Oct 13, 2025
5min read
Authors
Chloe Paramatti
A deep dive into how AI is reshaping executive compensation and how founders can build competitive packages.
⏳ Why This, Why Now
The advent of AI is changing compensation faster than founders can keep up. Executive roles are being redefined, early-career AI engineers are commanding premiums, and most available benchmarks are still anchored in traditional tech companies rather than AI-native ones. As the sector matures, we expect better data, but for now, founders need new ways to think about compensation.
We’ve written this guide because the answer is not simple. In the last two years, the integration of AI in the making of companies has fundamentally reshaped their structure, and that shift has had a direct knock-on effect on executive compensation design.
Today’s strongest AI companies didn’t start scaling with headcount. They scaled with leverage: raising more capital, hiring fewer people, and using AI to build faster with smaller teams. That means every hire matters more and gets paid more.
Founders need sharper frameworks. The old frameworks don’t fit the way AI companies are being built.
For European founders, the challenge is sharper. You’re not just competing with local peers, but with US companies that increasingly pay US-level packages in Europe. To win, you need to know where to adjust, where to hold firm, and how to use equity and benefits strategically.
⚡ What’s Changed
Org design is changing the math of compensation
In AI companies, leaner teams and higher capital efficiency mean compensation is concentrated in fewer, more strategic roles. Valuation per employee is spiking, revenue per employee is becoming a standard early-stage metric, and new questions are emerging:
What is the valuation per employee?
What’s the right equity-per-employee ratio?
Who do I need, by when and what are they worth if we succeed?
“The best way for companies to benchmark against others in the space is to look at what the equity and valuation per employee are, because that is what candidates will look at.”
— Špela Prijon, Founder, EquityPeople
Benchmarks are the starting point
AI job titles are exploding (up 578% YoY from the latest Ravio report), but data can lag behind when it comes to executive compensation.
Benchmarks still matter, especially for cash compensation, but they should be treated as a starting point, not the answer. From there, founders need to adapt: layering in company type, role impact, and reverse-engineering from target valuations.
As Špela points out,
“Benchmarks are useful until they’re not. In AI, they age out fast. Founders need a way to model the upside, not just copy comp tables.”
Premiums are real — but not for everyone
Ravio data shows AI engineers now command an average 9.5% salary premium. In companies built on AI, that premium often extends across the C-Suite, product, and technical leadership. But in companies where AI is mostly an enabler, the impact is more targeted to specific AI roles.
In practice, higher valuations and leaner teams mean AI companies can afford to pay more, but the distribution of that premium depends on your model.
Buying speed
Defining the right compensation should always consider the element of time. Hiring metrics like days-to-fill and offer acceptance rates matter to every company but they are absolutely top of mind for AI leaders. Why? With any hype comes a fierce race. A race to customers and a race for talent.
In AI, compensation’s attribute of buying time and the right people being worth double (you secure them on your team and prevent the competition from having them) is emphasised.
Executive Compensation Gets Trickier
These shifts affect all roles but make executive packages particularly complex. Would you pay the same for a Head of AI in a company integrating AI into existing products as you would for a Head of AI in a business built entirely on frontier models? The answer is obvious, but structuring the right package isn’t.
📑 The Report
In collaboration with EquityPeople and with insights from executive search partners and compensation data providers, we’ve built this report to give founders practical recommendations on navigating executive compensation in AI companies today. It’s a framework for thinking about:
What percentiles to target based on company type and talent pool
What role equity should play and how to structure it
What models help you justify compensation internally and externally
While this report touches on broader shifts in compensation across AI-related roles, the focus is on executive and other high-impact roles. The recommended frameworks and models are designed to give founders grounded guidance for scalable decisions.
What Makes AI Companies Different
Not all AI companies are the same, and neither are their leadership roles.
The type of AI company you’re building shapes who you need to hire, how much you need to pay, and what kind of packages you can offer candidates.
Founders who miss this either overpay for prestige hires or underpay for truly business-critical roles.
What you’re building and how deeply AI is embedded in your product should drive how you design and value every leadership hire.
🧱Three Types of AI Companies and Why It Matters
We use three company types to frame the compensation strategy:
AI Foundational | These companies are building infrastructure-level models (e.g. OpenAI, Mistral). They compete at the very top of the market.
👉 Benchmarks are often irrelevant here; modelling form outcomes is essential. |
|---|---|
AI | AI-Native companies build products directly on top of foundational models (e.g. Synthesia, Lovable).
👉 Product and GTM leaders are often just as critical as technical ones. |
AI | AI-Enablers are traditional SaaS/product businesses integrating AI into existing offerings (e.g., Revolut and Spotify).
|
“We see that AI-Native companies target higher cash percentiles than AI-Enablers when setting their compensation philosophy in Ravio. Interestingly, we see AI-Native companies disproportionately target the 90th percentile and above when using Ravio, relative to the rest of the market. AI-Enablers show no clear difference in target percentile to the wider VC-backed market.” - Raymond Siems, Co-Founder and CPTO, Ravio
🤖 The Roles Are Changing, Too
CTO vs CAIO / Chief Scientist
In AI foundational and AI native companies, the Chief Scientist or Chief AI typically builds the model itself, the IP. Engineering leadership (CTO, VP Eng) becomes more execution-focused: scaling, tooling, and delivery.
The CTO role has not diminished, but it is no longer the origin point of core technology in many AI companies. Roles like CAIO now command larger equity packages than CTOs in similar-valuation tech companies, especially in the U.S.
The distinction between these company types directly shapes who you hire and what you pay.
“For AI Native companies, engineers who can ship products on top of AI models are the most relevant hires, and their comp reflects the broader talent pool. Foundation model researchers are a highly specialised group with total comp ranging from £700k–£4M+ at top labs. Unless a company is truly operating at the frontier of model development, recruiting these scientists isn’t just costly, it’s misaligned with their business needs.” - Laila Shaban, AI Talent Lead, Adamas Knight
This distinction also applies to technical and research executives, so it’s critical to be clear about which type of AI company you are. Otherwise, you risk overpaying for non-frontier roles or under-investing in those that define your moat.
Commercial, Finance, and Ops Leaders
Many AI-native companies are founded by research-driven teams that often lack experience translating a product into a business. Commercial, finance, and ops executives fill that gap, and these roles are now commanding:
Higher cash comp (especially in EU-based hires)
Title elevation (GM → CRO)
Competitive equity (0.25–0.5% common at $100–300M valuations, where capital discipline starts to matter)
This is particularly important in Europe, where many founders come from deep tech or academic backgrounds and under-resource commercial functions early.
Title Inflation Is Real
Founders often inflate titles to attract technical talent, and compensation misalignment is also visible in how early-stage founders title and price senior hires.
“In many Series A and B companies, the so-called CFO is effectively operating at the Controller or VP Finance level. The combination of inflated titles and rising salaries risks creating a real mismatch between cost and capability.” - Neil French, Partner, STOIX
This is especially true for early-career PhDs. A Head of Research with no team or ownership scope ends up looking equivalent on paper to a Chief Scientist from DeepMind.
💥 Key Takeaways for Founders
Company type defines comp: AI Foundational = top of market, AI Native = competitive across the board, AI Enabler = selective premiums.
CTO ≠ CAIO: Technical leadership roles are splitting, equity follows IP creation, not job title.
Commercial leaders matter: Don’t under-resource finance, ops, and revenue roles; they’re often the missing piece.
Be careful with titles: Over-titling early hires creates long-term comp headaches.
You can’t outpay the giants: Build strategy around equity, milestones, and story, not cash alone.
Structure around value creation: Pay frontier premiums only where IP defines your moat.
Competing for Talent: US vs EU
Founders in Europe often believe they’re offering a competitive package, only to lose their dream hire. Why? Because “competitive” in Berlin or London doesn’t match “competitive” when a candidate is also considering offers from San Francisco or New York.
Note: we did not say “when the candidate is based in San Francisco or New York” on purpose - location is no longer the driver.)
AI has accelerated the globalisation of talent. Location-based pay is collapsing, and candidates benchmark against the best offer they’ve ever seen, regardless of where they live.
🌍 The Playing Field Has Changed
A candidate in Paris may receive offers from San Francisco, London, and Stockholm in the same week. The old logic (SF pays 20–30% more than London, London pays more than Berlin) no longer holds for this global talent pool.
As notes:
“We’re seeing US-based companies expanding into Europe without adjusting comp, just shifting the currency. They pay US rates, in euros or GBP.” - Alex Howman, Principal, True Search
Borders are blurring, and in Europe, as Alex shares,
“ICs and leaders in Europe on $600–700k packages matching US peers.”
Founders who still anchor offers to London or Berlin percentiles are competing with the wrong market.
🇪🇺 Europe’s Compensation Gap
Many European founders still use UK, or Germany benchmarks. The problem is these reflect local startup norms, not the global AI market.
The traps we see most often:
Only sticking to local benchmarks: Feels safe, but wasn’t designed for globally mobile AI execs.
Worrying about “overpaying”: 90th percentile or >1% equity may be necessary to land top hires.
Not telling the story: Candidates and boards need to understand why the package makes sense. Without a narrative, offers stall.
Weak equity modelling: US companies size grants backwards from target outcomes, while this is still rare in Europe, and it shows in lost hires.
"We are seeing European executive cash packages trending upward for Foundational, AI-Native and AI-Enabler companies, but not a convergence to US levels, because both markets pay a premium across AI categories based on Ravio data. It is, however, true that we see some European AI company executive packages approaching and in some cases exceeding median US levels for non-AI companies of the same size, funding and stage." - Raymond Siems, Co-Founder and CPTO, Ravio
👉 Ravio’s insights highlight a positive shift. European packages are rising, but the key to competing globally lies in smarter equity and storytelling, not simply chasing US cash levels.
The “Move to Europe” Myth
Many founders believe top US execs will take a pay cut to relocate. In practice, almost none do.
Those who move expect:
Similar net OTE as in the US
Same equity they’d get in the US
Relocation support packages
Harder negotiation, because relocation is disruptive
From recent searches, we’ve yet to see a US hire move to Europe for a discount. Everyone still expects a proper package.
Europe’s True Hidden Advantage
Benefits and social protections are valuable, but they rarely close a hire. Europe’s real edge is the chance to compete differently, especially on equity.
Europe is full of top-tier talent, but too often we lose it to the US because our equity numbers and structures are conservative.
If European founders can be forward-looking on equity (larger initial grants, smarter vesting, and consistent refreshers), they can retain their best talent at home. Of course, this also requires a push at the government level to harmonise the tax treatment of startup options across the EU.
💥 Key Takeaways for Founders
Benchmark globally, not locally: For high-impact roles, assume your competition includes San Francisco and New York or you’ll lose top talent.
Build full packages, not just salary: Balance cash and equity. Don’t overpay in cash when equity upside could close the candidate.
Design smarter equity structures: Blend time-based vesting with milestone triggers tied to valuation, revenue, or strategic events.
Use equity as your differentiator: Forward-looking grants and refreshers are Europe’s best chance to compete with US offers.
Educate and align stakeholders: Model total cost (cash + equity + benefits) so boards understand the investment and narrative.
Stay flexible: Some execs will require US-level pay; others can be won with balanced European packages.
Cash Compensation: Where to Anchor
Even if benchmarks are ageing quickly than usual, founders need a starting point.
In AI companies, especially at the AI foundational and AI native level, percentile benchmarks (50th, 75th, 90th) are only helpful if you adjust for context: company type, valuation, role impact, and geography.
In practice, the 75th percentile is often the floor, not the ceiling, in foundational and native AI companies.
What we’ve seen in the market, for AI-Native companies at ~$1bn valuation, is:
These are directional ranges, and the exact range will depend on your company's context and the impact this role will have on your company’s growth.
🚫 Why “Stage” Doesn’t Work Anymore
Founders and boards often default to stage labels (Seed, Series A, Series B) when thinking about compensation. But in AI, these markers are increasingly irrelevant.
“Stage should influence is the composition of total comp, not total comp itself” - Tamas Varkonyi, Founder at Equity People
Valuation, ARR and CAGR are stronger anchors than stage. A $500M “seed” competes for talent in the same market as a Series B or C.
Equity vs. cash mix shifts with maturity: earlier = equity-heavy, later = more cash.
Headcount is a weak proxy. A 20-person team may grant more equity per head than a 200-person team, but valuation sets the pool.
👉 For founders: stop thinking in stages and anchor your comp strategy in valuation, ARR, and equity pool design.
🌍 Using a Geo Approach
Sometimes you’ll apply a geo lens instead of going fully global.
For executives in Europe, local data is not always accessible. A good alternative is to use US benchmarks and apply a conversion factor that considers not just currency but relative market differentials.
On average, Europe pays ~25% less than the US across exec roles, with variations:
UK, Netherlands, Denmark: ~20% lower
France, Sweden, Spain, Norway: ~30–40% lower
Equity differentials are smaller (5–10% below the US)
⚠️ These are broad guidelines. For globally competitive execs, assume that you might have to reach US-level comp.
💡 Where AI Premiums Show Up
As noted earlier, company type drives the premium on both cash and equity when looking at average salary data from traditional tech companies.
In AI-Enabler companies, premiums are usually limited to high-impact roles like the Chief AI Officer or Chief Science Officer; for most other roles, compensation remains close to SaaS norms.
In Foundational and AI-Native companies, premiums vary by impact: the closer a role is to IP creation or value generation, the higher both the cash and equity weighting tend to be.
Equity is the biggest lever: grants in AI Native and AI Foundational companies are often 2–3× larger, frequently tied to performance milestones.
“In many AI-native companies, CROs are now out-earning CTOs (not CAIO/CSO), underscoring a fundamental shift: revenue generation is becoming as valuable as infrastructure." - Ben Erskine-Hill, Partner, ARCH
🧮 Cash + Equity Levers by Role with Recommended Practices
This grid shows how comp flexes by role and AI company type. Use it to balance cash vs. equity, avoid overpaying, and frame board discussions.
💥 Key Takeaways for Founders
Benchmarks are anchors, not answers: Use them as a starting point, then adjust for company type, valuation, and role impact.
Context matters: The same role in a AI Native company will expect more than in an AI Enabler.
Start with outcomes: Ask what the role needs to deliver and size the package backwards from there.
Geo adjustments have limits: They work for domestic hires, but global execs expect US levels.
Equity in AI: How It Really Works
Founders often assume equity in AI companies works like equity in SaaS, but it doesn’t.
The legal instruments (stock options, RSUs) may look the same, but the dynamics differ. In AI, valuations, talent scarcity, and leaner team structures change and reshape how equity is designed, distributed, and valued.
📈 Valuations: The Gold Rush Effect
AI companies trade more on belief than on revenue. Valuations often assume explosive growth — Palantir traded at ~200x 2026 revenue (Aug ’25) vs SaaS peers at ~10x. Even “rational” rounds, like OpenAI at 17x, are still at a premium to SaaS norms.
Upside: high early valuations reduce dilution and increase the $ value of grants.
Downside: they create tax headaches (high strike prices early, especially in the US), fragile liquidity (uneven payouts in acquihires like Microsoft/Haiper).
SaaS contrast: slower, steadier valuations, with liquidity tied to ARR milestones.
💎 Talent: Scarcity Premiums
Foundational AI talent is scarce, cultivated mostly in Big Tech labs (Google, Microsoft, Meta). Cash alone won’t close them - equity is the bridge.
Equity packages for top engineering and research talent can blow past SaaS comp grids; for more available talent, they converge to what we are used to.
Team Structure: Smaller, Senior, Pricier
AI companies start leaner, with smaller but more senior teams. That makes equity a bigger upfront lever and refreshers more common earlier on.
Through ~$200M valuations, inflated rounds offset pool burn, granting rich packages in $ terms without depleting %.
But by ~$500M+, ARR and scaling begin to matter, and without foresight, founders risk running out of the pool.
Early pool planning is essential.
Think crypto 2021: Everyone assumed the good times would last forever. Know how much is needed for those executives and sensitive roles, and bake in a downward shock to the valuation.
🔍 The Limits of Equity Benchmarks
Benchmarks are a starting point, not an answer. They show averages by role, level, and stage, but don’t capture context or the real value (net of strike price, future growth potential, etc.) of an equity grant. Notes on benchmarks:
Stronger at junior/mid-levels, weaker at exec/frontier roles.
75th/90th percentile figures often rely on thin samples.
Always cross-check multiple sources and refresh as markets shift. If they don’t make sense, they are probably not representative.
Check where your top talent comes from and where they leave, and benchmark your comp approach to these companies to compete with your target talent!
“Most of our AI clients will want to target the 75th percentile for the lower and middle segments of their organisation, and move up to the 90th for key and senior talent. But the air gets thin at those heights - the dependability of data points starts to crumble. Use common sense: if something looks fishy, it probably is.” - Tamas Varkonyi & Špela Prijon, Founders, EquityPeople
How to Size Equity by Role
Instead of relying on titles, use an “impact lens” to size equity. For AI-Native companies at ~1bn valuations, directional ranges are:
ARR, CAGR, PMF status, and business model also affect where you land. This makes reverse engineering even more critical.
🏢 Reverse Engineering Grants from Valuation
In AI, the most reliable approach is to work backwards from target valuation.
Instead of asking, “What’s the market? " ask, “If we hit $10B, what should this grant be worth?” and size grants accordingly.
As Tamas and Špela state:
“When it comes to equity, most AI clients will be able to remain around the 60–75th percentile of traditional benchmarks for most roles due to the higher potential baked into their stock. However, the thought process does not start with choosing the target percentile. It starts with choosing the future target valuation that the leadership and the board can stand by and working back from there to identify the size of initial grants.”
🤖 Why AI Benchmarks Are Even Trickier
Equity norms in AI are still in price discovery mode. Valuations swing, comp data lags, and executive data points are thin.
Sam Altman (Aug ’25): “AI is in a bubble,” likening it to the 1990s internet.
MIT study (Forbes, Aug ’25): 95% of enterprise AI projects deliver zero ROI — proof that “richer” equity packages reflect uncertainty as much as talent competition.
👉 EquityPeople data: In mid-2025, four AI companies ($1–4B valuations) hired CROs. Initial equity grants ranged from $1M–$5M. The difference wasn’t “market” but growth story and candidate profile.
🧑🤝🧑 Designing Equity for Founders & Execs (Conceptual Principles)
Equity packages must strike a balance: big enough to motivate, but flexible enough to protect shareholders and preserve runway.
Vesting: Longer cycles (5–6 years) are common in Foundational AI; application-layer companies often shorten to 3. Acceleration remains standard in M&A.
Hybrid vesting: A mix of time and milestone grants is on the rise, starting from earlier stages than before.
Option Pools: Early pools are larger (15–20%+) in AI, as computation-heavy businesses require earlier, more frequent fundraising.
Refreshers: Annual or milestone-based refreshers are expected at Series C+ ($2–5B valuations). Before then, use promotion or tenure grants.
Long-Term Alignment: Regular refreshers + milestone-based vesting help prevent attrition in volatile exit environments.
AI Nuances: 1) Scarce technical hires command outsized grants. 2) An IP contribution should weigh more than a job title. 3) ARR-based milestones often fail; tying vesting to valuation or strategic outcomes.
Hybrid Vesting - Deep Dive
Hybrid vesting (a mix of time + milestone grants) is becoming more common at earlier stages.
🖐️Risk: At earlier stages, when ARR and sales motion aren’t yet repeatable, grants should skew more toward tenure than milestones. At later stages, with stable ARR and proven growth, milestones can be more confidently set and therefore take up a larger share of the total grant.
Milestones need to be dependent on a financial event, like a funding round or an IPO, but linked to company milestones.
Area | Example Milestone | Why it Matters | Equity Treatment |
|---|---|---|---|
Valuation | Post-money | Leadership is directly tied to company value creation | Releases a % of milestone equity with a min and max %, depending on the goal achievement I.e achieve 90% of the goal, gets 85% of the milestone grant |
Revenue / ARR | Annual Recurring Revenue threshold | Leadership linked to commercial traction/scale | |
Product / Quality | Model accuracy/ | CAIO/CSO linked to IP creation |
💥 Key Takeaways for Founders
Equity in AI isn’t about copying the SaaS playbook: High valuations, scarce talent, and leaner teams change the math.
Pay for impact, not titles: Size equity based on role value (IP, revenue, scaling), not just job labels.
Reverse-engineer grants: Anchor packages to your target valuation, not to percentile benchmarks.
Pools need to be bigger: AI equity burns faster, plan for 10–20%+.
Refreshers matter: Expect annual or milestone-based refreshers by $2–5B valuations.
Clarity closes hires: Show candidates what their grant could be worth at your target valuation, not just the % today.
Storytelling is the real differentiator.
Founders have always had to be great storytellers. Especially at the start, when your company is/was unknown and high-risk, you have/had to convince great people to join you with nothing more than a story.
Now, in AI, storytelling has become even more critical. The best executives have no shortage of offers, from Big Tech, from hot AI companies, from well-funded competitors. What convinces them isn’t just the OTE or equity %, but the narrative of why joining you now matters.
📖 The Storytelling Playbook for Offers
1. The Narrative: Why Now, Why Us, Why You
Set the stage: why this role, at this moment, is critical to the company’s future.
Show the candidate how they fit into the journey and the impact they’ll have.
Be honest about risk, but show conviction in the path forward.
2. The Numbers in Context — Show Scenarios, Not Just Percentages
Provide a strong offer letter that breaks down total comp, especially equity.
Model upside scenarios: “Here’s what your grant could be worth if we 5× or 10×.”
Put equity in context of company milestones, not just abstract percentages.
👉 A great story won’t fix a weak package, but even the strongest package will fall flat without the story to bring it to life.
3. The Delivery: Founder-Led, Personal, and Inclusive
The founder should walk the candidate through the offer at the C-level, not HR.
Treat the offer meeting as the moment to reinforce trust and alignment.
If relocation is involved, include their partner. Show why this is the right move for their family, not just their career.
💥 Key Takeaways for Founders
Numbers alone won’t close execs: you need a compelling narrative.
Frame the offer as a journey: Why now, why us, why you.
Model equity upside clearly: show what the grant could be worth at your target valuation.
Founder-led delivery is non-negotiable: it signals commitment and conviction.
Bring partners into relocation: their buy-in often decides acceptance.
Turning Benchmarks into Offers
Building an executive package isn’t just about copying data; it’s about balancing equity pools, cash, equity, and benefits into a package that tells a compelling story to both your board and the candidate.
“Founders get stuck trying to fit global offers into local structures. They end up overpaying in cash or giving away equity they didn’t need to, just because they couldn’t explain the package properly.” - Špela Prijon, Founder, Equity People
Rather than approaching this mechanically, think of it as building a narrative: does this package reflect the role’s value, your company’s ambition, and the candidate’s opportunity? If those three things line up, the numbers will fall into place.
📋 Before the numbers
1️⃣ Start with the Equity Pool
Every competitive package begins with the pool. Before you even consider salary bands or equity percentage, ask: Is there enough equity to hire this executive and others, and do we have enough for potential refreshers?
AI pools are bigger than SaaS pools, typically 15–20%+ versus 10–15% in SaaS, because AI talent demands more.
Always buffer and plan for an initial grant plus 2–3 years of refreshers.
2️⃣ Define the Role’s Value
Role definition isn’t about titles but about value creation. Ask: What outcomes must this role deliver in the next 12–36 months?
CRO → drives revenue → skew cash-heavy.
CAIO → builds IP → skew equity-heavy.
CFO/COO → scale and discipline → balanced.
Use the role’s impact to decide the weighting between cash and equity.
🔢 Getting into the numbers
3️⃣ Cash Compensation
Here, it depends on whether you have or don’t have benchmarking data that you can use as guidelines.
If you do: Use trusted providers like Pave, Carta, or Ravio. If their data isn’t already adjusted for AI company type, you’ll need to apply premiums yourself (see below).
If you don’t: Apply the premiums below if you’re using traditional tech data, or use the conversion grid if you’re comparing across AI company types.
<> Premiums by Company Type (vs. traditional tech roles)
Company Type | Cash Premium | Equity Premium |
|---|---|---|
AI Foundational | +30–50% | +100–300% |
AI Native | +20–40% | +50–200% |
AI Enabler | +5–15% (AI roles only) | +0–50% (AI roles only) |
<> Converting Between AI Company Types
Conversion Path | Cash Adjustment | Equity Adjustment |
|---|---|---|
AI Foundational ↔ AI Native | ±10% | ±50–100% |
AI Foundational ↔ AI Enabler | ±20–35% | ±100–250% |
AI Native ↔ AI Enabler | ±20–35% | ±100–250% |
A CAIO may still need to be paid at Foundational levels if their role is genuinely IP-defining (the company’s moat).
4️⃣ Equity
Equity is where AI diverges most from SaaS. In SaaS, equity is more predictable and milestone-based on ARR. In AI, it is reverse-engineered from outcomes and sized with higher risk premiums.
How to reverse engineer grants
Define the target outcome by answering:
Where are we today in terms of valuation?
Do we calculate grants off the last priced round or a different valuation?
Where do we want to get to (realistic, optimistic, pessimistic)?
How long will that take?
Rule of thumb: if you are moving someone from a $3B company, aim to give them a 2–3× higher package when you reach that same value.
Back-calculate % ownership, factor in dilution, and add a risk premium depending on role and company type.
Structuring the Grant
Legal vehicle | Tax-optimise where it makes sense. Weigh the setup cost with the expected headcount of that jurisdiction and/or the impact of employees based there. |
|---|---|
Default vesting | 4-year linear vesting with a 1-year cliff. |
Ideal vesting | 50% time-based (usually 3-6 years, depending on business model) 50% company milestone-based (should be “double trigger” e.g. founding round raised at minimum $Xbn valuation) If your metrics/product/exit timelines are unclear, it's better to stick with time-based vesting. Extending to 5-6 years for larger grants can achieve similar risk protection for the company and stay aligned over longer build cycles. |
Multi-role coverage | If temporary (e.g. CRO overseeing CX + Sales + Marketing), use cash instead of time-based equity. |
Strike price | At FMV for tax-optimised plans, at nominal value for non-tax-optimised (or phantom) plans IF the granting entity is not US-based. |
Refresher Grant Logic
Many execs (especially from US) expect annual, performance-based refreshers. You don’t have to offer them early, but you must be aware of expectations.
If introduced, define a 4-tier performance grid, like the one below, to determine the size of the refresher grant.
Outstanding | Receive 50% + of the new hire grant - only 1-5% of the company should qualify for this |
|---|---|
Above expectations | Receive 20-50% + of the new hire grant - only 5-15% of the company should qualify for this |
Meets expectations | Receive 10-15% + of the new hire grant - most employees can qualify for this |
Needs to improve | No refresher. |
Performance-based refreshers usually appear at $2–5B valuations. Before then, use promotion grants or tenure grants if key talent is 75%+ vested.
Equity is not just a % number, but your primary long-term retention lever.
Milestone vesting aligns candidates with growth targets.
Refreshers prevent attrition as grants vest out.
Pool planning avoids a crunch when scaling senior hires - If you don’t plan for this now, you’ll run out of equity just when you need it most
5️⃣ Benefits & Relocation
Benefits alone rarely close an executive hire, but they can strengthen your overall value story, especially for globally mobile leaders relocating with families.
At a minimum, you should be competitive on healthcare, pension, leave, and flexibility. Other benefits can help attract and retain top executives, but they should be seen as complements rather than dealmakers.
Relocation support is mostly expected from executives moving from abroad. The scope will depend on their seniority and your company’s maturity, but strong support usually includes:
On-site visit: The candidate and their partner will fly to their new city to see the city, meet the CEO and team, and understand the culture.
Clear explanation: Walk both the candidate and their partner through the full package in a transparent session.
Concierge-style support: Help with housing, schools, visas, and local systems. Assign a point person to handle logistics and answer questions.
👉 Not every company can afford all of this, but the key is to make the package feel worth it to the candidate within your means. A well-told benefits and relocation story can close the gap when cash and equity are already competitive.
🧩 Putting the offer together
Once you’ve done the work the final step is packaging it into an offer the candidate can actually say “yes” to.
Great candidates don’t just evaluate numbers; they evaluate clarity, upside, and intent. That means your offer should cover:
Future value: Show the candidate what their package could be worth at your target valuation.
Fairness: Explain how you benchmarked and why this is competitive.
Alignment: Tie equity and milestones directly to outcomes they will drive.
Security: Highlight benefits and relocation for family stability.
Trust: Deliver the offer personally, with conviction, ideally supported by a clear one-pager or deck.
📄 Offer Letter Framework (Top-Level): A strong offer letter should include:
Intro & Story – why the company, why now, why them.
Cash – base + bonus (with structure and guarantees).
Equity – grant size, vesting, and upside scenarios.
Benefits & Relocation – what’s included and how it supports them (and family).
Next Steps – clear acceptance timeline, founder-led walk-through.
Practical takeaway: When presenting the offer, together with the numbers, tell the candidate the story of their impact and upside, why this role matters, why now is the moment, and what they stand to gain if the company succeeds.
From Numbers to Narrative
Designing an executive offer is about telling a story that connects your company’s ambition with the package you put forward.
Start with pool planning, then define the role’s value to decide the balance of cash vs. equity. Add benefits and relocation to make the offer credible for the candidate and their family. Pull it all together into a clear, simple narrative (ideally a one-pager) that shows how the package ties to your company’s future.
💥 Key Takeaways for Founders
Pools first: Always check headroom and plan for 2–3 years of refreshers.
Design around outcomes: Role impact (revenue, IP, scale) should drive the cash/equity mix.
Premiums are real: AI Foundational and AI Native companies must pay well above traditional tech.
Use Equity as your differentiator: Structure grants with time + milestones and plan refreshers early.
Benefits are a value-add, not a discount: Relocation and benefits won’t win the hire, but they can close the gap when cash and equity are competitive.
Founder-led delivery is essential: offers should be personal, transparent, and tell the story of impact and upside.
Turning Insight into Hires
AI has rewired executive compensation.
Company type sets your market, use benchmarks as a base, equity is the primary lever, and storytelling is what turns a “competitive” package into an accepted one.
If you remember one thing: design compensation around impact and outcomes, not titles and stages.
In practice, that means starting with the pool, sizing cash and equity by role value (IP vs revenue vs scale), reverse-engineering grants from target valuation, and making benefits/relocation a visible part of the value story. Then deliver it founder-to-founder: clear, fast, and personally.
The founders who win aren’t the ones who outspend; they’re the ones who out-explain. Your edge is a coherent narrative that shows why this role matters now, exactly how the candidate creates value, and what they stand to gain if you succeed.
Things are constantly changing and that’s why this report focuses on principles and structure, not fixed numbers. The data you have today might already be outdated tomorrow, but if you understand the logic behind compensation design, you’ll always know how to adapt it.
🚀 Founder Next Steps (1-page playbook)
Decide your type: AI Foundational / AI Native / AI Enabler — this sets your competitive set and premiums.
Plan the pool: Model initial grants + 2–3 years of refreshers (expect 10–20%+ in AI).
Define outcomes: Write the 12–36 month impact for each exec role; weight cash vs equity accordingly.
Anchor cash: For global talent, use US benchmarks; adjust for type and only apply geo discounts when truly domestic.
Size equity from valuation: Apply the reverse-engineering logic on your target outcome; use time + milestone vesting.
Package the whole offer: Include benefits and relocation; show upside scenarios in a simple one-pager.
Tell the story: Founder-led, fast, and specific — why now, why us, why you.
You don’t have to pay like OpenAI to win talent, but you do have to act like a top-tier employer: precise on numbers, ruthless on fit, and compelling on the future.
More to read
20 articles
