In this interview, Vlad Gozman, CEO of involve.me, discusses incentive misalignment in venture capital, the architectural boundary between generative freedom and production risk, and why compliance discipline shapes sustainable AI systems.
You’ve built inside a venture-backed scale company before. With involve.me, you chose to bootstrap and went unpaid for over two years. What exactly were you protecting by refusing capital this time?
Consider the structural misalignment at the heart of venture capital.
You accept $1M for 20% of your company — a mere 1% of the fund’s $100M pool. You spend nearly a decade building at a below-market salary, skip additional funding rounds, and land a $30M acquisition. Your 80% nets you $24M. Life-changing by any measure.
But the fund needs to return $300M+ to satisfy its LPs. Your deal’s $6M payout covers roughly 2% of that target. It barely registers.
This is where incentives diverge. The fund is fundamentally only optimizing for portfolio-level returns. That means relentless pressure to raise more capital and expand your risk surface. “Hire aggressively.” “Launch the next product.” “Hit every conference.” Each suggestion serves the same logic: push toward a venture-scale exit, even if it jeopardizes your perfectly meaningful $24M one.
Resist that pressure without board control, and you’ll learn quickly who the company actually belongs to.
If you take venture capital, you’re implicitly agreeing to optimize for outlier outcomes at the expense of personal financial certainty. A high-variance game by design.
Before involve.me, you attempted a VR CMS that didn’t gain traction. Was that a failure of timing, distribution, or misreading demand? What permanent scar did that leave on how you evaluate visionary markets?
It was largely a timing and misread demand issue. We started stereosense at a time when VR was heavily hyped and big players like Meta and Apple were pushing the space. At the time it did feel like the next big platform shift, and we even delivered major projects. The interest was there.
But companies didn’t really know what to do with it in a sustained way. VR wasn’t embedded in their core operations. It was exciting but not essential. And unfortunately, fascination doesn’t equal recurring demand.
After running some pilot projects around websites and lead generators, we began to see a clearer, more scalable pain point. That’s when we pivoted and built what eventually became involve.me.
The biggest lesson for me is not to chase hype, but to build products that are essential to business operations and easy to scale. Today, when I look at new technologies, I ask much tougher questions. Is there recurring business behind this? Are we addressing a genuine need or just riding a trend? Would customers feel real operational pain if this solution disappeared tomorrow?
You’ve criticized blind vibe coding in production systems. Where is the exact architectural boundary where generative freedom becomes operational risk?
The boundary is where experimentation turns into dependency. As long as you’re prototyping, generative freedom is fine. But once customers rely on the output, unpredictability becomes risk. That’s when you need structure, not just creativity.
If AI inside involve.me is an orchestrator rather than a builder, what prevents it from mutating business logic over time? Where does determinism reassert control?
There are two safeguards. First, the Agent operates within a predefined, enterprise-ready system of components. It can assemble, but it cannot fundamentally rewrite the code of these components. Second, execution logic, including conditional rules, scoring formulas, and routing mechanisms, remains explicit and rule-based.
Many SaaS companies bolt LLMs onto legacy workflows. What would actually break inside involve.me if you pursued AI velocity over compliance discipline?
In the long run, customer trust would be the first thing to break. Especially here in Europe, compliance and strict adherence to GDPR are non-negotiables. In enterprises in particular, procurement processes are rigorous and the moment you collect customer data, you’re under scrutiny. Some industries are more heavily regulated than others, but the baseline expectations around privacy and data handling are the same across the board.
Sure, if we didn’t have to think about compliance with every product update, we could probably move faster. But that speed would come at big legal and reputational risks.
Because compliance is always in the back of our minds when we build, we’re immediately eligible for more customers, especially in regulated industries. In many cases, that discipline becomes a competitive advantage.
You position zero-party data as strategic in a post-cookie world. At what point does involve.me stop being a form tool and start becoming infrastructure?
It becomes infrastructure the moment it stops being just a simple plug-and-play form builder and starts actively influencing business outcomes. Involve.me measurably lifts conversion rates, increases engagement, raises average order value, and captures meaningful customer insights that companies use to improve their products — that’s infrastructure.
When our customers integrate our AI elements thoughtfully into their customer journeys, the impact goes far beyond surface-level marketing or sales touchpoints. The data collected doesn’t just lie somewhere on a CRM for statistical purposes, but is actively fed into segmentation models, informs product development, and shapes CRM logic. In that sense, the funnel becomes part of the company’s internal intelligence system.
We’ve seen cases where our AI-powered product finders significantly reduced return rates because customers are guided to the right product from the beginning. When personalization works well, people buy more confidently and more accurately.
Your AI credit model suggests careful inference economics. Is that cost control, behavioral governance, or a way to prevent architectural abuse?
For us it’s more of a way to align incentives. We grow in tandem the more the usefulness of our AI Agent grows for the customer.
If inference costs drop dramatically, does SaaS pricing collapse into commoditized AI usage, or does differentiation move elsewhere?
I think pricing compresses, but it doesn’t collapse; it rather migrates.
When inference costs approach zero, the AI layer becomes table stakes. Every SaaS product will have “AI-powered” everything, the same way every product today has “cloud-based” everything. Nobody pays a premium for cloud anymore.
What holds pricing power in a post-cheap-inference world is likely three things. First, workflow lock-in, which means products deeply embedded in how someone works, where the switching cost is behavioral, not financial. Second, proprietary data loops, so compounding datasets like conversion patterns and industry benchmarks that a generic model can’t replicate. Third, trust and accountability, where enterprises will always pay a premium for a vendor who owns the outcome, not just provides a capability.
Operating from Vienna under EU AI regulation, do you feel constrained or structurally advantaged? Would involve.me look riskier if built in Silicon Valley?
I see it as a structural advantage. Being forced early to think about GDPR compliance, consent design, and data infrastructure shaped our architecture differently.
We built guardrails by default. In Silicon Valley, we might have moved faster initially, but we might also have accumulated hidden regulatory risk and not be able to easily operate in some markets.
As models shift from prediction to multi-step reasoning, does the traditional SaaS interface become obsolete? Is no-code eventually replaced by pure intent?
I mean, the vision is seductive in theory: state your intent, AI handles everything, interfaces disappear. And for simple tasks, this will happen. Nobody will manually build a basic form when they can just describe it.
But complex workflows require legibility, not just execution. When a process touches revenue or compliance, people need to see it, adjust it, and trust it. Not just hope a black box got it right.
I don’t believe no-code will die. It will most likely evolve into the governance layer between intent and execution. AI scaffolds, the visual interface lets you inspect and own the logic. The builder becomes an orchestrator and a reviewer.
If AI removes too much friction, users may stop thinking. Do you see a responsibility to design tools that preserve human judgment rather than replace it?
Absolutely. Our goal is to eliminate repetitive assembly work. Marketing teams should still think deeply about positioning, ICP, differentiation, and messaging strategy. The AI should handle layout scaffolding, scoring setup, and formatting tasks. If AI replaces thinking entirely, you get generic templates. If it augments thinking, you get productivity gains and smarter use cases. That distinction is important to us.
Imagine a future where AI agents fill out involve.me funnels on behalf of buyers. When agents negotiate with agents, what part of today’s conversion logic becomes irrelevant?
Surface-level persuasion would matter less, and emotional headline optimization would likely become secondary. If agents start interacting with agents, our customers will need to optimize for LLM logic and agent architectures rather than for human attention spans.
Anything that relies purely on emotional appeal would lose relevance in those interactions, because agents are data driven.
Editor’s Note
This interview explores how capital structure, compliance design, and AI governance intersect in modern SaaS, and why production discipline may outlast generative velocity.
