The Hotfix Podcast

#020: Every PM now has a team of agents. Here's what changed in how we work.

Every PM now has a team of agents A few months ago, Stefan and I recorded an episode about the future of product management, and at the time we were still debating whether PMs would need to become builders, whether AI would change the shape of the role, and whether the strongest PMs would be the ones who could move much closer to the actual creation of software. Back then, some of that still sounded a bit speculative, but after the past few months it feels much less like a prediction and much more like something that is already happening in front of us. The biggest change is not only that the models became better, even though they clearly did, but that the way we interact with them has changed. For a long time, most people used AI as a better writing assistant: write this email, summarize this document, make this sound more professional, turn my rough notes into something readable. That is useful, but it is also leaving most of the potential on the table. The bigger shift starts when AI stops being a chat window and starts becoming your actual working environment. The terminal is becoming my team I now spend a surprising amount of my day in front of a coding agent, not because I write production code all day, but because this agent has access to the knowledge I care about, the tools I use, the data I need, and the context of what I am trying to achieve. That changes the feeling of work quite fundamentally, because it no longer feels like I am asking a chatbot for help, but much more like I am talking to a small team that sits next to me all day. I can ask it to research how something currently works, inspect meeting notes, customer conversations, tickets, product data, or previous strategy work, create a mockup so I have something concrete to show, query data to check whether the problem is real, turn the findings into a slide deck, and, if the idea is promising enough, build a small prototype that I can put in front of someone. That entire loop used to require multiple people, multiple tools, and a lot of waiting, but now it can often happen in one working session. This does not mean that the agent replaces the PM, but it does mean that the PM suddenly has leverage that previously only existed inside a team. The new PM setup The important part is not just the model, because while a great model clearly helps, the model alone is not the system. The real setup consists of a coding agent, a well-maintained context repository, and access to the tools that matter, which for a PM might include the data warehouse, product analytics, meeting notes, customer feedback, ticketing systems, CRM data, Slack, Notion, Linear, Jira, Figma, or whatever else contains the real knowledge of the company. The magic starts when the agent does not need you to manually explain the whole situation every time, because it can find the relevant context, inspect the source material, understand what changed recently, and connect the dots across systems. That is the difference between “please help me write a PRD” and “please figure out what is happening with this customer segment, check the data, review recent conversations, propose three solution directions, create a prototype for the strongest one, and prepare a stakeholder briefing.” The first version is a writing assistant. The second version is a product team in a box. Memory is not really memory One thing we discussed in the episode is that the word “memory” can be misleading, because the goal is not necessarily that your agent remembers everything in the human sense, but that your agent knows where to look. A good analogy Stefan used is an exam where you are allowed to bring the law book, because you do not need to memorize every law, but if you need ten minutes to find every relevant paragraph, you still fail. The same is true for agents. The quality of the setup depends less on stuffing every piece of information into the prompt and more on helping the agent navigate your world: where customer interviews are stored, where product decisions are documented, where the current roadmap lives, where past strategy documents can be found, where the data model is explained, where known risks are captured, and where the latest meeting notes are stored. Once the agent can find the right information quickly, the whole interaction changes, because you stop prompting from scratch and start delegating actual work. This changes product work more than most people realize The PM job has always involved a lot of connective tissue, because you need to understand the customer, the business, the constraints, the product, and the data, and then translate between all of these worlds until the problem is clear enough that a team can make progress. AI does not remove that responsibility. It raises the bar. Because now a PM can move from a vague thought to a concrete artifact much faster, a customer complaint can become a structured problem analysis, a meeting transcript can become a stakeholder-specific briefing, a strategy idea can become an interactive deck, a product hypothesis can become a prototype, a prototype can be shown to users, and a user reaction can become the next iteration. The cycle compresses, and once the cycle compresses, the emotional cost of exploring ideas also goes down, because you do not need to defend every idea as if it took three months to build. You can test, learn, kill, and move on. That is a healthier way to build products. The interface may disappear Another interesting part of the conversation was whether we still need all the interfaces we use today, because for years software meant screens, dashboards, tables, forms, Kanban boards, admin panels, and settings pages. But once agents can read, write, update, and reason across systems, the interface becomes less obvious. For some workflows, the best interface might simply be a conversation, not because chat is always the best UX, because it clearly is not, but because many internal tools exist mainly because humans needed a structured way to enter, update, and retrieve information. If the agent can do that for us, some of those tools start to feel like implementation details. The to-do app is a good example, because many people do not fail with to-do apps because the UI is bad, they fail because maintaining the system is work. You forget to update it, a meeting changes your priorities, a task becomes irrelevant, a decision happens in a call, and the board is outdated after two days. An agent with access to meeting notes, messages, and your current goals can maintain that system with far less manual effort, and at that point the app itself becomes less important than the underlying context. Agents will become more proactive Most agents today still wait for instructions, which means you ask, they respond, you trigger, they execute. But the next shift is obvious. Agents will increasingly react to signals: a user signs up and gets stuck, a customer shows rage clicks in the product, a support conversation reveals a repeated pain point, a key account mentions a competitor, a metric starts drifting, or a meeting creates follow-up work. Today, a human needs to notice most of these things, but tomorrow agents will notice and reach out. I already see this in my own side project, where I built small, highly specific agents that monitor signups, activation data, product behavior, and user friction. One of them acts like an onboarding coach. It looks at what a new user did, checks the relevant product events, and sends a personalized message. When it works, it feels surprisingly human, not because it pretends to be human, but because it has context. It knows what happened, what the user tried, and where the user may be stuck. That is where agents become useful, not as generic assistants, but as context-aware workers with a narrow job. The future company will be agent-readable One of the more subtle points from our conversation is that companies with good written context may gain a real advantage, because remote companies already have a lot of their communication in calls, transcripts, Slack messages, tickets, documents, and product systems. That used to be seen mostly as a tradeoff, because there was less hallway conversation, less spontaneous alignment, and less information in the room. But in an agentic world, that written context becomes an asset. If the company’s knowledge exists in accessible systems, agents can work with it. If decisions only happen verbally at the coffee machine, agents cannot, unless someone writes them down afterwards. This may sound small, but I think it is a big deal, because the future company does not just need good documentation for humans, it needs context that agents can navigate. What PMs should do now The best PMs will not be the ones who use AI to write slightly better documents, because that will become table stakes very quickly. The best PMs will build working systems around themselves. They will maintain a context repository, connect their agents to the tools that matter, become better at translating messy business and customer context into clear instructions, and use agents to explore more options, test more ideas, and produce more concrete artifacts. Link to Podcast Episode * 📹 YouTube [https://www.youtube.com/@hotfix-podcast] * 🔊 Spotify [https://open.spotify.com/show/6kswvKwh28iixpZaeK8BH6] * 🔊 Apple Music [https://podcasts.apple.com/at/podcast/the-hotfix-podcast/id1777707758] In case you want to reach out, please do so on LinkedIn: 🔥 Follow Hotfix: https://pal.bio/the-hotfix-podcast [https://pal.bio/the-hotfix-podcast]🎙 Follow Christoph: https://www.linkedin.com/in/christophbodenstein/ [https://www.linkedin.com/in/christophbodenstein/]🎙 Follow Stefan: https://www.linkedin.com/in/stefan-pernek-629901107/ [https://www.linkedin.com/in/stefan-pernek-629901107/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thehotfixpodcast.substack.com [https://thehotfixpodcast.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

The Optimistic Case for PMs in an Agentic World

A few weeks ago, Stefan and I debated whether PMs need to become builders. We agreed. Then we had a heated WhatsApp argument and realized we disagree on something fundamental: Will agents make the PM role obsolete, or will they create the golden age of product management? Stefan believes the 100-person software company will become a one-person company within three to five years. I think the work changes, but the core skill, understanding humans and their problems, stays. We decided to argue it out on the podcast. Here is what we landed on. The case for extinction Stefan joined an AI-first startup three months ago. Everyone there uses AI for 95% of their work. The compounding effect is real: when the entire team operates this way, speed multiplies. When only one person does it, they are bottlenecked by everyone else. His experience with OpenClaw pushed his thinking further. He gave it tasks he assumed it could not complete. It found creative solutions on its own. Research that used to take him hours, such as cross-referencing analytics tools, downloading CSVs, running analysis, now takes a single prompt. His conclusion: if a solo developer can ship a working product from a WhatsApp chat, most of the coordination work PMs do will disappear. Companies that don’t adopt this speed will lose to companies that do. And those fast companies are small by definition. The case for survival I recently built DocReady, an app that helps Austrian doctors categorize tax documents. I built the entire product solo. Website, iOS app, web app, ads. The code part was impressive, sure. But it solved maybe 5% of the problem. The hard part is acquisition and activation. How do I let doctors know this exists? How do I make them use a tax preparation app regularly (for a task they hate)? One of my beta users told me she didn’t see why she should use DocReady instead of her Google Drive. She already had a system. This single conversation changed my product strategy. I realized I needed to extract structured data from documents, not just store them. That insight opens the door to Excel exports, spending dashboards, reminders, summaries. No agent would have made that strategic observation from a user interview. Not yet. Where we actually agree The PM role as defined in 2020 is dead. The person who writes tickets, manages backlogs, and coordinates standups will be replaced. That work is pure overhead in a world where agents handle execution. But the core skill, talking to customers, understanding their real problems, and translating that into product decisions, is more valuable than ever. Execution is cheap now. Context is expensive. We also agree that customer success managers, implementation specialists, and support agents are equally well-equipped for this new world. They’ve had direct customer conversations for years. They just lacked the power to act on what they learned. Agents give them that power. The PM title might survive. The PM job description won’t. The golden age of tiny companies Stefan made a point that stuck with me. If a single person can build and ship a hyper-specialized agent, say, one that handles support for Shopify stores selling sneakers, they could replace a company’s entire support team and their SaaS stack. A company spending 100k a year on humans and tools might happily pay 50k for an agent that handles everything. That is SaaS, just built by one person instead of a hundred. These companies won’t attract VC money. A 10 million ARR ceiling is boring for investors. But for a solo builder, 10 million a year is life-changing. And there will be thousands of these niche opportunities. The interface changes everything Stefan now does half his work through a WhatsApp chat with his agent. He vibe-codes, debugs deployments, and runs research. All from his phone. He took a three-hour bath and felt more productive than any day at his desk. I use multiple AI subscriptions for different tasks. I brainstorm complex strategy through my phone. When I bought a house recently, I resolved hundreds of questions through an LLM. I felt more secure and informed than I ever expected. The medium has changed. The screen-and-keyboard era is ending for many types of knowledge work. Agent monitoring might happen from a chat interface on your phone. People might not need to sit in front of a computer for their entire workday anymore. What PMs should do right now Forget most product frameworks. They were optimized for a world that no longer exists. Rebuilding an entire app was a crazy idea a year ago. Now it’s not anymore. Stay at the frontier. If you know what the latest agent tools can do, you are already ahead of most people in tech. If you tried vibe coding once in early 2024 and gave up, try again. The gap between then and now is enormous. Be the crazy person. Take risks. The likelihood of a positive outcome is higher than it was six months ago. The worst thing you can do is assume that the way you worked for the past ten years will keep working. And most importantly: be curious. This is the first technology shift in years that is not just hype. Everyone can feel how their job is changing. The people who lean into that change will build the next generation of products. It has never been this exciting to be in product. Even if the job title changes. Links Link to Podcast Episode * 🎥 YouTube * 🎧 Spotify * 🎧 Apple Music In case you want to reach out, please do so on LinkedIn: * 🔥 Follow Hotfix: https://pod.link/the-hotfix-podcast [https://pod.link/the-hotfix-podcast] * 🔗 Follow Christoph: https://www.linkedin.com/in/christophbodenstein/ [https://www.linkedin.com/in/christophbodenstein/] * 🔗 Follow Stefan: https://www.linkedin.com/in/stefanpernek/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thehotfixpodcast.substack.com [https://thehotfixpodcast.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

#018: Can Legacy Software Companies Become AI-native?

A few months ago, we talked about how AI would change the way software gets built, and at the time it still felt like something slightly abstract, interesting to think about but not yet forcing immediate decisions. That has changed. AI-native companies are now shipping faster, with fewer people, and at a fundamentally different cost structure, and they are no longer operating in a separate experimental space but competing head-on with established SaaS companies. The question is no longer whether this matters, but whether existing organizations can adapt fast enough. What AI-native actually means AI-native does not mean that a company has added AI features to an existing product or sprinkled AI into a few workflows. It means the company could not have been founded before this wave of AI, because the way work gets done would simply not have been possible. In these companies, the first line of code is written by an agent, documentation and contracts start with AI drafts, research and support replies are generated by default, and humans mainly step in to review, correct, and provide direction. Using AI is not a strategic decision anymore; avoiding it requires justification. Why this is hard for legacy companies Most legacy software companies are not badly run, and the structures they operate with were rational responses to the constraints of the past. They hired large engineering teams because building software was slow and expensive, they introduced product managers to translate customer needs into specifications, and they added layers of coordination because that was the only way to scale delivery reliably. AI changes one thing fundamentally: the cost and speed of translating customer intent into working software. What used to require weeks of alignment, handoffs, and planning now often happens within hours, but organizations are still optimized for the old reality. The people problem AI-native companies hire very differently from traditional SaaS organizations. They delay hiring for as long as possible, prioritize people who can both think and build, and try to avoid roles whose primary purpose is coordination rather than value creation. Legacy companies already have many of those roles in place. As a result, pushing AI adoption often means questioning why certain jobs exist at all, which is uncomfortable, politically difficult, and often avoided, leading transformation efforts to slow down or stall entirely. Distribution isn’t enough anymore Established companies often argue that their main advantage lies in distribution, and historically that has been true. However, AI-native companies operate with a cost structure that allows them to price their products very differently, because a team of ten can now compete with what previously required a team of one hundred. That pricing flexibility itself becomes a powerful distribution mechanism, and it is extremely difficult to out-market a product that is cheaper, improves faster, and is built by a much leaner organization. Product management changes the most The role that changes most visibly in AI-native companies is product management. Product managers are no longer primarily facilitators or coordinators, but builders who combine customer understanding with the ability to turn insights directly into working software. They talk to customers in the morning, prototype during the day, and often ship something meaningful by the evening, which causes discovery and delivery to collapse into a single continuous motion. This strongly favors people who deeply understand customers and can act immediately, while it puts pressure on roles that exist mainly to coordinate work between others. Fewer people, more context As execution becomes cheaper, context becomes more valuable. The most important assets of future software companies are the codebase itself, rich and ongoing customer conversations, and clearly written strategy, constraints, and decision principles that guide both humans and AI systems. AI systems need this context to act well, and humans do too, which creates a new leadership responsibility focused less on managing output and more on maintaining shared clarity. Can legacy companies win? In theory, legacy companies have everything they need to compete. They have distribution, long-standing customer relationships, and people with deep domain knowledge who understand the problems better than any newcomer. If they manage to radically reduce coordination overhead, turn product managers and engineers into customer-centric builders, and accept smaller, more empowered teams, they can remain competitive. In practice, this is rare, not because leaders are incompetent, but because the organizations they built were optimized for constraints that no longer exist. What stays true Despite all the change, some fundamentals remain. Customer understanding still compounds over time, clear strategy still matters, and good judgment is still scarce. What disappears is the need to scale through headcount. Software companies will likely become smaller again, not simpler, but leaner, and very different from what most of us are used to. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thehotfixpodcast.substack.com [https://thehotfixpodcast.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

#017: Product Management in 2025: What Broke, What Stayed, What Matters

Seven months ago, we talked about how product management was changing. We focused on discovery over delivery and outcomes over outputs. These topics felt urgent then. Now they’ve faded into the background. AI has taken center stage. And this shift is justified. Canva disrupted design. Shopify disrupted e-commerce. AI is doing the same to product management. The way product management was done six months ago will not exist in the future. At least not in most companies. The PM as builder AI coding agents have become so good that every product manager needs to evolve. The shift goes from facilitator and communicator to builder. Not just building prototypes to validate. Actually touching code and shipping features. This was only enabled in the last two or three months. Before that, AI wasn’t capable of it. Now even semi-technical people can ship features on their own. Six months ago, most PMs used AI for writing and discovery. Bouncing ideas back and forth with ChatGPT. Now we spend hours in tools like Lovable or Claude Code. Creating things that go beyond clickable prototypes. The old way: Sit with designers in Figma. Create linear prototypes where only one button works. A yellow highlight shows which element is clickable. This took three weeks. The new way: Have an idea in the morning. By afternoon, have something that feels like production. Everything is clickable. Actions happen in the background. Hand it to a developer and they can turn it into production code in a fraction of the time. The death of the feature delivery pipeline Claude Code blew me away when I started using it. It feels optimized for product managers. Like working with a senior engineer who checks in at the right time. When planning a new feature, it spends 10-15 minutes exploring the code base. Finding out what needs to be touched. Then it launches a planning agent. Comes up with a comprehensive plan for review. This is exactly how great engineers worked before. They surfaced risks. They surfaced increased costs. They suggested what should be a follow-up feature versus what to build now. The old feature delivery pipeline looked like this: PM talks to customers. Forms an idea. Prioritizes it. Does a first check-in with UX. Works on wireframes for two weeks. Checks feasibility with the tech lead. It doesn’t work. Back to the drawing board. Six weeks of coordination overhead. Then six weeks of implementation. This is already outdated. Rethinking the feature delivery pipeline will be one of the biggest challenges for organizations in 2026. Teams have to change In a company with eight engineers per product manager, the PM can only be a facilitator. A communicator. Someone who makes sure everyone works toward the same thing. If engineers get faster with AI tools, this coordination work only increases. Andrew Ng, founder of Coursera, recently suggested his team is moving to two product managers per engineer. More PMs than engineers. Six months ago, this would have sounded insane. But delivery is no longer the bottleneck. Before, there was a clear cut. PMs focused on discovery. Engineers focused on delivery. Sometimes in forward-thinking organizations, engineers did some discovery too. But PMs rarely touched delivery. Now the lines blur. While doing discovery, you can already deliver. Talk to a customer in the morning. Understand an insight. Prototype something during the call. Polish it in the afternoon. Push to production by evening. Discovery and delivery happen in the same motion. The risk of feature soup If everyone can deliver quickly, the risk of Frankenstein platforms increases. Products that don’t make sense anymore. Features piled on features without a clear thread. Organizations without a clear vision will have a hard time if they enable everyone to be builders. Products need to become more specialized. The old economics didn’t justify building a product with an ICP of 10,000 people. Now it does. But this requires discipline. Stick to a strategy. Don’t pivot every quarter. Otherwise you pile up features that become hard to maintain. Not just technically. Also organizationally, contractually, and when onboarding new people. Feature bloat without strategy is one of the biggest risks ahead. Distribution becomes the differentiator Before AI, success required a pyramid. The lowest level: ability to build. You needed engineers or money. This level is completely gone. Second level: having a great product. Simple, solving the right problems, intuitive, accessible. Third level: distribution. This was always there. But it’s becoming the main differentiator. What Shopify did to e-commerce is what AI will do to software. Building a good product is no longer a competitive advantage. It’s the ultimate baseline. A fitness instructor working at 10 different fitness centers has access to 200 potential customers. That’s an unfair advantage. They can test their app. If it’s good, word of mouth spreads. The skills that will matter for product managers: networking, sales, messaging, and marketing. When aligning with engineers about feasibility becomes less relevant, focus shifts to business fundamentals. The ability to create something great is not as valuable anymore. It was very valuable a year ago. What’s next Predicting the future is hard. Seven months ago, the rate of change felt crazy. It was even crazier than expected. Small improvements compound into fundamental shifts. Prompting was the hot topic six months ago. Now the hottest topic is keeping the most relevant context without having to retype it. In six months, we’ll probably laugh about manually attaching screenshots. People overestimate what can be achieved in the short term. They underestimate what can be achieved in the long term. A lot has changed. But it has never been more exciting to work in product and software. Links Link to Podcast Episode * 📹 YouTube [https://www.youtube.com/@hotfix-podcast] * 🔊 Spotify [https://open.spotify.com/show/6kswvKwh28iixpZaeK8BH6] * 🔊 Apple Music [https://podcasts.apple.com/at/podcast/the-hotfix-podcast/id1777707758] In case you want to reach out, please do so on LinkedIn: * ❤️🩹 Follow Hotfix: https://pal.bio/the-hotfix-podcast [https://pal.bio/the-hotfix-podcast] * 🎙️ Follow Christoph: https://www.linkedin.com/in/christophbodenstein/ [https://www.linkedin.com/in/christophbodenstein/] * 🎙️ Follow Stefan: https://www.linkedin.com/in/stefan-pernek-629901107/ [https://www.linkedin.com/in/stefan-pernek-629901107/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thehotfixpodcast.substack.com [https://thehotfixpodcast.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

#016: What makes a great PM in the era of AI?

Product management has never been an easy job. But the rise of AI has changed the rules and raised the bar. In this post, we unpack what separates good PMs from great ones today, and why AI will amplify that gap even further. The 6 Core Traits of Great PMs We started the conversation by listing the timeless traits we’ve seen in standout product managers. * They own problems, not features.Great PMs don’t stop when something is shipped. They stop when the underlying problem is solved. * They think in outcomes, not outputs.Great PMs don’t care too much about building the thing right. They care about building the right thing and they know how to measure its impact. * They think beyond the sprint.Strong PMs can zoom out. They hold a strategic roadmap in their head and adjust it as they learn more. * They collaborate across the product trio.They work deeply with UX and engineering to manage the four product risks: value, usability, feasibility, and viability. * They ship.Strategy without execution is just a deck. Great PMs reliably get things into users’ hand. * They’re curious.Curiosity is what drives the best PMs to go beyond the backlog. They explore ideas no one asked for. They chase problems no one sees yet. The Hard Skills That Can’t Be Skipped We also added some harder skills that separate truly high-performing PMs: * Data literacy.Not just reading dashboards, but understanding bias, false positives, and what your metrics really mean. * Business acumen.Knowing how your company makes money, what gross vs. net revenue retention is, and how to influence key drivers. * Being easy to work with.Saying no without creating drama. Collaborating with sales and CS without becoming a feature factory. This isn’t “fluffy”, it’s critical. So What’s Changing with AI? Here’s the big shift: AI lowers the floor but raises the ceiling. What gets easier: * Writing specs * Generating test cases * Creating dashboards * Synthesizing user interviews What becomes more dangerous: * Jumping to conclusions from data you don’t really understand * Delegating product sense to a prompt * Acting “strategic” without doing the hard thinking What AI can’t do: * Understand the messy reality of your customers * Discover the root of a real problem * Own a business outcome and make it move As Stefan put it: “AI won’t replace PMs. But it will replace the ones who were never really doing the job.” Why Thinking in Outcomes Is Still Rare One of our favorite heuristics: If you haven’t thought about something you launched in the past 2 weeks, you’re probably not outcome-driven. Most PMs live in the future and about what’s next on the roadmap, what’s being released. But very few revisit the past.They treat launches as finish lines. Great PMs treat them as checkpoints. The New Bar for Entry (and Why Juniors Will Struggle) One hard truth we discussed: AI may kill the junior PM role. Entry-level PMs often relied on manual tasks writing tickets, transcribing interviews, summarizing feedback. But those can now be done by AI. This raises the bar for getting into product. But it also creates new adjacent roles: * Setting up AI tools for discovery and prototyping * Creating the infrastructure for prompt-driven interfaces * Curating and structuring data that AI can use Final Thoughts PMs who want to thrive in this new era need to embrace one big idea: You can’t outsource product sense. AI will accelerate your work. But it won’t do the work for you. In fact, it will expose anyone faking it. If you want to stay great: * Stay close to customers * Learn how your business makes money * Think in problems, not projects * Use AI—but don’t hide behind it And most of all: stay curious. Links Link to Podcast Episode * 📹 YouTube [https://www.youtube.com/@hotfix-podcast] * 🔊 Spotify [https://open.spotify.com/show/6kswvKwh28iixpZaeK8BH6] * 🔊 Apple Music [https://podcasts.apple.com/at/podcast/the-hotfix-podcast/id1777707758] In case you want to reach out, please do so on LinkedIn: * ❤️🩹 Follow Hotfix: https://pal.bio/the-hotfix-podcast [https://pal.bio/the-hotfix-podcast] * 🎙️ Follow Christoph: https://www.linkedin.com/in/christophbodenstein/ [https://www.linkedin.com/in/christophbodenstein/] * 🎙️ Follow Stefan: https://www.linkedin.com/in/stefan-pernek-629901107/ [https://www.linkedin.com/in/stefan-pernek-629901107/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thehotfixpodcast.substack.com [https://thehotfixpodcast.substack.com?utm_medium=podcast&utm_campaign=CTA_1]