I'm writing to think, learn, and remember in public. I'll be here everyday.
You’re normal, right? For sure, I’m normal.
Let’s grab a beer and hang.
Quickly, we’ll start to have our doubts about the other. I still think I’m normal, of course. But now I’m not so sure about you.
After a little while, I’m sure of it. You’re not normal.
Am I normal?
Of course, not.
Once we understand that, we can stop worrying and judging, and we can get down to the business of connecting with each other.
It’s the time of year at my house when the baby birds are jumping out of their nests.
The moments right after they jump out are critical. Can’t fly. Wobbly legs. Not sure of themselves or where they can hide. They’re vulnerable.
And they’re on their own.
The parents might be hanging around. They might dive-bomb or otherwise try to distract a predator, but they can’t really do anything.
The bird has a decent chance of making it if they can get to cover and stay hidden long enough. Just long enough for their wings to start working. Once they get their wings, then they’re in business. They can evade. They can move. They can feed themselves. They can be a bird.
All baby birds have a moment of critical vulnerability.
But if they make it through, they can soar.
Visible and understandable.
You may have the data, but unless you make it visible and understandable, it doesn’t matter.
You may have the test results, but unless you make them visible and understandable, it doesn’t matter.
You may have the requirements, but unless you make them visible and understandable, it doesn’t matter.
Where do people want to look? Where do they normally look? Where does it make intuitive sense to look?
Make it visible there.
What is it trying to say? What does it need to say? What’s the story?
Make that understandable.
Show them.
Sure, the system insists you play its game its way. It’s set up that way. They’re invested in keeping it that way. The system self-regulates.
However, here’s a reminder that if the system doesn’t work for you, or you can’t find your place in it, it’s time to find a new system, make a new system, or break down the system.
0.0.
“But why, some say, the Moon? Why choose this as our goal? And they may well ask why climb the highest mountain? Why, 35 years ago, fly the Atlantic? Why does Rice play Texas?
We choose to go to the Moon. We choose to go to the Moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win, and the others, too.”
— John F. Kennedy, Rice University, September 12, 1962
JFK berthed the term “moonshot” with this famous passage from his speech that day.
Today, we overuse it everywhere. We ask our kids about their moonshot. We ask our teams about our moonshot. Our investors ask us about our moonshot. Politicians trying to latch onto the JFK luster talk about moonshots.
What we’re really saying is, “What big ideas do you have? What’s your dream? What seems impossible but would be awesome? If we forget about reality, what would we do?”
Make the seemingly impossible real, and do it in one big transformational step, rather than incremental improvement. Like a PowerBall ticket that hits.
Moonshot discussions are great, especially with a bottle of red wine. In fact, keeping a moonshot on your personal “I will be” list is a great life hack.
But let’s remember how we got to the moon.
First, there was the X-15, then project Mercury, and then Gemini. All incremental steps before Apollo. And each of those was chock full of incremental steps. Plus, the post-war, capitalistic industrial complex outside of the space program was itself creating new technologies every day that folded into the march to the moon.
What’s the next step? 10% better. 5% better. 1% better each step. Try this little thing. Test this little thing. Make it better.
Maybe we got to the moon relatively quickly.
But it certainly was no moonshot.
SaaS is dead.
Well, probably not dead, but there is/will be a new and improved version of SaaS:
TaaS.
Tokens as a Service.
Start building a business through which your customers flow their AI tokens. You scrape a little off the top. This is the perfect model for broker, arbitrage, “as a Service”, connector, and provider types of businesses.
Mark my words.
AI today is even more than an intern.
The event that brought me into today was an “Oh, shit” moment. I asked AI to do something, and not only did it do it, but it thought through the process, created the tools to do it, and then talked to me like a human.
Today, I can give it tasks, ask it to do research, and ask it to create requirements and then design, build, and test operational code. Here’s an example:
“We need to build a new automated test framework. The system must be trustworthy, run lights-out, analyze its results and publish them, self-heal when systems crash, and run the testing as fast as possible. Do the research reagrding our systems and industry best practices, generate a requirements document, and design an system architecture. For each functional item, decide whether it should be realized through COTS or custom-built tools. When you have the design, generate a proposal for me with pros and cons so we can discuss before we take the next steps.”
It will access the internet. It will use my computer and credentials to access all of our internal knowledge base, servers, and repos. It will use its reasoning skills. It might even prototype some parts along the way. And once we’ve discussed and agreed upon its design, it will do the implementation, testing, and debugging.
It’s moved up the chain from intern to a contractor brought in for a specific purpose or a fully sentient junior employee, and it’s moving toward mid-level.
How about the future?
You’re going to hire AI agents just like you would any other team member. Agents will take direction and talk to other agents. They’ll propose solutions. They’ll find problems. They’ll produce results. They’ll find ways to do it better.
All on their own.
The AI Agent Workforce is coming.
How will you get AI working for you?
When ChatGPT first hit in November of 2023, I was on board.
Here’s my first post about it. At that time, people didn’t really know the difference between an AI chatbot and Google. So searching for answers was one of its main use cases. However, ChatGPT didn’t have access to the live internet. It had a snapshot of it from a year prior. So you could ask it about the Ben Franklin’s role in the American Revolution, but you couldn’t ask it what time the Eagles played on Sunday.
It was a response engine, not a generative tool.
I, however, was already experimenting with it generatively for both writing and coding. Although it felt amazing, it wasn’t very good and had a very small context.
For example, I could ask it to “generate me a bash script that will execute these 4 python scripts and put all of the output in a CSV table.”
It would give me something that I could copy out into my editor and then run it myself. It wouldn’t work right, so then I’d fix it and keep going. It was a helper. An intern who needed micromanagement.
Over the next year and a half, this was how I worked with it. Over that time, it got better and better. Larger context so it could understand larger problems and write more code and create longer technical documents. Better at writing code that works and fixing bugs when given the failing output.
It also gained access to the internet, which greatly improved its ability to help me with everyday things like “find me a train route from Frankfurt to Nuremburg on March 12.”
Then along came the agent with access to your whole computer.
Now I could say to it: “Write a bash script that executes these 4 python scripts, put all of the output into a CSV table, and you run and test it until there are no bugs. Sort the CSV table on test results and date, verify that the table is correct, and then generate an Excel file with a bar graph of Pass/Fail for each week. You can find my credentials for the Jenkins server in ~/code/.env.” I don’t even have my code editor open.
That’s a mind-blowing advance. Because AI is a useful intern. You’re the boss, and you can tell it to go do things and you trust that it does it.
Next, I’ll talk about where we are today and what’s coming next…
It’s happening everywhere now.
We’re increasingly using AI to evaluate humans. We do it with performance reviews. We do it with job descriptions, candidate screening, interviewing, and new hire 30/60/90 day plans.
We allow the AI to pick the metrics, and then we ask the AI to evaluate the person against those metrics.
Now the humans are getting wise (as we do) and using AI to address the AI metrics. And to combat that, AI tools are popping up that claim “anti-AI” detection.
It’s a fun back-and-forth.
Whose AI is better?
But that’s a distraction.
The real question is, how are you getting AI to work for you, because if you’re not, you’ll be working for AI.
You can spend $1000 on a new driver.
Many do. Drivers have personality. They’re fun to talk about over a beer after the round. If you got $1000 burning a hole, the fun conversation is probably worth it.
But is your driver less than 10 years old?
If so, you don’t need a new one. It won’t really help improve your game. What you need is more practice with your mid-irons, wedges, and putter. Boring.
Real improvement starts with recognizing what’s worth improving.
