Tiny Teams, Bigger Stakes
An equity playbook for founders and joiners in the age of AI
I’ve been fascinated by the concept of Tiny Teams — small, AI-augmented companies that punch way above their headcount — because I think this is the economy of the future. In fact, I’m building one of my own :)
The numbers are hard to ignore. Bolt hit $20M ARR in 60 days with 15 people. Midjourney reached $200M ARR with around 40. Gamma serves around 50 million users with roughly 30 employees. These aren’t outliers anymore. They’re proof that the minimum viable team to build a serious company has collapsed.
So I started thinking: if the structure of the company is changing this dramatically, shouldn’t a founder also rethink how equity is allocated to their tiny team? Isn’t there a better way to align incentives with the new way we work with AI?
I think there is. But to see it clearly, we need to understand how equity has worked up to now — and why that model was built for a world that’s disappearing.
How Equity Has Worked (and Why)
When a startup typically raises money, the founders set aside a pool of shares — typically 10% to 15% of the company — to give to employees as stock options. The idea is straightforward: you can’t afford big-tech salaries, so you offer ownership instead. Skin in the game.
The standard mechanics: a four-year vesting schedule with a one-year cliff. You earn nothing in year one, then your shares unlock gradually over the remaining three years. This protects the company from giving equity to people who leave early.
Now here’s the thing most people don’t think about. That 10-15% pool was designed to be split across a lot of people. A typical venture-backed startup might plan to hire 50, 100, 200+ employees before the next funding round. When you divide 10% among that many people, the individual grants become very small:
A senior engineer might get 0.01% to 0.05%
A VP-level hire might get 0.1% to 0.5%
Everyone else gets fractions of fractions
At a $50M exit — which is actually a decent outcome — a 0.03% stake is worth $15,000. Before taxes. After four years of vesting. To turn that same 0.03% into even a single pre-tax million, the company would have to exit at over $3 billion. In other words, the only realistic path to life-changing money was betting that you’d joined the next Google or Facebook — and almost nobody does.
This system was built for a world where companies needed hundreds of people to operate. You needed 10 engineers to build what AI can now scaffold in an afternoon. You needed a 20-person support team because there were no intelligent agents handling tickets. You needed layers of middle management because coordination costs scaled with headcount.
The equity pool was thin because it had to be thin. Too many people, too little to go around.
The Structural Shift
Here’s where things get interesting. And I think most people’s mental model hasn’t caught up yet.
AI didn’t just make individual workers more productive. It collapsed the minimum viable team size for building a serious company. We went from “you need 100 people to build a real product” to “you need 20, maybe fewer.”
This even has a name now. Shawn “swyx” Wang has been writing about “Tiny Teams” — which he defines, memorably, as teams with more millions in ARR than employees. After surveying some of the best examples at the AI Engineer World’s Fair, his takeaway was that this is the next major transition of the org chart: knowledge work can now be augmented, automated, and scaled on demand, and organizations that don’t reflect that reality have their head in the sand. The examples I mentioned above — Bolt, Gamma, and others — come straight out of that body of work.
Sam Altman has predicted one-person billion-dollar companies. Whether or not that happens literally, the direction is clear. The number of humans required to create massive value is dropping fast.
And when your company only has 20 people instead of 500, something important changes: each person’s leverage is enormous. Your lead engineer isn’t one of fifty. She’s one of three or four. Your head of product isn’t managing a department — she is the department, augmented by AI tools that multiply her output.
In a 500-person company, losing one person is manageable. In a 20-person company, losing the wrong person is a crisis.
So why are we still using equity structures designed for the 500-person model?
The New Playbook: Concentrate Where It Counts
Here’s what I think the equity model should look like for an AI-native startup with ~20 people and a 10% option pool.
Tier 1 — Founding-Level Key People (2–3 people): ~6% of the pool. These are your co-founders in practice — the CMO, the lead engineer, the head of product. People who could have started their own company but chose to bet on yours. Give them 2% each.
A 2% stake at a $10M valuation is worth $200K. At $100M, it’s $2M. At $500M, it’s $10M. That’s life-changing money. That’s deep alignment — the kind you can’t create with a salary bump.
Tier 2 — Senior/Critical Individual Contributors (4–6 people): ~2% of the pool. Strong engineers, senior sales leads, key ops people. Not founding-level, but essential. Give them 0.3% to 0.5% each.
This is still 10 to 20 times what they’d get at a typical Series A startup. A 0.5% stake in a $100M company is $500K.
Tier 3 — Remaining Employees (~9–12 people): ~1% of the pool. 0.05% to 0.15% each. A 0.1% stake in a $50M exit is $50K — real money for junior roles. In a $500M exit? $500K.
Unallocated Reserve: ~1%. For future critical hires or refresh grants. Don’t spend it all upfront.
That accounts for the 10% employee pool. But there’s one more group worth carving out, and it sits outside the headcount entirely: advisors and key partners.
Advisors & Strategic Partners: ~1–2%. Most startups already do this. The experienced operator who opens doors, the domain expert who saves you from expensive mistakes, the early partner whose name on the cap table signals credibility — these people get equity too, usually a fraction of a percent each, often on a shorter vesting schedule. For a tiny team this group matters even more, because when you only have 20 people, the leverage you borrow from a great advisor is proportionally larger. Add this on top and the total grant footprint lands at roughly 11–12%, which is exactly why the standard pool guidance is usually quoted as a 10–15% range rather than a flat 10%.
The principle stays the same throughout: concentrate ownership wherever it buys you the most leverage — whether that leverage comes from a founding-level operator, a critical IC, or an advisor who can’t be on the payroll but moves the company forward all the same.
How This Compares to the Old Model
Most people evaluating startup equity are still using reference points from the traditional world. Here’s how the numbers look side by side, for the same role in each kind of company:
Senior IC equity → Traditional startup: 0.01% – 0.05% → Your Tiny Team startup: 0.3% – 0.5%
Key leader equity → Traditional startup: 0.1% – 0.5% → Your Tiny Team startup: 2%
What a senior IC’s stake is worth at a $50M exit → Traditional startup: $5K – $25K → Your Tiny Team startup: $150K – $250K
Your people get roughly 10 to 30 times more equity per person. Same pool size. Completely different outcome.
Remember the old math — that 0.03% senior-IC stake needed a $3 billion exit just to clear a single pre-tax million. The new equivalent is a Tier 2 grant of 0.3% to 0.5%. At 0.5%, the company only has to exit at $200 million for that stake to be worth $1M; at 0.3%, around $330 million. So the bar for life-changing money drops from “you joined the next Google” to “you joined a company that had a solid, fairly ordinary exit.” That’s the whole shift in one sentence: the same outcome that used to require a generational unicorn now just requires a good one.
And here’s the mindset shift that makes this work as an execution strategy: every role you don’t hire for is equity that stays with the people already on the team. This flips the usual incentive. In a traditional company, employees lobby for more headcount — bigger teams mean bigger empires and easier workloads. Here, the opposite is true. Because each person holds a meaningful stake, they’re personally motivated to lean on AI and automate rather than add a new hire, since every avoided hire keeps the pool concentrated and protects their own equity. You don’t have to mandate efficiency from the top. The cap table does the convincing for you.
The Opportunity Cost: Should You Actually Leave Big Tech?
This is the question that matters most to the senior talent this model is designed to attract. The answer depends entirely on which big-tech salary you’re actually walking away from. Three cases, each with a different verdict:
FAANG hotshot (Google L5–L7, Meta, Netflix): Walking away from a $430K–$700K guaranteed package. The hardest case to win — a startup has to offer ~1% equity to compete, and the break-even against three years of that comp lands around a $190M outcome. Doable, but it’s a real bet.
Solid big tech (Apple, Adobe, Salesforce, Oracle, Cisco): Total comp of $200K–$340K, not $600K+. This describes far more engineers than FAANG ever will. A standard 0.3–0.5% Tier 2 grant plus a competitive base shrinks the cash gap to ~$150K–$450K over three years — and one decent exit erases it.
Non–Bay Area / adjacent sectors (Canada, EU, Asia, banking/media tech): Earning $95K–$115K. The startup doesn’t even need to outbid — it can match their pay, and the equity is pure upside on top. For this group the “opportunity cost” barely exists. And because tiny teams are also far easier to manage remotely, this global talent pool is genuinely accessible — making it the most attractive hire for founders and candidates alike.
The takeaway across all three: the better-paid the candidate, the more equity it takes to move them — but the further you get from the FAANG bubble, the more the math tilts toward the startup on its own. Here’s the detail behind each.
The FAANG case. A Google L5 earns a median of around $430K total comp; an L6 is closer to $600K, global median ~$630K. Over three years that’s $1.3M–$1.9M, guaranteed and liquid. To pull this person you have to meet the math: roughly 1% equity — top of Tier 2, bottom of Tier 1. At a $100M valuation that’s $1M; at $500M, $5M. The ~1% grant pulls level with the L6’s $1.9M at about a $190M valuation and climbs from there, uncapped.
But the headline total hides the most important detail, so let me split it. That $600K L6 package is roughly $235K base, ~$300K in stock, and ~$60K bonus — meaning over half of it is equity, not cash. That changes the comparison entirely, because the right way to read any offer is cash on one side and equity on the other. Here’s the FAANG L6 next to a startup offer, over three years:
FAANG — Google L6 → Cash (base + bonus): ~$885K → Equity: ~$900K in RSUs — if the stock holds and you stay to vest
Startup — Tier 1, ~1% → Cash (base + bonus): ~$450K–$600K → Equity: $1M at a $100M exit, $5M at $500M — uncapped
Read it that way and the picture flips. On cash, the gap is real but not enormous — a few hundred thousand over three years. The rest of the FAANG package is also an equity bet; it’s just a bet on a $2 trillion company that, by definition, can’t double the way a startup can. You’re choosing between equity in something that won’t 10x and equity in something that might. The “safe” choice is mostly stock too.
The solid-big-tech case. Most strong engineers don’t have a FAANG seat — those are scarce and not on the table for everyone. The honest comparison is the company that pays well but not Google-well. Across the broader industry, senior engineers land roughly in the $200K–$340K range: Apple around $340K, Adobe near $280K, Salesforce and Intuit in the $250K range, and Oracle or Cisco closer to $190K–$250K (Levels.fyi medians, 2026) — not the $600K+ of the top tier.
Same split applies. Apple’s ~$340K senior package is roughly $220K base, $130K in stock, and $20K bonus — about a third equity. Laid out the same way over three years:
Big tech — Apple senior → Cash (base + bonus): ~$720K → Equity: ~$390K in RSUs, vesting-locked
Startup — Tier 2, 0.5% → Cash (base + bonus): ~$450K–$600K → Equity: $500K at a $100M exit, $2.5M at $500M — uncapped
Now the cash gap is small — on the order of $150K–$300K over three years — and the startup equity, in a single decent outcome, dwarfs the RSU side. These engineers are exactly the battle-tested ICs Tier 2 exists for. The comparison only works because it’s honest on both sides: base-plus-equity against base-plus-equity, never startup equity stacked on top of a cash-only number. And the “guaranteed” RSU only lands if you stay through the vest, with every refresh resetting the clock. The golden handcuffs are made of the same material as the startup offer — they’re just locked to a company whose stock won’t 10x.
The non–Bay Area case — and why it’s the best hire of all. The “you’d give up a fortune” argument assumes a US engineer in a high-cost city. Even there it’s misleading: that $400K comes with Bay Area rent, taxes, and childcare, so the net premium is smaller than the headline. But the bigger point is that the best talent isn’t only in San Francisco. A strong senior engineer in Canada earns ~CAD $135K (~$95–100K USD); in Germany, ~€100–110K; across much of the EU and Asia, less — same for adjacent sectors like banking and media tech. I know they’re good. I’ve worked with some of them. They just don’t live in Silicon Valley.
Run the numbers. Take someone earning ~$110K. The startup doesn’t need to outbid them — it offers $100K–$110K, right around what they make now, or even 90% of it. Cash is roughly a wash; no pay cut to live on. Then comes what their current employer can’t offer: a 0.5% Tier 2 grant, worth $500K at a $100M outcome and $1.5M at $300M. The proposition isn’t “take a pay cut and gamble.” It’s “keep your paycheck, and get a six-or-seven-figure call option on top.”
Now flip to the founder’s side, because this is the hire that should make you smile. You brought on FAANG-caliber talent for ~$110K — a fraction of a Bay Area equivalent — without overpaying on cash, using equity from a pool you’re keeping deliberately concentrated. Candidate keeps their standard of living and gets real upside; you get exceptional talent at a sustainable burn. Geography arbitrage, done honestly, is one of the strongest cards a tiny team can play.
And the tiny-team structure is exactly what makes this practical rather than theoretical. The usual objection to hiring globally is that distributed teams are hard to manage — but that pain comes from scale, not from distance. A 200-person remote org drowns in coordination overhead, timezone-spanning handoffs, and the management layers you need to keep everyone aligned. A 12-person remote team has almost none of that. Few enough people that everyone fits in one conversation, high enough agency that nobody’s waiting on a chain of approvals. Small teams are simply easier to run remotely, which means the entire global talent pool is open to you in a way it never was for the headcount-heavy company down the road. Tiny teams and remote hiring reinforce each other.
Why the risk is smaller than it looks
“Sure,” you’re thinking, “this all assumes the startup succeeds.” Correct — it’s a bet, not a guarantee. But three things make that bet far less risky than the old startup-lottery framing suggests.
First, the modest outcome isn’t a zero. The median venture-backed M&A exit in 2026 is around $70M, and two-thirds of exits are acquisitions, not IPOs — so yes, a $190M break-even sits above the median. But that median is dragged down by small acqui-hires, and the distribution is a barbell with a real cluster of billion-dollar-plus outcomes at the top where AI-native companies are overrepresented; venture-backed acquisitions crossed $100B in H1 2025 alone, up 155% year over year. And when a company does get acqui-hired, the equity is only half the story — acquirers buy the team, so they negotiate big-tech salaries, sign-ons, and fresh grants to retain them. The tiny-team structure even helps on the way out: an acquirer that would normally drag a sizable team into headquarters to justify the deal is far more willing to keep a small, high-functioning group intact — and often remote — because a handful of high-agency people is so much easier to absorb. The thing that made the team easy to manage as a startup makes it easy to retain after the sale. The genuine zero — equity worth nothing, no soft landing — is rarer than the averages make it feel.
Second, the timeline is compressing. AI-native companies reach meaningful valuations faster than any prior generation. When Bolt goes zero to $20M ARR in 60 days, the old 7-to-10-year slog to liquidity shrinks to 3–5 years or less.
Third, liquidity no longer requires an IPO. US venture secondary transaction value hit $106B in 2025 (PitchBook); the global secondary market reached a record $220B, up 42% year over year; and for the 12 months through mid-2025, VC secondary value surpassed the combined value of all VC-backed IPOs. You can increasingly sell part of your stake to institutional buyers before a traditional exit. The old objection — “equity is paper money until an exit, and exits take forever” — is getting weaker on both halves.
Put it together and the bet is asymmetric. The downside is capped and cushioned: even without breaking even, the acqui-hire soft landing means you don’t lose much — and outside the Bay Area, you may give up nothing at all on a net basis. The upside is uncapped and arrives faster than ever. The old framing treated joining a startup as betting your career on a lottery ticket. The honest framing today is closer to “heads I win big, tails I’m roughly fine.”
And then there’s a factor that doesn’t show up in any spreadsheet: the work itself. In a 20-person company, you’re not writing design docs that go into a review queue. You’re not waiting three sprints for a dependency to clear. You’re not sitting in meetings about meetings. You’re shipping. Every day. With AI tools that multiply your output. For a lot of senior people, that feeling of leverage and visible impact matters more than the compensation gap suggests.
The Hidden Strategy: Keep the Team Small On Purpose
Most startups think about hiring as a sign of progress. More people equals more growth. The tiny-team model flips this.
In this model, not hiring is a feature. Every role you can automate or eliminate with AI is a role that doesn’t dilute the equity pool, doesn’t add coordination overhead, doesn’t slow down decisions, and doesn’t increase your burn rate.
This creates a feedback loop. The team stays small → each person gets more equity → you attract better talent → the team stays productive without growing → the equity stays concentrated. The loop reinforces itself.
The moment you start hiring to fill org charts instead of solving real gaps, you’ve broken the model.
Rethinking What a “Hire” Even Is
There’s a deeper shift hiding underneath all of this, and it’s about how we decide who to bring on in the first place.
The traditional approach goes like this: you take the functions in a company — marketing, engineering, sales, design — and you assume each one needs a person stationed at it to man the post. That headcount becomes job descriptions, and then you hire a body per description. The hidden assumption is that a function existing means someone has to be assigned to staff it full-time. But that’s no longer true. One person plus the right AI tooling can now cover ground that used to require a dedicated hire per function — so the old reflex of “we have a marketing function, therefore we need a marketing person” quietly stops holding.
A better way is to start from the work itself. What are the jobs that actually need to get done in the company? Once you list those out as concrete responsibilities, you can bundle or unbundle them in ways a rigid job description never would. The unit of thinking stops being “a marketing person” and becomes “this cluster of problems that needs an owner.”
Here’s a concrete example from my own thinking. Say my startup needs what looks like a “marketing engineer.” Instead of writing that exact JD and hiring for it, I might step back and ask: is there a broader band of non-product engineering work scattered across marketing, design, and sales — integrating services, wiring up tools, automating handoffs — that no single function owns today? If so, maybe what I actually need is one high-agency person who can navigate that whole problem space, amplified by AI, rather than three half-roles bolted onto three different teams.
This is what high agency looks like in practice. You’re not hiring a title. You’re handing someone a problem space and the tools to dominate it. And it pairs perfectly with the equity model: fewer, more versatile owners means a more concentrated pool and deeper alignment per person.
A Word of Caution
Not every company can or should stay at 20 people. Some businesses genuinely need scale — in manufacturing, in compliance-heavy industries, in anything requiring physical presence. This model works best for software, AI-native businesses, and knowledge-work-intensive startups where the ratio of output to headcount can be radically altered by technology.
And concentration of equity also means concentration of key-person risk. If your 2%-equity lead engineer leaves, you have a much bigger problem than if one of a hundred engineers walks out. Your retention strategy can’t just be competitive comp. It has to be culture, mission, and the genuine feeling that each person’s contribution is visible and valued.
Which, in a 20-person company, it should be. That’s the whole point.
One Last Thought
I know where some people’s minds go when they read all this. Smaller teams, more automation — isn’t this just a story about AI taking jobs?
I don’t see it that way. The amount of problems available to solve across companies is enormous, and we’re nowhere near running out of them. What changes is what people work on. We’ll need people who can work alongside AI to do two things: tackle bigger chunks of the existing backlog through automation, and solve higher-complexity problems that simply weren’t possible before — the kind of work nobody even bothered to put in the backlog because it was out of reach.
And here’s the thing about that backlog: a problem is demand that stays attached to it, regardless of who solves it. Big Company A can have the problem in plain view and still drop it — approval layers, misaligned incentives, a roadmap captured by bigger bets, plain inefficiency — and the problem just sits there, unsolved, with the need still attached. It doesn’t evaporate because the incumbent couldn’t get to it. What changed is that the barrier for someone else to step in has collapsed. Startup B — or a solo builder with AI — can pick up exactly what the incumbent dropped and ship it, fast and cheap, in a fraction of the time and cost it would have taken a few years ago. And they can choose their own shape for it: a cash-making lifestyle business, or a venture-backed company chasing the kind of asymmetric win this whole piece has been about. Either way, the problem gets solved — and someone captures the value the incumbent left on the table.
Because building useful products and starting companies is getting easier, the total pool of opportunity isn’t shrinking, even if some large companies are freezing headcount. It’s redistributing — toward smaller, higher-agency teams where one person plus AI can take on what used to require many. That’s not a threat. That’s an opportunity.