Israeli AI lacks a “Flywheel”. Hetz Ventures wants to change that

“One of the biggest gaps right now is structural. In cyber, Israel had a clear national strategy - a network effect driven by talent, units, companies, and buyers. That flywheel worked,” explained Hetz Ventures Managing Partner Judah Taub. “In AI, we’re not seeing that same coordination. Many countries are making deliberate moves to lead in AI - you can see it in developing policies and investments. Israel is not. If anything, the thinking around it and the policy here are heading in the wrong direction.”

At Hetz Ventures, early-stage AI investing isn’t about hype or hockey-stick growth projections, it’s about deep tech, fast execution, and founders who think in systems.

Judah Taub

“That said, there’s still world-class talent here. The founders we’re looking to back now are the ones who don’t just build cool tech, but who think in systems,” he explained. “These are people who understand how to plug into global demand, how to move fast, and how to turn technical edge into real commercial advantage.

You can learn more in the interview below.

Fund ID
Name and Title: Judah Taub, Managing Partner
Fund Name: Hetz Ventures
Founding Team: Judah Taub, Pavel Livshiz
Founding Year: 2017
Investment Stage: Early
Investment Sectors: AI, Cyber, Data Engineering and Infrastructure

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?

8 - AI has meaningfully changed how we work day to day, but not in the way people usually talk about. We're still a people-first fund. Early-stage investing is about conviction in the founders, shared values, and the belief that we want to spend years building together. That's not something AI can do.

That said, our entire investment team comes from technical backgrounds, and AI is a core focus of the fund. So we've naturally been quick to adopt and experiment. Some tools are already in use across our internal workflows, from sourcing to technical diligence. We’ve also stayed close to the evolving edge of AI infrastructure and model development, which helps us be sharper partners to the founders we back.

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

We haven’t had AI exits yet, but we expect that to change soon.

All of our most recent investments - literally the latest 10 companies - are deep AI or data engineering startups. AI and data-focused startups make up over half of our portfolio since 2017.

They’re working across security, agents, infrastructure, monitoring, and more. What we’ve seen consistently is that when AI companies work, the growth isn’t incremental. It’s nonlinear and often exponential. The value they bring to market isn’t just a better version of something that already exists. It’s usually a dramatic leap forward.

One thing that stands out is the pace. The speed at which strong AI teams move, especially in the early stages, is on another level. But it also means being globally plugged in is critical. Unlike cybersecurity, where Israel has a large base of top-tier experts and buyers, early-stage AI still depends heavily on what's happening in the US. That’s where we’ve leaned in, by giving our founders direct access to a network of around 70 leading AI practitioners and potential buyers, most of them based in the US.

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

Yes, and it starts with understanding that AI is a technology, not a vertical. Cyber, for example, is a vertical. AI can be applied across nearly every industry - fintech, healthcare, autonomous vehicles, you name it. So the first question we ask is: what kind of AI company is this, really?

We don’t invest in every AI use case, even if it’s very exciting. For example, we don’t do AI for healthcare, despite how important and impactful it might be. Where we do go deep is in infrastructure AI, or in vertical AI where the buyer is someone like a CIO, VP Cloud, or Chief Data Officer. That’s where we know how to evaluate real technical edge, product fit, and go-to-market path, and where we can be genuinely helpful as investors.

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 invest very early, mostly before there’s revenue, and sometimes even before there’s a product. At that stage, traditional KPIs don’t tell you much.

What matters most is the team and whether the company is positioned to move fast once the pieces are in place. We focus on helping founders line up what they’ll need ahead of time. That means early relationships with buyers, strong technical advisors, potential channel partners, and thought leaders who are willing to engage. We also help set them up for the kind of follow-on capital that can scale the company in a serious way.

In the earliest stages, the question isn’t “what’s your ARR” but rather, “can this team build something people will rally behind once it exists?”

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

We’ve led and continue to lead large seed rounds where there’s often no revenue or other meaningful metrics yet. Some of our companies have gone on to raise Series A or even B without similar metrics.

At this stage, valuation is less about spreadsheets and more about conviction. It comes down to the strength of the team, the clarity of the opportunity, and the pace at which they’re able to execute. In AI especially, when the fundamentals are right, things can move fast. We’re comfortable backing that early, even before the numbers show up, as long as we believe the company is building toward something big and defensible.

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

The biggest financial risk in AI right now is how quickly the space is moving. What felt cutting-edge six months ago might already be outdated. This isn't like hardware or mobile, where you get an annual product cycle. In AI, we're seeing major leaps every two to three months.

That pace can create real pressure, ranging from infrastructure costs, to shifting benchmarks, to buyer expectations changing faster than a startup can adapt. It also means a team can execute well and still get leapfrogged. So while the upside is huge, staying relevant is a serious challenge, and one we factor into every investment.

Do you focus on particular subdomains within AI?

We see those as technologies, not domains. We’re very familiar with them (NLP, ML, computer vision, generative models), but we don’t invest just because the tech is interesting. It depends on where and how it's being applied.

For example, we wouldn’t invest in computer vision for surgeons or generative AI for protein folding, even if those are fascinating areas. But we have invested in AI to AI communication, AI-for-developer tooling and for security, where we know we can be highly valuable to the founders.

Our approach is to go deep on the technology, but only invest when it’s applied in markets where we have real conviction and can materially help the company succeed.

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

In my view, people underappreciate the transformative power AI will have, even with all the chatter about how quickly it is advancing.

AI is going to have a massive impact on traditional industries, but probably not the way most large companies are pitching it. A lot of S&P 500 companies are telling the market that adding AI to their existing business will unlock major value. This might be true, however it’s also likely that a small and focused team can potentially disrupt their entire business with an AI-native solution built from the ground up.

We’re especially interested in areas that are research-heavy and where there’s clear inefficiency. Israel has a few very strong teams in these spaces, but the talent pool is still limited. Where Israel really shines is when AI is combined with serious engineering to create something particularly valuable; that’s where we’ve seen the most promising opportunities.

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’m not sure “exit potential” is the right metric. We’re starting to see a new breed of entrepreneurs who aren’t just building to exit, but building to stay. Some of these founders are among the most ambitious and capable Israel has produced.

That said, we’re seeing real momentum in areas like AI infrastructure, developer tools, security-adjacent applications, and data-heavy enterprise use cases. These are domains where Israeli teams bring a strong mix of technical depth and execution speed — and where global buyers are actively looking for innovation. We keep an open mind and are transparent with every team we meet about where we can actually help in a meaningful way.

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?

One of the biggest gaps right now is structural. In cyber, Israel had a clear national strategy - a network effect driven by talent, units, companies, and buyers. That flywheel worked. In AI, we’re not seeing that same coordination. Many countries are making deliberate moves to lead in AI - you can see it in developing policies and investments. Israel is not. If anything, the thinking around it and the policy here are heading in the wrong direction.

That said, there’s still world-class talent here. The founders we’re looking to back now are the ones who don’t just build cool tech, but who think in systems. These are people who understand how to plug into global demand, how to move fast, and how to turn technical edge into real commercial advantage.