AI has “significantly enhanced operational efficiency” in the VC space, says Israeli Mapped in NY

“AI has significantly enhanced operational efficiency, particularly in deal sourcing, due diligence, and market analysis,” said Guy Frankin, Founder and Managing Partner of Israeli Mapped in NY. “I use AI-driven tools to evaluate technical depth and identify emerging trends.”

Franklin joined CTech for its new “VC AI Survey”, which explores how the technology is impacting the investment space. He shared how he also applies AI to improve “the mapping and coverage of the Israeli tech ecosystem” in New York.

Guy Franklin

“As New York rapidly becomes a center of global AI innovation—with OpenAI’s growing presence and an influx of AI startups—this moment presents a major opportunity for Israeli founders looking to scale,” he added. “I’m committed to connecting these founders with the right investors, partners, and corporates in NYC and helping them navigate this critical market.”

You can read more below:

Fund ID
Name and Title: Guy Franklin, Founder & Managing Partner
Fund Name: Israeli Mapped in NY Ventures
Founding Team: Guy Franklin
Founding Year: 2021
Investment Stage: Seed to Series B
Investment Sectors: AI, Cyber, Healthcare, Enterprise Software

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 significantly enhanced operational efficiency, particularly in deal sourcing, due diligence, and market analysis. I use AI-driven tools to evaluate technical depth and identify emerging trends. Internally, I also apply AI to improve the mapping and coverage of the Israeli tech ecosystem in New York—surfacing new companies, tracking growth, and strengthening founder and investor connections. This has become especially important given the rapid expansion of the AI sector in NYC, including the growing presence of players like OpenAI and major AI startups. While human judgment remains central, AI enables faster and more insightful decision-making, especially when assessing highly technical startups.

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

Yes. Two notable exits include Talon Cyber Security, acquired by Palo Alto Networks, and Gem Security, acquired by Wiz. While these companies were not solely focused on AI, both had significant AI components integrated into their products—Talon developed a secure ChatGPT-based assistant for enterprise environments, and Gem applied AI and machine learning for real-time cloud detection and response. The key characteristics were a clear technological edge, strong product-market fit in high-demand security domains, and founding teams with deep technical and operational expertise.

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

Yes. With AI startups, technical validation and understanding data moats are critical early in the process. When needed, I bring in domain-specific experts to help assess models and proprietary data access. Unlike more traditional domains, where traction and customer pipelines weigh more heavily upfront, AI companies require more attention to technical scalability and talent quality—particularly in New York City, where I see Israeli founders entering a highly competitive and rapidly evolving ecosystem.

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?

In addition to standard business metrics like revenue growth, customer acquisition cost, and profit margins, I focus on a few key factors specific to AI companies. These include:

• How accurate and useful the AI model is compared to others in the market
• The cost of running the AI each time it’s used
• How often the model is updated with new data, and how quickly it improves as a result
• Whether the company depends heavily on third-party tools like OpenAI or AWS
• And most importantly, whether the AI helps customers solve real problems and save time or money

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

I assess team strength, IP defensibility, model differentiation, and data advantage. In the absence of revenue, traction is often defined by pilot programs, technical benchmarks, and the ability to attract top-tier AI talent or early design partners.

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

Major risks include:

• High compute and infrastructure costs, especially with generative models
• Heavy reliance on external platforms (e.g., OpenAI, AWS, Nvidia)
• Evolving global AI regulations that could affect data usage and model deployment
• Uncertain IP protection around foundational models
• Lack of explainability, which affects adoption in regulated industries

Do you focus on particular subdomains within AI?

I focus on applied AI in enterprise settings, particularly:

• Generative AI for productivity and security
• AI agents in cybersecurity and DevOps
• Verticalized NLP (legal, healthcare, finance)
• Hybrid AI systems (combining symbolic and neural methods)

This includes tracking and supporting the growing number of Israeli AI startups entering New York’s expanding AI ecosystem.

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 reshaping traditional sectors through automation, decision support, and personalization. For example:

• Generative AI is transforming knowledge work in law, finance, and marketing.
• Predictive models are improving logistics, supply chains, and preventive healthcare.
• Computer vision is advancing retail, manufacturing, and agriculture efficiency

In a city like New York, with strong concentrations of finance, media, and healthcare, I see AI’s impact accelerating in industry-specific SaaS and productivity tools.

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?

Israeli startups excel in AI for cybersecurity, edge computing, and dev-centric productivity. Trends with strong exit potential include:

• AI-native cybersecurity platforms
• LLM security and observability tools
• AI infrastructure layers (data pipelines, model monitoring)
• Verticalized AI agents in compliance and operations

Many of these startups are now expanding to New York, which is becoming a major global hub for applied AI, thanks to its enterprise customer base and the growing influence of OpenAI and leading venture firms in the region.

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?

I see an underrepresentation of AI startups targeting industrial automation, biotech, and consumer applications. I’m especially interested in Israeli founders building agentic AI systems, scalable AI infrastructure, and solutions tailored to regulated industries — particularly those looking to scale in New York City, where I focus much of my investment and ecosystem-building efforts. With NYC emerging as a leading AI hub, I look for founders with deep domain knowledge, strong AI fluency, and global go-to-market instincts from the start.