“Today, every investment is also evaluated through a GenAI lens”

“Today, every investment is also evaluated through a GenAI lens,” says Lior Litwak, Managing Partner at Glilot+. Speaking in CTech’s VC AI Survey, he highlights how AI has transformed the fund’s strategy and blurred the lines between traditional and tech-driven domains.

“No one can afford to ignore these developments, and companies must leverage them to perform their mission better. In the seed stage, most pitches now involve solving problems using GenAI, whether in cyber or B2B SaaS. Every company today is, in some way, an AI company, so there are no purely traditional investment domains anymore,” he added.

Lior Litwak

You can learn more in the interview below.

Fund ID
Name and Title: Lior Litwak, Managing Partner, Glilot+
Fund Name: Glilot Capital Partners
Founding Team: Kobi Samboursky, Arik Kleinstein
Founding Year: 2011
Investment Stage: Seed, Series A
Investment Sectors: AI, Cyber, B2B SaaS

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?

10 - AI has pushed the market to rethink how software integrates within organizations. The last comparable revolution was the shift to cloud computing, which significantly changed how software is developed, priced, and distributed. That cloud revolution took about a decade to see widespread adoption in the business world. In contrast, the adoption rate and impact of AI technologies are much higher, with serious attention being paid across all layers, including security. Organizations are eager to adopt new AI-based capabilities both to create better security solutions and to protect the newly adopted technologies themselves.

Nevertheless, there is still a lot of hype and uncertainty, it’s not yet possible to point on the winning technologies since the technology is evolving rapidly, concepts are shifting, capabilities are improving weekly – so these innovations are constantly top-of-mind for us.

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

Not yet, but I can say we believe strongly in the space of securing AI and Generative AI operations within organizations. We invested in Noma, which has recently raised a whopping $100M Series B thanks to its rapid widespread adoption. While other comparable companies such as Protect AI and Apex Security have been acquired already, we believe Noma is leading this market and will become a tremendous investment for Glilot. We believe that the first wave of AI security companies that exited early did so before the market was able to fully evolve. Those early exits were mainly technology acquisitions for talented teams rather than established business models. Noma had a broader AI security vision from the start, and addresses not only Generative AI but all machine learning operations and supporting data infrastructure.

We have other portfolio companies that leverage AI capabilities, such as LayerX, Cyolo, and Shift, to name a few.

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

Today, every investment is also evaluated through a GenAI lens. No one can afford to ignore these developments, and companies must leverage them to perform their mission better. In seed stage, most pitches now involve solving problems using GenAI, whether in cyber or B2B SaaS. Every company today is, in some way, an AI company, so there are no purely traditional investment domains anymore.

We place extra emphasis on assessing the company’s technical moat, whether its tech is hard to replicate quickly, and whether it offers long-term differentiation, not just from current competitors but also from those likely to emerge in the next 18 months. Many companies launched in the past 6-12 months are already disrupting those founded only two or three years ago. We want to make sure we invest in teams that offer solutions built from the ground up for the new age of GenAI.

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?

Since every company today should have AI capabilities, the metrics are not fundamentally different. We want to see accelerated development speed, including the ability to leverage advanced development environments like Cursor, and we naturally care about time to market and growth thereafter.

For later-stage investments with established business models, we also examine whether the product effectively leverages AI in a way that does not harm profitability, given the non-trivial costs of LLMs. Just as in the past, we wanted to ensure companies weren’t unsustainably reliant on cloud services, today we look for AI strategies that are cost-effective.

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

We primarily look for exceptionally strong teams. At the stages we invest in, valuations are driven more by market conditions and comparable deals than by revenue multiples. We still believe that early seed rounds are largely about building the best team first and foremost; AI has not changed that.

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

Innovation cycles are moving faster than ever, creating, on one hand, opportunities, but also aggressive competition. Large tech and cloud players have a significant advantage due to their resources, data access, and customer bases, positioning them well in the race toward GenAI advances (including, ultimately AGI). The risk is that today’s tech giants are in an even stronger position than they were a decade ago, during the cloud era, to dominate the field.

Do you focus on particular subdomains within AI?

As a cyber-focused fund, we naturally look for startups leveraging AI capabilities to better protect organizations, assuming that attackers themselves are using the most advanced AI innovations to improve both the speed and scale of attacks. To complement that, we look for companies, like Noma, that can strengthen the security posture of AI data and workloads within enterprises.

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 impacting all sectors, and its adoption in traditional industries will continue to accelerate. In Israel, we expect especially transformative applications in cybersecurity, defense technologies, and operational efficiency improvements across multiple verticals.

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?

Israel has been and will remain a global leader in cyber, with world-class talent and engineering capabilities. Contrary to some perceptions, in my view, Israel is not falling behind in AI. Many of Israel’s tech successes have come from engineering excellence rather than pure scientific breakthroughs. We expect successes in AI to emerge from Israel’s ability to engineer high-quality, well-optimized products that solve real problems. In particular, we see strong potential in AI-powered cybersecurity and defense tech, which has regained attention following Israel’s recent military successes.

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?

We see very few Israeli teams working on foundational models from scratch. Aside from one key Israel figure, Ilya Sutskever (who isn’t building his company here), and AI21 perhaps, there is no Israeli team that really threatens the market leadership of companies such as OpenAI or Anthropic. We are more focused on backing strong engineering-driven teams that can adapt foundational GenAI models into effective, defensible products – and that’s ok.