- Antler partner Jussi Salovaara isn't interested in investing in new vibe-coding startups.
- Salovaara said he's excited about founders with deep domain expertise instead.
- He said we may see existing AI coding firms acquire smaller ones.
He's invested in over 600 companies and oversees $72 million in assets. But you won't find a vibe-coding company in his portfolio of the earliest-stage startups.
Jussi Salovaara, the cofounder and managing partner at startup accelerator Antler Asia, said he doesn't see much more room for new entrants in the AI-assisted coding space.
"It's been fantastic, but starting new vibe-coding companies right now feels like it doesn't make sense anymore," Salovaara, who is based in Singapore, told Business Insider. "It's an exciting category, but the guys are already out there."
He said he sees his colleagues at Antler and founders he has invested in using AI to more efficiently write their code and build tools. The firm's Europe fund was an early investor in $6.6 billion Swedish vibe-coding darling Lovable.
Still, Salovaara said he doesn't see vibe coding as a viable category for new, early-stage investments.
He said he's constantly weighing how any startup would defend itself against industry advancements.
"A million people are going to do the same. How do you differentiate?Anthropic ships your product next week, what happens to you? Model costs go up 5x, what happens to you?"
The future of vibe coding and the businesses it has started to disrupt is one of the most hotly debated topics in tech this year. While professional developers continue to say that AI-coded software poses risks and ultimately requires human input, startups like Lovable, Cursor, and Emergent have raised billions, struck big deals, and seen their valuations soar.
Salovaara said that existing AI coding companies may see some consolidation.
"It's going to be like a holistic kind of setup with a few winners, and they're going to stay, they're going to start acquiring smaller companies and just going to stay on top," he said.
But there's one type of AI company the VC of eight years said he's excited about.
Salovaara said he wants to invest in companies whose founders have deep domain expertise, a longtime focus for Antler.
"They combine this deep AI capability with industry capability. So the customers would be automotive, advanced manufacturing," he said.
He said he thinks about whether a baseline model getting better makes the product better, or if it replaces it.
An example is one of his portfolio companies IndustrialMind.ai, where ex-Tesla employees are building an AI agent to optimize factory operations like manufacturing and process engineering. Another startup, founded by former professional filmmakers, is building a video editing tool for other professional filmmakers.
"Too often you see cases where people who haven't spent time building for that space," he said. "That's always a bit of a head scratcher, like, does that make sense?"
Not all accelerator programs are pumping the brakes on investing in AI coding startups. The latest cohorts of Y Combinator and Andreessen Horowitz's Speedrun, which invest in early-stage startups, include several companies building AI coding agents or AI app builders.
Facts Only
Jussi Salovaara is the cofounder and managing partner at Antler Asia, a startup accelerator.
Salovaara has invested in over 600 companies and oversees $72 million in assets.
He does not invest in new "vibe-coding" (AI-assisted coding) startups, believing the market is saturated.
Antler's Europe fund was an early investor in Lovable, a Swedish AI coding startup valued at $6.6 billion.
Salovaara predicts existing AI coding companies will consolidate by acquiring smaller firms.
He prefers investing in startups with founders who have deep domain expertise, such as IndustrialMind.ai (AI for factory operations) and a video editing tool for professional filmmakers.
Salovaara questions how new AI coding startups can defend against rapid industry advancements and rising model costs.
Other accelerators, including Y Combinator and Andreessen Horowitz's Speedrun, continue to invest in AI coding startups.
Salovaara is based in Singapore and has eight years of experience as a venture capitalist.
Executive Summary
Full Take
The strongest version of this narrative is that the AI coding market is maturing, with early winners like Lovable establishing dominance, leaving little room for new entrants. Salovaara’s skepticism is grounded in practical concerns—differentiation, cost volatility, and the risk of being outpaced by larger models. His preference for domain-specific AI startups reflects a broader trend: the shift from general-purpose AI tools to specialized applications where expertise matters. This aligns with a pattern of market consolidation in tech, where a few dominant players absorb smaller competitors.
However, the narrative also reveals an implicit assumption: that AI coding is a zero-sum game where only a few can succeed. This overlooks the possibility of niche innovation or paradigm shifts (e.g., open-source alternatives or regulatory changes). The focus on "deep domain expertise" as a moat is valid but could also be a form of gatekeeping—privileging insiders over outsiders who might bring fresh perspectives.
Root cause: The paradigm here is venture capital’s obsession with scalability and defensibility. Salovaara’s stance echoes the dot-com era, where early movers consolidated power, but it ignores how disruptive technologies often emerge from unexpected places. The implication for human agency is mixed: while domain experts gain leverage, the barrier to entry for newcomers rises, potentially stifling creativity.
Bridge questions: What if the next breakthrough in AI coding comes from an outsider, not an insider? How might open-source models or regulatory shifts reshape this landscape? Could the focus on consolidation blind investors to decentralized or community-driven alternatives?
Counterstrike scan: If this were a coordinated influence campaign, the playbook would involve framing AI coding as a closed market to discourage competition while promoting domain-specific startups as the "safe bet." However, the content doesn’t match this pattern—Salovaara’s arguments are specific and grounded in his investment thesis, not a broader manipulation strategy.
Patterns detected: none
Sentinel — Human
The analysis is strongly grounded in attributed quotes and specific portfolio examples, exhibiting a human journalistic style rather than generic AI synthesis.