Do you know whose job AI is not going to be replacing anytime soon? My plumbers.

Literally every week there is a new advancement in generative AI that gets us one step closer to AI that can achieve the “Turing Test equivalent output” for many functional roles within an organization.

A friendly agent to help you sift through the documentation and clarify any questions? You can set that up in less than an hour.

How about write a blog? That was so 2022.

What about someone who could provide a quick sounding board to test your ideas, or an intern you can research different business opportunities? This is all easy to do with tools that are either free or cost as much for a monthly subscription as a human might charge per hour or even per 15 minutes.

I have been following these developments relatively closely and I have yet to see an AI that can replace copper piping, install a water heater, or shut off the water if a pipe bursts under my house. While a huge number of programmers have been laid off here in the Bay Area, my plumber is doing just fine.

Knowing About

With LLMs accelerating daily, the value of “knowing about” something is approaching zero. To use an example that is relevant to my business polySpectra: previously it might have been valuable to be the person on your team or in your company that knows about “resin 3D printing”. In about 90 seconds I just tested (via Poe) GPT 4, Claude-2, Llama-2-70b, and PaLM 2. They all “know about” resin 3D printing (see below for examples).

If someone knows how to spell the thing you are curious about - they can get a pretty good overview of a topic instantaneously and for free. So your value as the person who “knows about” the topic isn’t very high, because in the time it would take me to find you, I already have the answers I need.

So how is a mere human supposed to stay ahead of the curve? Perhaps you can know something that is fundamentally unknowable to AI. Perhaps you can do something better and faster and cheaper than anyone else. (“Knowing how” is clearly infinitely more valuable than “knowing about”.)

Or perhaps you can choose a few things or maybe even just one thing - you can choose a topic on which you are going to be the absolute foremost expert in the entire world. Not a topic that you “know about”, a topic that you know in your bones, a topic that you live every single day.

There is something that always bothered me about the advice of productivity gurus. It has taken me a while to figure out what it is. I think a big part of it is that I was never particularly impressed with their examples.

A common example, especially when someone is writing a productivity book, would be examples of authors. The first problem with this is that I’m not particularly interested in writing a book (although maybe someday I will be). More over, the specific act of writing a book is not necessarily difficult or impressive.

It is definitely a lot of work to write a book. (Or was, before generative AI took off.) And I’m sure that if you actually want anyone to read your book, it is even more work. And if you actually want to make the book successful enough to make any money, it is even more work. But the example of the author who just writes for some specified period of time or some specified number of pages every single day really didn’t resonate with me.

Maybe the other piece of it is that the majority of the people giving advice on productivity themselves aren’t doing anything or achieving anything that I find particularly impressive or applicable to my own life. Especially where their profession now is simply writing books about methods for how to be more efficient at writing books. Again, I’m sure that is valuable for a lot of aspiring authors, but it never really stuck with me.

For me, I have always been more interested in the advice of operators and doers. For example, I am much more curious about Warren Buffett’s advice on investing because he is actively investing and has built up his expertise on the topic through doing it (well) for decades. His advice might get him a little bit of publicity, but it really isn’t his business model to be giving advice. His business model is to be a smart investor.

On the other hand, you have people like Jim Cramer, whose primary job is giving investment advice every single day on TV. That’s what he gets paid to do. And yes, he also had a hedge fund at one point. I won’t pretend to know anything about it, but I don’t think it was particularly successful. Regardless, his profession is giving advice about investing, not investing.

I tend to be more interested in productivity tips from people who are focused on doing and achieving things, not just talking about it. The hard-won learnings of an operator are richer and more rooted in reality.

I can do nothing for you but work on myself…you can do nothing for me but work on yourself!

Some days, I’m overwhelmed by how fast technology is improving. Today, I feel like I’m running Windows 98.

In 1998 “50% of all handwritten card and letter addresses were ‘read’ not by human eyes but by high speed sorting machines, a process that resulted in labor savings of $31 million.”

The fundamental challenge of a machine reading handwritten postal addresses was solved before 1998. So why can’t even the most advanced AI algorthims from the top tech companies read a handwritten note in 2023? (This is not a rhetorical question.)

Optical character recognition (OCR) for digital text is ubiquitous, free, and fast. The built-in camera app on your iPhone will scan digitally-printed text basically in real-time. Google Translate will overlay the translation of signs/receipts/packaging in augmented reality.

Ironically, one of the classic tutorials in machine learning education is recognizing handwritten digits. Apparently only the US Postal Service has the talent to put this into production. Either that, or no one can think of a way that handwriting OCR would make them money. An efficient way to digitize pen and paper means that people would spend less time on their phones/computers/tablets, which would mean fewer notifications and less ad revenue. It would also mean that a plain piece of paper might displace the need for a $1500 tablet with a cool “pencil” to boot…and that’s bad for hardware sales. (Not to mention less data to spy on.)

I’ve been obsessed with finding the best tools for handwriting OCR, as a way of digitizing my analog notes (with the goal of reducing my screen time). Ironically, my journey thus far has only added to my screentime, trying to find and try “handwriting to text” tools that might unlock this workflow for me.

My conclusion as of today: the best handwriting OCR tools are already built into your phone. For iOS, use the “Scan Documents” feature inside of Notes. For Android, it’s called Google Lens. Don’t waste your time or money on any of the other apps for this — they all suck. Adobe gets a dishonorable mention for how terrible their OCR is for handwriting. (Given that they have monopolized PDF and “regular” OCR forever, they should be the best, not the worst.)

If you find a handwriting OCR app that doesn’t suck, please contact me immediately. I am hoping that maybe in 15 more years, I can finally digitize my handwritten notes.

Stories <> Data

Humans want stories, not data. Algorithms want data, not stories.

The major breakthrough of LLMs is commuting between these two domains.

Don't Get X'd

I am proposing yet another definition of X. It is a verb this time.

You know that you’ve been X’d when the platform that you’ve been sharecropping on suddenly pulls the rug out from under you.

Are you familiar with Nassim Taleb’s parable of the turkey?

What If The Web Worked For You?

The largest tech companies in the world are selling your attention. At the same time, the internet is the great democratizer, and is giving you access to the vast majority of the world at lightning speed. You can’t possibly read everything on your many screens, and you can’t possibly disconnect completely. What’s a human to do?

Yesterday, I found out about TreeCard, an app from Ecosia that pays to plant trees based on how many steps you take.

Today I’m at a 95-days streak of walking 10,000 steps a day - so my first reaction was that I wished I had found out about this a few months ago! (Why so many steps? I read Built to Move.)

My second reaction: Where does the money come from? Here’s the official TreeCard answer:

Brands pay us to feature their eco products within our rewards structure. These can be earned by planting trees!

So basically they’re selling my attention. (What’s new?) I’m not sure that this alone is going to save the planet, but so far my review is: why not? I’m going to get those steps anyways, why not get some carbon offsets along the way?

I’m still figuring out how the app works, but apparently if you join with my Treecard link and enter the code ray-5d7 then we both get extra trees (or something like that).

A Recipe for Forgiveness