The AI funding frenzy actively destroys innovation. This will annoy my peers, but someone needs to say it.
According to PitchBook data, AI startups claimed over 52.5% of all venture capital deployed globally in 2025. American VCs directed 62.7% of their dollars toward AI firms last quarter. This represents groupthink at an unprecedented scale.
The result? Alternative, but still, genuinely transformative technologies die on the vine.
What Gets Lost in the Feeding Frenzy
I recently spoke with a founder building advanced robotics for sustainable manufacturing. Brilliant technology. Clear market need. Customers ready to pay. But because her pitch led with “precision manufacturing automation” instead of “AI-powered,” she couldn’t get meetings.
The irony? Her system uses machine learning extensively. The AI serves as infrastructure. The innovation lies in solving precision manufacturing at scale. That nuance disappears when investors treat “AI” as a magic keyword that absolves them from understanding actual problems.
This is how markets miss entire categories of breakthrough technology.
Following my problem-first investment framework, I’ve watched companies solving fundamental problems: clean energy production, industrial efficiency, infrastructure resilience all struggle to raise seed rounds while vaporware “AI platforms” raise nine figures on PowerPoint decks.
The market operates backwards.
Why This Time Actually Is Different (And Worse)
Every investment cycle has its darlings. Dot-com in 2000. Mobile-first in 2011. Blockchain in 2017. The AI bubble operates at a different scale with different consequences.
The AI monopoly comprehensively starves everything else. EY’s analysis found AI companies drove over 70% of all VC activity in Q1 2025. Non-AI companies compete for the remaining 30% while fighting for investor attention that has moved elsewhere entirely.
This creates second-order effects previous bubbles didn’t produce:
- Talent drain – The best engineers migrate toward where capital flows, regardless of problem importance. We’re seeing PhDs abandon climate tech research to build the 47th generative AI wrapper.
- Timeline mismatch – AI companies raise on potential. Deep-tech companies must prove feasibility first. When capital concentrates in one category, the entire ecosystem optimizes for that category’s success metrics. Patient capital disappears.
- Narrative lock-in – Once “AI” becomes the filter for what’s fundable, founders reframe legitimate non-AI innovations as “AI-powered” just to get in the door. This creates selection pressure for founders willing to mislead rather than founders solving hard problems honestly.
What Romanos Vetridis and Team Are Doing About It
I built Ruvento SEED to fund technologies solving real problems regardless of their AI positioning. This stems from practical necessity.
The robotics companies focused on manufacturing problems need capital. IoT infrastructure solving resource optimization deserves funding. Clean energy systems that happen to use machine learning require patient investors who understand their actual value proposition.
These technologies will matter more in 20 years than most current billion-dollar AI rounds. They require investors who understand problems deeply enough to evaluate solutions independently of funding narratives.
That’s increasingly rare.
Through our cross-border initiative, we build infrastructure that helps deep-tech founders access resources while maintaining their actual value proposition. Fast-track commercialization pathways connect Asian and European ecosystems. Talent networks value domain expertise over AI credentials. Institutional partnerships focus on problem validation rather than technology categorization.
The Uncomfortable Truth About AI Returns
Most AI investments will generate terrible returns. Nobody wants to say this publicly, but the math speaks clearly.
AI transforms industries. That part holds true.
The problem emerges when 63% of all venture capital chases the same category. You stop investing in innovation and start investing in distribution scarcity. The handful of companies that win will win enormously. The vast majority will return nothing.
Traditional diversification logic suggests investors should spread capital across multiple categories to manage this risk – into commodities, etc.
Instead, the opposite happens: concentration into a single narrative because missing the winners looks catastrophic.
This represents career preservation masquerading as investing. Passing on the next OpenAI makes you look stupid. Funding a robotics company that takes eight years to prove out makes you look patient—behind the curve in industry parlance. The incentive structure rewards following the herd even when the herd operates on flawed premises.
I can afford to take a different view because I spent 15+ years building companies before investing. I know what actual technology development looks like. I know that the best opportunities often hide in the gaps that popular narratives create.
My portfolio results from Point One Navigation to Solugen validate this: problems persist while solutions—and funding narratives—evolve. When you invest based on problem understanding rather than category popularity, you can support founders through the pivots that destroy narrative-driven companies.
What Founders Should Do
You have two paths forward if you’re building deep tech outside the AI story.
Option 1: Reframe your pitch. Call everything “AI-powered.” Emphasize the machine learning infrastructure even when it serves as commodity technology. Play the game investors want.
Some founders will need this approach. Getting funded beats maintaining messaging purity.
Option 2: Find investors who understand your actual problem and get excited about solutions rather than keywords.
We exist, though we’re rarer than we should be. We ask about your manufacturing process challenges before your model architecture. We focus on your customer’s workflow problems ahead of your training data specifications.
For founders in Southeast Asia building robotics, IoT, clean manufacturing, or autonomous systems—technologies that incorporate AI as infrastructure while solving fundamental problems—Ruvento SEED was built for you.
We provide capital, operational expertise, and global infrastructure to help you succeed from seed stage forward, based on the problems you solve.
Where This Ends
Markets correct. Always. The AI feeding frenzy will end the same way previous bubbles ended—with a reckoning when reality meets valuation.
The correction itself matters less than what gets destroyed along the way.
Promising technologies that could have scaled with patient capital will run out of runway. Founders who could have built transformative companies will give up or pivot to whatever the next narrative demands. Research that should have reached commercialization will stay locked in university labs.
That represents the real cost of the AI monopoly. Venture capital can absorb wasted money on overfunded AI companies. The cost shows up as opportunity: all the technologies we could have built while capital and attention concentrated elsewhere.
I’m placing bets that when this cycle ends, the technologies solving real problems will matter more than the ones that fit the narrative.
If you’re building something real, let’s talk.
Romanos Vetridis is a Partner at Ruvento Ventures, investing in AI, robotics, IoT, and emerging technologies that reshape industries. He built technology companies for 15+ years before investing and operates across Silicon Valley, Europe, and Asia.
References
- BestBrokers.com. (2025). “The State of AI Venture Capital in 2025.” https://www.bestbrokers.com/forex-brokers/the-state-of-ai-venture-capital-in-2025-ai-boom-slows-with-fewer-startups-but-bigger-bets/
- Technology.org. (2025). “AI Startups Claim 63% of Total Venture Capital Money in 2025.” https://www.technology.org/2025/10/06/ai-startups-claim-63-of-total-venture-capital-money-in-2025-non-ai-companies-struggle/
- EY. (2025). “Major AI deal lifts Q1 2025 VC investment.” https://www.ey.com/en_us/insights/growth/venture-capital-investment-trends
