When asked if there are niches where he believes Israeli startups particularly excel, Ori Striechman believes that “most domains” make the cut. But according to the investor, there lies a more interesting question: “Which trends could mint the first Israeli Hectocorn?”
“I’m very bullish on the intersection of infrastructure and AI,” Striechman told CTech. “Unlike other regions that are strong in B2C and viral expansion, Israel excels in enterprise sales and deep tech, combining that with the maturity of our ecosystem and the shared expertise among founders, and that makes those areas particularly interesting to me as well.”
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Orri Streichman, Swiss Ventures
(Photo: Nadav Margalit)
Swish Ventures partners with few ‘exceptional’ founders each year in their journey to build generational companies, it believes in a concentrated approach and to target larger outcomes over quicker exits. The fund invests on behalf of leading endowments, Sequoia, pension funds and more with $150M under management.
Fund ID
Name and Title: Ori Striechman, Investor
Fund Name: Swish Ventures
Founding Team: Omri Casspi, Dana Alexandrovich, Ori Striechman
Founding Year: 2022
Investment Stage: Early stage
Investment Sectors: AI, Cyber, Infra
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?
9 – At Swish we partner with a select few exceptional founders every year on their journey to build generational companies, we focus our resources and work closely with them. AI lets us execute it better. And for a boutique fund like ours, AI already reshapes daily work: it helps draft memos, checks numbers, speeds up research, and takes care of daily administrative work. The time we save goes straight into working closely with our founders.
Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?
Success is measured by significant outcomes like IPOs and massive acquisitions, not by quick and small exits. We can see our companies compounding value at an incredible pace. One great example is Eon, an amazingly talented team of repeat founders attacking a core data-and-AI infrastructure problem. They achieved unicorn status in just nine months after inception and are still gaining massive speed. When an exceptional team captures a fundamental need in a big market and keeps executing, its valuation and revenue can explode faster than ever before.
Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?
If I have to be honest, there are no longer any traditional domains – everything is impacted by AI. I would categorize evaluation into two main areas: (a) For AI startups addressing complex infrastructure challenges, the assessment follows traditional methods, with AI serving as a compelling “why now” factor. (b) For AI Agents performing previously unimaginable workloads (such as coding agents), we develop mental models to understand the market. If we believe it’s a promising market, the evaluation then focuses on identifying the strongest team that can build moats and execute rapidly. Actually, that’s a key point for the AI era, companies must execute faster than ever before, and that’s another key factor we consider.
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?
This question is more relevant to the growth funds that invest in the later rounds of our companies when there is a mature product, but at the early stages, we look at the strength of the founding team and the scale of the addressable market rather than at traditional financial metrics. We’re especially interested in indicators and metrics that reflect expansive market dynamics and the potential for breakout growth.
How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?
In my opinion, early-stage valuation is almost irrelevant in the AI era. When we talk about companies that may reach valuations of tens of billions, it’s far more important to select the right team pursuing the right idea. At Swish, we focus on identifying the right teams and ideas that will make a significant impact, rather than on quick exits. The valuation of a company is a function of how quickly it can continue to grow, and for the type of AI companies we invest in, the answer is very quickly.
What financial risks do you associate with investing in AI companies, beyond the usual technological risks?
The greatest risk facing AI companies is commoditization. For most new ideas, competitors emerge almost instantly, and the market can be unforgiving to those who fall behind.
Do you focus on particular subdomains within AI?
We focus on the business outcomes, and not on the specific technologies. We love solutions for enterprises, particularly in data-and-AI infrastructure (backup, cost efficiency, data pipelines), cybersecurity, and AI enhancing the workforce (system integrators, pentesting, coding agents). These are massive markets that will look completely different with AI, and Israeli startups have a proven track record of excellence in enterprise solutions, and we know that once they have the right product, they’ll excel in the underlying technology.
How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?
I think that AI will massively reshape traditional industries and create significant global value, and those who fail to adapt may be left behind. Within AI, I’m especially excited about reinforcement learning (RL). The best way to think about it is that RL allows you to solve any benchmark, which is quite crazy to think about, and means that if you have a clean set of environments and a good feedback loop to decide what’s correct and what’s not, then you can train a model with RL to solve that.
A good demonstration is through the game of chess, which is close to my heart. Earlier computer chess programs used the min-max algorithm and were quite good; however, the next significant development came with AlphaZero, the first chess program trained with RL. It introduced many original ideas unknown to humans and quickly became the best chess program ever. This shift from imitating the smartest humans to innovating completely new strategies is the power of RL. If RL can help create new strategies in coding, math challenges, and other areas, it will be incredibly interesting and valuable.
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?
I think the answer is “most domains,” but a more interesting question is: which trends could mint the first Israeli Hectocorn?
I’m very bullish on the intersection of infrastructure and AI. Unlike other regions that are strong in B2C and viral expansion, Israel excels in enterprise sales and deep tech, combining that with the maturity of our ecosystem and the shared expertise among founders, and that makes those areas particularly interesting to me as well.
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?
Israel’s smaller size has historically meant we lagged in infrastructure and areas needing big upfront investments. However, we have world-class academic research, with pioneers like Noam Shazeer and Ilya Sutskever leading global breakthroughs. It’s important to keep building on our strengths and nurturing our AI talent, including researchers and engineers. I’ve seen many talented individuals, who could become top AI experts, often pursuing other fields.
For founders, that just made us focus more on finding the best ones that are after massive markets.