Pave

Benchmark pay, price jobs, build pay ranges, run merit cycles, and communicate total rewards in one end-to-end platform.

[Pave @pavecomp]

Read the full Hot Jobs Index: https://t.co/gpARGjn5rP

[Pave @pavecomp]

🔥 Some of the fastest-growing roles in the market barely existed on org charts two years ago.

Pave’s Hot Jobs Index tracks the jobs on the rise across our real-time compensation data, and this quarter the shift is hard to miss.

[Pave @pavecomp]

📈 GTM Engineers went from showing up at 0.3% of companies in early 2025 to 3.2% today.

Swipe through to see which roles are taking off: AI Transformation Lead, GTM Engineer, AEO/GEO Specialist, and AI Product Manager. 👉

[Matt Schulman @Matthewschulman]

Getting comp right at @SymphonyAI means bringing data together from a lot of different places.

The more data you can layer into @PaveComp, the more the Pave Agent has to work with. Thank you to Leanne for sharing how SymphonyAI does it, both here and on stage at TRL.

[Pave @pavecomp]

Dig into the Q3 rankings 💰 https://t.co/mN4MDAcOei

[Pave @pavecomp]

Which jobs are the hottest in the market right now? 🔥🔥🔥

Our Q3 Hot Job Index is LIVE, ranking the roles in highest demand based on Pave data. Whether you’re planning headcount, setting pay, or building your hiring strategy, this is the pulse check you need.

[Pave @pavecomp]

Don’t miss your chance → https://t.co/dBOXXGzzzj

[Pave @pavecomp]

One day left to weigh in on the question every team is asking: what should our token budget be? 🤖

Our AI Token Use & Spend Policy Pulse Survey closes tomorrow, Oct 23. Complete it to get the full report and see how your approach to budgeting, governance, and measurement stacks up against the market.

[Zuhayeer Musa @zuhayeer]

Update. Got an anonymous tip that the first product designer at Ramp (within first 5 hires), received 0.25% over 4 years for their initial grant.

That’s ~24% under the low end I modeled for hire #5, so my estimates ran high. That 0.25% would be worth ~$28.5M at Ramp today.

I reached out to @Matthewschulman and @rithikaneti at @pavecomp, and they pulled their data on equity grants across US VC-backed tech companies (chart below).

Among the first 5 employees, 0.25% sits above the median (0.16%) but below the 75th percentile (0.42%). For a product designer in the first 5, it lines up almost exactly with the 75th percentile.

And more importantly, benchmarks for “founding” roles are a bit more of an art rather than a science this early in hiring, since grants can be ultra-specific to individuals and their worth to the organization.

It can change with role, seniority, stage, growth etc, so I wouldn’t treat it as prescriptive.

It’s also just a bet on the company from the candidate’s perspective, and sometimes employers will need to flex to close a stellar candidate.

Hope my post helped shed some light on what outcomes can look like at a high growth startup, post-dilution.

Some more exciting updates coming soon between @pavecomp + @Levelsfyi. This post sparked some awesome discussion, and there’s a lot of neat data between the two of our platforms 📊

[Zuhayeer Musa @zuhayeer]

Update on the Ramp equity grants for early hires. Got an anonymous tip that the first product designer at Ramp (within first 5 hires), received 0.25% over 4 years for their initial grant.

That’s ~24% under the low end I originally modeled for hire #5, so my estimates ran high. That 0.25% would be worth ~$28.5M at Ramp today.

I reached out to @Matthewschulman and @rithikaneti at @pavecomp, and they pulled their data on equity grants across US VC-backed tech companies (chart below).

Among the first 5 employees, 0.25% sits above the median (0.16%) but below the 75th percentile (0.42%). For a product designer in the first 5, it lines up almost exactly with the 75th percentile.

And more importantly, benchmarks for “founding” roles are a bit more of an art rather than a science this early in hiring, since grants can be ultra-specific to individuals and their worth to the organization.

It can change with role, seniority, stage, growth etc, so I wouldn’t treat it as prescriptive.

It’s also just a bet on the company from the candidate’s perspective, and sometimes employers will need to flex to close a stellar candidate.

Hope my post helped shed some light on what outcomes can look like at a high growth startup, post-dilution.

Some more exciting updates coming soon between @pavecomp + @Levelsfyi. This post sparked some awesome discussion, and there’s a lot of neat data between the two of our platforms 📊

[Pave @pavecomp]

We’ll also get into how pay for AI roles works, how AI hypergrowth companies compare with the rest of tech, and what is changing for more traditional roles. 🤖

Save your spot and join Jessica Pillow, Hannah Wells, and Josh Steinfeld. https://t.co/j54yerB7d5

[Pave @pavecomp]

Where is compensation headed in 2027? 👀

Pave and Alpine Rewards surveyed companies on their plans for next year, and we are sharing what we found live, alongside our friends at Deel. We’ll cover headcount growth, salary increase budgets, promotions, pay transparency, and pay equity.

[Rithika Neti @rithikaneti]

Anthropic just published its own workforce data showing the decline in junior share across its technical teams

At 10K+ companies in @pavecomp data, this trend is consistent

If entry-level roles keep shrinking, who’s being trained to replace today’s senior talent?

[Pave @pavecomp]

@Human_Capital https://t.co/FfcJacPzug

[Pave @pavecomp]

Who’s winning as AI changes work? Join Pave + @Human_Capital on Oct 27 to compare AI frontier companies to everyone else. 👇

[Pave @pavecomp]

The Pave October data release is live!

👉 This month’s update includes a refresh to our variable pay and participation methodology, so the numbers you see in the app better reflect what’s actually happening in the market.

A big thank you to Ari Stern and Kevin Cheung on our Data team for helping this release go out smoothly. 💚

[Pave @pavecomp]

See 70+ ways to put it to work. https://t.co/1SdnWJYehs

[Pave @pavecomp]

What if the offer check, the pay equity question, and the equity burn number all took minutes instead of hours? ⏱️

✅ Teams are doing exactly that with the Pave Agent. It recommends, and your team decides.

[Pave @pavecomp]

Who you gonna call? Pave will help you banish them for good. https://t.co/EPp4m9CzE4

[Pave @pavecomp]

Some of the scariest things in compensation don’t jump out at you. They sit quietly in a spreadsheet and haunt your decisions for months. 👻

Meet the ghosts of stale data.

The good news is that these ghosts are easy to banish. When you benchmark against current market data, your pay decisions reflect today instead of whenever the data was last pulled.