One Knight in Product

Kanika Tolver is a Senior Product Manager by day and the founder of Career Rehab. She's also the author of a book of the same name, "Career Rehab: Rebuild Your Personal Brand and Rethink the Way You Work" Kanika's hot take? That some project managers out there are actually product managers in disguise. Their responsibilities have changed, their job titles have yet to catch up, and it's up to them to seize the opportunities of the new world of product management. Find Kanika on LinkedIn: https://www.linkedin.com/in/kanikatolver/ or check out her website: https://kanikatolver.com/.

Maud Larpent, Chief Product Officer at Treatwell, a major European beauty booking platform, shares her journey from Reuters to product leadership roles at TripAdvisor and Expedia. She reveals how Treatwell shines by balancing B2B and B2C needs to enhance user experiences. Maud discusses building a product-led culture, tackling challenges in a sales-focused environment, and the indispensable value of transparent feedback. Plus, she shares her morning routines and the importance of learning from limitations, all while navigating opportunities in the beauty industry.

In this episode, Alex Rastatuev, a Senior Product Manager at Keyhole, discusses his strong belief that effective product onboarding is more crucial than merely showcasing features. He emphasizes how a user-centric approach can significantly improve activation rates. The conversation delves into balancing simple design with essential guidance, managing cross-functional collaboration in products, and the added complexity of ERP system onboarding. Alex shares insights on using AI for enhancing customer success and tailoring strategies for specific user needs.

Debbie McMahon, Interim Chief Product Officer at the Financial Times, shares her journey from the Department for Work & Pensions to leading product strategy at one of the UK's most iconic newspapers. She discusses the FT's evolution from a traditional paper to a digital powerhouse, emphasizing the importance of transparency in hiring and balancing idealism with real-world challenges. McMahon offers insights on transitioning to a CPO role, fostering collaboration among teams, and the growing influence of AI on journalism and product development.

Rich Mironov, a B2B product management expert and author of "The Art of Product Management," shares insights on bridging the gap between product teams and executives. He emphasizes that leaders care more about financial success than product creation nuances. Mironov argues for improved financial literacy among product managers and the need to communicate in terms executives understand. He also discusses his recent move to Portugal, highlighting cultural adaptation and its influence on product culture.

Join Tami Reiss, an executive coach and author known as "Tami from Miami," as she passionately discusses how all product managers are inherently leaders. She emphasizes the need for PMs to inspire their teams and cultivate personal visions for success. Tami reveals how true leadership comes from empowerment, regardless of titles, and shares insights on effective advocacy and communication strategies. Plus, she introduces her upcoming children's book that aims to inspire young minds about product management and leadership!

Alexandar Murauski is an expert in all things related to product localisation and the CEO of Alconost, a platform that aims to help product teams unlock global growth through AI-enhanced localisation. Alexander's hot take? That the language your product "speaks" is a fundamental part of the product's user experience, and is often left lacking. It's important to consider localisation upfront, and ensure that you take cultural considerations into account, not just Google Translate the text as an afterthought. Find Alexander on LinkedIn: https://www.linkedin.com/in/amurauski/ or check out his company, Alconost, at https://alconost.com/en. If you'd like to appear on Hot Takes, please grab a time: https://www.oneknightinproduct.com/hot

Andriy Burkov is a renowned machine learning expert and leader. He's also the author of (so far) three books on machine learning, including the recently-released "The Hundred-Page Language Models Book", which takes curious people from the very basics of language models all the way up to building their own LLM. Andriy is also a formidable online presence and is never afraid to call BS on over-the-top claims about AI capabilities via his punchy social media posts. Episode highlights: 1. Large Language Models are neither magic nor conscious LLMs boil down to relatively simple mathematics at an unfathomably large scale. Humans are terrible at visualising big numbers and cannot comprehend the size of the dataset or the number of GPUs that have been used to create the models. You can train the same LLM on a handful of records and get garbage results, or throw millions of dollars at it and get good results, but the fundamentals are identical, and there's no consciousness hiding in between the equations. We see good-looking output, and we think it's talking to us. It isn't. 2. As soon as we saw it was possible to do mathematics on words, LLMs were inevitable There were language models before LLMs, but the invention of the transformer architecture truly accelerated everything. That said, the fundamentals trace further back to "simpler" algorithms, such as word2vec, which proved that it is possible to encode language information in a numeric format, which meant that the vast majority of linguistic information could be represented by embeddings, which enabled people to run equations on language. After that, it was just a matter of time before they got scaled out. 3. LLMs look intelligent because people generally ask about things they already know about The best way to be disappointed by an LLM's results is to ask detailed questions about something you know deeply. It's quite likely that it'll give good results to start with, because most people's knowledge is so unoriginal that, somewhere in the LLM's training data, there are documents that talk about the thing you asked about. But, it will degrade over time and confidently keep writing even when it doesn't know the answer. These are not easily solvable problems and are, in fact, fundamental parts of the design of an LLM. 4. Agentic AI relies on unreliable actors with no true sense of agency The concept of agents is not new, and people have been talking about them for years. The key aspect of AI agents is that they need self-motivation and goals of their own, rather than being told to have goals and then simulating the desire to achieve them. That's not to say that some agents are not useful in their own right, but the goal of fully autonomous, agentic systems is a long way off, and may not even be solvable. 5. LLMs represent the most incredible technical advance since the personal computer, but people should quit it with their most egregious claims LLMs are an incredible tool and can open up whole new worlds for people who are able to get the best out of them. There are limits to their utility, and some of their shortcomings are likely unsolvable, but we should not minimise their impact. However, there are unethical people out there making completely unsubstantiated claims based on zero evidence and a fundamental misunderstanding of how these models work. These people are scaring people and encouraging terrible decision-making from the gullible. We need to see through the hype. Buy "The Hundred-Page Language Model Book" "Large language models (LLMs) have fundamentally transformed how machines process and generate information. They are reshaping white-collar jobs at a pace comparable only to the revolutionary impact of personal computers. Understanding the mathematical foundations and inner workings of language models has become crucial for maintaining relevance and competitiveness in an increasingly automated workforce. This book guides you through the evolution of language models, starting from machine learning fundamentals. Rather than presenting transformers right away, which can feel overwhelming, we build understanding of language models step by step—from simple count-based methods through recurrent neural networks to modern architectures. Each concept is grounded in clear mathematical foundations and illustrated with working Python code." Check it out on the book's website: https://thelmbook.com/. You can also check out Machine Learning Engineering: https://www.mlebook.com and The Hundred-Page Machine Learning Book: https://www.themlbook.com/. Follow Andriy You can catch up with Andriy here: LinkedIn: https://www.linkedin.com/in/andriyburkov/ Twitter/"X": https://twitter.com/burkov True Positive Newsletter: https://aiweekly.substack.com/

Sam Greenwood, an emotional resilience coach for product managers, offers a candid view on the looming societal collapse and the urgent need for a mindset shift in tech. He argues that product managers are too focused on AI while ignoring broader societal challenges. Greenwood emphasizes the importance of emotional resilience and leadership skills to navigate today’s uncertainties. He stresses that product professionals must transform their fears into innovative solutions, adapting designs to meet evolving market needs amid environmental and political instability.

Olha Yohansen-Veselova, a product growth and optimization advisor and startup mentor, shares her insights on the evolving role of product managers. She argues they should transition to growth managers, emphasizing a focus on financial impact and performance. Olha highlights the necessity for product managers to adopt an entrepreneurial mindset, embracing proactive decision-making. Networking and continuous learning also emerge as vital components for success in this evolving landscape, pushing product managers to reach their full potential.