“The cybersecurity M&A market remains highly active, and we believe the growing adoption of AI will further accelerate this momentum,” said Oren Yunger, Managing Partner at Notable Capital. “Alongside a new wave of independent cybersecurity startups emerging in the Israeli ecosystem, we anticipate more acquisitions as cyber giants race to both secure AI applications and embed AI into their own platforms.”
Yunger joined CTech for its VC AI Survey to share insights on the state of the investor ecosystem in the era of AI. Notably, how Israel will be impacted and what VCs should look for in Startup Nation.
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Oren Yunger, Notable Capital
(Photo: Notable Capital)
“Much of this M&A activity will likely be talent-driven, with Israel poised for strong outcomes in areas where technical depth is strongest, namely computer vision, diffusion models, and infrastructure optimization,” he added.
You can learn more in the interview below.
Fund ID
Name and Title: Oren Yunger, Managing Partner
Fund Name: Notable Capital
Founding Team: Sarel Eldor, Danny Akerman, Amit Pilowsky
Founding Year: 2000
Investment Stage: Early-to-growth-stage
Investment Sectors: AI, Fintech, Cyber
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?
5 – We’re using AI to support parts of our investment process that are routine or process-oriented, but not as a replacement for the core of our business—our investment decisions and interactions with founders.
AI is particularly helpful in sourcing and initial diligence. We have been experimenting with using online signals to identify promising companies and potential founders since well before ChatGPT, but have greatly improved the effectiveness of those tools since incorporating LLMs that can constantly scan the internet for signals. We also utilize AI to provide us with quick yet in-depth background information, ensuring we come prepared to meetings.
Additionally, it helps us address high-level initial diligence questions, such as understanding the history of different technologies and mapping out competitive landscapes. AI has improved both the number of opportunities we see and the depth of initial research we can conduct on interesting opportunities. Ultimately, venture capital is a people business rooted in conviction, relationships, and long-term belief, and we see AI as a tool to deepen, not replace, the human judgment and trust that define our work.
Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?
Yes—two great examples are Streamlit and Neon, where we led early Series A investments. Streamlit, an open-source framework that made it radically easier for data scientists and AI engineers to build and share custom ML apps, was acquired by Snowflake for around $1 billion. Neon, a serverless Postgres database designed for the modern AI stack, was recently acquired by Databricks for a similar amount.
In both cases, the teams had built strong product-led growth or bottom-up adoption among technical users by solving real pain points, and that grassroots momentum made them highly attractive to strategic acquirers in the AI ecosystem.
Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?
The pace of AI innovation changes how we evaluate both founders and competition. Speed is often the key differentiator, especially in a space where younger founders, even without traditional industry experience, are outpacing legacy teams by building and shipping faster. In this environment, it’s critical to understand not just what a company is building today but how quickly it can adapt as the ecosystem evolves. A great example is Torq, which wasn’t born in the AI era but rapidly evolved its core platform to deliver agentic AI security operations—demonstrating how quickly category leaders can adapt and lead in this new landscape.
At Notable, we’re focused on investing in both infrastructure and application-layer companies solving greenfield problems or unlocking new workflows rather than competing head-on with foundational model providers like OpenAI or Anthropic (where Notable is an investor). The most compelling companies are those that stay close to model advancements and customer pain points and move with urgency to deliver value as the underlying capabilities improve.
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?
Most of the metrics we focus on haven’t changed, but in the AI space, we tend to pay special attention to gross margins and retention metrics. Many AI companies face high COGS due to GPU usage or LLM API spend. That’s not a dealbreaker for us, as some of our best investments, like Vercel, started with lower gross margins, but we do want to understand how margins improve over time.
Since many of the best AI products are being adopted from the bottom up, product velocity remains critical. But just as important, we track revenue and usage retention metrics to ensure that growth is durable and tied to real customer value, rather than initial experimentation.
How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?
Our approach to valuing AI companies is consistent with how we think about any investment: we focus on the size of the opportunity and whether the round will meaningfully position the company for continued momentum. We believe that the opportunity for AI companies is, in many cases, much larger than for non-AI companies. We’re now seeing AI companies hit revenue earlier and grow faster than non-AI companies, which reinforces our conviction in the scale of the opportunity
What financial risks do you associate with investing in AI companies, beyond the usual technological risks?
High COGS is a common risk we evaluate with AI companies, but it’s one we are often comfortable taking, especially when there’s a clear path to margin improvements. Many AI companies employ a bottom-up sales approach rather than traditional software contracts, so understanding retention and engagement is just as important as understanding the cost structure. A notable example is Fal, the leading generative media AI inference platform designed for developers. The company initially had a high cost structure, but as usage scaled and efficiency improved, it achieved more traditional software gross margins. Today, Fal is one of the fastest-growing AI companies, run-rate serving many thousands of customers, demonstrating that execution makes all the difference.
Do you focus on particular subdomains within AI?
We’re most focused on LLM-enabled agents across domains, as well as the infrastructure required to build those agents. We invest heavily in technologies geared for technical personas inside organizations – developers, data practitioners, and security professionals. We constantly seek new use cases being disrupted for these personas.
How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?
We believe AI will fundamentally reshape nearly every traditional industry, and create new ones. In sectors such as healthcare, logistics, and insurance, companies have long struggled to modernize their operations with tools like RPA, often falling short of achieving meaningful automation.
What’s different now is that technologies like LLMs capable of navigating software interfaces, combined with headless browser clouds, are unlocking a new level of process automation that actually works at scale. This shift won’t just improve efficiency—it has the potential to redefine how entire industries operate.
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?
The cybersecurity M&A market remains highly active, and we believe the growing adoption of AI will further accelerate this momentum. Alongside a new wave of independent cybersecurity startups emerging in the Israeli ecosystem, we anticipate more acquisitions as cyber giants race to both secure AI applications and embed AI into their own platforms.. Much of this M&A activity will likely be talent-driven, with Israel poised for strong outcomes in areas where technical depth is strongest, namely computer vision, diffusion models, and infrastructure optimization.
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?
While many of the most successful AI companies globally (OpenAI, Anthropic, Cursor, etc.) have grown rapidly through bottom-up adoption, we’ve found that most AI startups in Israel still lean heavily on traditional software sales motions. That presents a real opportunity. We’re excited about Israeli founders who are not only reimagining how products are built with AI but also optimizing for scale and speed from day one. Founders who pair deep technical insight with novel go-to-market strategies are the ones we are most eager to back today.
