How Emergent went from Zero to $10Mn+ ARR in 60 days
About the project
Date:
Aug 15, 2025
Client:
emergent
Services:
1. Problem statement: Where AI coding fell short
Before Emergent launched, the AI coding market was crowded with tools like Replit, Lovable, and Bolt. These platforms made writing code easier but stopped short of completing the full product journey. Developers still had to manually manage backend logic, databases, and deployment, which slowed down real-world product building.
Emergent was designed to close this loop. It introduced an AI vibe coding system that could write the frontend, backend, and even deploy the complete web app in one flow. The mission was simple: help builders with strong ideas but limited technical background bring their products to life quickly and cleanly.
2. Setting the stage: From discussions to execution
In April 2025, I began working closely with Mukund Jha, Founder and CEO of Emergent, along with their GTM and Growth team. The goal was clear: design a go-to-market strategy for the US audience that would help Emergent break through the noise and dominate conversations around AI development tools.
By August 2025, the results spoke for themselves. Emergent scaled from zero to over $10M in ARR, achieved product-market fit within 60 days, and closed a $23M+ Series A led by Lightspeed.
3. Defining the GTM thesis
Our core belief was that while AI tools compete on capabilities, real adoption comes from cultural resonance and community pull.
We built the GTM framework on three pillars:
Cultural alignment: Make the product and brand feel native to US developer culture.
Volume and consistency: Maintain a constant presence across multiple high-velocity channels.
Distribution loops: Create feedback systems that connect creators, content, and community conversations.
4. Building the foundation before launch
Virality is never instant. It is engineered through patience and sequencing. For Emergent, it took nearly two months of dedicated work to set up the entire campaign pipeline before the first piece of content went public.
This stage followed the YC principle of doing things that do not scale. At 0 to 1, automation adds little value. Instead, manual testing, direct outreach, and iterative learning matter more. Every campaign and asset was handcrafted to discover what worked best.
We experimented across multiple channels including newsletters, X campaigns, directory listings, short-form videos, and long-form explainers. This phase was about exploration rather than optimization. Only after identifying signal did we start doubling down.
The key learning was that every 0-to-1 GTM needs a phase of deliberate chaos, testing small bets across channels to isolate the few that can later scale exponentially.
5. 360° growth levers that drove traction
Once the system was ready, execution moved in full swing.
Influencer and creator marketing: Led cross-platform outreach on X, TikTok, Instagram, and YouTube. The breakout came when a creator posted a TikTok/Instagram/Youtube shorts video about Emergent that went viral, triggering a surge in signups and conversions.
AI UGC engine: Scaled user-generated content that highlighted real product outputs and developer reactions, strengthening credibility.
Offline activations: Organized events and hackathons and San Francisco-based events to anchor digital buzz in real communities.
Product readiness: Worked closely with the product teams to build genuine use cases of the product that would resonate with the audience in our content marketing.
Short-form content emerged as the single strongest lever. In prosumer categories, it serves as a growth multiplier by combining discovery, education, and conversion in one surface.
6. Timing the launch for maximum impact
The launch timing became a critical strategic call. We postponed it several times to avoid overlapping with noise from larger AI product launches. The goal was to dominate the conversation window and own the internet for a few days.
When all campaigns finally went live together, everything clicked. The internet had space, creators had a fresh topic, and audiences had context. The momentum was organic yet coordinated. Every platform conversation looped back to Emergent, amplifying reach across ecosystems.
This synchronization between content, timing, and cultural readiness became the ignition point for viral lift-off.
7. From traction to PMF
The viral moment sparked curiosity, but retention confirmed substance. Within 45 days, user signups, active usage, and referrals all pointed toward strong product-market fit. From there, growth became compounding.
By the end of the second month, Emergent had crossed $10Mn+ ARR and established itself as the most complete AI coding solution in its category.
8. Lessons from Emergent’s GTM journey
a) Volume drives visibility: Sustained content flooding across key channels keeps the brand top of mind.
b) Virality follows frameworks: True virality comes from systematically engineering the right message, medium, and moment.
c) Track to scale: Granular campaign tracking creates the feedback loops needed to convert spikes into consistent growth.
Closing reflection
Emergent’s journey was the outcome of intentional effort, creative experimentation, and precise timing. The collaboration between Mukund Jha, the product team, and the GTM function proved that real momentum is built, not discovered.
When distribution, product readiness, and cultural timing align, even a new entrant in a crowded market can not only capture attention but define the conversation.
