---
title: "He Left a $300M Company for a Startup… Here's Why"
episode: 73
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Oliver Manojlovic"
guest_title: "Chief Revenue Officer, Dash0"
date_published: 2026-05-15
date_modified: 2026-07-22
duration: 00:53:28
word_count: 9337
topics: ["consumption-revenue", "sales-compensation", "sales-leadership", "forecasting", "ai-in-gtm", "gtm-strategy"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/oliver-manojlovic-left-300m-for-startup/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# He Left a $300M Company for a Startup… Here's Why

_Oliver Manojlovic (CRO, Dash0) on pure pay-as-you-go GTM, ARR without contracts, consumption comp, hiring SEALs, and motivation vs. morale_

**Episode 73 · The LeanScale Podcast**  
Oliver Manojlovic, Chief Revenue Officer, Dash0 · Hosted by Anthony Enrico  
Published May 15, 2026 · Updated July 22, 2026 · 00:53:28  
Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/oliver-manojlovic-left-300m-for-startup/

**Topics:** Consumption Revenue · Sales Compensation · Sales Leadership · Forecasting · AI in GTM · GTM Strategy


## Executive summary

Oliver Manojlovic spent seven and a half years helping scale Personio from $3M to $300M in ARR — then walked away from one of Europe's biggest unicorns to become CRO of Dash0, a seed-stage observability startup going head-to-head with Datadog. His reasoning isn't nostalgia or money. It's that the problems that land on your desk at a $300M-ARR company had become problems he'd rather not solve, and at an early stage you get to choose your problems and shape the DNA of the company itself. He wanted to test whether what he learned scaling Personio could work again from zero — maybe knock it even harder out of the ballpark.

The reason Dash0 tipped him over the hill is how founder Mirko Novakovic (who previously sold Instana to IBM for roughly $500M and builds now for the thrill, not the money) wants to run it: radical customer-centricity expressed as no commitments — no contracts, no renewals, pure pay-as-you-go, even on multi-million-dollar enterprise deals. There are no annual renewals because the company has 'everyday renewals.' The bet only works, Oliver stresses, because observability is inherently sticky and the product is best-in-class: if you take care of the customer, it genuinely doesn't matter whether you invoice on consumption or once a year. If your product is bad, the fix isn't a lock-in clause — it's a better product.

The middle of the conversation is the operational hard part. Without contracts you can't pull a contracted number, so ARR becomes collected revenue extrapolated forward twelve months, with disciplined, repeatable consumption projections — built from a customer's telemetry data or their prior-vendor usage — stacked on top. Comp is the toughest problem he's ever wrestled with: Dash0 pays commissions up front on a projected 'synthetic ACV' with security-margin haircuts, a two-person review, rep-adjustable estimates, and a consume-to-earn provision so nobody inflates a projection and walks. Leadership told the team openly the plan will change. His governing frame: the comp plan is a derivative, not a root cause — if five deals go sideways at once, you don't have a comp problem, you have a hiring or product problem.

On leadership and talent, Oliver borrows a military analogy he admits he half-dislikes: your first hires are SEALs who operate confidently without supervision in uncharted terrain, then Marines who build the foundation the broader infantry scales on — and the quiet killers of fast-growing companies are B-players whose management cost and lowered standards drag everyone down. He hires for agency and urgency because you can't reliably train them on a fast-growth timeline. The sharpest distinction of the episode: it is not a leader's job to motivate people — motivation must already be within them — but it is the leader's job to manage morale, supporting people through hardship without taking it away, the same way you raise self-reliant kids.

On AI, Oliver is a heavy Claude Code user, but his point is that the real unlock isn't the model — it's the discipline of knowledge management and context engineering. Organize and maintain clean context and a former one-hour subject-matter meeting becomes a five-minute prompt that's 95% right, bridged by a ten-minute human review; Anthony's live example is an agent that turns an hour-long discovery transcript plus a diagnostic into a finished SOW, better than doing it by hand. The open question both wrestle with: how does a junior operator learn judgment when AI hands them an average answer they can't yet evaluate — and do we now need 'mental gyms' to keep thinking sharp? Who should listen: founders weighing startup versus scale-up, CROs and RevOps leaders building consumption or usage-based models, and anyone designing comp or forecasting with no contracts to lean on.


## Key takeaways

1. **The real reason to leave a unicorn for a startup: you choose your problems** — Oliver didn't leave Personio because he stopped caring — he left because the problems that landed on his desk at a $300M-ARR company had become ones he preferred less to solve than the problems of building something new. Every business has problems; the privilege is getting to choose which kind you work on.
   _Why it matters:_ Evaluate a startup-vs-scale move by the texture of the daily problems, not the size of the logo or the title. If shaping DNA excites you more than optimizing an established machine, that's the signal.
   _For:_ Founders, Revenue Executives, Sales Leaders

2. **At early stage you work ON the company, not just IN it** — In the early days you're creating the DNA — deciding mechanisms that sometimes don't survive a day and sometimes stay in place, evolving, for five, six, seven years. At a larger company those things are already set, and you evolve rather than rip out and rebuild.
   _Why it matters:_ Founders and early operators should treat foundational decisions as things they may live with for years — and stay willing to rip out what clearly isn't working fast, while the cost of change is still low.
   _For:_ Founders, Revenue Executives

3. **No commitments, everyday renewals — pay-as-you-go even for enterprise** — Dash0 sells with no contracts and no renewals, even on multi-million-dollar deals. A customer can leave any time, so instead of annual renewals the company runs 'everyday renewals.' It's a deliberate expression of customer-centricity, backed by a founder who refuses to use penalty clauses to hold customers.
   _Why it matters:_ A no-commitments model is viable when your product is critical and hard to rip out — but it forces you to earn retention continuously through value and care, not defend it with a contract.
   _For:_ Founders, Revenue Executives, Sales Leaders

4. **The model only works with inherent stickiness and real product confidence** — Pure pay-as-you-go requires a product with built-in stickiness (swapping out observability mid-flight is painful) and genuine confidence that it's the best in the market. Contracts to lock in a weak product are the wrong instinct — if the product is bad, improve it until it's good, then sell it.
   _Why it matters:_ Don't copy the no-commitments motion unless your product is critical and you'd bet on it head-to-head. Otherwise you're using contracts to paper over a product problem.
   _For:_ Founders, Sales Leaders

5. **Report ARR without contracts by extrapolating revenue and stacking projections** — With no contracted amount to pull, Dash0 takes collected revenue, extrapolates it forward twelve months, and stacks disciplined consumption projections on top — built from a customer's telemetry data or their prior-vendor usage. Investors who understand the stickiness treat that consumption as genuinely recurring.
   _Why it matters:_ Usage-based companies can present a credible ARR by pairing extrapolated actuals with a repeatable projection method — and can argue their revenue is more recurring, not less, because it survives without a contract forcing it.
   _For:_ Revenue Executives, RevOps Leaders, Founders

6. **Being a 'bad forecaster' upward is fine early; missing on the downside is the real problem** — Dash0 has corrected its guidance upward for twelve to fifteen months straight — 'bad forecasters,' Oliver jokes, but in the acceptable direction. The dangerous miss is falling short of the forecast; and as a company matures, consistently beating by too much starts to look like a loss of control.
   _Why it matters:_ Set forecasts you're likely to beat at early stage, but treat forecasting maturity as a goal: eventually the number needs to land inside a tight band so the board trusts you're in control.
   _For:_ Revenue Executives, RevOps Leaders

7. **Pay commissions on a projected 'synthetic ACV' — with guardrails** — Comp was the hardest problem Oliver has faced. Dash0 pays up front on a projected/synthetic ACV (the customer's estimated one-to-three-year spend), then protects the company with security-margin haircuts, a two-person review, rep-adjustable estimates, and a consume-to-earn provision so reps can't book a wild projection and walk before consumption materializes.
   _Why it matters:_ In a consumption world, tie commission to a disciplined, reviewed estimate that reps must actually deliver against. Balance lucrative upside against the company's cost-of-sales ratio — both sides have to win.
   _For:_ Sales Leaders, RevOps Leaders, Revenue Executives

8. **The comp plan is a derivative, not a root cause** — If five deals turn sour at once, Oliver argues you don't have a comp-plan problem — you have a bad-hire problem or a product problem that merely surfaces through comp. The comp plan doesn't cause churn and doesn't cause reps to over-promise; tweaking it only mitigates fallout from a deeper issue.
   _Why it matters:_ When a plan seems to be 'breaking,' resist re-engineering it first. Diagnose whether the real problem is who you hired or what you built, and fix that.
   _For:_ Sales Leaders, Revenue Executives, RevOps Leaders

9. **Hire SEALs, then Marines — and fear the hidden cost of B-players** — The first hires should be SEALs who operate confidently without supervision in uncharted terrain; the next wave are Marines who build the foundation the infantry can scale on. SEALs never lose their value and elevate everyone around them; the thing that quietly crushes companies is B-players who seem 'good enough' but carry a heavy management cost and set a poor standard.
   _Why it matters:_ Over-index on hiring self-directed operators early — they reduce the need for management layers — and move decisively on mediocre performers whose real cost is the drag they put on the team.
   _For:_ Sales Leaders, Founders

10. **Agency and urgency can't be trained on your timeline — hire for them** — Oliver believes agency (finding a way, refusing to blame the tool or the hiccup) and urgency are largely formed by the time someone enters an interview. Even if you could train them, at startup speed you don't have one to five years to develop it — and a salesperson who can't sell themselves in the interview is already a signal.
   _Why it matters:_ Screen hard for agency and urgency up front rather than betting on coaching them in. At high growth, hiring for these intangibles beats trying to install them.
   _For:_ Sales Leaders, Founders

11. **Motivation is theirs; morale is your job** — Oliver rejects the idea that a leader's job is to motivate people — motivation is why someone joined your cause and must already be within them. What a leader can and must manage is morale: supporting people through a hard moment without removing the hardship, and keeping the game fair (hygiene factors like not yanking an account from the rep who earned it).
   _Why it matters:_ Stop carrying the guilt of 'making' people motivated. Focus on fair conditions and being present in the weak moments so people build resilience — if the underlying motivation isn't there, that's a hiring problem, not a morale one.
   _For:_ Sales Leaders, Revenue Executives, Founders

12. **AI's real unlock is knowledge-management discipline — and it creates a junior-learning problem** — Oliver is a heavy Claude Code user, but insists the transition that matters is the discipline of managing knowledge and context, not the tech itself. Well-engineered, maintained context turns a one-hour expert meeting into a five-minute prompt (95%, plus a ten-minute review). The catch: AI accelerates an experienced operator 10–100x but only lifts a junior to 'average,' leaving open how the next cohort absorbs real judgment.
   _Why it matters:_ Invest in context engineering and knowledge upkeep as an operating discipline, and be deliberate about how juniors learn — because AI can hand them answers they can't yet evaluate. Anthony's analogy: build 'mental gyms' to keep thinking sharp.
   _For:_ RevOps Leaders, Sales Leaders, Founders


## Frameworks

### No Commitments / Everyday Renewals (04:32)

**Definition:** A go-to-market model with no contracts and no renewals — pure pay-as-you-go, even on multi-million-dollar enterprise deals — so the customer can leave at any time and the company effectively re-earns the business every single day.

It's an expression of radical customer-centricity: don't tie customers up with penalties, make them stay through value. It only works when the product is sticky and best-in-class, and it shifts retention from a renewal event to a continuous obligation.

### Big Ship vs. Jet Ski (and Working ON vs. IN the Company) (07:39)

**Definition:** Anthony's framing for the startup-vs-scale-up choice: a big ship already has a direction and goes far; a jet ski is nimble and yours to steer. Oliver's corollary: at early stage you work ON the company (creating its DNA), and at scale you work IN it (executing).

Neither is better — they're different, and different people belong on each. The early-stage draw is the chance to set foundational mechanisms you'll live with for years, and to change the core cheaply when there's little to lose.

### ARR Without Contracts (Extrapolate + Project) (13:43)

**Definition:** Define ARR by taking collected revenue and extrapolating it forward twelve months, then stacking disciplined, repeatable consumption projections — derived from a customer's telemetry data or their prior-vendor usage — on top of the extrapolated actuals.

Investors who understand the stickiness of the business treat consumption as genuinely recurring. Oliver argues it can prove more recurring, not less, because the revenue survives without a contract forcing it — you never face the 'what if none of this were contracted?' question.

### Projected 'Synthetic ACV' Commission (22:28)

**Definition:** Pay commissions up front on a projected ACV — the customer's estimated one-to-three-year spend — protected by security-margin haircuts, a two-person review of the estimate, a rep-adjustable projection, and a consume-to-earn provision that only fully vests the commission once the customer actually consumes.

The design balances lucrative upside for reps against the company's cost-of-sales ratio while avoiding annuity-style payouts that are too slow to motivate. The precautionary elements stop reps from booking a crazy projection, earning on it, and leaving before consumption materializes.

### The Comp Plan Is a Derivative, Not a Root Cause (26:17)

**Definition:** A comp plan surfaces problems but rarely causes them. If deals collapse at scale, the root cause is bad hires or a product that isn't doing its job — not the incentive structure, which only mitigates or escalates the underlying issue.

Oliver uses the doomsday scenario (five deals turn at once) to show that the failure mode is a hiring or product problem wearing a comp-plan costume. Changing the plan treats a symptom; you have to find and fix the actual root cause.

### SEALs, Marines, Infantry — Hiring in Waves (29:42)

**Definition:** Staff a company in waves: SEALs first (operate confidently without supervision in uncharted terrain, mission evolves as they gather intel in the field), then Marines (build the foundation), then infantry (scale once the foundation exists).

Get as many SEALs as you can — they retain their value, need fewer management layers, and elevate everyone around them, becoming a 'fusion reactor' of productivity. The counter-pattern is the B-player whose real cost is management overhead and a lowered team standard.

### Motivation vs. Morale (35:09)

**Definition:** Motivation is why someone joined your cause — intrinsic, and not the leader's job to install. Morale is how someone feels in a given moment — situational, and squarely the leader's job to manage by supporting people through hardship without removing it.

A highly motivated person can have low morale from a bad day or a perceived unfairness (an account yanked from the rep who earned it). Leaders keep the game fair and stand with people in weak moments so they build resilience and self-reliance — the same principle as raising self-reliant kids.

### Knowledge Management as the AI Unlock (Context Engineering) (46:24)

**Definition:** The real leverage from AI isn't the model — it's the discipline of organizing and maintaining company knowledge so an agent has clean, current context to draw from, turning a one-hour subject-matter meeting into a five-minute prompt that's 95% right (plus a ten-minute human review of the output).

A Claude Code / agent instance is only as good as the repo and connected systems behind it. Setting up context is the easy part; maintaining it and keeping it relevant as the company grows is the excellence piece — and it forces a retraining in how teams manage information.


## Quotes

_Speakers inferred from an undiarized transcript — verify before attributing._

> "It's simply because the problems that ended up on my table on a day-to-day basis started to become problems I prefer less to solve versus the problems that I brought back to my table to solve."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (01:21)

> "We don't do renewals because we have everyday renewals, and it's very refreshing going to market with that type of approach."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (04:32)

> "If the product does what it's supposed to do, if you take care of your customer the way you're supposed to, it actually doesn't really matter whether you send them an invoice on a consumption base or once a year."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (10:27)

> "In the last 12, 15 months, we continuously corrected our guidance upwards, so we're bad forecasters. It's okay to be bad in that direction."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (17:47)

> "I think Charlie Munger said it: show me the incentive and I'll tell you the outcome. So I'm a firm believer in that."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (19:09)

> "If the company is making a shit ton of money and the sales team is not benefiting from it, they're not going to get excited. If the sales team is making a shit ton of money and the company's not, then the company won't be there for long."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (21:49)

> "The comp plan is just a derivative of something. It's not a root cause. The comp plan doesn't cause people to churn."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (26:17)

> "The challenge with hiring salespeople is that if they don't know anything to sell, at least they know how to sell themselves. If they don't know how to sell themselves, that's already the first thing where you're like, okay, you know already."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (28:21)

> "The first people you hire, they are like the SEALs. They can operate confidently without supervision, you go into uncharted terrain."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (29:42)

> "The more SEALs you can get into your company, it turns into this fusion reactor of productivity and talent."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 73 (31:03)

> "Imagine 40, 50 years fast forward, you're sitting with your grandchildren, telling them about your life, and you say: we were really onto something in this amazing company, and there was a technical issue that prevented us from building a generational company. Is that the story you want to tell?"
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (32:27)

> "It's actually not your job as a leader to motivate people. Either they know why they joined your cause, or they don't. What you can do, and what you need to do, is manage morale. That's a different thing."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (35:09)

> "It's your job to be there and support them through that hardship. It's not your job to take it away. Because if you take it away, they always come back to you to take it away."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (36:30)

> "Your cloud code instance is only as good as your repo and the data in the systems you connected to."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 73 (46:59)

> "The biggest transition is not necessarily that we're applying technology. The biggest transition is the discipline you need to have around managing your knowledge and information in your company."
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (46:59)

> "Something that would take hours, and I would probably miss something, now takes minutes and is better than anything I did manually."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 73 (49:07)

> "If you're experienced, AI can accelerate you 10, a hundred times. But if you're junior and you don't know, then it just brings you up to this average level. And then what happens afterwards? How do you absorb that knowledge?"
>
> — Oliver Manojlovic, The LeanScale Podcast Ep. 73 (50:20)


## Practical advice by role

### Founders

- Choose your problems deliberately — the reason to leave scale for a startup is that you prefer the problems you'll get to solve (shaping DNA, working ON the company), not the size of the logo.
- Only run a no-commitments / pay-as-you-go model if your product is genuinely sticky and best-in-class, and fund it with a capital structure and cash-flow management that can absorb the upfront investment. Never use contracts to lock in a weak product — fix the product first.
- Treat early foundational decisions as things you may live with for years, but stay willing to rip out what clearly isn't working while the cost of change is still low.

### Sales Leaders

- Hire SEALs first (self-directed, no supervision needed), then Marines who build the foundation the broader team scales on — and move fast on B-players, whose real cost is management overhead and a lowered standard.
- Screen for agency and urgency up front; you can't reliably train them at startup speed, and a candidate who can't sell themselves in the interview is already telling you something.
- Manage morale, not motivation. Keep conditions fair (don't yank an account from the rep who earned it), stand with people in weak moments, and support them through hardship without removing it so they build resilience.

### RevOps Leaders

- Make every system, tool, and process serve one of two buckets: helping the team execute more productively, or improving the strategic decisions that steer the business. Pure-governance work that gives the team nothing back is missing the point.
- Build ARR without contracts by extrapolating collected revenue forward twelve months and stacking disciplined, repeatable consumption projections (from telemetry or prior-vendor usage) on top — then track and improve their accuracy over time.
- Invest in knowledge management and context engineering as an operating discipline: an agent is only as good as the repo and data behind it, and maintaining current context as you grow is the real work.

### Revenue Executives

- Pay consumption commissions on a projected 'synthetic ACV' with guardrails — security-margin haircuts, a two-person review, rep-adjustable estimates, and a consume-to-earn provision — so reps can't inflate a projection and walk.
- Be transparent that the plan will change: state your intent (reps raving about it AND the company winning) and let the team hold you accountable to both sides.
- When a plan looks like it's 'breaking,' diagnose the hiring or product problem underneath before re-engineering incentives — the comp plan is a derivative, not a root cause.


## AI takeaways

**Thesis:** AI's biggest unlock in go-to-market isn't the model — it's the discipline of knowledge management and context engineering. Organize and maintain clean context and AI levels up everyone's execution at the critical moments; neglect it and the output is average or wrong. The open question is how the next generation of operators learns judgment when AI hands them answers they can't yet evaluate.

- **Context is the constraint** — A Claude Code / agent instance is only as good as the repo and connected systems behind it. Setting up context is the easy part; maintaining and updating it as you grow is the excellence piece.
- **95% in five minutes** — Organize knowledge so a former one-hour subject-matter meeting becomes a five-minute prompt that's 95% right — then bridge the last 5% with a ten-minute human review of the output, never taking it at face value.
- **Agents that do the work, not just answer** — Anthony's live example: an agent turns an hour-long discovery transcript plus a diagnostic into a finished SOW in minutes — more complete than doing it manually — because the context is engineered to build it the way he would.
- **The junior-learning problem** — AI accelerates an experienced operator 10-100x but only lifts a junior to 'average.' The unresolved question: how do people absorb real judgment when they skip the experience that used to build it?
- **Train the mind like a gym** — Anthony's analogy: as AI removes daily cognitive 'exercise,' teams will have to deliberately train critical thinking and creativity — mental gyms — to keep judgment sharp.

**Agent & automation ideas**

- SOW / proposal agent: feed a discovery-call transcript plus the diagnostic findings and generate a finished, client-ready SOW in the house format — catching the details a human misses on a live call.
- Enablement knowledge agent: index deep product / observability knowledge so any rep can get a tailored, ~95%-accurate answer in a five-minute prompt instead of a one-hour expert meeting.
- Consumption-projection engine: build repeatable 12-month usage forecasts per account from telemetry or prior-vendor consumption data, then measure and improve accuracy to forecast consumed revenue.


## Operations takeaways

### Revenue operations

- **Two buckets only.** Every system, tool, or process must either help the team execute more productively or sharpen the strategic decisions that steer the business — otherwise it's governance theater that gives the team nothing back.
- **ARR without a contract.** Extrapolate collected revenue forward and stack disciplined, repeatable 12-month consumption projections on top; prove recurring-ness through stickiness, not contract length.
- **Forecast direction matters.** Being a 'bad forecaster' by beating guidance is acceptable early; the dangerous miss is on the downside, or losing control of the number as you mature.
- **Context engineering is the new hygiene.** AI leverage depends on the repo and connected data behind it; maintaining relevant, current context as you scale is the real operating discipline.

### Customer operations

- **Everyday renewals.** With no contracts, every day is a renewal — retention is earned continuously through product value and care, not defended by a penalty clause.
- **Stickiness is the moat.** A no-commitments model only works when the product is critical and hard to rip out (e.g., observability); the invoice cadence then doesn't change churn.
- **Consumption budgeting is a service.** Help customers budget by modeling their expected spend up front — projecting from their own telemetry data or their prior-vendor consumption.


## Metrics mentioned

| Value | Metric | Context |
| --- | --- | --- |
| $3M → $300M over 7.5 years | Personio ARR growth | Oliver's tenure scaling Personio; the unicorn he chose to leave for a seed-stage startup. |
| ~$500M (Instana → IBM) | Founder's prior exit | Dash0 founder Mirko Novakovic's earlier company sold to IBM — why he builds Dash0 for the thrill, not the money. |
| ~6-7 months | Dash0 commercial traction at interview | Dash0 started selling at the end of 2024; by Oliver's interview it already showed strong signal, including a very large early deal. |
| Communicated end of Feb, still rolling out | Comp plan rollout | Oliver expected the comp plan to take a couple of weeks; it slipped past the start of 2026 into implementation — consumption makes it harder. |
| Corrected upward 12-15 months running | Guidance revisions | Dash0 keeps beating its own forecast — the acceptable direction to be 'a bad forecaster' at early stage. |
| Hours → minutes | SOW generation | An agent turns an hour-long discovery transcript plus a diagnostic into a finished SOW, better than doing it manually. |
| 95% in a 5-min prompt + 10-min review | AI answer quality | Well-engineered knowledge turns a former one-hour subject-matter meeting into a five-minute prompt, verified by a short human review. |
| 10-100x for experienced, 'average' for juniors | AI acceleration | AI dramatically accelerates an experienced operator but only lifts a junior to average — raising the question of how they learn judgment. |


## Entities mentioned

- **Dash0** (company) — Oliver's current employer — a seed-stage observability startup going head-to-head with Datadog, built by founder Mirko Novakovic around a pure pay-as-you-go, no-commitments GTM. Started selling at the end of 2024 and had ~6-7 months of commercial traction (including a very large early deal) when Oliver interviewed. · https://leanscale-knowledge-hub.netlify.app/company/dash0/
- **Personio** (company) — The European HR-software unicorn where Oliver spent seven and a half years and helped scale from $3M to $300M in ARR before leaving; also the 'prior company' behind his RevOps 'do more with less' governance anecdote. · https://leanscale-knowledge-hub.netlify.app/company/personio/
- **Datadog** (company) — The observability incumbent Dash0 is going head-to-head with — the market Oliver's product has to win against. · https://leanscale-knowledge-hub.netlify.app/company/datadog/
- **IBM** (company) — Acquirer of Dash0 founder Mirko Novakovic's prior company (Instana) for roughly $500M — the exit that lets him build Dash0 for the thrill rather than the money. · https://leanscale-knowledge-hub.netlify.app/company/ibm/
- **OpenAI** (company) — Cited as a company most of whose customers are likely on a monthly / usage-based billing model; its heavy burn comes from research headcount and infrastructure, not the billing model. · https://leanscale-knowledge-hub.netlify.app/company/openai/
- **Nvidia** (company) — Named in passing as where OpenAI's spend ultimately goes — the compute/infrastructure cost base, not the billing model. · https://leanscale-knowledge-hub.netlify.app/company/nvidia/
- **Snowflake** (company) — Cited alongside AWS as a consumption-model business with very similar characteristics to Dash0 — though they use commitments and are less 'pure play' than Dash0's no-commitments approach. · https://leanscale-knowledge-hub.netlify.app/company/snowflake/
- **AWS** (company) — Cited with Snowflake as an established consumption-model business whose characteristics resemble Dash0's, but which still relies on commitments. · https://leanscale-knowledge-hub.netlify.app/company/aws/
- **JPMorgan Chase** (company) — Anthony's anecdote from a prior usage-based company: a multimillion-dollar deal that would only sign on a pure usage contract — the case that raised the question of whether such deals can be reflected in financial metrics. · https://leanscale-knowledge-hub.netlify.app/company/jpmorgan-chase/
- **Riverside** (company) — Used as a hypothetical example of a sticky SaaS customer for a monitoring product — a growing recording software can't simply stop using observability day to day, illustrating why consumption is genuinely recurring. · https://leanscale-knowledge-hub.netlify.app/company/riverside/
- **Oliver Manojlovic** (person, guest) — CRO of Dash0 (observability); previously helped scale Personio from $3M to $300M ARR before leaving the unicorn for early stage. · https://leanscale-knowledge-hub.netlify.app/guest/oliver-manojlovic/
- **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/
- **Claude Code** (tool, AI Dev Tool) — Referenced by Oliver ('Claude Cook') and Anthony ('cloud code') as the AI tooling Oliver is a super fan of — connecting it to Dash0's systems to build enablement 'infrastructure at scale,' and behind Anthony's SOW-generation agent. Both stress it's only as good as the repo and context behind it.


## FAQ

**Q: Why did Oliver Manojlovic leave Personio for a seed-stage startup?**

A: Not for the logo or the money — because he'd rather solve the problems of an early-stage company (shaping its DNA, working on the company itself) than the problems that land on your desk at a $300M-ARR unicorn. He also wanted to test whether what he learned scaling Personio from $3M to $300M could work again from zero.

**Q: What is Dash0's 'no commitments' go-to-market model?**

A: Pure pay-as-you-go with no contracts and no renewals — even on multi-million-dollar enterprise deals. Customers can leave at any time, so instead of annual renewals the company has 'everyday renewals.' It works because observability is sticky and the product is best-in-class, so care and value retain customers rather than a penalty clause.

**Q: How do you report ARR when you have no contracts?**

A: Take the revenue you've actually collected and extrapolate it forward twelve months, then stack disciplined, repeatable consumption projections — built from a customer's telemetry data or their prior-vendor usage — on top. Investors who understand the stickiness of the business treat that consumption as genuinely recurring, and you avoid the 'what if none of this were contracted?' question.

**Q: How do you design comp plans in a pure consumption model?**

A: Dash0 pays commissions up front on a projected 'synthetic ACV' — the customer's estimated one-to-three-year spend — with guardrails: security-margin haircuts, a two-person review, rep-adjustable estimates, and a consume-to-earn provision so reps can't inflate a projection and walk. Leadership is transparent that the plan will change and holds itself accountable to both the reps and the company.

**Q: What is the difference between motivation and morale in leadership?**

A: Motivation is why someone joined your cause — it must already be within them, and it's not the leader's job to install it. Morale is how someone feels in a given moment, and managing it is the leader's job: support people through hardship (without removing it) and keep the game fair, so they build resilience and self-reliance rather than dependence.

**Q: What is the SEALs / Marines hiring model for startups?**

A: Your first hires should be 'SEALs' who operate confidently without supervision in uncharted terrain; the next wave are 'Marines' who build the foundation the broader 'infantry' can scale on. Get as many SEALs as you can — they retain their value and elevate everyone around them — and beware B-players, whose management cost and lowered standards quietly crush fast-growing teams.

**Q: What's the real unlock of AI for a go-to-market team?**

A: Not the model itself but the discipline of knowledge management and context engineering. When knowledge is organized and maintained, a former one-hour expert meeting becomes a five-minute prompt that's about 95% right (with a ten-minute human review), and agents can turn a call transcript into a finished SOW. An AI instance is only as good as the repo and data behind it.


## Timeline

- **00:00** — Why he left a $300M company
- **03:00** — What makes early-stage startups different
- **08:00** — The "no commitment" SaaS model explained
- **12:00** — How to think about ARR without contracts
- **19:00** — The hardest part: comp plans
- **27:00** — Hiring elite sales talent (SEALs vs Marines)
- **35:00** — Motivation vs morale (critical leadership insight)
- **41:00** — How AI is changing sales execution
- **49:00** — The future of work and learning


## Related episodes

- **Ep. 95: Why AI Means More RevOps Hires, Not Fewer** (Jimmy O'Halloran) — The consumption-revenue companion — quota design, hunter/farmer comp, and forecasting owned by a centralized data-science function. · https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/
- **Ep. 91: Why Outcome-Based Pricing Is a Trap for Most AI Companies** (Roee Hartuv) — Pricing & packaging counterpart — where pure usage / pay-as-you-go fits and where outcome-based pricing becomes a trap. · https://leanscale-knowledge-hub.netlify.app/podcast/roee-hartuv-outcome-based-pricing-trap/
- **Ep. 2: How to Measure New Business With Usage-Based Pricing** (LeanScale) — The measurement problem underneath articulating ARR when revenue isn't contracted. · https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-alves-usage-based-pricing/
- **Ep. 6: Why Your Forecast Is Broken** (LeanScale) — Foundational forecasting episode behind Dash0's 'bad forecaster upward' approach to guidance. · https://leanscale-knowledge-hub.netlify.app/podcast/why-your-forecast-is-broken/
- **Ep. 88: Why AI Won't Close Your Biggest Deals** (Michael Kiernan) — A CRO's view on AI's real limits in GTM — pairs with Oliver's junior-learning and human-judgment concerns. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-kiernan-nextdoor-ai-wont-close-deals/
- **Ep. 85: Why AI + GTM Engineers Can't Replace RevOps** (Tessa Whittaker) — AI plus judgment: the sibling to Oliver's context-engineering thesis and his worry about how juniors learn. · https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/


## Full transcript

_Machine-transcribed and not diarized; speaker attribution is inferred._  
_Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/oliver-manojlovic-left-300m-for-startup/transcript.md_

### 00:00 — Why he left a $300M company

**[0:00]** Today, I'm sitting down with Oliver Manojlovich, who's the CRO at Dash Zero, an observability startup going head-to-head with Datadog. Before this, Oliver spent seven and a half years at Presanio helping them scale from three to three hundred million in ARR. Then he left one of Europe's biggest unicorns to go back to early stage. And now, he's building a company with a GTM philosophy that's going to raise some eyebrows. No commitments, not on small deals, not on enterprise deals, pure pay-as-you-go, even on multi-million-dollar accounts. We're going to get into why he thinks that's the future, what it means for how you should

**[0:42]** structure Sales Comp, and what made him walk away from the unicorn. Oliver, you were a VP of Sales at a company doing over three hundred million in ARR, then left to go to a seed stage company. Most people would call it crazy, but why did you see a Dash Zero that made it worth the reset? Yeah, I mean, first of all, thanks for having me. It's a good question, but I don't think it's actually that crazy. I think it's a preference topic, so there's multiple elements that go into or went into that decision. So, number one, I mean, like when you spend seven and a half years at a place, you know,

**[1:21]** it's dear to your heart, the people especially, so you can't just disconnect and say, "I don't care." And that's also not the reason why I did it. I think it's simply because the problems that ended up on my table on a day-to-day basis started to become problems I prefer less to solve versus the problems that I brought back to my table to solve. So I think that's the primary driver, because ultimately, if you, whatever you do, there's always problems. You know, the business you have has problems. The business I have has problems. My prior company, everyone has sort of problems, otherwise they wouldn't be a business, and

**[1:56]** I think it's always a - or it should be a deliberate choice in what type of problems you want to solve. And if you can make that choice, I think that's already, you know, quite awesome. Like not many people actually can do that. And yeah, I felt like the itch to go back into something and see if what I've learned, can I apply this and, you know, maybe knock it even harder out of the ballpark than we did with the prior company. And yeah, the good thing is like if you have some, let's say, decent success on your professional side, then you also get more opportunities and you can choose what you're doing next. And Dash Zero was a deliberate choice.

**[2:36]** So typically when I look - or when I was looking now, again, I was looking into, okay, well, the people that I would be working with, that's an important factor on multiple levels, like good people, but also smart, capable, and ambitious people need to be kind of like in that same combination. And then the product itself, like you have to have when you bring in salespeople like myself into the game, you have to have something that you actually can sell. So it ideally has gravity so that it's not something you need to brute force into the market. Dash Zero was like they just started selling at the end of 2024, so basically when I interviewed

### 03:00 — What makes early-stage startups different

**[3:13]** six, seven months of commercial traction, but you could already see in these six, seven months, there's a lot of things like working out really nice. A lot of things started cooking, including like a very large deal we closed beginning of the year. And yeah, that was, that was good, like, good signaling, but the really interesting thing that tipped me over the hill was how Mirko, our CEO and founder, wants to build the company. So you have to know it's a third company, so he sold his prior company to IBM for 500 million, so he's a, he doesn't need to do it for the money, I mean, you can argue how

**[3:51]** much you need, but like, if he's a very like, easy going person, he doesn't need fancy sorry stuff and whatever, like, therefore he's good. So he's doing it for the, for the, for the thrill, you would call it, and one thing that he figured is like, he wants to be very customer centric, like, actually, like, customer centricity is the thing that like, is for him the most important piece, and many people say that, but I think it's important to back it up. And the way how he decided to back it up is to say, a customer can leave at any point in time. I don't want to, I don't want to tie them up in a contract that makes like give them

**[4:32]** penalty to, to alter their choice, if they have a good reason to. And that's where we come to this funny thing of no commitments, and that's at the centerpiece of it. So we don't do renewals because we have everyday renewals and it's, it's very refreshing going to market with that type of approach. It gives you also like new challenges and new tasks to resolve, but gives you a lot of like, you know, interesting conversations. I'm sure it does. I'm sure it does. Before we dive into that, I want to go back to one thing that you're mentioning. And a lot of people listening, they're probably in the fence, Hey, should I leave this bigger

**[5:13]** company to go to an early stage company like that? And you mentioned there's some problems that are interesting for you to solve. Typically, what problems do you get to solve at an early stage startup that maybe you don't get to at a scale that organization that would give somebody an idea of if it's a good fit for them or not? I mean, I don't think there's a very general answer that is applicable everywhere. I think it's more like what type of problems you're emerging. So when you are at this early stage of a company, you're working a lot on the company more than

**[5:49]** later on when you're working in the company executing, because you're still in the phase where you're creating part of the DNA. Like many things, and that was a very interesting experience that you decide in the early days. I don't know, they many things don't sustain many things, like barely sustained a day, you know, that you decide you think like, you know it. And then the next morning, like shit, oh, we have to do differently. But there's also other things you put in place and you look back and you realize, oh, like that principle or that mechanism is now in place for four, five, six, seven years.

**[6:26]** And like it has evolved, but it hasn't fundamentally changed. And that's something that you could then say it's a little bit of the DNA of how, you know, whatever company system you're building there is operating on. And that's a very fascinating thing, in my view, because you only get so much opportunity to do this and experience this versus when you are at a larger company where these things are already established. So you don't go back and like if there's no big pain, change something at the core, at the heart of the company, you evolve it, but you don't like go in and say, hey, let's rip it out and see what happens if you do something else.

**[7:03]** And this is something you can do here literally every day, because it's also easier here. We don't have, like, there's not much to lose on some of these items, you know, yeah, okay, you lose a customer, unfortunate, but if you have 10 customers, that's 10% of your custom base. If you have 10,000 customers, and you lose a thousand, that's a different story. Yeah, 100%. I think a lot of people that we work with are, they're interested in going into startups, but might not know exactly what it means. And I think the analogy I've always liked is, hey, you can you could get on a big ship

**[7:39]** that already has a direction and really going far, or you can hop on a jet ski. And for some people, hopping on that jet ski is kind of fun. Absolutely. And again, it's not even like a judgment in the sense like the one or the other is really better. Like, I think there's people who should like stay on the big boat, and there's people who definitely need to get on the jet ski, and that's like not better or worse. It's just different. And I mean, from a personal perspective, you know, I love my sister dearly, but I think she would hate an environment like this. At the same time, she's working at a company with a lot of continuity, a lot of stability,

### 08:00 — The "no commitment" SaaS model explained

**[8:14]** I would like die of boredom there. So it's an important company she's working in, but like, it simply is not my operation. Going back to what you were bringing up earlier, pure pay as you go, I have seen many hybrids where hey, we get a commitment, and then you have some usage based above that. And I know that that being a VC backed startup, that creates all kinds of operational complications, one, like how you're reporting to the board, how you're reporting for future fundraising events, I can only imagine the sales and marketing complications it creates. Amazing that it's great for the customer, and I love that you're leading with that.

**[9:02]** But how are you solving for all those problems? So I think not every business is like, how to say, feasible to run a model like that. So the product you're selling needs to have an inherent stickiness. So if someone like implements an observability solution, if it's not breaking, or if it's not complete disaster, like, you're thinking twice whether you want to change, you know, it's not just like it has to be a little bit cheaper. So that's, I think, one, you know, factor, basically, the nature of the business we're in. The other thing is, we have a very high confidence in the product we're building.

**[9:46]** So if you if you're not really convinced that this is, you know, product that can, or is maybe already the best in the market, you should I mean, it's hard to say, like, you could then raise other questions. But yes, there's a certain risk to it. And like, it's not about like, oh, yeah, you have a bad product, and therefore ask for a commitment so that you lock in people that they don't leave. I mean, if you have a bad product, maybe don't sell it, like, try to improve it until it's good, and then you can sell it. But again, the thing is, like, if the product does what it's supposed to do, if you take

**[10:27]** care of your customer the way you're supposed to do, it actually doesn't really matter whether you send them an invoice on a consumption base, or once a year. Yes, you have casual topics, but you called it out or a VC funded startup. So you resolve with the type of funding for that type of like upfront investment you need to make. And you can also manage cash flows in multiple ways and address that, how you manage your costs on other sides as well. So you're not powerless. It's not like if you don't do this, like, it's no, there's no feasible way. I mean, there's also other businesses who inherently have a similar model.

**[11:06]** I mean, we build on a monthly basis. So some of them just offer monthly and that type of, you know, contractual model is favored by their customers. I think, like, OpenAI probably also has most of their customers on a monthly billing model or like user based thing. And yeah, okay, they have a crazy burn, but not because of the billing model, but because of the investments and everything they're like managing, that's a, that's a different problem. Yeah, they're having thousands of AI researchers on their, on their payroll, absolutely not because of the infrastructure. Yeah, I think like Nvidia, like knows where the money goes, so yeah. Exactly.

**[11:48]** For most startups, the North star key metric is going to be annual recurring revenue. Yeah. I'm sure you're pressed to come up with a proxy for that. I know it's not as, since you don't have contracts, you can't just go pull the contracted amount and use that as your annual recurring revenue. How do you approach that? And practically, because we work with a lot of usage based companies, at least they have a component that's usage based, and they're trying their best to articulate what their ARR is, because that's what investors want to know. How are you guys doing that? What's your approach and method to articulating that?

### 12:00 — How to think about ARR without contracts

**[12:26]** I mean, we really don't have that problem, like fundamentally, so our investors understand what we're doing. So they understand the field, they have a hypothesis, an opinion on it, they understand the stickiness of the business we're in. And so even if we are consumption based, they consider this really recurring, because think about it. If you are a monitoring solution and you're monitoring, let's say, the SaaS application of that recording software, Riverside, sure, they have maybe volatility in their business, but if it's a growing company, they're going to continue using your product.

**[13:02]** They can't just say, "Okay, today we're not using it, tomorrow we're doing it." So the criticality of the product that we are providing is significant, and therefore, it leads to this recurring usage. That's kind of, I think, at the core of when people thought about SaaS, when people thought about cloud, what's in there? They didn't think about the recurringness in the sense that, "Yeah, you need to have a contract that sticks in a recurring way, you need to have a recurring value that comes out of it," and I think this is at the core of it. So specifically, if you're going to a board meeting or you're fundraising, are you mainly

**[13:43]** showing, "Hey, here's the revenue that we've achieved," and you can just extrapolate that out 12 months if you want to come up with an ARR number? Exactly. I mean, that's how most people also do ARR in early stage, because they do bookings, but then they have to also kind of split out and then calculate it upwards or forward. So it's not much different, like, yes, you could argue, is consumption-based recurring, but I think if the nature of your business illustrates it clearly, then I think it proves it even to be more recurring because you don't have the question, "What would happen if all of this wouldn't be contracted?" Would it collapse?

**[14:27]** And if it doesn't, then I think you're actually demonstrating strength in your business in a different way. Right. Let's talk about that because this is something I've run into. I've worked as an operator in usage-based companies, not pure pay-as-you-go, so that's a whole new world. But one of the scenarios that would happen, and I'm curious how you handle this, one of the deals we close, we close JP Morgan Chase, and it was a massive deal. We'd been talking to them for six months, and we knew it had multimillion-dollar value, but they were on a pure usage contract, and that was the only way they would sign.

**[15:15]** Even though we knew the use cases, we knew how they would use it. When we close one of those deals, and I'm curious, when you close a big deal like that, are you discrediting yourself by not being able to put that in your financial metrics or future revenue projections, or are you able to do that? No, we are. Why wouldn't we? We know we have consumption estimates. Our customers ask us, "Okay, how much do we need to budget for whatever you're doing there?" And we're like, "Okay, let's look at your telemetry data," either by already instrumenting it and measuring it clearly, or by taking their, so far, consumption numbers from other

**[15:53]** vendors because typically, it's kind of also a symptom of the maturity of the business, like the sector. I mean, yes, you have some net new clients like startups who just started their business, and therefore, they didn't have any incumbent solution beforehand, but any larger business that has business vertical software and that we've been over as a customer has at least one, and in many cases, multiple other vendors already in place. And from that, we make projections, and then it's also the way how we, for example, capture it in the system that we make 12 months consumption projections.

**[16:29]** There at that point, consumption projections, but as we kind of mature, we'll know the accuracy of those because we do this in a repeatable method, and in an ideal world, we'll actually be able to forecast our consumed revenue with a high accuracy. I mean, I think there are examples like Snowflake or AWS and so on, other businesses who are on this type of model. They do commitments, though. I mean, they are not as pure play as we are running it, but in the end, the characteristics of the business are very, very similar. Yes, so let me make sure I understand. If you're walking to a board meeting and you're going to present the financial metrics of the

**[17:12]** company, you say, "Hey, this is how much revenue we've collected. You can extrapolate that out 12 months." That's what our ARR is, but by the way, we close these big logos. We have a methodology for forecasting where the consumption is. We don't have the revenue yet, but we do have an idea of what it will be, and we can stack this on top of what we expect revenue to be in the future. Is that how you're approaching? Exactly. Then we have, obviously, an expectation of what on top of that will be closed. I mean, again, I think any early stage startup that is at this level where we are in, giving

**[17:47]** accurate 12-month forecast is, I would say, there's, let's say, at least a portion of luck in it. Ideally, you're wrong in the way that you're beating it. Ideally, you're never wrong in the way that you're not beating it. Then you have another problem, but typically, I mean, in the last 12, 15 months, we continuously corrected our guidance upwards, so we're bad forecasters. It's okay to be bad in that direction, as you mentioned. Yeah. At that early stage, I mean, when you mature, at some point in time, they don't care so much about then beating the forecasting, or let's say you have a margin that you can or

**[18:27]** should beat it, but if you go beyond that, then they're like, "Okay, are you in control or what's happening?" Right. Do you even know how to forecast? Yeah. Talk to me about comp plans, because this has been one of the toughest conundrums I have spent hundreds of hours trying to rack my brain on, the best way to approach it, so I'm curious what you see working with the team and how you actually do this. Yeah. I mean, look, there's a couple of factors that you have to try to bring together, and the consumption piece doesn't make it easier. So very clearly, and I thought, coming in, "Hey, I've built comp plans before, that

### 19:00 — The hardest part: comp plans

**[19:09]** should be a thing of a couple of weeks, let's go," and before we even enter 2026, comp plan is going to be built. Turns out, we're just rolling it out, so it's end of February, we've communicated it, we've shared quotas, but we're still in implementation and roll out phase. So we wanted to make sure that comp steers behavior, and I think that's a critical piece. I think Charlie Munger said it like, "Show me the incentive and I'll tell you the outcome," so I'm a firm believer in that. We have to factor a few things together, and I think the biggest problem is at that early

**[19:51]** stage when you are not super confident in quota setting and predictions and so on. How do you make sure that on the one hand, you protect the cash flow profile of the company? At the other side, you have lucrative incentives that excite your salespeople to go above and beyond and completely knock it out of the ballpark. And we basically did a lot of modeling, we did a lot of calculations, we did a lot of number crunching to how to say, acquaint ourselves with many scenarios how this could play out and check on the ratios between cost of sales and the revenues that we're bringing in and stay within a ratio.

**[20:40]** So, not to be super detailed on that, at the risk I might even tell you a wrong number because it's now a couple of weeks ago that we did those calculations, but I mean you know all of these principles like quota to OTE ratios and so on, so we're following that method because ultimately we don't have yet territories, we can then build out territory-based quotas. That will be probably something for the future, so you take the financial engineering element and then start to say, okay what's attractive, what's compelling for the market, how do you get the best talent, because that's another component, you know like the best sellers

**[21:14]** also pick the companies not only by what kind of upside is there, but what kind of development potential do they have, how is the compliance structure, do the people know what they're doing, so they're kind of like sensing this and ultimately throughout this year what I want to happen is my sales folks go out there and talk to their peers and saying, hey it's crazy here, this is absolutely fantastic and the fantastic needs to be also fantastic for the company, so both need to win. If one is winning and the other not, then you have an unhealthy relationship with everything.

**[21:49]** If the company is making a shit ton of money and the sales team is not benefiting from it, okay, they're not going to get excited. If the sales team is making a shit ton of money in the company not, then the company won't be there for long. So you need to balance that, there's no answer, like yeah you need to tweak it in this or in this advantage, you need to be really trying to find the optimum. So long story short, we settled on, yes we'll do upfront compensation or commissions based on this synthetic or projected ACV that we're estimating, so it's kind of like tying back to this process that is anyway part of the sales process.

**[22:28]** So we talk to our customers about their first, I don't know, one to three year spendings and then we take a couple, let's say, security margins of that estimates. We have two people look at that and say, okay yeah, that sounds about right and then people can get comment on that projection and we also allow them if they feel like it's risky and they don't know it, like to reduce that number because ultimately what they need to do is they need to like consume that to fully earn that commission. If they don't, let's say, I mean you need to have a little bit of a precautionary element

**[23:07]** in it, otherwise people just put a crazy projection and then consumption is 10% of that and then they move on and then you'll have to chase it, we don't want that. And we've been very transparent to the team and explained to them like these are the problems that we're having in this model. So we've not pretended that we know all of it and we said there's a few questions that we might have to answer as we go because we can't like how to say anticipate all possible scenarios and I think that's the other piece that is important. So you know, make it lucrative, make it financially stable but also be very transparent with your

**[23:39]** team at that early stage that there might be changes so that they don't think that this is like now set in stone for the next 25 years because it is not, I mean, it rarely is even in more mature companies but if you're outlining your intentions, if you're outlining and what I said to you about like I want the team to be raving about it, that's the same thing I said to the team and they should hold me accountable for that. So if they're not happy with it, then I have not done my job or if the company's paying too much, then I have not done my job. So like I'm accountable towards both. It's such a complicated problem to solve for.

**[24:18]** Like you mentioned, you want to incentivize performance, you want to incentivize closing, that's what the sales teams are to do but the company does take on some of the risk but if you pay out in like an annuity fashion, it's just too slow and it's not going to incentivize them enough. So I think I appreciate that you leave it, hey, this isn't fully solved. There's pros and cons and things we may tweak but also I appreciate that that's not scaring you away from solving the actual problem of, hey, what do we need to incentivize the team to do in order to get our company to the next stage of growth? Yeah, I mean, you need to find a way forward.

**[25:00]** It's very easy to now come up with like, I don't know, I mean, we had this discussion, okay, so what happens if we do this and then five deals turn at the same time? And I said, yeah, I mean, theoretically, that's possible and would be bad. I mean, that's why we have some provisions in there that protect us but let's think about this scenario. So if that happens, then we have like, not the problem you think we have, then we don't have a problem with the comp plan. We have either the problem that we have a bad team that is bullshitting us and over promising to the client that we've hired the wrong people, then we need to fix this differently.

**[25:39]** Or the product is not doing what it's supposed to do. So that's not a comp plan problem. It's just, you know, like it materializes with the comp plan or it gets escalated with it. But the problem is either we've made the wrong hires or we're not building the right product. And like, yeah, when you figure out which one of those it is, then you need to fix it. A change in the comp plan will just like mitigate some of the fallouts, but it's not going to solve the actual problem you have. I love that you put it that way, because that's absolutely right. Let's go find the real problem and solve that.

**[26:17]** If all these doomsday scenarios do end up happening, then there's other reasons for that. Yeah. I mean, the comp plan is just a derivative of something, you know, it's not a it's not a root cause. I mean, sure, you can design it in such a bad way that it becomes a problem for itself. But like the comp plan doesn't cause people to churn. Right. You know, like the comp plan is not the root cause why a customer says, hey, this is not for me. You know, I mean, it could be like leading people to do stupid shit and like say wrong things and whatever and over promise. But like if you have that problem, like at scale, so multiple people are doing this,

**[26:57]** then you should ask yourself, who have you hired? Let's talk about that, because I think that is usually the biggest bottleneck for fast growing companies is getting the right talent that has the right skill, but also the right level of commitment and intangibles that are important for a growing team. And you've seen this at so many levels. You've scaled from three to three hundred million. You're back at an early stage. So I imagine you've gone through a serious amount of hiring and firing. What are you looking for in sales talent? How do you know if this person is going to be successful or not?

### 27:00 — Hiring elite sales talent (SEALs vs Marines)

**[27:34]** And how do you avoid any critical mistakes as much as you can? I mean, you'll make mistakes. Number one, as as much as it pains me, I've already made here mistakes like I've already hired people that are not anymore with us. And I'm sure some of the people that are hired still here might not work out. So but you obviously try to to minimize it as much as possible. The hard reality is the faster you go, the more mistakes you'll make. And you need to kind of like account for that and learn very fast on the same level. But the challenge with hiring sales folks is like if they don't know anything to sell, at least they know how to sell themselves.

**[28:21]** So they don't know how to sell themselves. That's already like the first thing where you're like, OK, you know already. So but what is it that I look for? And I think it changes over time to a slight extent. It's not like radically different because ultimately, I mean, I'm a firm believer that at any point in time you need to hire people who have agency who are like genuinely curious who want to help the client. So have the intent of not just closing a deal and getting away with commission, but actually resolving a problem and being rewarded for helping to solve that problem.

**[29:01]** And then a few other traits that kind of like construct the how to say route right foundation because you need to grow. So, for example, coach ability and the ability to learn fast, ability to communicate complex things in a simple fashion and so on. But the first two or three things that I said, I think are like a critical foundation that you like distinguish top performers versus, let's say, average or people are not performing at all, especially at early stage, I've I've I'm not the biggest fan of like military analogies, but I've stole this from from a person and the way how that person said it is like when

**[29:42]** you start to build a company, the first people you hire, they are like the seals, you know, they can operate confidently without supervision. So you go into uncharted terrain. Their mission is like, I don't know, could be multifold, could be evolve as they see, you know, like it could change like as they're in the field, because typically you try to do some, you know, like gathering intel on what's happening there, but you don't know it until you deploy. And then the second wave are basically the Marines and they're like building the foundation that then the infantry can full scale follow up and sorted out finally.

**[30:19]** And I think that's when you're building a company very comparable process, because you don't start with hiring 100 people, you hire the first people, and they set the foundation, they have learnings, and then you follow on with a few more. And this is a little bit like how I think also in sales it operates. But if I can choose, like I'll have as many seals as I can get, because even later, they're very useful, they don't they don't lose their value, it's just hard to find those people. I love hiring seals. They don't need supervision, you don't need more management layers. They don't just execute the job, they elevate everyone around them.

**[31:03]** I think the more seals you can get into your company, it turns into this fusion reactor of productivity and talent. And one person who, and then next you need at least Marines. And then I think the biggest thing that crushes companies is B players, the ones that seem like they're doing good enough, they listen well, usually, and they kind of mess up sometimes. But the you don't realize the cost of management for that type of person, then setting a poor standard for those around them. Somebody who's just underperforming a little bit really brings the rest of the team down. Yeah, absolutely.

**[31:42]** And I mean, there's a there's a thing, you know, people like that, there's so many stories for that. So like, why something is not possible, or why something might not fail, is actually very simple to figure out, you know, it's it's not really super smart, it can sound very smart. And you know, when you sometimes have conversations around, let's say minor issues, and that often like in sales, there's a conversation around like the effort and the energy you have to put into something and sales is a is a numbers game is an energy game, you know, as much as it is a smartness game, it is also about the energy that you invest into it.

**[32:27]** And when it comes down to those type of energy investments, number of calls, meetings, whatever you have and set and then off you hear things like, yeah, something little is not working. I know the tools not doing right, there's a hiccup, there's this and that. And then it's maybe a little bit too much. But then I say, look, imagine 40, 50 years fast forward, you're sitting with your grandchildren, and you're telling them about your life. And then you're saying, hey, we're really onto something in this amazing company. And you know what, there was a technical issue that prevented us from building a generational company.

**[33:12]** Is that the story you want to tell? Like, you couldn't watch the Superball because the batteries in your like, whatever, remote control died like, like, is that where you stop? Like, is that is that what defines you, I think, like, and because we often end up in situations where we look at things and something's not working out. And that's what I mean with agency, you know, like, people with real agency, they wouldn't even discuss this, they wouldn't even like, like, wouldn't even enter their mind that this is a conversation they want to have with anyone, they would just find a way. And that's what you need in the beginning.

**[33:52]** And like, the longer and the more people you can have and accumulate around like that who have that type of, you know, behavior, and I frankly think like after being a while in this whole funny game, I'm not sure if this is something you can train, I think this is something that people like, you know, build out as they grow up become humans, probably it's not purely genetically, I think there's an element of it, but like, it's it's baked by the time you enter an interview process for a sales job, and then either you have it or you don't. And sure, some have it more, some have it less, some have it not even at all, but hard

**[34:30]** to hard to create, it's a bit like urgency, it's either they are not either your leg is broken, or you just have a scratch. Even if you could train it, let's let's do that thought experiment, you're not going to have enough time to coach them in the current mission that you're in. So yeah, yeah, I mean, that's like when you're going at that speed, that's the other problem that you have time, I agree, I mean, sure, I mean, probably like if you talk to a psychologist, there is a process or an approach on how to get people into this, because if people have enough pain, like, they change. But yeah, everyone has a different tolerance for that.

### 35:00 — Motivation vs morale (critical leadership insight)

**[35:09]** Yeah, but you don't have one to two to five years to develop someone into someone who has a sense of urgency. And I also don't want to inflict pain on anyone just to kind of like, how to say get through that. So so, you know, like, everyone needs to understand why they are doing what they're doing. Because like, that's a conversation that I had with my leadership team about motivation, because motivation, I think is a term that has been used a little bit inflationary, you can motivate people, you know, it's not about like, it's also actually not your job as a leader to motivate people, so either they know why they joined your cause, or they don't.

**[35:50]** But then that's a different problem. What you can do, and what you need to do is like to manage morale. That's a different thing. Because like, people can have high motivation, but the morale is like, how to say, not there for that moment, because of whatever reasons, you know, it could be that bad day at home, they don't feel well, I know they only had bad calls, like it's getting to them, you know, I mean, everyone has a weak moment. And then it's your job to be there and support them and like support them in a way through that hardship. It's not your job to take it away. Because if you take it away, like they always come back to you to take it away.

**[36:30]** No, you need to help them to find a way how they like, you know, go through that how they how they build that resilience and assertiveness to go through these things. Because then they become self-reliant and grow and so on. But the motivation to do that has to be already within them, you can't install that you can inspire some people and it's the thing over time, but to your point, if you hire someone and they don't really have the motivation, they go yeah, it's not there. I think that's a really important distinction. I've never I've never heard it put that way.

**[37:06]** The difference between motivation and morale and how sometimes I think those two can get conflated. Have you seen times where you have a highly motivated person held back by low morale and organization? And what might that look like? And how do you solve it? I mean, they absolutely I mean, it could be it could be internal, external, give you an example hygiene factors. So someone is a high performer, like high motivation, like the personal goals and the company goals are super aligned. And then there's I know something that happens that this person perceives is super unfair,

**[37:48]** you know, like personally for them, they like an account gets taken away for whatever reason, you know, someone comes in and says, no, this gets assigned to Bob and Jack is not like, why I'm, I'm working this like for the last six months, I've been working my ass off and now when we have access to the decision maker, Bob gets it like because he's your whatever brother in law. I mean, we've all seen sort of weird stuff like that play out. That's a very simple example how you ruin morale, because why would that person keep up the morale to do the things if they see it's not paying off for them? Very simple.

**[38:26]** I mean, it's a little bit of a constructed and like toxic example that could also be more easy examples like that. There's all kinds of I think this is kind of, let's call it like the everyday struggle you go through. Yeah. And I think I think as a leader, hey, taking the burden off of yourself to say, I'm responsible for instilling motivation or turning this person into a motivated person, because I think a lot of people do carry that on their shoulders are thinking like, I'm supposed to do this. And if I have somebody on the team who's not motivated, they don't seem like they're driven. It's my job to do that for them. But it's not.

**[39:06]** But focusing on morale and making sure, hey, let's keep the game fair. Let's keep the vision clear. And then let's go help people when, you know, they're having a bad day, they have a bad day at home or they have a bad day in work and, and keeping them going. And I think that's a really important distinction that a lot of leaders don't think about. No, I think it's the same thing when you raise kids, you know, we often think about it in a way like, hey, how can we make sure our kids have a bright future? I think the best way that we can do is like help them understand how to go through hardship

**[39:41]** and how to learn everything else or figure it out themselves. Like you can train for them, you can learn for them, you can fix problems for them. Because if you do, they'll always come back to you and ask you to fix problems like, and at some point in time, you're dust and you're gone and then what are they doing? They're 40 years old and they can't help themselves. I don't know. I mean, and it's the same thing in leadership. Like I don't like analogies, like when you compare employees with kids, but when sometimes it fits. Yeah, no, I like, I really don't like it because it's a different relationship.

**[40:18]** I think it devalues both relationships, but some of the principles, how humans simply operate like they are, like they are independent of, like in what constellation you're, you're, you're how to say intertwined. As you're building these teams, scaling these teams, you're focused on getting the morale high. You're bringing in highly motivated people. I'm always curious, you know, at LeanScale, we do GTM ops, we're helping people set up systems or helping people implement AI. How are you enabling your team to be successful? What tools are you using? Everyone wants to know how everyone's using AI, so if you have any like tips and tricks

**[40:58]** of things that you're doing, how are you helping your team win? So I remember, like there was a situation in one of my prior companies where we had like planning session and the ops team, the RevOps team was around and I said like, look, going into next year, we need to make sure that whatever we're doing also like has a tangible impact on the team so that they can do more with less, because everything that we did this year was purely governance and didn't have any value for the team. And everyone was super upset. Like I, I, I had so much slack from my boss at the time, like that I was so like offensive

### 41:00 — How AI is changing sales execution

**[41:52]** and everyone felt really, really bad and I'm like, I'm really sorry, I didn't mean to offend anyone because I was part of that exercise, I've included myself. So and I think when you when you think about these things, you often forget why are you doing this? So the only reason why we're bringing these things into place is that we, you know, help people to get more productive and that we can run a company efficiently. So that surely there's let's say some governance requirements, risk, whatever, but let's park that for a moment, like, because these are the things you have to do, like, if you if

**[42:29]** you can comply with the regulation and law, like different problem, but let's park that for a moment. The other thing is like you want to make be able to execute and you want to be able to like build your strategic decisions on whatever you need there and then to steer the business properly. And if anything that you do in like providing systems tooling enablement processes is not steering into one of those two buckets, you're missing the point. And it's easier said than done. But that's kind of like the fundamental premise I try to manage this and operate it. Am I always successful? Do I always get it right? No, 100% not.

**[43:07]** But I think we're living in a pretty interesting time where AI is is a big thing. I mean, I'm a big user of quote, quote, I don't know if I should like advertise it, but I'm I'm sure they don't even need it. I'm I'm a super fan of Claude Cook. So I'll give him an advertisement if you want it like it's like I'm not I'm not really a technical person. But what's interesting also going back to an early stage company, you can like try these things out on a much, much like how to say blank or sheet because, you know, we don't have we have a few systems in place and we're actually I think pretty advanced, like some

**[43:45]** of the things that we're doing, like, we're already ahead of what our like my prior company was doing at the time that when I left, other things are less mature. I mean, that's kind of like not surprising. So and then you just try things out, okay, and like connecting Claude Cook to some of our systems and then using the core work functions and so on. I mean, don't like it's it's not like that this is perfect. But it's so good that you're like, this is so easy. And it's crazy. So now we're working on basically building and let's call it like infrastructure at scale

**[44:23]** so that we level up the quality bar of everyone in their execution at those critical moments. Because one thing that when you're like growing that fast, super, like difficult is enablement. So we're a very technical product, like understanding observability on a principle level is easy, but on a fundamental like, you know, you go into the weeds level is very, very difficult. I mean, even our CEO, who's a who's a software engineer by trade, I mean, I think he just posted today that he hasn't written a line of code in the last 10 years, and he's turned into a salesperson. Yes.

**[44:59]** But he still understands a lot of about technology and he himself says, like, I don't I don't have a clue about the details. There's other people who can do that much better. They have, like, super bright technical minds. And when we bring them in play, like, those things become much easier. So you shouldn't even attempt as a salesperson to, like, in our space be like, appear super like, like, like, like a subject matter expert in the field, like you should understand it, you should understand our audience, like the problems they're dealing with and so on. But even to get to that point is hard.

**[45:37]** So how do you how do you accelerate that process without just going through the typical classical cycles? And I think that's where I can play a fundamental role, because you can you can you can organize knowledge now in a way that people can ask questions in a way which would have required beforehand, like a one hour meeting to resolve that. Now it's a five minute prompt. That's maybe not 100 percent, but it's 95 percent and the last five percent you bridge with a 10 minute conversation about the output. So you don't just take at face value, whatever comes out. But to get to this 90 percent, like, takes you much, much, much less.

**[46:24]** And maybe it sounds a bit theoretical, but that's kind of like direction where you're going so that knowledge is always and everywhere available in a tailored way so that people can leverage it specific to the question they have currently on that plate. And that could be a customer that could be someone internally. That's kind of like the idea. And I think the I think the biggest transition actually is not necessarily that we're applying technology here. I think the biggest transition is what type of discipline you need to have around managing your knowledge and information in your company.

**[46:59]** I think this is something where people will have to get like retrained in a way like we have like, let's say, entirely understood. Yeah, your your cloud code instance is only as good as your repo and the data in the systems you connected to the context engineering. So very simple, you know, and the context engineering is is a simple part, but maintaining the context and keeping it up to date and keeping it relevant like as you grow and as you grow, that's that's I think the excellence piece. One hundred percent. And I know they're getting better. Now you can send out teams of agents to do things so they all have their own context

**[47:46]** windows and you can roll things up. But I think getting people hands on early and getting the leverage and you were alluding to this earlier, too, it's not just an efficiency play. I guess, of course, maybe you can run leaner, but it's also an effectiveness. So it's not one example we use, which historically has been very hard to scale like an agency type of company because it requires a lot of people and then to enable those people at the level of when you're a boutique size is unbelievably difficult. But now, if you can run the playbooks with with AI and it can follow it really well,

**[48:30]** as well as like do the implementation of the work, it completely unlocks something. And one specific example we use in our selling process is putting together the S.O.W.s, because ours look different every single time. There's different projects and different things. And every single time I would miss something on a call as diligent as I was being on listening, taking notes, reviewing the transcript. But now we have an agent that just says, hey, take that transcript from that hour long meeting I had with a potential client and the diagnostic that we ran through and showed all the problems and the things that they said they cared about the most.

### 49:00 — The future of work and learning

**[49:07]** Turn that into an S.O.W. for me and then just send it and something that would take hours and I would probably miss something now takes minutes and is better than anything I did manually. Absolutely. And especially if your like context is engineered in the right way so that the S.O.W. was like built the way how you know, like you would ideally build it. That's because that's the beauty of it, you know, like it takes you onto an interesting level. And I think that they're like for me, there is like two fundamental like situations. Number one, like when you're an experienced person, you know, you do whatever you do already

**[49:42]** for like a good amount of time and you can assess the output that is coming out of AI and so on. And you can, you can like you, you basically like move yourself up and accelerate from that point. And then on the other side, there's rookies who are coming into this, they're looking at that output and they can't judge what they're seeing because they don't know better. And that's going to be a very interesting exercise. Like, like, what's that going to do to like this cohort of people like coming in? Because I think if you are, let's say on a certain experience level, AI can accelerate

**[50:20]** you, I don't know, 10 whatever hundred times, but if you're, if you're like junior and you don't know, then it brings you on to this average level. And this is good because like it helps you get quicker there, but what happens afterwards? How do you, how do you absorb that knowledge? How do you learn this? Because most people who like didn't have this opportunity, they had to learn it through like experience and now you're there. So will you learn it the same way? Will you learn it differently? That's I think that goes nearly a little bit into a philosophical question, but when you

**[50:54]** train people and we hire in sales often, you know, rookies from university, they come in as an SDR, they get promoted to an AE and so on. Like will this be the same path or will this also evolve through that type of knowledge management and knowledge access that this is like opening up? That's an interesting longterm. I think we're going to have to get really intentional about training our critical thinking and creativity skills. Just like a hundred years ago, you didn't have gyms on every single corner of every single city because people didn't need to go into a room and lift a bunch of metal weights because

**[51:35]** what they were doing in their day to day was giving them enough activity. But now when we're mostly sitting around and on video calls, you need to go train your body so it doesn't atrophy. I think we're going to have to do something similar to maintain that mental clarity. It's a good thought. I mean, it goes back to, you know, this like learning thing when you're like, what do you need to teach people? They need to, you need to teach them how to solve problems on their own without you constantly helping them. And I think it's, it's a similar, it's a very comparable bucket. Yeah. So we'll see what that looks like.

**[52:09]** I don't know if there's going to be mental gyms or you go and solve puzzles or do things or escape rooms. I'm not sure, but I think we're going to have to do some things in manual mode to keep our brain sharp. Great. Oliver, this has been fantastic. I really appreciate you sharing tangible, practical solutions to problems that you're seeing, especially going through the ride from a 3 million to 300 million era company, sharing what it means to get back into a startup and assessing if that's a good fit for you. And then something I think so many products and platforms are going this direction, but

**[52:44]** navigating how to build your operation around a pure pay as you go product offering. It's very challenging and I appreciate you sharing all of the specifics of how you forecast, how you present financial metrics to the board, how you do things like comp planning. I think this is going to be so helpful for anybody who's listening and more people are going to be needing that advice soon. And of course, appreciate all the insights on AI, how you're leveraging and where you think the future is. So Oliver, I just want to thank you so much. I can't wait to see what you do next and appreciate you being here. And thank you for having me.

**[53:22]** It was a really, really good conversation. Thank you for the time. Thank you.


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