Friends,

These regular two day weekends just don’t hit like the four-day one us Aussies enjoyed last weekend.

Speaking of Australian things (terrible segue incoming), something that comes up often in my discussion with growing tech companies (in Australia and beyond), is “what the heck are we doing with equity?” (see, I told you it would be terrible)

We’re in a unique landscape here. While Australia’s technology sector punches above it’s weight, it’s still maturing, and equity hasn’t had the same impact to the individual, as it has in the States. Outside the US, it can be rare to know someone who got rich from equity, often resulting in a mental discount on the equity businesses offer their employees.

But hire in the US and you know just how different it is. There’s a greater education/understanding around what equity is, and what someone should expect in grant size. Each present challenges to companies scaling in these locations.

In today’s edition, we ground these matters in data from Pave’s latest New Age of Equity report. It’s an incredible breakdown of precisely how equity is being managed across the market, and an amazing resource for those grappling with how to establish or augment their own.

So let’s dive in and see what the data says.

Enjoy ✌

Matt

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

The teams furthest along are building systems that learn. What works for each manager. Which signals predict risk. When to act and when to step back. Where a human still needs to be in the loop.

These are systems where trust is earned over time and autonomy follows. HR manages exceptions and escalations. The system handles the rest.

Pyn is building the platform for that shift. If you're a People leader who wants to shape what it looks like, this is your invite.

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

I've worked with enough scaling startups to know that most equity programs are often held together with good intentions and little else.

Who gets equity? Depends on the hire.
How much? Whatever it takes to close the offer.
Vesting terms? Whatever the template says.

The problem isn't that these decisions are wrong individually. It's that they don't connect into anything coherent. 

Pave just published their New Age of Equity report, analysing data from 4 million grants across 4,500 companies, and it shows what a coherent program looks like.

Here's how to use that data to pressure-test your own setup and make smarter calls going forward.

If your equity program was set up a few years ago and hasn't been revisited, you might assume there's wide variation in how companies structure their grants. There isn't.

At private companies, 85% of new hire grants now use four-year vesting. 92% use a linear structure (meaning equal amounts vest at regular intervals, rather than larger chunks vesting later or earlier). And 90% vest on a monthly schedule.

That's a strong consensus. Four-year, linear, monthly. That doesn't mean it's automatically right for you, but if yours looks wildly different, it's worth understanding what's driving the market in that direction and whether those same forces apply to your company. Especially in markets where equity understanding is low, and candidates may have a hard time wrapping their head around the difference.

The more interesting trend is cliffs. For new hire grants, cliffs remain standard: 80% of private companies include them. But for ongoing grants to existing employees, that number has dropped to just 17.6%, down from 34.6% in 2020.

A cliff for a new hire gives you a window to assess fit before any equity transfers. For someone who's been with you for two or more years, that same waiting period can feel like you're second-guessing their value. Whether you keep cliffs on ongoing grants or drop them is a design choice, but the trend suggests most companies have decided it's not serving them for tenured staff.

Worth asking: Are your new hire and ongoing grants using the same cliff policy by default, or have you made a deliberate call on each?

This is where most scaling startups have the biggest gap. No documented participation framework, no clear rules about who qualifies and at what level, and grant sizes decided ad hoc during hiring.

Pave's data gives you a useful reference point.

Seniority is the strongest predictor of who receives equity: about 55% of new hires at entry level receive a grant, rising to 94–95% at Director level.

Department matters too. Among early-career employees, 84% of R&D new hires receive equity compared with just 49% in G&A.

At junior levels, companies are weighting equity towards the functions where talent competition is fiercest.

Then there's ongoing grants. A median 95% of promoted employees receive refresh equity, compared with just 44% of high performers who weren't promoted.

Most companies are using ongoing equity to reinforce career progression rather than reward excellence in the current role. While there's no single right answer, it's a question worth having a position on to ensure your plan design is efficient, and focused where it has the most impact.

If your program doesn't have clear rules for any of this, you're in good company. A practical starting point: map your last 12 months of grants and look at the patterns. At each level and department, who received equity and who didn't? Were grant sizes consistent for similar roles? Can you explain the rationale, or was each one a standalone negotiation?

If you can't answer those questions, build a simple participation grid. By level and function, decide who is eligible for new hire equity and who qualifies for ongoing grants. Even a one-page table ("P1–P2 in R&D: yes. P1–P2 in G&A: case by case. P3+: yes across the board") gives you and your hiring managers a framework instead of making it up each time.

Two areas from the Pave data are worth planning for, even if they're not urgent yet.

The first is burn rate: the percentage of your total equity pool you're granting each year. The median across Pave's dataset is 2.95%, but that number on its own doesn't tell you much.

Context matters. Companies growing headcount by more than 10% run a median burn rate of 2.9%. Flat headcount sits at 2.6%. Shrinking companies run at 2.3%. Your burn rate should match what you're trying to do with talent. If you're hiring aggressively, a higher rate is expected. If your headcount is stable and your burn rate is still climbing, your grant sizes or participation rules may have crept wider than intended.

AI-native companies are a good example of intentional trade-offs. They're running a median burn rate of 3.9%, nearly 40% higher than other tech companies at 2.8%. They can afford this because they're typically well-funded, and competition for AI and ML talent is fierce enough that equity is one of the most effective tools they have. Most companies won't need to run that hot, but the principle is useful: your burn rate should reflect a conscious choice about what kind of talent you're competing for, not just accumulated decisions nobody's reviewed.

The second is the shift from stock options to RSUs. Early-stage companies overwhelmingly use options (97% at fewer than 100 employees), but RSUs become the dominant vehicle around the 501–1,000 employee mark. If you're approaching that range, start the conversation with your board now. Candidates from later-stage companies will expect RSUs, and if you haven't planned the transition, you'll end up making it reactively when a key hire asks.

Pave's full New Age of Equity report goes deeper, with breakdowns by company stage, industry, and function. If you're planning to review your equity program this year (and after reading this, you probably should), it's the most practical starting point I've come across.

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