Michael Browne recently shared something that caught my attention: "fascinating times we live in. If you're not seeing Moltbook right now, tune in. Understand it, and start asking questions." He added that he is "awaiting other thought leaders to chime in" and even name-checked Ethan Mollick as someone he would like to hear from.

That short post is doing a lot of work. It is not a hot take. It is a signal flare. And I think Michael is pointing to a skill most of us are still learning: how to notice a new idea early, sit with it long enough to understand it, and then ask questions that move the conversation forward.

In a landscape where feeds reward instant certainty, his approach is a quiet counterweight. Tune in. Understand it. Ask. Then wait for smart people to respond after they have digested it.

What Michael Browne is really saying

When Michael says to "tune in" to Moltbook, he is not just recommending a tool. He is describing a pattern for how new waves in tech and work actually arrive:

First, something new starts showing up in your peripheral vision.
Then, the early adopters talk in fragments and screenshots.
Then, the hype and backlash arrive before most people have tried the thing.
Finally, a few credible voices put language around what is real, what is not, and what changes next.

Michael is suggesting we intervene earlier in that cycle, but with more discipline.

Key insight: Pay attention early, but do not rush to a conclusion.

Why "tune in" matters (and what it looks like in practice)

Most professionals either ignore a new trend until it is unavoidable or obsess over it until they burn out. "Tune in" is the middle path.

Tuning in means creating just enough exposure to answer three basic questions:

What problem is this trying to solve?
Who is it for right now (and who is it not for)?
What new behavior does it encourage?

If Moltbook is appearing in your feed, treat that as a data point, not a verdict. Watch a demo. Read a thread. Scan a few use cases. Notice what people are excited about, but also what they are struggling to explain.

A helpful mental shift: you are not trying to decide whether Moltbook is "good." You are trying to understand what category it belongs to and what it might replace.

"Understand it" is the hard part

Michael pairs "tune in" with "understand it" for a reason. Consumption is easy. Understanding requires structure.

Here are a few ways I like to turn vague awareness into real understanding when a new AI product, workflow, or concept is trending:

1) Identify the new unit of value

Every meaningful tool makes a new unit of work cheap.

Spreadsheets made calculation cheap.
Search made retrieval cheap.
Generative AI made drafting and variation cheap.

If Moltbook is a new wave, ask: what does it make cheap? Is it ideation, synthesis, planning, studying, writing, collaboration, or something else?

2) Map the workflow, not the features

Feature lists mislead. Workflows reveal impact.

Try to describe, step by step, what someone does before and after this tool exists. If you cannot do that, you do not understand it yet.

3) Find the constraint it removes (and the constraint it creates)

New tools remove friction, but they also create new problems:

Faster output can create quality drift.
Easier publishing can create noise.
More automation can hide assumptions.

Understanding means holding both sides at once.

"Start asking questions" that are worth asking

This might be the most important line in Michael Browne's post. The best question is rarely "Is this the future?" That invites a fight.

Better questions sound like:

What would need to be true for Moltbook to become a daily habit for teams?
Where does it fit in an existing stack (docs, notes, chat, project management)?
What does it replace, and what does it simply add on top of?
What are the failure modes (hallucinations, privacy, version control, attribution, plagiarism, shallow thinking)?
Who benefits first: students, creators, analysts, product teams, executives?

If you want to go one level deeper, ask questions about incentives:

What does the company need users to do repeatedly for the product to work?
What does it optimize for: speed, accuracy, novelty, engagement, learning?

Those questions turn a trend into an investigation.

Why waiting for "thought leaders" is smart (if you do it right)

Michael says he is "awaiting other thought leaders to chime in" and clarifies that he does not want them to respond immediately. That is a subtle, healthy expectation: expertise should include digestion time.

But there is a trap here. If you wait passively for someone famous to tell you what to think, you outsource your judgment. The better stance is:

Use the waiting period to form your first-pass model.
Then compare your model with the best analyses.
Update your view publicly and specifically.

That is how you build your own credibility while still learning from others.

Ethan Mollick is a great example of the kind of voice many people look for in moments like this: someone who tests tools, explains implications, and separates capability from narrative. The value is not that a known name speaks, but that rigorous thinking enters the chat.

A simple framework for evaluating a new AI "thing" in your feed

If Moltbook (or anything else) keeps popping up, run it through this quick framework before you amplify it or dismiss it.

The 10-minute test

Define the job: "I want to use this for X."
Try one real task, not a toy prompt.
Save what it produced.
Note what you had to correct.
Ask: would I do this again tomorrow?

The credibility test

Are claims paired with examples?
Do reviewers show failures, or only wins?
Is anyone measuring time saved or quality improved?

The adoption test

Does it require a behavior change that most people will resist?
Does it fit existing habits (notes, docs, meetings, studying), or fight them?

The risk test

What data touches the system?
Who owns the outputs?
What is the cost of being wrong?

This framework does not make you an expert overnight, but it makes you harder to fool.

The content lesson hidden inside Michael's post

Even though Michael's post is short, it models a strong content strategy:

He names a moment: "fascinating times we live in."
He points attention: "If you're not seeing Moltbook right now, tune in."
He gives a next step: "Understand it, and start asking questions."
He invites credible dialogue rather than instant outrage.

That is a useful template for anyone trying to write better LinkedIn content without resorting to hype. You do not need a full essay to contribute. You need a clear signal and a smart prompt.

Where I land

Michael Browne is right to treat Moltbook as something to watch and to approach it with curiosity rather than certainty. In fast-moving AI cycles, the winners are rarely the loudest voices. They are the people who:

notice early signals,
build a working understanding,
ask better questions,
and update their beliefs in public.

If Moltbook is the next wave or just the next experiment, the practice Michael is pointing to will still pay off.

This blog post expands on a viral LinkedIn post by Michael Browne. View the original LinkedIn post →