HockeyStick Show

About HockeyStick Show

Steal breakthrough ideas in tech, business & performance from world-class experts www.hockeystick.show (https://www.hockeystick.show?utm_medium=podcast)

Publisher

Miko Pawlikowski

Category

business · technology

Language

en

Explicit

No

First episode

16 Mar 2024

Latest episode

27 Sept 2026

Latest episodes

59 episodes in the feed.

1. 

   27 Sept 2026

   Hacking your attention, with Luca Belli - HockeyStick #56

   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 Story Much 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 Recommendation Building 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 Influences Luca’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 Fast Recommendation 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 Control Towards 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 Thoughts Recommendation 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 (https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   20 Jun 2026

   Just Use Postgres?, with Denis Magda - HockeyStick #55

   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 Postgres Many 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 Database One 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 Databases Throughout 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 Story Another 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 More One 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 Matters A 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 Thoughts Denis’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 (https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   11 Apr 2026

   The Human Blind Spot in Cybersecurity, with Robert Siciliano - HockeyStick #54

   Welcome to Episode 54 of The HockeyStick Show I’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 Firewall We 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 Appreciation One 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 Frontier We 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 Touch As 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 Action For 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 Thoughts Robert 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 (https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1)

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

6 ratings

Spotify

5.0 / 5

3 ratings

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