A Product Manager's Thoughts on AI Brayden Haws

  • Move The Rise of the AI Product Manager
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    The Rise of the AI Product Manager

    In the 1980s, AT&T commissioned McKinsey to estimate the size of the cellular telephone market. Their report estimated that at the peak there would be 900,000 cellphone subscribers. That is a far cry from reality, today it is estimated that 85-90% of the world owns a phone. People love to bring up this example to poke fun at McKinsey and at humans' general inability to estimate. But if you look a little deeper this miss makes more sense. At the time they did the study, phones were expensive, unreliable, clunky, and just plain ugly. It is hard to feel like that is a product that will take off. Until recently, AI has been in somewhat of a similar boat. It can be expensive, requires technical expertise, and things can get ugly if it is used incorrectly. But things are changing.

    Kaggle released their st

    The Rise of the AI Product Manager 3,879 words
  • Move Uncovering Opportunities in AI
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    Uncovering Opportunities in AI

    The contents of the most important library in the world are unreadable. Not because you lost your library card three years ago. They’re locked in ash and carbon. Nineteen hundred years ago, Mount Vesuvius erupted. We all know what it did to Pompeii, but what it did in Herculaneum is as important. The mud and ash spewing from the volcano buried one of the largest libraries in the ancient world. Thousands of scrolls, containing the history and wisdom of the ancient world were lost. Or so we thought until technology and AI opened a path to recovering the lost text. Right now, teams from all over the world are working to unlock the writing in these texts.

    Rediscovering the ancient world is only one of the near-infinite uses of AI. The capabilities grow daily. Few people can keep up. But I found a couple who might be able to and tried to learn everything I could from them.

    [Daniel Gross](https://www.perplexity.ai/search/Who-is-Daniel-k1YQ.dolT6O

    Uncovering Opportunities in AI 1,175 words
  • Move When AI Draws the Owl
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    When AI Draws the Owl

    If you have worked in tech for any period of time, you have probably seen this image:

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    It is also likely that you have been asked to do this in one form or another. At some point, we all have to draw the owl. We start with a vague idea or set of instructions and have to get to a complete, compelling outcome. It can be hard. It's uncomfortable. And it doesn't always end in a piece of art you want to share with others. But if you find drawing the owl to be too challenging you're in luck! Because we live in a day and age where AI can draw the owl for you.

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    Or at least many hope that AI will draw the owl for them. There is novelty and time savings in this, but there is also risk.Sure, AI drew the owl. But it didn't get it 100% right. A

    When AI Draws the Owl 467 words
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    AI: Automated Intellingence

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    I am too controlling of most things to hand them off to AI. For instance, writing this article or writing code. I want my writing to be in my voice and I am still learning to code, so no taking shortcuts there. But, I have found AI to be helpful and practical in automating lots of things. I wanted to share some of these in case they spark ideas for others on how to use AI in practical ways each day. Let's get into some examples:

    Conversation Starters

    I help to run a local product management group. We use Slack as the hub for our community. Our members requested that we have a weekly topic to start discussions in the group. I took it upon myself to post those conversation starters. But I ran into two problems. It turned out that I wasn't that good at coming up with topics. I was even worse at remembering to post them. So I turned the job over to our Slackbot. I ga

    AI: Automated Intelligence 1,544 words
  • Move I Taught a Robot to Read the News So I Don’t Have To
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    I Taught a Robot to Read the News So I Don’t Have To

    Newsletters are dead! AI is now the first and last word in the daily news. If you are a journalist or a content creator, it is time to find a new line of work.

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    By now, you have probably heard a declaration like this in the context of every field and every job. Maybe one day, AI optimists will be correct, and computers will do everything for us. We aren't there yet but we are getting closer.

    I have spent the last few months working to build a newsletter compiled and written entirely by AI. This newsletter grew out of my frustrations with product management news. Product is a field that is rich with long-form and strategic content. But searching for daily product management news doesn't return much. And I wanted to consume this content, not create it. So I decided to see if AI, specifically LLMs and GPT could liv

    I Taught a Robot to Read the News So I Don’t Have To 1,665 words
  • Move Don't Miss the Boat on AI
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    Don't Miss the Boat on AI

    Like most of the world, I have gotten swept up in the ChatGPT hype. I was using it a ton when it launched. Using it on personal projects, at work, and looking for ways to incorporate it into products. But as time went on the shine faded. It gave wrong answers, made things up, or I often felt I could do higher quality work without it. But the ChatGPT plug-ins have reignited my interest. I currently have access to web browsing and code interpreter plug-ins. I wanted to share a recent exercise I went through to test how plug-ins might impact how I work and build products.

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    Working with Plug-Ins

    With the recent waves of layoffs, I was interested in future job trends for Product Managers. I asked ChatGPT to help build a data set on PM job trends. This pr

    Don't Miss the Boat on AI 1,941 words
  • Move The AI Treadmill
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    The AI Treadmill

    I was recently poking around with some of my old code. It is a daily newsletter that is AI-generated. It reads the news for the day and then emails me a newsletter of things I should care about as a PM.

    Even though it wasn’t that long ago that I built this, it is crazy how things have changed. GPT-4 wasn’t out yet, and the OpenAI API didn't offer web search. LangChain was around but was still in its infancy. I also didn't understand how to use LLM chains or that they were called chains. Despite those limitations, I was able to build something that has run for the last eight months.

    This code is centralized around a chain (I built one without realizing it ⬆️). I built it using Python and many calls to GPT 3.5. It is also packed with super-specific prompts around output formats. And there are lots of guardrails and output cleanup functions.

    The AI Treadmill 631 words