You Probably Don’t Need to Keep Up With AI
Dots, Muse, and a better way to decide when AI is worth your time
Key Takeaways
If you are interested in using AI to free up some of your personal time, Dot or Muse are worth trying. These new agents are remarkably easy to start using.
Staying current with AI does not require learning every new product. It is more useful to keep up with what AI has become capable of doing.
Early adopters often spend time learning complicated tools and terminology that become unnecessary once the technology gets easier to use.
Try a new AI tool when it solves a real problem for you. If it does not, waiting may be perfectly rational.
A useful question is no longer “Am I keeping up with AI?” but “Can AI do something now that would actually make my life easier?”
If you feel like you are falling behind on AI, I have some good news.
You probably are.
So am I.
There is simply too much happening. New models, new tools, new agents, new interfaces. Something that takes an afternoon to learn today may simply appear as a feature in ChatGPT, Claude, or another major AI product a few months from now.
That has changed how I think about “keeping up.”
I no longer think the goal should be to learn every new AI product as it appears. A more useful goal is to understand what has become possible and recognize when one of those capabilities has finally become easy enough—and useful enough—to deserve your time.
And this week, I think AI agents crossed one of those thresholds.
If You’re Curious About Agents, Start Here
I have written quite a bit over the past couple of months about AI agents—systems that can do more than respond to a prompt. They can work through multiple steps, use tools, and take actions on your behalf.
Until fairly recently, getting the most out of this technology often required some effort.
I started paying for Manus in early 2025, when general-purpose AI agents still felt very new. Later, I briefly experimented with more technical approaches such as OpenClaw.
Along the way, I learned plenty of new terminology, unfamiliar systems, setup procedures, and ways of getting these tools to work.
Some of that was useful.
Some of it, frankly, I probably never needed to learn.
The AI companies kept making the technology easier.
That is why OpenAI's new Dots and Meta's Muse are interesting to me.
Not because agents themselves are new.
They aren't.
What is new is how little you increasingly need to know about agents to use one.
OpenAI describes a Dot as an always-on agent inside ChatGPT. It has its own cloud computer—essentially a computer running remotely on OpenAI's servers rather than on your laptop—and can work across services you choose to connect and continue working on tasks over time.
I recently gave a Dot a decidedly unexciting assignment: handle the logistics for a work trip.
I needed a flight, a hotel, and a rental car.
Normally, that means bouncing among websites, comparing options, keeping dates straight, checking locations, and eventually making several reservations.
Instead, I had the agent work through much of that process.
I still reviewed the important choices. I was still responsible for what ultimately happened.
But the value was immediately obvious: I got some of my time back.
That is why, if you have access to Dot and are curious about agents, I think it is worth tinkering with one.
Don't start by trying to build some elaborate professional workflow.
Give it something mundane.
Have it help organize a trip. Research something you have been meaning to buy. Work through some tedious planning task. Give it something where you will know whether the result is useful.
Meta's Muse is worth watching for the same reason. Meta introduced Muse in September as a personal agent intended to work out of the box, without requiring technical expertise. It can use its own browser, work through multi-step tasks, continue working after you leave, and come back when it needs your input.
I have not used Muse yet, so I can't give you a firsthand comparison.
But if you have access and the idea of giving an AI a task rather than simply asking it a question sounds useful, Muse is another reasonable place to start.
I would be surprised if Claude and the other major AI platforms do not keep moving rapidly in this same direction.
You Do Not Need to Learn Everything Else
There is an important second lesson here.
You don't need to become an “AI expert” by learning every tool that comes out.
In fact, trying to do that may be a particularly inefficient way to use your time.
One of the strange things about being an early adopter of AI is that you pay what I think of as an early-adopter tax.
You learn the complicated version.
You figure out the new website. You learn unfamiliar terminology. You connect things that don't quite want to connect. You troubleshoot. You find workarounds.
And then the technology improves.
What required considerable effort suddenly becomes something you can accomplish by typing a sentence.
I have experienced this repeatedly.
That does not mean early experimentation is pointless. It has helped me understand what these systems can and cannot do, and that understanding lasts longer than knowledge of any one product.
But I increasingly think there is an important distinction between keeping up with AI capabilities and keeping up with AI products.
You probably should have some idea of what AI can now do.
You do not need to know how to use every product that can do it.
The products will change.
The interfaces will change.
The terminology will change.
The capabilities are what matter.
A year ago, knowing that an AI system could operate a computer, work independently through multiple steps, or continue pursuing a goal over time was relatively cutting-edge information.
Increasingly, those abilities are becoming ordinary features.
So instead of asking:
“Am I keeping up with AI?”
I think a much better question is:
“Is there something AI can do now that would actually make my life easier?”
If the answer is yes, try it.
If the answer is no, you probably do not need to spend Saturday afternoon learning it just because somebody on LinkedIn says it is the future.
Sometimes waiting is a perfectly reasonable technology strategy.
The AI companies may make the difficult parts much easier for you.
Wait Until the Benefit Is Bigger Than the Hassle
I think there is a simple way to decide whether a new AI capability is worth trying.
Does it solve a problem you actually have?
Is it easier than whatever you are doing now?
Can you tell whether it did the job correctly?
If the answer to those questions is yes, it may be time to experiment.
That is why I think Dot and Muse are worth highlighting right now.
The learning curve has fallen enough that you can spend your time seeing whether an agent is useful rather than learning how to build one.
And there is absolutely nothing wrong with letting other people pay the early-adopter tax until that happens.
I usually keep this blog fairly close to psychology, school psychology, and AI. I don't see Dot or Muse primarily as tools for school psychology practice, and I certainly would not start putting sensitive student information into a consumer AI agent.
But freeing up time matters too.
School psychologists—and psychologists, professors, researchers, teachers, and pretty much everyone else—have lives filled with things that have nothing to do with their professional expertise.
We book travel. Compare purchases. Schedule things. Research options. Organize plans. Keep track of tasks.
If an AI agent gives you an hour of that time back, you still got an hour back.
And that may be the most compelling reason for many people to start experimenting with agents.
Not because you want to become an “AI expert.”
Because you have something better to do with your time.
So my advice is becoming simpler:
Keep up with capabilities, not products.
Notice what has become possible.
When something becomes easy enough to try and useful enough to matter, try it.
And don't worry too much if somebody else got there six months before you did.
Sometimes being a little late means arriving just after somebody else made the technology much easier to use.
There is another side to this story.
Making agents easy enough for almost anyone to use does not just mean they can arrange our travel or take tedious work off our plates. It also means AI can increasingly enter systems that were built around the assumption that the entity clicking, typing, and answering questions is a human being.
Amazon Mechanical Turk shut down this week after more than 20 years.
In my next post, I want to talk about why I think AI killed MTurk—and what that may mean for online survey research, AI agents, human participants, and synthetic respondents.
Sources and Further Reading
Current tools
OpenAI — Getting started with your Dot
Meta — Introducing Muse: The World's First Personal AI Agent Built for Everyone
Earlier posts in my AI agent series
The Future of AI Agents in School Psychology
From Chatbots to Agents: What School Psychologists Need to Know
Before You Hand Over the Keys: Using AI Agents Safely in School Psychology
Goals and Loops: How to Give an AI Agent a Job Without Losing Control
I Built a School Psychology Video Game with an AI Agent
My Research Agent Worked. So Why Wasn't I Using It?
AI Disclosure: Generative AI was used to assist with drafting and editing this post and to create the accompanying image. I reviewed and revised the content and take responsibility for its accuracy and final form.