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16 places to launch your startup (v2): 1. OpenAlternative (DR 52) 2. Fazier (DR 83) 3. SideProjectors (DR 71) 4. Launching Next (DR 53) 5. Startup Fame (DR 83) 6. EarlyHunt (DR 63) 7. SourceForge (DR 52) 8. Aura++ (DR 73) 9. Startup Ranking (DR 61) 10. PitchWall (DR 69) 11. Startup Stash (DR 65) 12. Tiny Launch (DR 73) 13. Startup Buffer (DR 43) 14. Smol Launch (DR 74) 15. LaunchIgniter (DR 74) 16. OpenHunts (DR 71)

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The Latest CRO Confidential with @samdblond s out! "$0 to $600M in Under 4 Years. The ElevenLabs GTM Playbook" Carles Reina was the fourth employee and first GTM hire at ElevenLabs. Four years later: $600M+ ARR and an $11B valuation. Today he's a Partner at Baobab Ventures, helping the next generation of startups scale. In the latest CRO Confidential, hosted by Sam Blond (CEO and Co-Founder of Monaco), Carles breaks down exactly how they built it. The revenue ramp alone is jaw-dropping: $0 to $100M ARR in 20 months $100M to $200M in another 10 months $200M to $330M in another 5 months $330M to $600M+ in another 6 months #1. Distribution from day one ElevenLabs never bet on a single channel. Every market got its own thesis: why launch here, direct or reseller, what does GTM look like in the first 90 days? Core markets were sold direct; everywhere else, resellers got the technology into market fast. Distribution plus relentless experimentation was the whole game. #2. The grants program that killed the competition Carles came back from holiday with one question: how do I kill my competition? The answer: give startups with 25 or fewer employees free credits for 3 months. They gave out tens of thousands of grants, made a ton of noise about it, and pulled demand away from every competitor in the market. Over 10% of ElevenLabs' enterprise revenue eventually came from upselling those grant recipients as they grew. #3. The 20X quota model This one's controversial. At ElevenLabs, a $100k base salary meant a $2M quota — 20x — with uncapped commissions. Reps regularly hit 300-600% of target, and average attainment across the entire GTM org ran 167% every single quarter. Carles' theory: he'd rather have a lean team of killers crushing quota than a bloated team missing it and complaining. And when a market genuinely broke, they'd grant quota relief — the commitment was that comp would always stay fair. #4. Only pay commissions on recurring revenue Sign a giant one-off or a 2-month POC? Zero commission — because it added nothing to valuation. Roughly $1M of real recurring revenue was worth ~$33M in valuation, and reps were paid generously on that. Incentivize the outcome that matters, not the inputs. #5. What he'd do differently: enablement and senior sellers, earlier Two regrets. First, sales enablement came too late — it's always underinvested until the team is too big to onboard properly. Second, hire senior sellers earlier. Hungry young reps are great, but people with 20 years of relationships who know procurement compress timelines dramatically. Either regret fixed might have gotten them to a billion faster. #6. Wire AI into GTM, but sell the team on productivity, not replacement Carles pitched the founders on building an AI SDR, an AI AE, and an AI customer success manager. The team's first reaction: "am I going to get replaced?" They proved the opposite. The AI SDR answered inbound emails in minutes and improved conversion rates. The AI CSM worked long-tail and mid-market upsells automatically — and the human account owners still got paid the commission. Productivity story, not replacement story. #7. Test 100 things. You only need one to work Carles told the team he only needed one experiment to hit to add another $100M in ARR. So run everything in parallel, kill what fails fast, and treat a failed experiment as a win — you learned something. Add to that: make it insanely easy to do business with you, from pricing to onboarding to time-to-value. 👉 Distribution is everything, incentivize recurring revenue (not meetings), be famously fair to your salespeople — reps who get rich make everyone around them believe — and let AI do the work agents are best at while humans do the relationships and the creative bets no agent would ever come up with. Watch the full episode: https://t.co/H4d5GI1GfP