HockeyStick Show

Hello everyone!I’m Miko Pawlikowski, and in this #56 episode of The Hockey Stick Show, I sat down with Luca Belli, AI safety lead at Spring Health and author of the upcoming book Hidden Influences.Luca spent more than five years at Twitter, where he co-founded the Machine Learning Ethics, Transparency, and Accountability team. Our conversation explored something most of us interact with every day without thinking much about it: recommendation algorithms.They decide what appears in our feeds, what products we discover, which people we meet, and increasingly, how we understand what is happening in the world.How Much of Your Life Is Chosen by an Algorithm?Probably more than you think.Social media has become one of our main gateways to the internet. We use it to keep in touch with people, read the news, discover ideas, and make sense of what is happening around us.Almost everything we see there has been selected by a recommendation system.But the influence goes much further than social media.Dating apps recommend people we might want to meet. Event platforms suggest where we should go. Shopping sites decide which products are worth showing us. Those recommendations can eventually affect who we meet, what we buy, what we become interested in, and what we do offline.The technology has become so deeply embedded in everyday life that we often stop noticing it.Generative AI Is Only Part of the StoryMuch of the current debate about AI focuses on generative models.Fake images. AI-generated video. Misinformation.Luca pointed out that creating misleading content is only part of the problem.For that content to matter, somebody has to see it.And very often, the mechanism deciding who sees it is a recommender system.A fake video sitting somewhere on the internet has limited impact. A system that decides it is engaging and pushes it in front of millions of people is a different problem entirely.Recommendation algorithms have been influencing information distribution for years, long before generative AI became the centre of the conversation.The Algorithm Is Learning From You. You’re Learning From It.One of the most interesting parts of our conversation was the difficulty of separating our own preferences from the systems around us.Recommendation systems learn continuously from our behaviour.You click something. You watch a video. You follow someone. You like a post.Those actions become signals that influence what you see next.But the influence also runs in the opposite direction.An algorithm might repeatedly expose you to a topic you had never previously considered. Eventually you become interested in it.Was that interest always there, waiting to be discovered?Or did the recommendation system help create it?There isn’t a clean boundary.Luca described recommendation systems as socio-technical systems. You cannot properly study the algorithm without also studying the people interacting with it.The user changes the system, and the system changes the user.That feedback loop is part of what makes recommendation algorithms so powerful.There Is No Correct RecommendationBuilding a system that recognises whether an image contains a cat or a dog has something recommendation systems don’t: a ground truth.Most people can agree whether the image contains a cat.But what is the objectively correct recommendation for a person?There isn’t one.Something I find interesting might be completely irrelevant to you. Even my own preferences change depending on the situation.The things I want to read in the morning might be completely different from what I want after a long day at work.That creates a fundamental problem.If there is no universal definition of a good recommendation, what exactly should these systems optimise for?Clicks?Time spent?Satisfaction?Something else entirely?There is no simple gold standard against which a recommendation algorithm can be measured.And that makes questions about whether one is behaving “correctly” much harder than they initially appear.Hidden InfluencesLuca’s book Hidden Influences grew out of his work studying responsible AI and the societal impact of recommendation systems.At Twitter, he worked particularly on algorithmic amplification and the amplification of political content.After Twitter was acquired in 2022, he decided it was time to move on. The book became a way to put several years of thinking and research into one place and, as he put it, get some closure before starting the next chapter.It isn’t a manual for engineers trying to build a recommender system from scratch.Instead, Luca wants to explain what these systems do, how they interact with people, and why their social consequences can be difficult to predict.One audience he would particularly like to reach is policymakers.Regulating recommendation algorithms requires understanding what actually happens under the hood, rather than treating “the algorithm” as a mysterious black box.Moderation Gets Complicated FastRecommendation and moderation are closely connected.If a platform is deciding what people should see, it also has to decide what they should not see.That sounds straightforward until you try to write rules that work globally.Even something seemingly simple like banning nudity requires a precise definition of what counts as nudity. Cultural expectations differ between countries, communities, and contexts.Then there is the problem of who is doing the moderation.A moderator may be reviewing content created in a culture they know very little about, intended for an audience with an entirely different set of assumptions.Luca described this as a form of audience collapse.Something originally created for one audience suddenly reaches another audience without the context required to interpret it properly.That is one reason global moderation policies become extraordinarily difficult once you get into the details.Taking Back Some ControlTowards the end of our conversation, I asked Luca for something practical.If recommendation systems are designed to keep our attention, what can somebody actually do about it?His answer was refreshingly simple.Use limits.Most phones now allow you to set daily time limits for individual apps. Luca uses them himself because they interrupt the moment when a few minutes of scrolling quietly turns into an hour.The other recommendation was to become more deliberate about what you consume.There is nothing wrong with watching something funny or completely pointless.The question is whether you intended to spend three minutes doing it or somehow ended up spending three hours.Simply recognising that many of these systems are optimised for engagement changes the way you interact with them.Final ThoughtsRecommendation systems are easy to overlook precisely because they work so smoothly.They sit between us and huge amounts of information, filtering the world into something manageable.That can be incredibly useful.But those same systems influence what we discover, what we pay attention to, who we interact with, and sometimes even what we believe.Hidden Influences attempts to remove some of the mystery around how that process works and give people better tools for thinking about their relationship with algorithms.The book is already available through Manning’s Early Access Program, where readers can access completed chapters while Luca continues writing it.You can follow Luca and his work at lucab.phd or find him on LinkedIn.It was a pleasure having Luca on the show, and I hope you enjoy the episode as much as I enjoyed the conversation.Thanks for reading, and see you in the next one. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Hello everyone!I’m Miko Pawlikowski, and in this #55 episode of The Hockey Stick Show, I sat down with Denis Magda to talk about one of the most widely used databases in the world: Postgres.Denis recently published a book called Just Use Postgres through Manning, and our conversation explored why this decades-old technology continues to power modern applications, often replacing entire categories of specialized databases.Rediscovering PostgresMany engineers have a complicated relationship with Postgres.For some, it’s the dependable database that’s always there. For others, it can feel like an aging piece of infrastructure overshadowed by newer and more specialized alternatives.Denis first started using Postgres in 2009 while working on social networking applications. Over the years, it became his preferred database because of its reliability, maturity, and flexibility.His perspective deepened when he joined Yugabyte, a company building distributed Postgres solutions. There, he began exploring capabilities that many developers overlook, including JSON support, full-text search, and vector similarity search.Those discoveries ultimately inspired him to write Just Use Postgres.Beyond a Relational DatabaseOne of the central themes of our discussion was the difference between the “best” tool and the “right” tool.Postgres may not outperform every specialized database in its respective niche. Dedicated document databases, search engines, or vector databases often offer deeper functionality for specific use cases.That doesn’t mean you should automatically add them to your stack.Denis advocates starting with a proof of concept to determine whether Postgres can meet your requirements before introducing additional infrastructure. In many cases, it can.This philosophy isn’t about replacing every database with Postgres. It’s about reducing unnecessary complexity and making informed architectural decisions.The Swiss Army Knife of DatabasesThroughout our conversation, Postgres increasingly resembled a Swiss Army knife.Its extensible architecture allows developers to add capabilities through a rich ecosystem of extensions. Whether you need geospatial functionality, scheduling capabilities, advanced indexing, or AI-related features, chances are there’s already an extension available.This adaptability has helped Postgres remain relevant through multiple generations of technology trends.Rather than trying to reinvent itself every few years, it continues to evolve while maintaining the stability that production systems depend on.A Community-Driven Success StoryAnother fascinating topic was the governance model behind Postgres.Unlike many popular technologies, Postgres isn’t controlled by a single vendor. Instead, it thrives through a community-driven approach that prioritizes collaboration and long-term sustainability.Denis compared its ecosystem to Linux. Multiple companies contribute to the project, but no single organization dictates its direction.This balance has allowed Postgres to maintain exceptional quality, resilience, and independence while continuing to innovate.According to Denis, that community stewardship is one of the key reasons why Postgres has remained relevant for so long.Message Queues, Job Scheduling, and MoreOne of the more surprising parts of our discussion was how far Postgres can stretch beyond traditional database workloads.Denis explained that Postgres can serve as a job queue or lightweight messaging system for many applications.It’s not intended to replace platforms like Kafka for large-scale event streaming, but for simpler workloads, it can often handle job scheduling and message processing effectively.Features such as partitioning, indexing, and extensibility make these use cases practical without introducing additional operational overhead.Why Simplicity MattersA recurring theme throughout the episode was simplicity.Every new component added to a system introduces operational costs, maintenance requirements, and potential points of failure.Denis argues that engineers should challenge the assumption that every problem requires a new database or service.Sometimes the simplest solution is already running in production.Sometimes the answer is simply: “Just use Postgres.”Final ThoughtsDenis’s book offers a fresh perspective on a technology many developers think they already know.Whether you’re a long-time Postgres user or simply curious about what modern Postgres is capable of, Just Use Postgres is packed with practical insights and real-world examples.You can find the book through Manning, Amazon, and other major booksellers.To follow Denis and his work, connect with him on LinkedIn and X, where he regularly shares thoughts on databases, distributed systems, and software architecture.It was a pleasure having Denis on the show, and I hope you enjoy the episode as much as I enjoyed the conversation.Thanks for reading, and see you in the next one. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 54 of The HockeyStick ShowI’m Miko Pawlikowski, and this week we explored the evolving world of cybersecurity with Robert Siciliano.Robert, CEO of Protect Now LLC and creator of the Strategic Human Firewall, joined us to unpack how organizations can adapt to increasingly sophisticated threats. The conversation moved beyond tools and tactics into something deeper: how people, not just systems, define the strength of modern security.Understanding the Human FirewallWe started with Robert’s core idea: the “human firewall.”At its essence, it’s about transforming employees from passive liabilities into active defenders. Traditional firewalls filter traffic. Human firewalls filter intent.Instead of relying solely on technical controls, this approach builds awareness, judgment, and instinct across the organization. Employees aren’t just following rules. They’re recognizing risk in real time.Robert drew a sharp contrast with standard security training. Most programs focus on compliance and minimal engagement. His model pushes toward something more durable: personal ownership.Security Awareness vs. Security AppreciationOne of the most important distinctions Robert made was between awareness and appreciation.Awareness is surface-level. It means knowing the rules.Appreciation goes further. It means understanding why those rules matter and acting accordingly.He illustrated this with what he calls the “kitchen table effect.” When employees internalize security lessons deeply enough, they bring them home. They talk about them with family. They apply them in everyday life.That’s when behavior actually changes.Security stops being a corporate requirement and becomes a personal value.AI: The New FrontierWe also spent time on AI and its impact on cybersecurity.Robert was clear: AI raises the stakes. Deepfakes, synthetic voices, and hyper-personalized phishing attacks make deception more convincing than ever.Old mass phishing campaigns are fading. What’s replacing them is precision targeting at scale.But there’s a flip side.This shift creates a moment to re-engage people. When threats feel more real and more personal, training can become more relevant. More urgent. More effective.The Personal TouchAs the conversation wrapped, Robert emphasized a simple principle:Security works best when it feels personal.Titles don’t matter here. Whether you’re a CTO, CISO, or team lead, your role is to make security relatable.That might mean sharing stories. Running discussions during all-hands meetings. Talking about real-world examples, including how these threats show up at home, not just at work.When people see themselves in the problem, they start to care about the solution.Taking ActionFor organizations looking to improve, Robert suggested starting with a basic question:How does security currently show up in your company?If it feels like a checklist, that’s the problem.Reframe it as something empowering. Something human.Build training that invites participation. Encourage questions. Create space for real conversations instead of one-way instruction.The goal is to make security part of the culture, not just a requirement.Final ThoughtsRobert Siciliano’s perspective is straightforward but often overlooked.Technology matters. But mindset matters more.If people care, they pay attention. If they pay attention, they catch things machines miss.Security isn’t just a systems problem.It’s a people problem.And that’s exactly where the opportunity is. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 53 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Richard Heimann, Director of AI for the State of South Carolina and author of “Sutskever’s List”, to talk about the papers that built modern AI, the man behind OpenAI’s biggest breakthroughs, and what happens when living doubts become explosive decisions.Richard walked me through Ilya Sutskever’s legendary reading list: 27 papers that supposedly explain 90% of what’s happening in artificial intelligence, and why understanding this curated canon matters more than drowning in the weekly flood of new research. The conversation moved fluidly between deep learning history, the Sam Altman firing saga, bubble economics, and the challenge of separating genuine progress from AGI fever dreams.The Reading List That Became a BookWe started by exploring how a simple recommendation from Ilya to John Carmack turned into a full book project. When Ilya shared his reading list in 2021 or 2022, he made a promise: read these papers and you’ll understand 90% of what’s going on in AI.Manning Publications initially wanted an anthology: 27 chapters analyzing each paper in isolation. Richard pushed back. The papers weren’t just standalone artifacts; they built on each other and told a larger human story. Ilya’s story. The publisher agreed, and Richard spent the last year weaving the technical breakthroughs into a narrative that makes sense for people who aren’t writing these papers themselves.The book is done. The final chapters just went up on Manning’s early access program. Print release is scheduled for May 2025.Who Is Ilya Sutskever and Why Should We Care?For those who only know Ilya from the Sam Altman firing drama, Richard provided crucial context. This is the person responsible for AlexNet in 2012: the moment that launched the modern deep learning era. He’s behind Word2Vec, sequence-to-sequence models, and the scaling of transformers at OpenAI. GPT-1, 2, 3, and beyond.But beyond the technical contributions, Ilya has this mystique. He doesn’t say much. When he does, it’s high signal. And his work has consistently centered on safety concerns, which makes him both a technical innovator and someone genuinely worried about the implications.The reading list reflects his mental model. It gives insight into what he sees, what he values, and why he makes the decisions he makes.The Sam Altman Firing: Living Doubts Gone WrongWe spent significant time unpacking the OpenAI board saga. Richard’s take was fascinating: he traced it back to GPT-2 in 2019, when OpenAI deemed the model “too dangerous to release” and staged its rollout over nine months.At the time, researchers were skeptical. It looked like hype-building. But Richard sees it differently now: it was a living doubt. Ilya and OpenAI acted on their safety concerns in a transparent, reversible way. They could always say “we were wrong” and release the full model, which they eventually did.The Sam Altman firing was different. It was explosive, irreversible, and impossible to unwind once initiated. The lesson from a safety perspective: whatever your doubts are, structure them so you can reverse course if you’re wrong.Bubble Economics and the Free Lunch EraI asked the question everyone wants answered: are we in an AI bubble?Richard’s response was nuanced. Yes, it’s bubbly. But bubbles aren’t inherently bad. Nothing important happens without bubbles. You don’t get this kind of capital, talent, and momentum from purely rational actors making measured bets.The key difference from 2008: there’s real underlying technology here. It’s more like the dot-com bubble: bad ideas will get flushed out, valuations will correct, but the fundamental shift is genuine.What’s remarkable isn’t the diminishing returns everyone’s complaining about. It’s that scaling worked at all. For 50-60 years, AI progress required genuine innovation: new architectures, new training tricks. For the last five years, we just made models bigger and threw more data at them. That free lunch was unprecedented.Now the free lunch is ending. Ilya himself recently said the era of scaling is over. We’re going to need good ideas again.AGI: Paper Hopes vs. Living TechnologyRichard was refreshingly direct about AGI hype. He doesn’t find the concept appealing. It’s a paper hope: something people talk about but don’t actually build toward in meaningful ways.The substrate we’re working with isn’t going to produce human-like intelligence. And we don’t need it to. The technology is already powerful and will continue improving linearly. But the exponential curves and S-curves are done. We’re hitting asymptotes.The implication: a lot of the AI safety concerns about alignment and existential risk become less urgent. He doesn’t see an existential threat from his computer.What’s Underrated and OverratedI asked Richard what people are sleeping on and what’s empty hype.Overrated: AGI and the entire AI safety research agenda focused on existential risk.Underrated: The technology itself, at least among skeptics. Too many people dismiss these models as “stochastic parrots” or “just databases” without understanding what they actually are. The technology will be pervasive in five to ten years, and the skeptics are needlessly rounding down.Working in Government AIWe also covered Richard’s day job: Director of AI for South Carolina. He evaluates use cases from 80+ state agencies, all interested in adopting AI. Some have clear ideas, others need help defining their approach.About 80% is advisory: looking at use cases from technical, governance, privacy, and security perspectives. The remaining 20% is an informal accelerator developing strategic use cases in-house.The scale is what attracts him. Even in a small state of 5 million people, the potential impact is enormous.At its core, this episode was about understanding foundations in a field that rewards chasing novelty. How to build mental models that persist beyond the next model release. How to act on doubts without making irreversible mistakes. And what it takes to write a book that captures not just the papers, but the worldview behind them. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 52 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Maggie Engler and Numa Dhamani, co-authors of “Introduction to Generative AI (Second Edition)”, to talk about navigating the AI landscape without getting swept up in hype, fear, or misinformation.Maggie and Numa shared what it’s like to write a technical book in a field moving so fast that a second edition became necessary just a year after the first. The conversation moved fluidly between AI agents, copyright battles, bubble economics, and the challenge of staying grounded when headlines scream about both utopia and apocalypse.When Your Book Needs an Update Before the Ink DriesWe started by exploring why a second edition was needed so quickly. The answer wasn’t just new models or better benchmarks—it was a fundamental shift in how people think about and use generative AI.When the first edition came out, people were still asking “What is generative AI?” By the time they started the second edition, the question had become “How do I actually use this in my daily work?” The technology moved from experiment to infrastructure in less than two years.Maggie and Numa described the challenge of writing about a field where specific results and capabilities change weekly. Their solution: focus on teaching people how to interpret new developments rather than chasing the latest numbers.Agents: Promise, Limitations, and RealityWe spent significant time on AI agents—one of the biggest additions to the second edition. The conversation was refreshingly balanced. No wild predictions about fully automated workflows next quarter. No dismissive skepticism either.They explained how agents show real promise in constrained domains like coding, where you can verify results against tests. Tool use capabilities have improved. Infrastructure like Anthropic’s Model Context Protocol is maturing. But we’re still far from the autonomous systems some headlines suggest.The key insight: agents work best when you can clearly define success and verify outcomes. The further you get from that, the more human oversight you need.The Legal Wild West and Copyright ChaosThe copyright discussion was particularly interesting. Maggie and Numa didn’t dance around the obvious: large-scale model developers are training on copyrighted material. The question isn’t whether it’s happening—it’s what happens next.We talked about the recent SORA controversy, where OpenAI initially told anime studios they could opt out character by character, then reversed course within days. The lawsuits, the settlements, the attempts at licensing frameworks—it’s all still being negotiated in real time.Their take: we’re converging on some baseline principles around transparency and accountability, but the intellectual property questions will take much longer to resolve.Bubble or Revolution? Yes.I asked the question everyone wants answered: are we in an AI bubble?Their response was nuanced. Yes, there are bubble characteristics—high valuations, massive investment, limited returns, lots of speculation. But no, the underlying technology isn’t a passing fad. The comparison to the dot-com era felt apt: real value underneath, correction likely, but the fundamental shift is genuine.Maggie predicted we’ll see market consolidation and some valuations adjusting. Numa emphasized we’re moving from wild optimism toward more measured metrics and tempered hype. But the core technology will keep evolving, and returns will materialize.Starting Points and Practical AdviceWe closed by discussing how people should actually get started with generative AI today. Their advice was simple: just play with the tools. Try Gemini, Claude, ChatGPT. Most have free tiers. Experiment with prompting. See what works for you.The hesitation people feel—not knowing the “right” use cases or perfect prompts—is the main barrier. The best way through it is hands-on exploration, not more reading.At its core, this episode was about maintaining perspective in a field that rewards extremes. How to stay informed without getting overwhelmed. How to evaluate capabilities honestly without falling into either hype or cynicism. And what it takes to write a book that stays relevant when the field updates faster than publishing cycles allow. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 51 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Tommy Breedlove (the author of the book “Legendary”) to talk about the long road from survival mode to self-worth, and how money, identity, and purpose get tangled together along the way.Tommy shared his personal story, from growing up around addiction and incarceration to building a successful career, losing himself inside of it, and ultimately redefining what “success” actually means. The conversation moved fluidly between money, masculinity, relationships, and the quiet damage caused by chasing external validation.Money, Identity, and the Cost of ApprovalTommy started by unpacking how early trauma and instability shape our relationship with achievement. For him, success became a shield. Money, status, and performance were ways to feel safe, respected, and untouchable.He explained how this pattern shows up for many high performers, especially in tech and business. On the surface, things look great. Underneath, there is burnout, resentment, and a constant fear of being exposed. The more approval you chase, the more expensive it becomes to maintain the image.When Net Worth Becomes Self-WorthWe spent time digging into how money quietly becomes a proxy for value. Tommy talked about how easy it is to confuse financial success with identity, and how that mindset erodes relationships, health, and joy over time.He challenged the idea that more is ever enough when the underlying wound is unresolved. Without self-respect, success only amplifies insecurity. With it, money becomes a tool instead of a scoreboard.Redefining Success on Your Own TermsThe conversation shifted toward what it actually takes to step off the treadmill. Tommy described slowing down, setting boundaries, and getting honest about what you want rather than what you think you should want.That process often involves hard tradeoffs. Letting go of roles, relationships, and expectations that no longer fit. Learning how to say no. Building a life that feels aligned, even if it looks smaller from the outside.At its core, this episode was about sustainability at a human level. How to build a career without losing yourself. How to pursue ambition without outsourcing your self-worth. And what success looks like when nobody is watching.Thanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 50 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Peter Farkas to dig into the messy reality of modern infrastructure, open source licensing, and what really happens when companies try to protect their products from hyperscalers.We walked through his recent LinkedIn post, the story behind it, the unintended consequences of “defensive licensing,” and what the future might look like for teams trying to build sustainable businesses on top of open source.Cloud Providers, Open Source, and the Licensing SqueezePeter started by explaining the background behind his post: why companies shift to restrictive licenses like SSPL, what they’re trying to defend against, and why it often snowballs into confusion for both users and vendors.He shared examples of how cloud providers respond, how this changes the economics of running a service, and why certain licensing decisions end up punishing the wrong people. The conversation unraveled into a broader point about how blurry the line has become between infrastructure, managed services, and full-blown products.Why “Open Source Alternatives” Aren’t Always What They SeemWe also talked about the wave of drop-in replacements and forks that appear every time a company tightens its license. Peter explained the real costs behind “just run it yourself,” the pressure it puts on engineering teams, and why some of these forks still depend heavily on the original maintainers.Underneath it all is a bigger question: who actually pays for the innovation that everyone wants to remain free?The Realities of Building a Business Around InfrastructurePeter broke down the challenges of turning infrastructure into a viable product: operational burden, attack surfaces, compatibility expectations, and the never-ending stream of breaking changes that users don’t see.The theme kept coming back to sustainability. What does fair monetization look like? How do you protect your company without alienating your community? And what options do founders realistically have when cloud giants can replicate their service within months?Thanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 49 of The HockeyStick Show! I’m Miko Pawlikowski, and this week I sat down with three platform leaders who’ve lived through the messy, unglamorous reality of building internal platforms that actually help teams ship better software: Ajay Chankramath, Sean Alvarez, and Nic Cheneweth.We unpacked what platforms really are, why they’re misunderstood, and how good platform work is far more human than technical.Platforms Aren’t Magic — They’re Just Good Engineering Done at ScaleAll three guests pointed out a simple truth: most companies don’t need fancy platform branding, they just need to fix the basics. Shared tooling, stable environments, repeatable patterns — the “boring stuff” is what creates real leverage.A platform isn’t a product you install. It’s a consistent way of working that reduces chaos and duplication.Lesson: A platform is not the shiny thing — it’s the reliable thing.Action: Identify one repeated pain your teams face and solve it once, centrally.Internal Customers Matter More Than Internal TechnologyA theme that came up repeatedly: platform work only succeeds when the platform team treats engineers as customers, not as people who should “just use what we built.”Ajay talked about how teams often skip discovery and jump straight into building. Sean emphasized empathy. Nic highlighted that many “platform failures” are really product failures — misaligned expectations, poor communication, and unclear value.Lesson: If no one is using your platform, it’s not a platform — it’s shelfware.Action: Before building anything new, interview five developers about what they actually need.Reduce Cognitive Load, Don’t Add to ItEvery engineer knows the pain of juggling too many deployment paths, tooling options, and config formats. A good platform reduces cognitive load by removing decisions that shouldn’t matter.This isn’t about limiting freedom. It’s about letting teams spend their energy on product, not plumbing.Lesson: The best platform decisions remove decisions.Action: Pick one workflow today that your team repeats and standardize it.Developer Experience Is a Business MetricNic made a point that stuck with me: no executive wakes up excited about “platform engineering.” They care about throughput, reliability, cost, and time-to-market. A platform only earns its place when it moves those numbers.You don’t justify platform work with architecture diagrams. You justify it by showing how much faster teams deliver because of it.Lesson: If you want executive support, speak the language of outcomes.Action: Track one metric affected by platform friction — and show the before and after.Platforms Fail When They Become Mandates Instead of ChoicesSean raised this repeatedly: forcing a platform onto teams rarely works. The healthiest platforms are opt-in, because they’re useful enough that teams choose them.Mandates hide problems. Adoption exposes them.Lesson: If you have to force adoption, the real issue isn’t adoption — it’s value.Action: Ask a team why they didn’t choose your platform. Their answer is your roadmap.Culture Makes or Breaks the PlatformAjay described how teams often treat platform issues as technical problems, when they’re usually cultural ones: trust, communication, ownership, and the willingness to collaborate across team boundaries.The best platforms grow in environments where experimentation is allowed, feedback loops are short, and teams feel safe saying “this isn’t working.”Lesson: A platform is a cultural artifact as much as a technical one.Action: Start including platform updates in your engineering ceremonies — make it part of the conversation, not an afterthought.A final thought from meThis conversation reminded me that platforms aren’t about abstraction layers or golden paths or YAML templates. They’re about helping people do their best work without tripping over the infrastructure underneath them.If you take one thing from this episode: treat platform engineering as a service, not a structure. Talk to your teams, fix the pain that matters, and keep the human side front and center.Thanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 48 of The Hockey Stick Show! I’m Miko Pawlikowski, and in this episode, I had the pleasure of speaking with Amanda Brock, CEO of OpenUK and a leading voice in open technology, open source, and policy. Amanda’s experience spans law, tech, and community-building, and we dove deep into the realities of open source, AI, and what it means to build a collaborative ecosystem.1. Focus on People, Not Just CompaniesAmanda emphasized that OpenUK is about bringing together people from all backgrounds, not just companies. The UK’s open source community is diverse and global, and real progress comes from connecting individuals, not just organizations.Lesson: Community is built on people, not brands.Action: Reach out to someone in your field you haven’t met yet—collaboration starts with a conversation.2. Openness in AI Is Complicated, but CriticalWe discussed the difference between “open source” and “open innovation,” especially in the context of AI models like Meta’s Llama. Amanda explained that true openness means anyone can use and share code freely, but many new licenses add friction.Lesson: Don’t take “open” at face value—read the fine print.Action: Next time you use an “open” tool, check the license and see what you’re really allowed to do.3. The UK’s Role in Open Source Is Bigger Than You ThinkAmanda shared that the UK leads Europe in open source contributors, but most projects are small and globally connected. The focus isn’t on “UK projects,” but on people who choose to live and work here.Lesson: Impact isn’t about ownership—it’s about contribution.Action: Contribute to a global project, even if it’s just a small fix or a comment.4. Funding and Culture Shape InnovationWe talked about why so many open source companies and talent move to the US: better funding terms, risk-taking culture, and strong local networks. The UK and Europe have the talent, but need to foster environments that reward experimentation and accept failure.Lesson: Innovation thrives where risk is embraced.Action: Try something new in your work—even if it might fail, you’ll learn more than by playing it safe.5. The Future of AI and Openness Is GlobalAmanda’s final message: the future of AI and open tech isn’t just about national interests or big companies—it’s about global collaboration, access, and making sure everyone can benefit from new technology.Lesson: The best solutions come from working together across borders.Action: Share your knowledge and support open access, wherever you are.A final Thought from meTalking with Amanda was a reminder that technology is about people, policy, and the choices we make together. If you take one thing from this episode: be open, stay curious, and connect with your community.Thanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show

Welcome to Episode 47 of The Hockey Stick Show! I’m Miko Pawlikowski, and in this episode, I had the chance to chat with Mark Liu about AI, creativity, and how to think about the future of technology. Mark has worked on some really cool projects, and we went deep into both the technical and personal lessons.1. Understand the Basics Before Chasing the HypeMark explained diffusion models in a super simple way. Imagine starting with a noisy picture and slowly cleaning it up until it looks like your idea — like “a dog walks on the beach.” That’s how modern AI image generation works.Lesson: Don’t just follow the buzzwords. Pick one idea, break it into small parts, and understand it step-by-step.Action: Next time you hear about a new tech trend, try to explain it to a friend in one sentence. If you can’t, you don’t understand it yet.2. New Tech is Cool, But It’s Messy at FirstWe talked about video generation models like OpenAI’s Sora. They’re powerful, but they still make weird mistakes. The tech will get better, but it’s not “magic” yet.Lesson: Don’t expect perfection from new tools. Early versions always have flaws.Action: Experiment with new tools, but keep backups and don’t bet your whole project on them.3. Writing a Book or Building a Project Takes ClarityMark shared how writing his book forced him to clarify his ideas. You can’t just write for yourself — you have to make it easy for others to understand.Lesson: Teaching something is one of the best ways to truly understand it.Action: If you’re learning something new, write a blog post or record a short video explaining it.4. Don’t Be Afraid to Start SmallWe both agreed that starting is often the hardest part. Many people wait until they feel “ready,” but that day never comes.Lesson: Progress beats perfection.Action: Break your goal into the smallest possible next step and do it today — even if it’s just 15 minutes of work.5. Your Network is Part of Your SkillsetMark’s opportunities often came from people he’d met through side projects, talks, and collaborations.Lesson: Skills matter, but so do connections.Action: Share your work in public, even if it’s not perfect. You never know who’s watching.Final Thought from MeTalking with Mark reminded me that the future of AI (and any tech) isn’t just about algorithms — it’s about people who are willing to explore, make mistakes, and share what they learn.If you take one thing from this episode: be curious, start small, and share your work.Thanks for listening! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show