Blumberg Capital: "Ninety percent of companies we evaluate today are AI-focused"

For Blumberg Capital, artificial intelligence isn’t a niche anymore, but the norm. “Ninety percent of the companies we evaluate today are AI-focused,” says Managing Director Yodfat Harel Buchris, who notes that the firm now judges almost every startup through an AI lens.

“We continue to apply our 6Ts of venture capital: theme, team, technology, terrain, traction, and terms, she explained. “We pay close attention to the team’s technical depth, their understanding of data infrastructure, and their ability to build compounding advantages over time. AI companies with resilience and defensibility win in the long run—not just those with the most novel models.”

Yodfat Blumberg Capital

You can learn more in the interview below.

Fund ID
Name and Title: Yodfat Harel Buchris, Managing Director
Fund Name: Blumberg Capital
Founding Team: David Blumberg
Founding Year: 1991
Investment Stage: Early and growth stage
Investment Sectors: AI

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 - We consider ourselves a data-driven VC firm, and AI is becoming core to how we operate at global scale. With a presence across the U.S. and Israel, and a portfolio spanning North America, Europe, and Israel, we’ve built a proprietary AI algorithm to help us identify and evaluate opportunities across markets more efficiently. Since building a data science team last year, we’ve doubled our deal flow pipeline and are seeing a clear increase in both the quantity and relevance of inbound opportunities. Beyond sourcing, we’re actively testing AI tools across HR, recruiting, marketing, and internal workflows — all aimed at helping us operate smarter and deliver more value to our founders.

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

Blumberg Capital has invested in data-driven companies for decades.

We invest in B2B SaaS companies using AI and other data-driven technologies across enterprise software, fintech, data analytics and infrastructure, supply chain and mobility, healthtech, and cybersecurity. In 2024, among the companies presented to our investment committee for in-depth evaluation, nearly 90% leverage AI in their core product. In a 2024 survey, around 90% of Blumberg Capital portfolio companies said they use or plan to use AI in their core products.

We have a strong track record investing early in pre-revenue companies, leveraging AI to deliver meaningful value to customers.

Examples Include:

• Braze: utilizes AI for personalized marketing campaigns, analyzing user data to optimize engagement
• DoubleVerify: applies AI to ensure digital media quality and effectiveness, verifying ad placements and viewership
• Nutanix: helping enterprises with their AI transformation with infrastructure that supports control, privacy, and security.
• SeaLights: Acquired by Tricentis in 2024. SeaLights is not a general AI research or model provider, but a software quality intelligence platform that leverages AI and machine learning to make software testing smarter and more efficient.
• Cybellum (Acquired by LG in 2021): Cybellum’s GenAI assistant helps streamline product security processes by automating tasks such as risk management, evidence creation, and vulnerability monitoring.

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

Ninety percent of the companies we evaluate today are AI-focused, and we continue to apply our 6Ts of venture capital: theme, team, technology, terrain, traction, and terms. We pay close attention to the team’s technical depth, their understanding of data infrastructure, and their ability to build compounding advantages over time. AI companies with resilience and defensibility win in the long run—not just those with the most novel models.

We prioritize startups with access to unique, hard-to-replicate data; products that become more valuable as they’re used; and moats that strengthen with every technological leap. We’re drawn to teams building foundational infrastructure or demonstrating outsized growth compared to peers.

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 we look at AI companies, especially in the AI agents space, we want to understand how they can turn technology into a real, scalable business. Early signs include whether customers stick because the product is embedded in their daily workflows rather than just being a novelty. For AI-specific signals, we pay close attention to how inference costs trend, whether margins improve as the product scales, and how much work is truly being done autonomously rather than just assisted.

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

When we value early-stage AI startups with little or no revenue, we focus on defensibility such as proprietary data, unique models, and infrastructure that is hard to replicate. We look at speed to commercial deployment and forward-looking unit economics including inference costs and margin improvement at scale. We benchmark against recent AI deals in their niche and weigh the team’s ability to capture market share quickly.

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

Inference and infrastructure costs can erode margins if they do not decline with scale or if model efficiency lags. Heavy reliance on third-party model providers can compress margins or shift economics unexpectedly if pricing changes. Customer acquisition can be challenging in a crowded market. Finally, regulatory shifts around AI use and data privacy can introduce compliance costs.

Do you focus on particular subdomains within AI?

We focus on several subdomains within AI, with a particular interest in infrastructure that will power the next era where AI agents take on a much larger role inside companies. This includes the foundational layers that enable agents to run reliably at scale, managing cloud operations, orchestrating complex workflows, and enabling automatic scaling across different components of the stack. We see a major opportunity for companies that can solve challenges in latency, cost optimization, observability, and security for AI-native workloads. Israel is uniquely positioned for this wave, with deep talent in cloud infrastructure, distributed systems, and cybersecurity. We believe the same strengths that built world-class DevOps and cloud management tools can now be applied to building the operational backbone for AI agents, creating infrastructure that not only supports but accelerates their adoption in enterprise environments.

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 critical to some of our most critical industries like healthcare, supply chain and logistics, defense and financial services.

In healthcare, AI-augmented video management is helping surgeons capture laparoscopic and robotic video automatically, analyze and access cases on demand and securely. In cancer diagnostics, startups like Imagene are helping healthcare organizations continuously learn from imaging, molecular, and clinical data through proprietary foundation models, large language models, and biology-tuned analysis engines. It empowers biopharma and clinical teams with advanced capabilities for biomarker discovery, mechanism of action exploration, and indication expansion by deriving meaningful insights across modalities, enabling more predictive, insight-driven workflows that optimize trial design and improve therapeutic success.

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?

In Israel, we see strong exit potential over the next five years in AI infrastructure for the enterprise, serving as the backbone that allows AI to operate efficiently, securely, and at scale inside companies. This includes cloud orchestration, automated scaling, and cost optimization layers that make running AI workloads economically viable, as well as tooling for monitoring, governance, and integrating agents into complex business workflows. Israeli startups have a deep edge here thanks to world-class talent in cloud infrastructure, DevOps, and cybersecurity, much of it shaped by military tech units. We expect to see winners emerge in platforms that help enterprises deploy and manage AI models across hybrid and multi-cloud environments, reduce inference costs, and maximize utilization without compromising on performance or compliance.

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?

Israel’s AI ecosystem is thriving, but gaps remain in the infrastructure needed to run AI at scale. There is limited domestic compute capacity, underdeveloped model security and governance tools for regulated sectors, and talent shortages in AI-specific engineering. Closing these gaps in cloud infrastructure, compliance-ready operations, and skilled workforce could position Israeli founders to build globally significant companies in the next wave of AI adoption.