Chapter 1: What is the main topic discussed in this episode?
What's going on, everybody? It's Brian. Welcome to another Saturday conundrum to wrap up the month of January. That seems odd, right? We're wrapping up January of 2026 already. We're one twelfth of the way through 2026. However you want to look at that. It's gone fast, man. And in the world of AI, we had some... crazy changes and advancements in the last four to six weeks.
And that's sort of where we're going to start this particular conundrum. Um, now if you've never heard our conundrum episodes before, it's pretty simple, right? I want to bring to you every Saturday, just something for you to mentally chew on. Uh, I'm going to provide two different sides to a, to a topic. But that's what kind of makes it, you know, a debate.
But the conundrum side of it is like, there shouldn't be a really easy answer for either of them. As you're listening to the episode, I would imagine you might find yourself saying, makes sense, or, oh, that's also a problem.
Chapter 2: What recent changes in AI are impacting professional skills?
I didn't think of that. That's sort of, if I'm doing my job right here, that's where we should land. And boy, is it hard to get AI to give us really good conundrums. I struggle every single week to get AI to think like a human, which is very hard to do. And so there's a lot of back and forth. for me to get to a point where I feel really good about this idea, or it just inspires me.
So this one is kind of inspired by the recent AI developments over the last six weeks. I mean, anybody that's been in this world, in this AI bubble, as we say on the show a lot, has noticed the change. I would imagine you would, you know, yes. Claude code has been a huge topic. Um, but to be fair, opening eyes code X is in there too.
Um, then also we have things like just recently Claude in Excel. And then very recently, like this last week, um, what was originally called Claude bot and now it's called bolt bot due to it. a cease and desist, doesn't matter. It's a weird name anyway, but Lopat and all the things it can do for you. And then we started hearing terms like even swarms and okay. Now swarm of bees.
Like I would buy like Hornets. What is swarms? That doesn't sound good. So there's so much going on. That's the point of this. And yeah, How do you even define AI literacy at this point? I struggle with this. I teach AI to professionals all the time. And I struggle with what do they need to know today?
And how is that different than what I would have taught or did teach a year ago, six months ago, two years ago, three years ago? I've been doing AI and teaching AI since, well, three years now. So the generative AI side, not the machine learning side of AI, but the generative AI side of things. Yesterday was episode 650 of the Daily AI Show. I've been on over 90% of those shows live.
So that means I've spoken well over probably 600 hours in the last three years live about AI in conversations with the rest of the DOS crew. I say all to say, I'm in it. I'm in the bubble. I'm here. And yet this conundrum gets me. It's called the liquid literacy conundrum. Over the last six weeks, the center of gravity shifted.
People spent 2024 learning how to talk to one model, now they manage systems where models talk to each other. Problems still matter, but they increasingly hide inside workflows, agent routers, tool pools, and multi-step automation.
That shift rakes the normal way professionals build competence because the surface area you have to learn keeps changing faster than most teams can train, document, and standardize. So here's the conundrum. If AI skills now behave like a liquid, always taking the shape of the latest interface model or agent framework, what should you actually invest in?
If you focus on the current tools and patterns, you stay effective, but your knowledge can expire quickly and you end up rebuilding your playbook every quarter. If you focus mainly on durable fundamentals, you build long-term leverage, but you risk falling behind on the practice methods that deliver results right now.
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Chapter 3: What is the liquid literacy conundrum?
It's updating its own instructions in real time.
Exactly. It's the difference between writing a letter and having a telepathic conversation.
Wow. And then you combine that with what we saw on January 24th. You're talking about Claude and Excel.
Yes. And this is huge for the non-technical crowd. For $20 a month, anyone can now use conversational AI to build, say, a complex financial model right inside Excel.
Wait, pause on that. The source mentioned an 11-tab financial model built in 10 minutes.
10 minutes.
I've built those models. That's not a 10-minute job. That's a lock the door and don't talk to me for two days job.
Not anymore. You just ask for it. You describe the outcome and the system handles the formulas, the linking, all of it.
And then there's Moltbot. I saw this blowing up on GitHub. They used to be Claudebot, right?
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Chapter 4: How should professionals adapt to the evolving AI landscape?
This happened in a day. It signals a gold rush. The entire developer community looked at this and said, that, that is the future.
Why? What was so different?
It's an always-on agent. It doesn't just wait for you to type in a box. It acts across your email, your chat, the web. It's like a digital employee that doesn't sleep.
And this is where the shift becomes so clear. We're not talking about better prompts. We're moving to automated workflow orchestration. Exactly. And swarms. Swarms is another one of those terms that sounds a bit dystopian. Break that down for me.
It's like being a general contractor. You don't lay the pipes yourself. You hire a plumber, an electrician, and they coordinate. In a swarm, you have specialized sub-agents, a research agent, a drafting agent, a critique agent. You give the swarm a goal, like write a market analysis on EV batteries, and they just iterate among themselves.
So they collaborate without me micromanaging every single step.
You just get the final polished report. It's a fundamental change. And it leads to this huge structural mismatch. The report highlights, you know, universities operate on semester cycles.
Right. A professor writes a syllabus in August for a class in January.
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Chapter 5: Should you focus on current tools or durable fundamentals?
But by the time the students sit down, the tools from August are practically antique. The educational cycle is just too slow.
Which brings us to position one in this debate, the argument for investing in the current tools, the now skills.
OK, let's make the case for this side.
I'm a student. I'm looking for a job. The argument is simple. The market rewards people who can do the work today.
And the data backs that up. The report says 92 percent of employers would hire a less experienced candidate with a micro credential in these tools over a more experienced one who doesn't have it.
92 percent. That's that's basically saying years of experience matter less than knowing the latest patch update.
For a lot of roles right now. Yes, because the productivity gains are just so enormous. 74% want verified digital skills. They want proof you can drive the car.
And this leads to a concept in the report called the competence cascade. I love that term.
It's the idea that advantages compound. The person who masters the tools now, they get the internships, the projects. Why? Because they can deliver output on day one.
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