Friends,

Only a brief hello this week as my body finally relaxes, post Startup People Summit. This is my focus for the next couple of weeks.

In short, the content of the day was some of the most incredible, practical and insightful thought leadership I’ve ever seen. Whether people were arguing for or against the role of a manager, or talking about the journey to cultural decimation and getting out the other side — it was everything I hoped the day would deliver.

If you saw some of the event hype and want to grab the sessions, then post event access is available here.

Enjoy this week’s edition ✌️

Matt

In case you’re new here (or just missed it) here’s the past three editions of the FNDN Series:

Clarinet’s AI Fluency Assessment gives you an org-wide baseline of how effectively, safely, and strategically employees use AI, based on applied knowledge and behavioral observation, not self-reported confidence.

Join Tara Nouveau, Clarinet’s Head of AI Learning Products, for a 30-minute live demo of the assessment, including how it works, what the results look like, and how organizations are using them to decide what training comes next.

Thank you for supporting our sponsors, who keep this publication free.

Know a startup Head of People looking for answers 🙋 why not forward this to them for some instant karma? ✨

This edition is based on the latest episode of the FNDN Series podcast, with Colby Nesbitt.

Colby Nesbitt is the Senior Manager of Employee Listening at Netflix, and has worked in People Analytics, Talent Management, and Compensation at Lattice and Twitter. She draws on her background as a PhD in Organizational Psychology, and writes from an interdisciplinary perspective about the future of HR, the changing nature of organizations, and the impact of AI on talent strategy.

When I reflect on the times I did the best work of my career, it becomes immediately obvious how much of that came down to not just me, but my environment.

These are the kinds of things that will not be new to anyone reading this.

When I was doing my best work, I was in environments where I had:

When I think of the times I didn't feel I was doing great work, the opposite was usually true.

It reminds me of one of my favourite articles challenging the idea that excellent talent is excellent no matter where it is. It was also the focus of my recent discussion with Colby Nesbitt, Netflix's Senior Manager of Employee Listening.

I first reached out to Colby after reading her piece on 'Is Performance a Power Law?', which explores our relationship with performance, and our obsession with measuring the outputs of individuals versus the inputs that create them.

These outputs are believed to fall along a power law distribution, as opposed to a normal distribution.

If you're like me, you hear terms like high-performance, A-players, talent density, and other monikers increasingly used in a way that seems to be outsourcing 'achievement' to the individual, rather than the role of the environment.

Or put differently, the role of culture, in enabling that performance.

For many companies, there's an appealing logic to the idea of hiring your way to high performance. Get your hiring right, and you can find the 10x people, bring them in, and then get out of their way.

There’s a case, however, for why that logic is incomplete – and why the half of performance that gets ignored is the half that sits at the feet of the organisation.

I remember the first time I'd heard the term power law in a performance context was in the book Work Rules!, by Google's then SVP of People, Laszlo Bock.

The premise is that in the kinds of work many of us do now – knowledge work, as opposed to procedural or systematic work – the value generated by the highest performer can be exponentially greater than that of the average performer. It's typically used to measure outputs: revenue generated, deals closed, code shipped. Anything countable that starts at zero and has no ceiling.

A chart almost everyone will be familiar with is the bell curve, which has also been used to describe where performance falls in an organisation. Where the power law tracks outputs, the bell curve is better suited to behaviours – the things a person does. And most human behaviours, it turns out, are distributed normally.

So, we have two curves trying to describe performance in different ways, and both of them are real. That distinction is the centre of the issue.

Behaviours, the things a person actually does, are distributed normally. The outputs those behaviours produce can follow a power law, because outputs are countable, bound at zero and open at the top.

A simple way to see this: think about a sales leader.

The behaviours that make someone excellent in that role – the listening, the coaching, the strategic instincts – would fall on a bell curve if you measured them cleanly.

Plug those same people into a sales metric and what comes out looks like a power law. Same people, same capability, but the skew comes from the metric, not the individuals.

Colby came at this from another angle too, drawing on her time as a competitive rower. On a crew team, boats race eight people at a time. Coaches do something called seat racing: swap a pair of rowers between boats, race them, swap someone else, and repeat. Over time you can isolate the plus-minus of every individual in the boat. It's a stressful thing to go through. Colby said she often wonders whether we could do something equivalent in organisations, because most of our work isn't individual output either. It's a team producing something together, and the contribution of each person is hard to separate cleanly from the rest.

This is the fallacy of performance being seen as a power law in modern organisations. Roles are often and frequently collaborating with others, and one outcome is not easily divisible by the people within a team.

So what happens if you take the power law at face value and hire accordingly?

If you kept only the top 20% delivering 80% of the work and let everyone else go, the company falls apart.

The majority you cut were holding together the system that let those top performers do their best work. You don't escape the distribution by hiring only from the tail. You create a new one, where the people who were middling before become the new bottom (rank and yank, anybody?)

I've seen this described as the difference between ‘superstars’ and ‘rock stars’ within the excellent book, Radical Candor. Superstars are career-driven, after the big moves and the high-impact moments. Rock stars are ambitious about the quality of their work rather than their career trajectory. Both are necessary. Most companies would be in trouble without the rock stars, and most companies forget to recognise them.

It's also why your performance ratings can't answer this for you. Colby calls a rating distribution a tautology: it hands you back the philosophy you gave your managers.

It tells you what you asked for.

It's also the same point the ‘Myth of A-Players’ piece makes. Ron Johnson built the Apple Store, which was wildly successful, then took the same instincts to JC Penney, where it wasn't.

But look, none of which is new, which might be the uncomfortable part.

The conditions that make organisations perform well haven't changed much in decades. But in the rush to focus our performance measures on output, it seems we've stopped measuring them.

Look again at the three moments I opened with. None of them are talent problems. They're all things a company did for a person (me).

Here’s how that can be true for you and your company, too.

Start with alignment, because it's the precondition for everything else. Someone can be doing excellent work on the wrong thing, and it counts for nothing.

I increasingly hear this from organisations over-indexing on their AI rollouts. People who are so busy ‘producing’ through AI, that much of it isn’t in aid of the actual goal.

Colby gets a better signal for high performance from a listening strategy than from a rating scale.

Here’s four questions she suggested as a means of signalling whether you're on the right track (worth including in your next engagement survey):

Alignment is bedrock, but it's supported by consistency. Your culture is the decisions you make. The people you promote, and sometimes who you let go, are the most visible statement a company makes about what it will celebrate and tolerate. When those decisions don't match the stated values, people believe the decisions over the words.

While many of the things that build high performance can still be attributed to tried and true practices, maybe it won’t be that way forever.

If one person running a fleet of AI agents really can produce ten times the output of a peer, the output side of this argument gets stronger. ClickUp recently advertised a CMO role at a million dollars with no human team to manage, pointed instead at thousands of internal AI agents.

You can hire the best people on the market and still not have a high performance culture. Performance is what happens when a capable person meets an environment built for it. The environment is the half that companies own.

If you enjoyed this post or know someone who may find it useful, please share it with them and encourage them to subscribe.

A roundup of the most interesting stuff from the week:

When you’re ready, here’s three ways I can help you:

1. Tools & resources
Resources and tools that give you what you need to build your own startup compensation practices.

2. Comp consulting
Building startup compensation practices that are clear, fair and competitive.

3. Startup People Summit
A 1-day annual event for People professionals in scaling companies. Creating the playbook for startup people practices. Grab recordings from past events, or subscribe to join the next summit.