“The distinction between AI startups and non-AI startups will disappear entirely”

“At Magenta, we see AI not as a passing trend but as a foundational layer that will underpin the next generation of category-defining companies,” explained Ran Levitzky, General Partner at Magenta Venture Partners. “While the initial wave focused on core models and horizontal capabilities, we believe the next phase will be led by applied AI companies that embed intelligence deeply into products, solve specific and valuable problems, and show clear paths to monetization and defensibility.”

The firm joined CTech for its VC AI Survey, where venture capital companies are invited to share insights on artificial intelligence and its expected impact on every aspect of the sector and industry. It is focused on teams that treat AI as a strategic enabler, not just a feature, and ‘who combine technical excellence with sharp execution and commercial discipline’.

Ran Levitzky

“In the coming years, the distinction between AI startups and non-AI startups will disappear entirely,” he added. “The winners will be those who know how to build AI-native products that scale, deliver measurable value, and adapt fast in a rapidly evolving ecosystem. Israel, with its unique mix of talent, resilience, and global ambition, is well-positioned to lead in this transformation.”

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Fund ID
Name and Title: Ran Levitzky, General Partner
Fund Name: Magenta Venture Partners
Founding Team: Ran Levitzky, Ori Israely, Mitsui & Co.
Founding Year: 2019
Investment Stage: Series A
Investment Sectors: AI, FinTech, Cyber, Mobility, Healthcare, Supply Chain, Vertical SaaS, 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?

7 - We leverage AI across our entire workflow. Our custom GPT acts as a virtual agentic associate, helping assess companies in our dealflow and evaluate potential investments. We apply AI to analyze the environments surrounding our portfolio companies, enabling us to deliver deeper strategic value. AI copilots assist in identifying trends across industry benchmarks, business models, and other relevant signals. We also use generative AI for content creation, including social media, investor updates, and broader communications.

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

It’s still early for us to see a full exit from a pure AI company, but many of our portfolio companies have already embedded AI into their core strategy and are demonstrating clear business impact. AI capabilities are driving new monetization opportunities through enhanced product tiers and improving margin profiles across several sectors. We’re also seeing stronger sales efficiency, shorter sales cycles, and improved customer retention. Notably, companies leveraging AI effectively are showing a meaningful increase in ARR per employee, reflecting both operational leverage and disciplined execution. Tre below:

Fund ID
Name and Title: Ran Levitzky, General Partner
Fund Name: Magenta Venture Partners
Founding Team: Ran Levitzky, Ori Israely, Mitsui & Co.
Founding Year: 2019
Investment Stage: Series A
Investment Sectors: AI, FinTech, Cyber, Mobility, Healthcare, Supply Chain, Vertical SaaS, 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?

7 - We leverage AI across our entire workflow. Our custom GPT acts as a virtual agentic associate, helping assess companies in our dealflow and evaluate potential investments. We apply AI to analyze the environments surrounding our portfolio companies, enabling us to deliver deeper strategic value. AI copilots assist in identifying trends across industry benchmarks, business models, and other relevant signals. We also use generative AI for content creation, including social media, investor updates, and broader communications.

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

It’s still early for us to see a full exit from a pure AI company, but many of our portfolio companies have already embedded AI into their core strategy and are demonstrating clear business impact. AI capabilities are driving new monetization opportunities through enhanced product tiers and improving margin profiles across several sectors. We’re also seeing stronger sales efficiency, shorter sales cycles, and improved customer retention. Notably, companies leveraging AI effectively are showing a meaningful increase in ARR per employee, reflecting both operational leverage and disciplined execution. These companies share a strong alignment between AI use cases and real customer needs, coupled with product-led teams that move quickly and prioritize measurable business outcomes.

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

Yes, evaluating AI startups is meaningfully different, especially when considering the product, competitive positioning, and the founding team’s ability to turn AI into a lasting advantage. We look at whether AI is core to the product’s differentiation and if it creates a moat through performance, user impact, or speed of execution that cannot be easily copied. We assess how well the team can design and evolve AI-driven features that are deeply integrated into the product experience, not just layered on top. There is also a clear distinction between evaluating foundational AI infrastructure companies and AI-enabled vertical SaaS companies as each demands a different lens in terms of scalability, go-to-market, and defensibility.

Over time, we believe the term "AI startup" will become irrelevant, as every successful company will need to be AI-native at its core. The real question will shift from whether a startup uses AI, to how intelligently and strategically it does so.

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?

When assessing a potential AI company at the Series A stage, we focus on core financial indicators like revenue growth, gross margin potential, customer retention, and sales efficiency, while recognizing that many of these may still be in early stages. What matters most is how AI is expected to influence these metrics over time. We pay close attention to the assumptions around how AI will drive monetization, support prichese companies share a strong alignment between AI use cases and real customer needs, coupled with product-led teams that move quickly and prioritize measurable business outcomes.

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

Yes, evaluating AI startups is meaningfully different, especially when considering the product, competitive positioning, and the founding team’s ability to turn AI into a lasting advantage. We look at whether AI is core to the product’s differentiation and if it creates a moat through performance, user impact, or speed of execution that cannot be easily copied. We assess how well the team can design and evolve AI-driven features that are deeply integrated into the product experience, not just layered on top. There is also a clear distinction between evaluating foundational AI infrastructure companies and AI-enabled vertical SaaS companies as each demands a different lens in terms of scalability, go-to-market, and defensibility.

Over time, we believe the term "AI startup" will become irrelevant, as every successful company will need to be AI-native at its core. The real question will shift from whether a startup uses AI, to how intelligently and strategically it does so.

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?

When assessing a potential AI company at the Series A stage, we focus on core financial indicators like revenue growth, gross margin potential, customer retention, and sales efficiency, while recognizing that many of these may still be in early stages. What matters most is how AI is expected to influence these metrics over time. We pay close attention to the assumptions around how AI will drive monetization, support pricing strategy, or create stickiness through differentiated outcomes.

For AI-specific considerations, we look at early signals such as adoption and usage rates of AI features, and how those are projected to impact conversion, expansion, or retention. We also examine the cost and scalability of delivering AI-driven value, including inference or infrastructure costs relative to the unit economics. While some data may still be directional at this stage, we look for a clear, credible path showing how AI moves the business forward in ways that are both measurable and defensible.

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

When we evaluate early-stage AI startups, they typically have less than one million in ARR, so we place strong emphasis on team quality, product differentiation, and the strategic role AI plays in creating long-term value. We look for early signs of customer traction, whether through paid pilots, strong engagement, or clear willingness to pay, and assess how AI contributes to pricing power, retention, and overall business scalability.

Unlike in earlier hype cycles, we believe disciplined investors should still anchor valuation in reasonable multiples on actual or near-term revenue. While we recognize the long-term potential of breakthrough AI technology, we avoid inflated valuations that are unlikely to be justified by business performance. Our approach balances ambition with pragmatism, focusing on companies where strong technology is matched by clear commercial thinking and a realistic path to scale.

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

Beyond core technological risks, we see several financial risks that are particularly relevant to AI companies. One key area is infrastructure cost - AI workloads can be computing strategy, or create stickiness through differentiated outcomes.

For AI-specific considerations, we look at early signals such as adoption and usage rates of AI features, and how those are projected to impact conversion, expansion, or retention. We also examine the cost and scalability of delivering AI-driven value, including inference or infrastructure costs relative to the unit economics. While some data may still be directional at this stage, we look for a clear, credible path showing how AI moves the business forward in ways that are both measurable and defensible.

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

When we evaluate early-stage AI startups, they typically have less than one million in ARR, so we place strong emphasis on team quality, product differentiation, and the strategic role AI plays in creating long-term value. We look for early signs of customer traction, whether through paid pilots, strong engagement, or clear willingness to pay, and assess how AI contributes to pricing power, retention, and overall business scalability.

Unlike in earlier hype cycles, we believe disciplined investors should still anchor valuation in reasonable multiples on actual or near-term revenue. While we recognize the long-term potential of breakthrough AI technology, we avoid inflated valuations that are unlikely to be justified by business performance. Our approach balances ambition with pragmatism, focusing on companies where strong technology is matched by clear commercial thinking and a realistic path to scale.

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

Beyond core technological risks, we see several financial risks that are particularly relevant to AI companies. One key area is infrastructure cost - AI workloads can be compute-intensive, and without careful architecture and optimization, high inference or training costs can erode margins as the business scales. Another risk is dependency on third-party models or platforms, where pricing changes, access restrictions, or policy shifts can materially impact unit economics and roadmap execution.

We also pay close attention to regulatory risk, especially in sectors like healthcare, finance, and defense, where AI-driven products may face long and uncertain validation cycles or compliance hurdles that delay revenue. In some cases, uncertainty around IP ownership or the use of third-party training data introduces legal exposure that could translate into financial liabilities. We underwrite these risks carefully, especially at the Series A stage, and prioritize companies that demonstrate a clear understanding of how to build AI-native products with sound business foundations.

Do you focus on particular subdomains within AI?

We focus on applied AI opportunities where the technology delivers a tangible product and business value. Our interest spans generative AI in vertical domains, natural language interfaces that simplify complex workflows, and computer vision for industrial, security, and automation use cases. We also actively look at AI solutions in supply chains, where predictive and optimization tools can drive operational efficiency, as well as horizontal platforms that empower developers, analysts, or non-technical users across industries. In parallel, we are increasingly drawn to startups addressing the new challenges that AI adoption creates for enterprises - such as model governance, compliance, observability, and responsible deployment at scale. Across all these areas, we prioritize teams that pair deep technical expertise with experienced executioners who can translate innovation into scalable, commercially viable products.

How do you view AI’s impact on traditional industries? Are e-intensive, and without careful architecture and optimization, high inference or training costs can erode margins as the business scales. Another risk is dependency on third-party models or platforms, where pricing changes, access restrictions, or policy shifts can materially impact unit economics and roadmap execution.

Physical AI is emerging as a compelling opportunity, where Israthere specific AI technologies you believe will be especially transformative in certain sectors?

We see AI driving fundamental change across traditional industries by rethinking core workflows, improving efficiency, and enabling new business models. This is already evident across our portfolio. At Workiz, AI powers "Jessica," a virtual voice dispatcher that automates scheduling and customer interaction for field service teams, boosting efficiency and professionalism in a high-friction operational environment. Onebeat applies AI in retail to optimize inventory allocation and real-time merchandising, helping retailers respond dynamically to demand and increase margins. Sensos brings intelligence to logistics and supply chains, using AI to enable predictive tracking, risk monitoring, and real-time visibility for global operations.

We believe technologies like generative AI, computer vision, and domain-specific natural language models will continue to be especially transformative in industries such as logistics, retail, healthcare, and financial services. The most impactful solutions are those that embed AI deeply into existing workflows and deliver measurable ROI in complex, real-world environments.

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?

We see strong exit potential across a wide spectrum of AI-driven sectors in Israel, supported by a combination of deep technical expertise and strong execution. Cybersecurity continues to be a standout area, where AI is enabling more adaptive and proactive threat detection, creating real differentiation in a crowded global market. Fintech is another domain seeing strong momentum, with AI powering smarter decision making, automation of complex workflows, and better risk management.

Physical AI is emerging as a compelling opportunity, where Israeli startups are building systems that combine perception, reasoning, and real-world interaction. These technologies are gaining traction in environments that demand high levels of autonomy, precision, and reliability.

In parallel, we see increasing activity in emerging white spaces where AI can transform legacy processes and bring step changes in productivity and insight. There is also growing demand for tools that support AI governance, monitoring, and responsible deployment at scale. Israeli teams are particularly strong at executing in these areas, combining technical depth with a global, product-driven mindset that positions them well for meaningful outcomes.

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?

There’s a gap in the Israeli AI landscape when it comes to core infrastructure and foundational model efforts. While AI21 Labs has made impressive strides, I wish we had a few more companies in the spirit of Mistral or Anthropic emerging from Israel. Most of the local focus has been on applications, but the ambition to build low-level AI infrastructure is still limited. This is a missed opportunity, especially considering the strength of Israeli engineering talent and the way companies like NVIDIA are already tapping into it to build the infrastructure powering global AI workloads.

Looking ahead to the next phase of this big AI cycle, it's strategically important for Israel to ensure its innovation ecosystem covers the entire AI stack so we remain agile and well-positioned to capture opportunities at every layer of the stack, particularly when it comes to global tech collaboration and synergies.

On the AI founders aspect, we’re especially looking to back teams that combine deep technical expertise with a proven ability to execute on go-to-market in their eli startups are building systems that combine perception, reasoning, and real-world interaction. These technologies are gaining traction in environments that demand high levels of autonomy, precision, and reliability.

In parallel, we see increasing activity in emerging white spaces where AI can transform legacy processes and bring step changes in productivity and insight. There is also growing demand for tools that support AI governance, monitoring, and responsible deployment at scale. Israeli teams are particularly strong at executing in these areas, combining technical depth with a global, product-driven mindset that positions them well for meaningful outcomes.

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?

There’s a gap in the Israeli AI landscape when it comes to core infrastructure and foundational model efforts. While AI21 Labs has made impressive strides, I wish we had a few more companies in the spirit of Mistral or Anthropic emerging from Israel. Most of the local focus has been on applications, but the ambition to build low-level AI infrastructure is still limited. This is a missed opportunity, especially considering the strength of Israeli engineering talent and the way companies like NVIDIA are already tapping into it to build the infrastructure powering global AI workloads.

Looking ahead to the next phase of this big AI cycle, it's strategically important for Israel to ensure its innovation ecosystem covers the entire AI stack so we remain agile and well-positioned to capture opportunities at every layer of the stack, particularly when it comes to global tech collaboration and synergies.

On the AI founders aspect, we’re especially looking to back teams that combine deep technical expertise with a proven ability to execute on go-to-market in their domain.