands on support post investment • Great early stage support is not just advice, it is people, capital planning, and operating help that changes outcomes • AI investing gets easier when you start with category selection, avoid fickle demand, then hunt for non obvious wedges in real workflows • Long term winners tend to show compounding growth, improving efficiency, real demand, durable business models, founder strength, and an asymmetric risk reward at the price Timestamped Highlights 00:00 Marco’s quick intro and what ANSA invests in 00:36 Undercover markets, open source, and mission critical industries explained 01:54 The two open source filters that change how ANSA underwrites a deal 03:31 Why open source can work in defense, plus the Defense Unicorns example 05:29 How a new firm raises a first fund, and what the right LP partners look for 10:50 The three levers ANSA pulls with founders: people, capital, operations 15:22 Marco’s six part framework for evaluating investments 17:39 How to tell who wins in crowded AI categories, and why niche wedges matter 21:41 The first investment they will never forget, and the air gapped cloud problem A line worth stealing “You can’t outsource greatness. You can’t outsource people selection.” Pro Tips • If you are building open source, be intentional about what is free versus paid, security, compliance, and auditability tend to earn real pricing power • If your business depends on paid acquisition, test a path to organic growth early, it can unlock profitability and give you leverage in fundraising and exits • In crowded AI spaces, pick a wedge where documentation is heavy, complexity is low, and ROI is obvious, then expand once you own that lane Call to Action If this episode helped you think more clearly about investing and building, follow the show, subscribe, and share it with one founder or operator who is navigating funding, pricing, or go to market right now

AI is everywhere, but most teams are stuck talking about efficiency and headcount. In this episode, Dave Edelman, executive advisor and best selling author, shares a sharper lens, how to use AI to create real customer value and real growth. We get into the high road vs low road of AI, what personalization should look like now, and why data has to become an enterprise asset, not a bunch of disconnected departmental files. Key Takeaways • Efficiency is table stakes, the real win is using AI to build new experiences that customers actually want • Start with customer friction, find the biggest compromises and frustrations in your category, then design around that • Personalization is no longer limited by content scale in the same way, AI changes the economics of tailoring experiences • You do not always need one giant database, modern tools can pull and connect data across systems in real time • Treat data as an enterprise resource, getting cross functional alignment is often the hardest and most important step Timestamped Highlights • 00:46 Dave’s origin story, from early loyalty programs to Segment of One marketing • 03:33 The high road and low road of AI, growth experiences vs spam at scale • 06:51 Where to start, map the biggest customer frustrations, then build use cases from there • 16:31 The data myth, why you may not need a single mega database to get value from AI • 21:31 Data as a leadership problem, shifting from functional ownership to enterprise ownership • 25:14 Strategy that actually sticks, balancing bottom up automation with top down customer led direction A line worth stealing “Use those efficiencies to invest in growth.” Pro Tips you can apply this week • List the top five customer frustrations in your category, pick one and design an AI powered fix that removes a compromise • Audit your data reality, identify where the same customer facts live in multiple places, then decide what must be unified first • Run a simple test and learn loop, create multiple variations of one experience, measure what works, and keep iterating • Put strategy on the calendar, make room for a recurring discussion that is not just metrics and cost cutting Call to Action If this episode helped you think differently about AI and growth, follow the show, leave a quick rating, and share it with one operator who is building product, data, or customer experience right now.

Amanda Kahlow, CEO and founder of 1Mind, joins Amir to break down what AI changes in modern sales and go to market, and what it does not. If you lead revenue, product, or growth, this is a practical look at where AI creates leverage today, where humans still matter, and how teams actually adopt it without chaos. Amanda shares how “go to market superhumans” can handle everything from early buyer conversations to demos, sales engineering support, and customer success. They also dig into trust, hallucinations, and why the bar for AI feels higher than the bar for people. Key takeaways • Most buyers want answers early, without the pressure that comes with talking to a salesperson • AI can remove friction by turning static content into a two way conversation that helps buyers move faster • The hardest part of adoption is not capability, it is change management and trust inside the team • Humans still shine in relationship and nuance, but AI can outperform on recall, depth, and real time access to the right info • As AI levels the selling experience, product quality matters more, and the best product has a clearer path to win Timestamped highlights 00:31 What 1Mind builds, and what “go to market superhumans” actually do across the full buyer journey 02:00 The buyer lens, why early conversations matter, and how AI gives control back to the buyer 06:14 Why the SDR experience is frustrating for buyers, and where AI can improve both sides 09:42 Change management in the real world, why “everyone build an agent” gets messy fast 13:04 Why “swivel chair” AI fails, and what real time help should look like in live conversations 15:52 Hallucinations and trust, plus the blunt question every leader should ask about human error 22:26 Competitive advantage today, and why adoption eventually pushes markets toward “best product wins” A line worth sharing “Do your humans hallucinate, and how often do they do it?” Pro tips you can use this week • Start with low stakes usage, bring AI into calls quietly, then ask it for a summary and what you missed • Build adoption top down, define what good looks like, otherwise you get a pile of similar agents and no clarity • Focus AI on what it does best first, recall, context, and instant answers, then expand into workflow and process later Call to action If this episode sparked ideas for your sales team or your product led funnel, follow the show so you do not miss the next one. Share it with one revenue leader who is trying to modernize their go to market motion, and connect with Amir on LinkedIn for more clips and operator level takes.