Podcast Summary: Marketing Against The Grain

Episode: Inside the $130M AI Startup Growing Faster Than ChatGPT

Date: August 26, 2025
Host: HubSpot Media (Kipp Bodnar & Kieran Flanagan)
Guest: Elena Verna (Head of Growth, Lovable)

Overview of the Episode

This episode dives deep with Elena Verna, the growth leader behind Lovable—an AI-powered platform for building software—which has become the fastest-growing AI app, even surpassing the growth rate of ChatGPT. The discussion unpacks what makes Lovable’s operating model and growth trajectory unique, how AI is redefining startup structures and roles, and what established companies can learn from AI-native startups about autonomy, velocity, org design, and the future of growth and management.

Key Discussion Points & Insights

1. Elena Verna’s Journey & Why She Joined Lovable

• Elena planned to retire after Dropbox, feeling the work had become repetitive and overly political.
• Lovable’s potential as a generational, category-defining, AI-native company drew her back to full-time work.
• She wanted to see, from the inside, how AI-native companies operate differently.

Quote:
“I thought that I'll just live a cush lifestyle of advising… But Lovable came around out of left field… I saw a lot of potential of becoming a generational company that redefines… what entrepreneurship means, that redefines what software building actually entails.” — Elena Verna (02:17)

2. How Lovable Runs Differently From Traditional Startups

Autonomy and Ownership

• Lovable gives employees full autonomy; individuals handle projects end-to-end, minimizing cross-functional dependencies.
• Almost all GTM (go-to-market) execution is owned individually.

Quote:
“There’s no cross functional dependencies in order to complete really any project… That creates so much agency… and so much passion.” — Elena Verna (05:43)

Small Teams, Big Output

• Achieved over $100M ARR with only ~35 core employees (and 20–30 contractors).
• Most team members are generalists; specialists are brought in as contractors.
• Velocity is prioritized – “idea to customer impact” is measured in weeks, not months or years.

Quote:
“We have more generalists than specialists because of that and we do contract specialists quite a bit… Velocity above all—velocity is one of our biggest moats.” — Elena Verna (07:20)

3. The Rise (and Necessity) of Generalists

• Early-stage AI-native companies benefit from generalists who can build, ship, and iterate quickly.
• Specialists—via contracting—are used primarily for nuanced, later-stage tasks.
• As AI-native companies build rapidly, even post-$100M ARR, the role of long-tenured specialists is less central than in legacy SaaS.

Quote:
“I think that we're going to walk away from full time employees being highly specialized… the companies will be able to maintain a smaller headcount for much longer…” — Elena Verna (09:36, 11:38)

4. The Collapse of Middle Management

• “Pure manager” roles are viewed as obsolete for AI-native companies.
• Leaders must retain “vertical”/craft expertise and be willing to change altitudes between IC (individual contributor) work and strategic thinking.
• Traditional hierarchies, with many management layers, are seen as a hindrance to velocity and innovation.

Quote:
“I think that role of a pure manager is going to die… If a person has lost the ability to change altitudes… your role is on the line if you want to go into AI-native world.” — Elena Verna (13:22)

Memorable Moment:
“The best leaders are the ones that have best work on a team happen without them… If you need a leader to actually produce something in the team, I feel like you're a shit leader.” — Elena Verna (16:36)

• The expectation is that managers/“kingdom builders” shouldn’t equate status with headcount, but rather with impact.

5. Org Design & Team Dynamics in AI-Native Startups

• Fewer, larger teams per manager; fewer management layers (“ICs, leads, and heads—that’s it”).
• Fewer coordination roles, less “game of telephone.”
• Mistakes have a lower cost because the company can quickly correct and iterate.

Quote:
“The cost of mistakes is not so big anymore… The org charts, I hope, will collapse… just have ICs, leads, and heads.” — Elena Verna (17:53)

6. AI and the Evolution of Growth as a Function

• Traditional growth levers: product-driven acquisition, activation, monetization, retention.
• In AI-native products, the “activation” problem shifts—users interact mainly through a prompt box, not multi-step onboarding or flows.
• The focus is increasingly on how to monetize AI (with rapidly evolving unit economics) and retain users in continuously changing categories.

Quote:
“With AI, the problem statement of activation changes quite a bit… all of a sudden, it’s just a box.” — Elena Verna (21:30)

7. Defining & Segmenting Lovable’s Users

• Lovable’s core market: non-technical users, without alienating engineers.
• User archetypes: solopreneur building products (business builders) vs. “team players” (internal productivity, prototyping).
• Careful focus on not restricting platform’s horizontal appeal, while still prioritizing highest-value user groups.

Quote:
“Our mission fundamentally is to make it just work so you don’t have to know all of the details around it. But if you know the details, you can look under the hood.” — Elena Verna (30:03)

8. Adapting Growth Teams for the AI Era

• In the future, growth teams may morph into AI innovation teams—creating, managing, and deploying autonomous AI agents for tasks like onboarding, support, and sales.
• In legacy orgs, the introduction of AI is challenging; AI-native teams struggle to thrive in environments with entrenched silos and defined cross-functional boundaries.

Quote:
“Why do you need a really sophisticated growth team to do conversion rate optimizations…? You should buy an agent that does this for you or you should build your own agent that does this internally.” — Elena Verna (33:27)

9. No-Brainer AI Growth Use Cases

• Elena’s current growth workflow: idea → AI-generated spec → prototype via AI → direct to engineering → live in production—no waterfall of review/coordination/memo writing.
• AI dramatically shortens the cycle from idea to shipped product.

Quote:
“I have an idea… I go to ChatGPT, it writes the initial spec for me… In an hour I’m finished with the spec… There is no more of that coordination.” — Elena Verna (36:50)

• AI automates the routine; people should focus on higher-order idea generation and creative problem-solving.

10. Empowerment (Not Threat) by AI

• AI is best seen as unlocking potential, not as a threat; tasks made easier by AI allow teams to do more, faster.
• Opportunity expands for those who enjoy the craft itself and are ready to leverage AI to supercharge output.

Quote:
“I see it as an unlock of free time that I can spend on more meaningful things… there's probably things that you're capable of that you have never even reached that bar yet… AI is going to allow us to do.” — Elena Verna & hosts (40:28, 41:28)

Notable Quotes & Timestamps

• On Lovable’s uniqueness:
  “AI native companies are functioning very differently compared to traditional tech SaaS companies and I wanted to be in the middle of it selfishly to just understand what is going on.” (02:36, Elena Verna)
• On autonomy:
  “Everybody is responsible from end to end on everything that they touch… There’s no such thing as like oh, I need this person to do this part. No, you try to do it all by yourself and only involve other people if it’s absolutely necessary.” (06:13, Elena Verna)
• On management’s future:
  “Role of a pure manager is going to die… They lost all of their vertical expertise. They only have horizontal oversight.” (13:22, Elena Verna)
• On growth team work today:
  “Right now… my growth process is completely collapsed… I have an idea… ChatGPT writes the spec for me… direct to engineer… Sometimes a designer… It gets done and then they just ping me when it’s live and that’s it.” (36:50, Elena Verna)
• On AI as a creative unlock:
  “AI is a bazooka for the creative mind.” (41:28, Host referencing Kipp Bodnar)

Key Timestamps for Major Segments

| Timestamp | Discussion Segment | |-----------|----------------------------------------------------------------------| | 00:00–02:13 | Elena's background and decision to join Lovable | | 05:41–09:36 | Lovable’s operating philosophy: autonomy, end-to-end ownership, team design | | 13:22–17:30 | The decline of pure management, need for hands-on expertise | | 21:02–25:19 | Rethinking product growth levers and activation for AI products | | 26:48–31:03 | User segmentation, platform focus, product experience | | 32:43–36:34 | Growth teams as AI innovators, challenges for legacy companies | | 36:50–38:53 | Practical AI applications in growth: Elena’s workflow | | 40:28–41:28 | AI as empowerment, not threat—creative expansion |

Final Takeaways

• Lovable's success is built on autonomy, velocity, and a generalist ethos accelerated by AI.
• Managers must do real work and shed dependence on headcount and hierarchy for status.
• AI-native design collapses process layers, speeds innovation, and opens creative possibilities.
• Growth, in the context of AI, evolves rapidly—product features, monetization, and retention strategies must all adapt to a new reality.
• Adopting AI is less about tools, and more about reimagining structure, ownership, and roles.

For those curious about Lovable or the future of AI-powered company building, this episode is full of actionable frameworks and radical, firsthand insights from the frontlines.