Finding Good Information About AI (Part 1)

A model to filter down AI information sources to find the ones that are the most interesting to you

It's easy to show someone something interesting in ChatGPT, create an inspiring image or talk about huge multi-million dollar movies being made about AI. While it can be overwhelming, all this content shows just how many directions AI can go in. Nobody knows where this is going or what's going to happen, but many smart people are paying attention.

That said, there's also a lot of noise. You'll see courses being sold and brands being bolstered. Industry leaders showing off their tooling to garner support and funding. Scientists share findings aimed at understanding and measuring how AI changes. And a flood of TikToks, tweets, and news stories cover different events with a myriad of opinions and facts.

OpenAI’s DALLE 3 - generated prompt: ‘Adjust the Impressionist style image to show a man who is more clearly searching for a book on a bookshelf. He should have a focused, intent expression as he scans the titles, perhaps reaching up to pull a volume off the shelf. The woman remains assertive and in control, confidently seated at the table with her hand on a book. The modern Impressionist style should be maintained, with clear lines and a vivid color scheme that enhances the characters' engagement with their environment. Ensure the background remains simple and does not distract from the main subjects.‘

While reading I've noticed that I need some sort of categorization. An ability to filter down which sources of data are the most interesting to me. After some careful review, I’d like to share that model with you now in hopes that it helps you better understand where to find what you're looking for.

A quick disclaimer before we get started: This model helps to categorize in an approachable way that does oversimplify in many cases. Models just work that way. They're approximations of a complex universe and helps rationalize difficult to grasp things.

The Five Sources of AI Content

Commentators

This group is largely focused on opinion and perspective. They're storytellers discussing the topics surrounding AI. These can be people who are just interested or "true believers" who think the world has and will change completely from this new technology. While Commentators don't look to gain wealth from discussion directly, it may be related to the work they do as an influencer or related to producing other media.

I find that a lot of the topics and discussion coming out of this group pivots around a few details without much deeper context. If you want to find emotive stories that make you feel something, this is a great place to find it. If you're looking for a more comprehensive, accurate, or deeper understanding of AI, you likely won't find it here.

Commentators to look for:
Stratechery - A publication we frequently reference that hosts guests from a wide range of experience and expertise. While Ben Thompson has expert advice, he isn't directly in the industry and doesn't get paid for AI work directly.

Professionals

This group is largely focused on offering a service or benefiting financially from AI. That could be selling a course, investing in a business for a great return, or creating a news outlet focused on some aspect of AI. While there can be a deeper discussion about how to leverage AI, they're largely interested in short-term opportunities over pursuing a long-term vision.

I find that professionals are focused on a single track of thinking or experience. While they may connect AI with some other discipline, it's usually along a single line of thinking. For example: "AI art to create game assets". This limits the conversation to a specific marketable direction and can feel unauthentic for those looking for a deeper discussion.

What's more, oftentimes the products being built by this group are mostly marketing and don't hold up to a closer inspection. There's An AI For That is filled with demo applications being sold as if they fully solve real problems. Be careful what a professional promises.

Scientists also fall into this category. Great papers have a specific hypothesis in mind and great thinkers are looking for recognition for their ideas. While the science being done can fuel interesting conversations, findings, and ideas, it largely doesn't exist beyond a theoretical space until engineering applies it.

Professionals make a great source for learning something specific or understanding how to profit from AI in the short term. The motivations are obvious, but this space can still catch you off guard because of how interesting some of the stuff sounds. If you're excited about building something or want to see a bigger vision, Professionals might not have what you're looking for.

Professionals to read:
Everyday AI - Aimed at a general audience, looks to understand AI trends and communicate about AI. I have found this a balancing perspective to that of my engineering background.
Lilian Weng - A really fantastic blog, diving deep into the engineering and science of things to pull out the details. With mostly raw facts, it's up to the reader to put together more of the big pictures at play. One of my favourite blogs on the topic of AI.
LangChain - A major technology aggregator for things AI into a pipeline for engineering systems. With many partnerships, they have largely transitioned from Innovation content to Professional content aimed at being a one-stop platform. They still aggregate interesting ideas, but much of the content has shifted.

Innovators

This group is made up of hobbyists, engineers, and creatives who are looking at AI and thinking about what it can enable. This group is less concerned with profiting from AI and instead is interested in being a part of the movement. They have enough experience across multiple disciplines to focus on interesting problems that others might not be able to see.

Rather than being focused on the scientific aspects of AI, this group is pragmatic about creating new features and experiences from these tools. They reject a common understanding of what a tool can do and look for a deeper meaning. Generative AI isn't just a tool for generating art or content, it's a tool to generate rational responses to input. The distinction matters to this group, and it helps them open up a much wider pool of opportunities.

This group is great at looking into the gray of a problem and seeing things for what they are. Innovators offer a space to build something interesting and discuss interesting ideas. For strong opinions that follow a well-formed interest group, you'll need to look elsewhere.

You're in the right place for this kind of content already!

Other Innovators to follow:
Maggie Appleton - One of the people who focus on design and craft over profitability, largely due to her work focusing on academia. She regularly collects interesting findings and posts a lot of interesting ideas and thoughts on her blog.
Percy Liang - A professor at Stanford with his name on some of the most notable papers related to AI and LLM research. He helped author my favourite paper on AI agents. This system building and analysis helps really shape what's possible through applied experimentation.

Part 2

There are too many sources to go through in a single post. We'll cover the last two in a follow-up post next week! Hopefully, there won’t be any major announcements to disrupt that plan :)