How AI is reshaping the venture capital landscape

Artificial intelligence is no longer a distant concept - it is a transformative force that is redefining industries and reshaping the rules of innovation and investment. In venture capital, its impact is particularly profound, influencing not only what firms invest in, but how they operate, evaluate, and support the companies in their portfolios.

As part of an ongoing editorial initiative, CTech has launched the VC AI Survey, a series of in-depth interviews with dozens of leading venture capital investors to understand how AI is affecting their business routines, operational strategies, and long-term thinking. This project sheds light on how AI is embedded in the daily workflow of venture capital funds, offering a rare look at how technology is rewriting the internal playbook of the investment world.

Miri Fenton (from right), Shelly Hod Moyal, Nadav Shimoni, and Dorin Baniel.

As part of the survey, CTech reached out to Israeli venture capital firms to understand how the rapid rise of artificial intelligence is reshaping their investment world. In the coming days and weeks, we will be publishing the interviews conducted by CTech's reporters with investors, and we'll also be providing comprehensive overviews of the main insights gleaned from the investors' candid and detailed responses.

Among the core questions: How has AI affected the day-to-day work of VC funds over the past year? Have there been notable exits from AI-driven startups - and what set those companies apart? The survey also examined whether scouting and evaluating AI startups differs meaningfully from traditional sectors, and how investors are adjusting their criteria.

Investors were asked to share the KPIs and AI-specific metrics they rely on when assessing companies with little revenue but strong technological promise. Other topics included financial risks beyond the technical realm such as infrastructure costs, data dependencies, and regulatory uncertainty.

CTech also looked at which AI subdomains are drawing the most investor interest, how AI is impacting legacy industries, and what unique trends are emerging in the Israeli AI ecosystem. Respondents highlighted areas of strength, blind spots in the local market, and the qualities they seek in founders building the next generation of AI-native startups.

Despite recent market volatility and a general slowdown in venture funding, AI-related investments have experienced a dramatic surge. In 2024, global funding for AI companies surpassed $100 billion - an 80% increase from 2023 - accounting for nearly a third of all capital raised worldwide. Within that, generative AI alone attracted close to $45 billion, almost doubling the previous year’s figure. For many investors, AI has become more than a high-growth category - it is a resilient and foundational element of their portfolios, viewed as both a source of innovation and a hedge in uncertain times.

Inside VC firms, AI is already altering the way deals are sourced, analyzed, and managed. Most funds surveyed reported a significant impact on their day-to-day work, rating AI’s influence at levels ranging from 6 to 8 out of 10. Guy Franklin, Founder and Managing Partner of Israeli Mapped in NY Ventures, notes that AI has significantly improved efficiency in processes such as deal sourcing, due diligence, and market analysis. At his firm, AI is used not only to evaluate technical depth and identify trends but also to map the broader Israeli tech ecosystem in New York more effectively.

This operational shift is taking place across multiple layers. In deal sourcing, AI tools are now used to monitor thousands of startups in real time, scanning data from platforms like Crunchbase and LinkedIn, as well as job boards and news sources. Natural Language Processing is applied to suggest promising companies based on market activity, founder backgrounds, and other indicators. The due diligence phase, traditionally labor-intensive and intuition-driven, has been transformed by automated systems that analyze financial models, scan legal documents for red flags, and assess competitors. Ran Levitzky, General Partner at Magenta Venture Partners, describes their custom GPT as a virtual associate that helps screen dealflow and evaluate investment opportunities.

AI is also playing a critical role in decision-making, where it helps investors move beyond instinct by offering fast, data-rich insights that reduce bias. Dorin Baniel, Principal and Head of the EMEA office at NightDragon, describes ChatGPT as an “always-on thought partner,” used in daily tasks such as drafting investment memos, running financial analyses, and composing internal communications. Portfolio management is another area undergoing change. AI platforms now track key performance indicators in real time, flagging anomalies, suggesting growth strategies, and even predicting exit windows. At Arkin Digital Health, AI is used to model investment scenarios and analyze specific companies and sectors more deeply, enabling more effective collaboration with portfolio companies.

Still, the path is not without its challenges. One pressing issue is the shortage of professionals who combine deep technical expertise with business insight. Another is the critical dependency on high-quality data, as flawed inputs lead to poor decisions. Many AI tools remain black boxes with opaque logic, raising concerns about transparency and accountability. Investors are also wary of inflated valuations driven by AI hype, and they are actively seeking tools that help distinguish between companies with real substance and those built mostly on narrative. Meanwhile, growing regulatory scrutiny around data privacy, algorithmic fairness, and cybersecurity is adding further complexity to the landscape and increasing the burden of due diligence.

Identifying strong AI startups requires a different approach than assessing traditional tech companies. While revenue and traction still matter, more weight is placed on the strength of the technical founding team, access to proprietary data, and the defensibility of the intellectual property. Shelly Hod Moyal, Founding Partner at iAngels, emphasizes that her firm looks for companies in which AI is not an accessory but a core enabler of the product. Ran Levitzky from Magenta predicts that the line between AI startups and non-AI startups will eventually disappear altogether.

When evaluating these companies, investors examine not only standard SaaS metrics but also AI-specific dynamics, such as the relationship between model training costs and recurring revenue, the cost and exclusivity of data acquisition, time-to-value for customers, and the scalability and accuracy of AI-driven features. Security, compliance, and enterprise integration are also critical, especially in sectors like healthcare and finance. The efficiency of small teams using AI to achieve results that once required large engineering departments is increasingly viewed as a sign of operational excellence.

Valuing early-stage AI companies, particularly those that have not yet reached significant revenue milestones, is complex. At Maverick Ventures Israel, the focus is on the founding team and their understanding of a specific pain point. Miri Fenton, an investor at the firm, reinforces the idea that people remain at the heart of early-stage investing, regardless of technological context. The emphasis is on founder-market fit, defensible IP, customer urgency, and early validation through pilots or strong engagement. While many firms see long-term potential in AI, they are cautious about hype multiples and prefer to anchor valuations in reasonable financial expectations and a clear path to profitability.

AI-driven companies present new financial and operational risks as well. The high cost of computing infrastructure, especially for generative models, can erode margins. Heavy reliance on third-party APIs or non-exclusive datasets can introduce dependencies that are difficult to manage. Legal uncertainties around IP and data use may delay commercialization or lead to unforeseen liabilities. The market for foundational models is becoming increasingly crowded, and generic solutions can quickly become commoditized. As the competitive landscape evolves rapidly, even companies with strong models may struggle to demonstrate lasting business value.

Nonetheless, successful exits are beginning to emerge. BioCatch, a leader in behavioral biometrics, was acquired for $1.3 billion. Oddity, which integrated deep learning IP from Voyage81, went public with a $4 billion market cap. Other companies, such as Talon Cyber Security and Gem Security, were acquired by giants like Palo Alto Networks and Wiz, thanks to their deep AI integration. Base44 exited in just six months with an $80 million deal. These companies had a few things in common: strong technology, clear product-market fit, deep operational expertise, and a tangible ROI for enterprise clients. In contrast, other firms like Stability AI and Chegg experienced setbacks as they failed to maintain a defensible edge in the face of rapid commoditization. The lesson is clear - success lies not in branding a product as “AI-driven,” but in using AI to create value that cannot be easily replicated.

Venture funds are now focusing on applied AI opportunities that deliver measurable business impact. These include generative AI in specific verticals, conversational interfaces, industrial computer vision, and supply chain optimization. At iAngels, the view is that AI is not a vertical in itself, but a horizontal layer cutting across industries. From foundational models to domain-specific solutions, AI is embedded everywhere.

The impact of AI goes far beyond the tech sector. It is already transforming traditional industries by rethinking workflows, increasing efficiency, and enabling new business models. Shelly Hod Moyal notes that AI may ultimately affect more industries than mobile did, ushering in a new era of personalization, automation, and interface design. In professional services like law, finance, and healthcare, AI is reducing friction and streamlining decision-making. In highly regulated environments, technologies like retrieval-augmented generation, embedded AI copilots, agentic workflows, and continuous learning systems are redefining how tasks are performed. In healthcare, where burnout and cost pressures are high, AI is being adopted to relieve administrative burdens and improve outcomes without disrupting existing workflows.

Israel continues to play a central role in the global AI revolution. With a reputation for technical talent and speed of execution, Israeli startups are thriving in areas such as cybersecurity, defense tech, fintech, and AI infrastructure. They are also making strides in regulated verticals like healthcare and banking, as well as in “physical AI” - systems that interact with the real world in complex environments. The local ecosystem is defined by urgency, ingenuity, and a global mindset, giving founders the ability to do more with less.

Still, some segments remain underserved. There is room for more AI solutions in consumer finance, logistics, agriculture, and products for small and medium businesses. Investors are actively seeking founders who can build with real data advantages from day one, who combine technical skill with go-to-market instincts, and who understand how to navigate regulation while pushing the boundaries of AI autonomy. Independence, execution, and timing are key.

As part of the survey, CTech reached out to Israeli venture capital firms to understand how the rapid rise of artificial intelligence is reshaping their investment world. In the coming days and weeks, we will be publishing the interviews conducted by CTech's reporters with investors, and we'll also be providing comprehensive overviews of the main insights gleaned from the investors' candid and detailed responses.

Among the core questions: How has AI affected the day-to-day work of VC funds over the past year? Have there been notable exits from AI-driven startups - and what set those companies apart? The survey also examined whether scouting and evaluating AI startups differs meaningfully from traditional sectors, and how investors are adjusting their criteria.

Investors were asked to share the KPIs and AI-specific metrics they rely on when assessing companies with little revenue but strong technological promise. Other topics included financial risks beyond the technical realm such as infrastructure costs, data dependencies, and regulatory uncertainty.

CTech also looked at which AI subdomains are drawing the most investor interest, how AI is impacting legacy industries, and what unique trends are emerging in the Israeli AI ecosystem. Respondents highlighted areas of strength, blind spots in the local market, and the qualities they seek in founders building the next generation of AI-native startups.