InNegev: Israel’s next big AI exits will come from Enterprise, Cyber, and Robotics

“Looking ahead, exit potential is highest in enterprise generative AI, AI-powered cybersecurity, AI infrastructure/hardware optimization, autonomy (robots, vehicles, drones), and vertical AI for climate, agriculture, water, energy, and industrial automation,” explained Amir Tzach, Chief Investment Officer at InNegev. “Meaningful value-add beyond capital typically includes introductions to design partners, pilot orchestration, manufacturability support, access to compute/cloud credits, and guidance on IP, data rights, and regulatory readiness. Gaps remain in foundational model development, consumer AI, and hardware-integrated AI—prime openings for ambitious teams building defensible systems with real-world deployment in mind.”

InNegev team.

You can learn more in the interview below.

Fund ID
Name and Title: Amir Tzach, Chief Investment Officer
Fund Name: InNegev
Founding Team: Amir Tzach, Dror Green
Founding Year: 2019
Investment Stage: Pre-Seed
Investment Sectors: AI, Mobility, Foodtech, Deep Tech, Semi Con, Quantum, Novel Materials, Energy, Agri Tech, Climate.

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 is now embedded in almost every part of our operations – from sourcing and screening companies to market research, due diligence, and investment preparation. It has significantly improved efficiency and decision-making, though we are still working toward full integration.

Over the past year, we have adopted AI tools to speed up market mapping, analyze technical materials, summarize pitch decks, and surface potential risks and opportunities faster. In each process, we consult with leading global experts in the relevant technical domains to pinpoint where the strongest technological differentiation lies and to identify teams capable of executing at the highest level, quickly – because in this field, speed is critical.

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

We have not yet had a major AI exit, as our portfolio is still young. However, our strongest-performing AI companies share common traits: they combine hardware and software in a way that creates a defensible advantage – like Pickommerce in robotics – or they enable a genuine leap forward in their sector. These companies are led by exceptional teams, validated by top-tier experts, and focused on executing rapidly to capture market leadership.

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

Yes. In AI, we perform much deeper technical due diligence, often involving the most respected experts in the relevant field, to verify the originality and defensibility of the model and data. We put more weight on early proof-of-concept and pilot results than on initial revenue, as speed to market and technological lead are critical in this space.

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?

We focus on pilot success rates, depth of user engagement, technical performance metrics like accuracy and recall, and cost efficiency for computation and inference. We also look at early customer conversions and scalability of the technology, often validating these KPIs with domain experts to ensure the performance advantage is meaningful and defensible.

As a pre-seed investor, we don’t expect classic revenue KPIs on day one. We focus on milestone signals that predict traction: signed design partners/LOIs, speed to first pilots, and pilot-to-paid conversion. We look for early unit-economics direction—cost per inference and latency, GPU hours per experiment and cloud spend versus delivered value; for hardware-plus-software, initial BOM and expected yield. AI-specific metrics matter most: model accuracy/recall (or the domain’s equivalent), robustness/drift over time, on-device power for edge/robotics, data-asset build-up and labeling throughput, and release cadence. We validate these with top domain experts to confirm the technological edge, and we track burn multiple, runway, use of cloud credits, and de-risking items like IP filings, data rights, and a basic privacy/regulatory plan. Speed of execution is critical.

As companies progress, our lens shifts to scalable revenue quality: ARR and growth, gross margin (software vs. services mix), net dollar retention and expansion within design partners, sales-cycle length and payback, and pricing power. For hardware-enabled AI we add units shipped, field reliability (MTBF), return rates, manufacturing yield, and contribution margin per unit. On the AI ops side we watch inference-cost and latency versus SLAs, the cadence of model refreshes, and stability of production performance. The throughline is speed with discipline: keep compute/manufacturing costs trending down and convert expert-validated technical advantage into repeatable commercial wins.

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

We value these companies based on team quality, technological uniqueness, market size, and tangible signs of demand. We also consider expert validation of the technology’s potential and the company’s ability to execute quickly on key milestones, as speed is often decisive in AI markets.

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

Beyond general technology risk, we monitor high compute costs, reliance on third-party data, regulatory burdens, talent competition, and scalability of cost structures. We also consider platform dependency risks, particularly where startups rely heavily on external AI APIs, and evaluate how quickly the team can adapt if circumstances change.

Do you focus on particular subdomains within AI?

We focus on computer vision, robotics (including autonomous systems), edge AI, and industry-focused generative AI, as well as predictive and optimization ML in climate tech, agriculture, water, and energy—domains where tight hardware-software integration creates real moats and is where we add the most value. We also look closely at new AI models: domain-specific or multimodal foundation models, physics-informed and simulation-trained approaches, and efficient on-device/quantized architectures that materially reduce cost and latency or unlock new capabilities. We’re especially interested when proprietary data and close integration with sensors, chips, or robotics turn those models into deployable systems with clear ROI.

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 transforming industries by enabling automation, prediction, and optimization. In manufacturing, agriculture, energy, healthcare, and logistics, we see AI delivering leaps in efficiency and quality – especially when combined with hardware to create complete, deployable systems.

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?

Enterprise-focused generative AI, AI-powered cybersecurity, AI hardware and infrastructure optimization, autonomous vehicles and drones, and vertical AI in climate, agriculture, and industrial automation. We believe companies that combine hardware and software, validated by domain experts and able to execute quickly, will dominate in these areas.

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?

Gaps include foundational AI model development, hardware-integrated AI solutions, and consumer AI products. We seek multidisciplinary teams who combine top-tier AI expertise with deep industry knowledge, work closely with leading experts, and are committed to rapid, high-quality execution to deliver transformative, defensible technologies