“AI can’t replace trust”: Red Dot Capital Partners on the human side of venture capital

“AI is already woven into our daily work - from sourcing companies to drafting memos, helping us process information faster, map markets with more precision, and prepare for meetings with richer context,” said Danielle Ardon Baratz, Partner at Red Dot Capital Partners. “But we’re still only scratching the surface of what’s possible, and we’re continuously adding more internal capabilities.”

Ardon Baratz joined CTech for its VC AI Survey to share what makes the biggest impact when investing in a world full of AI.

“At the end of the day, venture capital is fundamentally about people,” she added. “AI can surface patterns and crunch data, but it can’t replace the trust and conviction that come from sitting with a founder, hearing their story, and judging their ability to win. For us, AI is an amplifier, not a substitute.”

Danielle Ardon Baratz

You can learn more in the interview below.

Fund ID
Name and Title: Danielle Ardon Baratz, Partner
Fund Name: Red Dot Capital Partners
Founding Team: Yaniv Stern and Yoram Oron
Founding Year: 2016
Investment Stage: Early Growth
Investment Sectors: AI, Fintech, Cyber, Enterprise Software, Deep Tech

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year - specifically in terms of the day-to-day work of the fund's partners and team members?

7. AI is already woven into our daily work - from sourcing companies to drafting memos, helping us process information faster, map markets with more precision, and prepare for meetings with richer context. But we’re still only scratching the surface of what’s possible, and we’re continuously adding more internal capabilities.

At the end of the day, venture capital is fundamentally about people. AI can surface patterns and crunch data, but it can’t replace the trust and conviction that come from sitting with a founder, hearing their story, and judging their ability to win. For us, AI is an amplifier, not a substitute.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

We haven’t had a major AI exit yet - most of our portfolio companies in the space are still scaling. But we’re starting to see a clear pattern in the strongest ones: they don’t just build impressive tech, they solve a real problem inside a workflow, and they think commercially from day one. They manage inference costs early, build defensibility through data or distribution, and show the discipline to turn pilots into long-term deployments.

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

Our evaluation framework for AI startups builds on the same fundamentals we apply to all of our early growth investments - team strength, market opportunity, defensibility, and scalability. That said, identifying promising AI startups does introduce additional layers of consideration, especially as many of these companies are exhibiting fast growth earlier on than before.

As an early growth fund, we typically focus on proven market traction and clear paths to operational efficiency. With AI startups, while those elements remain critical, we also pay close attention to whether the company truly owns or controls differentiated data, how they plan to reduce platform dependency on third-party models, and whether their unit economics hold up under enterprise-grade loads.

Another nuance is the balance between innovation and sustainability. Many AI startups can demonstrate impressive early capabilities, but we assess whether those capabilities translate into durable value creation, not just a technological edge that could be quickly commoditized.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

We typically invest in early growth companies that have reached an inflection point in their growth trajectory and are typically between $1-5M in sales. When we evaluate AI companies, we look for signs that the business can grow quickly while building something durable. Beyond the standard financial metrics, we pay attention to how deeply the product becomes part of a customer’s workflow, whether its economics improve as it scales, and how defensible its advantage is over time. The strongest teams show not only early traction but also the ability to turn pilots into lasting deployments, with a clear path to sustainable margins and capital-efficient growth.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

We invest at the early growth stage; therefore, we typically do not value very early-stage AI startups directly. Our focus is on companies that have already reached initial product–market fit, early commercial traction, and scalable business models.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

The biggest risk we see is how quickly the ground is shifting. Infrastructure costs are volatile, regulations are evolving, and new foundation models are released every few months, turning yesterday’s breakthrough into tomorrow’s baseline. Companies that don’t innovate relentlessly risk being leapfrogged by the next wave of players. That’s not just a missed revenue target; it’s the risk of becoming irrelevant.

The companies that proactively address these challenges by building efficient inference pipelines or securing proprietary data moats tend to have more predictable unit economics. We’re also seeing companies like our portfolio company Bria AI take it a step further. Their attribution engine powers a 'data economy' where original creators are compensated for AI-generated outputs. It’s a sustainable moat built through fair value exchange rather than data hoarding, and it directly addresses some of the core risks associated with GenAI adoption.

At the same time, this volatility creates opportunity. The pace of innovation is driving enterprise adoption faster than in any previous cycle. Pilots are moving to production quickly because the ROI is real and the urgency is high. The companies that stay ahead by adapting fast, defending their data advantage, and refining product-market fit again and again can scale at unprecedented speed. These are the founders we back: not just great product builders, but repeat builders who treat constant iteration as their edge.

Do you focus on particular subdomains within AI?

We don’t focus on specific AI subdomains as an investment thesis in itself. Instead, we look at how AI is being applied to create defensibility or accelerate adoption within large markets. What matters to us is whether the company utilizes AI to make the product stickier, improve margins, or unlock new go-to-market opportunities that drive faster scale.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

AI is already starting to reshape traditional industries, not by replacing them, but by rewiring how they operate. In sectors like healthcare, finance, logistics, and manufacturing, the shift isn’t just about automation, but about creating smarter infrastructure through reducing friction, increasing speed, and unlocking new cost structures.

For us, the most exciting companies aren’t building “AI for AI’s sake,” but using AI to solve specific, painful problems in industries where time, trust, and operational efficiency matter most.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

As mentioned above, we believe AI is doing far more than automating tasks - it’s rewiring entire business models and how value is captured. A prime example is how generative AI is enabling unprecedented monetization dynamics: think consumption-based pricing, AI credits, hybrid models, and forcing companies to rethink how they charge for innovation.

Our recent investment in Stigg aligns with that thesis. Traditional billing systems were built for flat subscriptions - the “billing monolith.” But with AI-powered features often billed per API call, per inference, or floating between consumption and subscription, companies need infrastructure that adapts in real time. That’s exactly where Stigg excels: they offer modern, unified monetization for engineering teams that empowers them to launch AI-first pricing and packaging models instantly, without getting bogged down by billing complexity. This capability is vital for companies racing to capture value from AI.

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

AI doesn't change the type of founders we are looking to work with. We’re still backing the same type of people: humble and hungry builders who are clear on what problem they’re solving, obsessed with execution, and honest about the trade-offs. AI doesn’t change that, it just adds complexity. What stands out most today are founders who can stay focused, move fast, and cut through the noise to deliver real value. That’s always been true, and it still is.