REVIEW: Multipliers by Liz Wiseman (3-stars)

I recently finished Liz Wiseman’s 2025 book, Multipliers, and while I appreciate the intent behind the framework, I walked away feeling like I’d read a well‑packaged version of ideas that have been circulating in leadership circles for years. It’s not that the book is wrong — it’s that it’s not new. And in some cases, it oversimplifies the very real complexity of how people and organizations actually work.

Research to Support a Framework: My training in sociology makes me suspicious of repeated claims of quantification of things like "productivity."  Wiseman repeatedly cites percentages of “productivity” people report under different types of leaders. But the more I read, the more I wondered: How exactly are we measuring this?

The appendices describe structured interviews and multiple rounds of coding, but the core data is still self‑reported perception, not actual productivity. And in knowledge work, “productivity” is a slippery concept. Some of the best thinking happens:

• in the shower
• on a walk
• during a commute
• while knitting or exercising
• in the quiet space between meetings

If someone says they’re operating at “70% of their capability,” what does that even mean? It certainly doesn’t map cleanly to output. Insight doesn’t happen on a clock, and the brain’s default mode network  (the part responsible for creative leaps) activates when we’re not visibly “producing.” So if you're always "busy" -- can you actually be productive or are you just following the ruts in the road?

So the numbers make for good storytelling, but they’re not metrics. They’re sentiment.

The more I read, the more it felt like Wiseman started with the Multiplier/Diminisher idea and then went out to collect stories that fit the model. There’s nothing wrong with that, and most leadership books do it, but it’s different from discovering a pattern organically.

Once you’ve worked inside large organizations, you’ve seen dozens of these frameworks come and go. At Abbott, for example, we had the “in the box / out of the box” model -- another metaphor wrapped around basic human behavior.

Some of Wiseman’s recommendations are genuinely solid:

• run 30‑day experiments
• give people ownership
• ask better questions
• encourage people to bring solutions, not just problems

But these aren’t new ideas. They’re foundational leadership practices. They show up in Agile, Lean, design thinking, and every decent management training program of the last 30 years.

The User Manual Trap: One section encourages leaders to identify their “native strengths” and create a personal user manual so others know how to work with them. In theory, this is great. In practice, it can go very wrong.

I once worked with a CMO who had a user manual that was… memorable. Snarky, rigid, demanding, and completely inflexible. Instead of creating clarity, it broadcasted:

“Here are all the ways I refuse to adapt. Please adjust yourselves accordingly.”

A tool is only as healthy as the person using it. And a user manual can reveal more about a leader’s blind spots than their strengths.

Culture is the real missing ingredient in this book, and this is the part most leadership frameworks gloss over.

You can teach people any model you want: Multipliers, Radical Candor, Situational Leadership, “in the box,” “above the line,” whatever the flavor of the year is — but unless the organization has:

• psychological safety
• aligned incentives
• leaders who model the behaviors
• trust
• clarity
• and buy‑in at every level

…nothing changes.

Frameworks don’t transform organizations. People create culture, and there are many conditions required for cultural shifts.  Rarely are culture changes top-down. Without the right environment, a leadership model becomes vocabulary, not behavior.

FINAL THOUGHTS:

If someone wants a leadership book that actually grapples with complexity, I’d recommend:

• David Marquet’s Turn the Ship Around - a true operating model for distributed decision-making (see my review http://www.livegreenwearblack.com/2017/12/review-turn-ship-around-true-story-of.html) 
• Daniel Coyle’s The Culture Code - a grounded look at psychological safety, belonging, and high‑performing teams (see my review http://www.livegreenwearblack.com/2018/02/review-culture-code-secrets-of-highly.html)

Both authors understand that leadership isn’t a set of behaviors you adopt, it’s a system you design.

Multipliers isn’t a bad book. It’s just not a deep one. It offers a tidy framework, some useful language, and a handful of practices that can help leaders reflect on their impact. But the real work of leadership - the messy, human, systemic work - lives far beyond any model. If you want to change an organization, you don’t start with a framework. You start with culture, safety, and trust. Everything else is just packaging.

Why I Won’t Be Paying $20/Month for OttnoAI — Even Though I’m Glad I Tested It

 After three sessions with OttnoAI — one long session, one that disappeared when I closed the tab, and a third that produced a long set of recommendations — I’ve come away with a clear sense of what this tool is, what it isn’t, and why I’m not going to subscribe at $20/month.

This isn’t a negative review. I’m genuinely glad I tested it. I think the founder is building something interesting, and I appreciate the privacy‑first stance. But the product, as it exists today, isn’t something I can justify paying for — especially when the core value should be built directly into Garmin Connect or even MyFitnessPal.

Here’s why:

1. Every session starts from zero — no memory, no continuity, no way to save

OttnoAI has no way to:

• save a chat

• resume a session

• pick up where you left off

• maintain context across conversations

If you close the tab, the entire session is gone. My second session — which included a long, detailed back‑and‑forth — simply vanished.

This means you have to:

• copy/paste everything into Word or Notes

• re‑explain your context every time

• get very good at prompt engineering

• manually reconstruct your own history

For a tool that’s supposed to help interpret long‑term health data, starting from zero every time is a major limitation.

This alone makes it hard to justify a subscription. I get the same level of service from the post-Amazon acquisition One Medical level of care by physician assistants acting as primary care practitioners.

2. The trial banner never updated — the UI feels unfinished

For three days straight, the banner at the top of the screen said:

“3 days left in your trial.”

It never updated unless I manually refreshed the window.

It’s a small thing, but it signals that the UI is still early and not fully wired up. Combined with the typing lag and occasional freezing, it reinforces the sense that the product is still in a prototype phase.

3. The hallucinations are frequent, and sometimes stubborn

I expect hallucinations from any LLM — that’s not the issue. The issue is the type of hallucinations and the fact that some persisted even after correction.

Examples:

• It told me to “contain the cats” (as if that’s ever happening).

• It invented a “coursework intensity timeline” out of thin air.

• It repeatedly insisted my L4‑5 spinal fusion was causing ongoing pain — even after I corrected it multiple times and explained that the surgery solved the problem completely.

• It assumed my midterm project was an “exam day” and blocked it out as a rest day.

• It confidently told me it didn’t have my 30‑day data… until I uploaded the CSV… at which point it said, “Oh yes, I do.”

These aren’t edge cases. They happened in every session.

The model did correct itself when prompted, but the fact that it needed repeated correction — especially about the spinal fusion — is a sign that the grounding and guardrails aren’t strong enough yet.

4. It overreaches into medical interpretation

This is where I get cautious.

OttnoAI drifted into:

• diagnosing causes of heel pain

• predicting recovery timelines

• interpreting autoimmune interactions

• prescribing caloric deficits

• making claims about hormonal patterns

• giving sleep‑architecture interpretations that sounded authoritative but weren’t grounded in my actual data

I understand the founder’s intent that this is meant to be a supportive tool, not a medical device. But the model’s tone sometimes crosses that line, and users may not always know when it’s guessing.

This is exactly why I think this kind of tool needs stronger constraints before it’s ready for a paid tier.

5. The helpful recommendations were good — but not $20/month good

To be fair, I did get a few genuinely useful insights:

• Stop taking melatonin every night as it’s more disruptive than helpful

• Add brown noise to my nightly routine

• Take progesterone at the same time every night (8:30–9pm) and give it 60–90 minutes to work

• Try a 5‑minute box breathing exercise after driving

• Warm the bed with the heated mattress pad, then turn it off when I get in

These are small, actionable, grounded suggestions which are exactly the kind of thing Garmin should be surfacing.

But here’s the thing:

These insights came after hours of prompting, correcting, and steering the model back on track. They weren’t the default output. They were the result of me doing the heavy lifting.

That’s not a $20/month experience.

6. This functionality belongs inside Garmin Connect (or MyFitnessPal), not as a standalone subscription

Garmin already has:

• the data

• the sensors

• the long‑term history

• the stress and HRV models

• the sleep architecture

• the recovery algorithms

What they don’t have is the interpretation layer — the connective tissue that helps people understand why their sleep tanked, why their stress spiked, or why their heel hurts after certain activities.

OttnoAI is trying to build that layer. But it shouldn’t require:

• exporting CSVs

• manually uploading files

• re‑explaining your context every session

• paying $20/month for something Garmin could integrate natively

This is the kind of functionality that should be built into Garmin Connect or MyFitnessPal as part of the existing ecosystem, not a separate subscription.

7. I’m glad I tested it — but it’s not ready for me to pay for

OttnoAI is ambitious. It’s privacy‑first. It’s trying to solve a real problem. And I genuinely appreciate the founder’s approach.

But the product today:

• loses sessions

• hallucinates frequently

• overreaches medically

• misinterprets context

• lacks grounding

• has no memory

• requires constant correction

• feels like a prototype

• and doesn’t yet deliver $20/month of value

I’m rooting for it. I want it to succeed. But right now, it’s not something I can justify paying for — especially when the core value should be built directly into the platforms that already hold my data.