One Knight in Product

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.

Elena Verna, a growth consultant with a rich background in tech and a flair for meme-making, dives into solopreneurship and its benefits. She highlights how this career path allows for optionality and personal passion. Elena shares her belief that humor, especially through memes, can address serious workplace issues and build empathy. The conversation also touches on product-led growth in B2B, emphasizing the need for authenticity in personal branding and the shifting dynamics of sales strategies. It’s a lively blend of insights and humor!

Zoe Laycock, Product Marketing Lead at Diffblue, is a passionate advocate for ethical AI development. She emphasizes the urgent need for product managers to shift from a 'move fast and break things' mentality to a focus on quality and accountability. Zoe discusses the ethical landscape of AI, stressing the importance of transparency and user data protection. She also highlights the challenges startups face in ensuring ethical AI oversight, urging for a proactive approach in addressing bias and fostering diversity in tech.

Myles Sutholt is a Germany-based product leader working for an Africa-based startup where he's helping to digitise the health supply chain across the continent, with a "laser focus" on creating user value alongside business value and fostering motivated, dynamic teams. His hot take? That leaders too often rely on gut feel and recency bias when performing performance reviews, relying on point-in-time assessments and trying to be nice rather than supporting the career growth of their teams. Find Myles on LinkedIn: https://www.linkedin.com/in/myles-sutholt/. If you'd like to appear on Hot Takes, please grab a time: https://www.oneknightinproduct.com/hot!

Martin Eriksson, co-founder of Mind the Product and author of "Product Leadership," discusses the common pitfalls in company strategies and emphasizes the need for clarity and alignment. He reveals that many companies lack coherent strategies, which hampers product development. The decision stack framework is introduced as a solution for strategic alignment. Eriksson also critiques vague company values, advocating for more specific principles that support decisive actions, and stresses the importance of compelling vision statements to inspire teams.

Martijn Versteeg is the founder of Group Effort, an organisation that fosters connections & facilitates the growth of scale-up leaders through peer groups, offsites and workshops. His hot take? That product people should stop looking for the "golden nugget" of knowledge. Martijn argues that instead of seeking a single breakthrough insight, product managers should focus on consistent iteration and learning through small, incremental steps. Find Martijn on LinkedIn or check out Group Effort. Also, remember to check out the conference that he's organising, and we'll both be speaking at: Product Mastery Conference If you'd like to appear on Hot Takes, please grab a time!

Martijn Moret is the founder of DataSquirrel.ai, a company focused on leveraging AI to humanise and simplify data analysis for product managers and non-tech managers. His hot take? Most product managers neglect data—not because they dislike it, but due to a lack of time and skills, which can lead to wrong priorities and poor decision-making. He also highlights the current limitations of AI in data analysis, emphasising that while AI accelerates workflows, it still requires human oversight for reliable outcomes. Find Martijn on LinkedIn or check out DataSquirrel.ai. If you'd like to appear on Hot Takes, please grab a time!