Israel's World-Class AI Powerhouse: Leading Through Applied Innovation
A Startup Nation Central report with commentary by NVIDIA
November 2024
OVERVIEW
“The energy around utilizing technology for positive impact is fantastic to see, and I am sure Israel will play a huge role.”
Sam Altman
Foreword by Avi Hasson, CEO of Startup Nation Central
Israel stands at the forefront of global AI innovation, driven by an exceptional ecosystem of startups, academia, and strategic support from both local and multinational players. Leading corporations—including NVIDIA, Microsoft, Google, Amazon, Meta, and Apple —have deepened their investments in Israel’s AI landscape through significant R&D and acquisitions, pointing to Israel’s standing as a global AI leader. Complementing these efforts, initiatives like Israel’s National AI Program, backed by a 1 billion NIS investment, are aimed at enhancing our AI sector, advancing research, and nurturing local talent.
With over 430 multinational corporations operating R&D and innovation centers in Israel, these tech giants are not only innovating but are also driving core strategic developments, especially in AI, from their Israeli centers. The synergy between Israel’s vibrant startup ecosystem, multinational investments, and academic collaboration provides a powerful foundation for continued sector growth. However, to fully leverage Israel’s potential, expanding government investment is essential. To maintain its position as a global AI powerhouse, Israel must address key challenges by enhancing academic talent, strengthening the National AI Program with its 1 billion NIS commitment, attracting top AI researchers locally and globally, and facilitating the transfer of research into business applications. This will support R&D, academia, and high-tech industries, and foster AI talent, to integrate advanced technologies for public benefit and to expand support for this critical growth engine.
Overview: Israel’s World-Class AI Powerhouse
by Yariv Lotan, VP of Product and Data at Startup Nation Central
Israel is rapidly establishing itself as a global leader in Artificial Intelligence (AI) innovation, boasting one of the highest densities of startups and scaleups worldwide. Its outstanding entrepreneurial DNA provides a quantitative advantage through the large number of AI-driven companies and the attraction of leading multinational research centers, thereby leveraging the principle that in AI, quantity begets quality. Complementing Israel’s robust ecosystem is a remarkable diversity of applied AI domains and exceptional quality of AI entrepreneurship.
However, sustaining Israel’s leadership in this competitive field presents significant challenges. It requires not only a continual influx of talented individuals and substantial resources but also ongoing excellence in academic and business research.
Unprecedented AI Density
Israel has long been recognized for having one of the highest densities of startups per capita. Currently, AI startups constitute 30% of its tech landscape, accounting for up to 40% of funding rounds and securing 47% of total investments. Despite a recent surge in U.S. AI investment driven by high-profile deals, Israel’s AI startup activity remains two to four times greater than that observed in the United States or Europe, highlighting the unparalleled density of its AI sector.
The Quantitative Growth Factor
Since 2014, Israeli AI startups have demonstrated remarkable growth, experiencing over 170% increase in active companies compared to a 12% rise in non-AI startups. This surge has created a quantitative advantage, resulting in over 2,000 AI-driven companies and more than 400 leading multinational research centers operating within Israel.
Sustaining this growth is supported by a significant funding advantage. While just under 50% of non-AI companies secure equity funding within the first two years of their establishment, a remarkable 68% of AI companies succeed in raising funding. This disparity indicates strong investor confidence in Israel’s AI ecosystem, the sector’s robustness, and the attractiveness of AI ventures to investors.
Applied AI Excellence Across Diverse Sectors
While infrastructural AI is predominantly dominated by global tech giants and necessitates multi-billion-dollar investments, Israel distinguishes itself through applied AI—leveraging AI models to address novel and specific challenges. Applied AI is critical for driving economic and business value from the rapidly increasing investments in AI. Israel’s unique startup density and the industry-wide diversity of its innovation ecosystem enable impactful applications across sectors such as healthcare, agrifood, cybersecurity, robotics, and more.
This report identifies that seven out of twelve key innovation sectors each attract over 60% of funding allocated to AI companies. The broad spectrum of AI applications, combined with Israel’s entrepreneurial agility, academic research talent, and robust funding growth, fosters heightened interest from leading multinational corporations. Six out of seven of the “Magnificent Seven”, such as Microsoft, Google, Amazon, Meta, NVIDIA, and Apple, are leveraging Israel’s AI ecosystem through significant R&D operations and acquisitions.
Microsoft and Google lead with AI development centers focusing on machine learning and natural language processing, while Amazon’s AWS center drives AI integration within cloud services. Meta enhances AI-driven communications, and Nvidia has bolstered its AI hardware capabilities through the acquisition of Mellanox. Apple’s Israeli centers work on AI-powered features, including facial recognition. These strategic investments highlight Israel’s pivotal role in global AI innovation.
Global Validation of AI Quality
Measuring the quality of AI is more art than science, as it blends academic research, intellectual property, funding, and revenues. Instead of traditional metrics, this report examines exit activities as a distinct signal of quality. Since 2021, Israeli AI companies have outperformed their overall ecosystem representation in exit activities. From 2014 to 2023, top multinational corporations (MNCs) acquired one Israeli AI startup for every four American AI acquisitions, showcasing Israel’s strong global appeal. In contrast, non-AI acquisitions by MNCs follow a much lower ratio of approximately 1:11. This stark difference underscores the strategic global interest in Israel’s AI sector and highlights the exceptional quality and attractiveness of Israeli AI ventures to leading international investors.
Challenges Ahead
To maintain its position as a global AI powerhouse, Israel must address key challenges by taking effective steps in four strategic directions:
1. Extend Academic Talent and Research
Double down and expand Israel’s National AI Program to attract more AI researchers both domestically and internationally, and facilitate the transfer of AI research into business applications. Backed by a 1 billion NIS investment released in two phases, the National AI Program aims to position Israel as a global AI leader by supporting R&D, academia, high-tech industries, and fostering AI talent.
2. Attract Multinational AI R&D
Israel must continue to incentivize global businesses to establish and expand their AI R&D operations within the country, thereby enhancing collaboration, innovation, and the integration of cutting-edge AI technologies.
3. Implement AI Across Israeli Economic Sectors
Promote the widespread adoption of AI solutions across various economic sectors, including healthcare, agrifood, finance, and transportation, to drive operational efficiency, innovation, and economic resilience.
4. Support AI Funding
In the face of a volatile economic environment and geopolitical tensions, the government should encourage international investments in AI startups and support AI R&D investments by scaleups to ensure sustained financial backing and growth in the AI sector.
Sector-Specific Insights
Health Tech
Artificial intelligence is revolutionizing healthcare by enhancing medical imaging, electronic health records, robotics, drug discovery, and clinical trials. These advancements promise improved patient outcomes and increased operational efficiency. However, the sector faces significant challenges, including data privacy concerns, data scarcity, complex business models, lengthy drug development cycles, and stringent regulatory requirements.
In Israel, digital healthcare stands out as a market leader, representing 58% of active AI health tech companies. This dominance is further reinforced by the majority of AI investments flowing into the health tech sector, underscoring Israel’s pivotal role in driving innovation and investment in healthcare AI solutions.
Robotics
AI is at the forefront of transforming the robotics industry, driving advancements in logistics, agriculture, and healthcare. Key applications include autonomous navigation for path planning, advanced perception systems for object handling, and automation in industrial environments. Despite these advancements, the robotics domain contends with challenges such as data scarcity for training AI models and the necessity for high precision in dynamic physical settings, which often require expensive testing and validation processes.
In Israel, AI-driven robotics companies have significantly outpaced their non-AI counterparts in attracting investment. Between 2019 and 2024, these companies secured 78% of total sector funding, highlighting the robust investor confidence and the critical role of AI in advancing the robotics industry within the country.
Climate Tech
AI holds transformative potential in climate tech, addressing critical challenges in weather modeling, geospatial monitoring, sustainable agriculture, wildfire prevention, and renewable energy optimization. Nonetheless, the widespread adoption of AI in climate tech is hindered by fragmented data sources, the need for high-resolution datasets, and limited computational resources.
Within Israel, energy tech and agritech are spearheading the adoption of AI in the climate sector, capturing nearly 70% of the funding compared to their non-AI counterparts. This focused investment reflects the strategic emphasis on leveraging AI to enhance sustainability and resilience across key environmental sectors.
Conclusion
Israel’s AI sector serves as a cornerstone of its technological innovation, driving substantial growth across diverse industries such as health tech, robotics, and climate tech. The nation’s unique combination of academic excellence, entrepreneurial dynamism, and robust global partnerships solidifies its position as a leader in AI development.
Looking ahead, while Israel’s vibrant AI ecosystem is poised to continue attracting significant investments and strategic interest from multinational corporations, however, sustaining this momentum requires addressing key challenges. By expanding academic talent and research, attracting multinational AI R&D operations, implementing AI solutions comprehensively across economic sectors, and ensuring consistent support for AI funding, Israel will not only preserve its current advantages but also unlock new opportunities for innovation and economic growth. Balancing these strengths with proactive measures to overcome obstacles will reinforce Israel’s status as a global AI powerhouse, ensuring sustained innovation and economic growth in the years to come.
Israel’s AI Ecosystem: Where Top AI Talent Meets Startup Culture
By Arik Kol, Sr. Director and Head of NVIDIA Inception Startups Program at NVIDIA Israel.
Israel is well known and recognized for being a powerhouse of innovation: the ‘Startup Nation’. When it comes to the AI ecosystem – it is no different. With the highest number of AI startups per-capita and third in the world for leading companies in the field of Gen AI [according to IIA], Israel is the home of world-leading AI startups, researchers and developers – in the industry and in academia, creating the applications and technologies for the next wave of AI.
The AI ecosystem in Israel goes back all the way to strong Israeli academia, which was the first to explore the capabilities of artificial intelligence. They built expertise in areas such as computer vision, with video analytics and image processing, as well as NLP – the roots of the LLMs we know today. The strong foundation in AI research with an agile and mature startup culture, made Israel highly competitive in comparison to other regions, despite its small size.
A key metric that sets Israel apart is its focus on applied AI solutions in areas like cybersecurity, health tech, and agriculture, which positions it as a leader in practical AI applications. Among the first to adopt the new technologies were the healthcare, smart cities, robotics, and finance industries. From those early days, about a decade ago, the Startup Nation has continued expanding in AI, with over 2,000 startups developing the next era of AI applications.
While the Israeli cybersecurity ecosystem was initially slower to adopt AI compared to some international counterparts, it has since recognized the critical role of AI in maintaining Israel’s leadership position in the field. In the past two years, there has been a significant shift, with both startups and leading cybersecurity firms, actively working to integrate AI technologies into their solutions. This transformation will not only enhance their existing offerings but also position them at the forefront of the evolving AI landscape.
Whether it’s the use of AI in improving cybersecurity capabilities, i.e., AI powered cybersecurity, or solutions developed to secure AI hardware and software infrastructure, i.e., cybersecurity for AI, the transformed cyber industry holds a tremendous leadership potential for Israel’s tech ecosystem.
But Israel is not all about cyber. We see an increasing number of startups operating in healthcare, M&E, robotics, automotive, biology, retail, climate, industry 4.0, logistics, agriculture, and many more – working to develop applications that will drive the next wave of AI.
The driving forces behind the Israeli AI ecosystem
Israeli universities and research institutions are crucial in advancing AI in Israel. They offer respected AI programs, producing cutting-edge research and skilled graduates – but are also the place of birth for some of the world’s leading AI startups. The academic focus spans various AI fields, including natural language processing, computer vision, robotics, and machine learning, and is closely linked to global academic circles, particularly in the U.S.
Many Israeli AI startups have roots in academic research, with founders often being academic researchers themselves, especially in healthcare, biotech, and AI infrastructure. A few notable examples include AI21 Labs, Mentee Robotics, Lightricks, Deci, Bria and Tabnine.
Additionally, close to 400 multinational organizations, according to the IIA, have established robust AI R&D operations in Israel, investing in local talent and providing essential resources for research and product development, while working closely with the local ecosystem and academia.
From startups programs to research groups, business operations and broad industry collaboration, their involvement significantly accelerates knowledge transfer and helps Israeli startups scale. Startups should actively seek resources and support from them, often offered at no costs, to fully realize their potential. These offerings can range from technical expertise to business and marketing support, providing startups, developers, and researchers with the tools to push the boundaries of AI.
NVIDIA itself employs over 4,000 employees in Israel, in dozens of product, engineering and research groups, including a substantial AI research team, working closely with universities across the country to form the company’s 2nd largest R&D center outside of the US. To power AI training and development of future products, while also igniting the local ecosystem, NVIDIA built Israel-1 – the country’s most powerful supercomputer.
We’ve also recently reached over 1,000 Israeli startups that take an active part in our global startup program, NVIDIA Inception, making Israel the No. 5 country in the program. Startups taking part enjoy technological and business support to help them drive global innovation. Those investments reflect confidence in Israel’s AI talent and its potential to innovate and develop cutting-edge technologies for the era of accelerated computing and AI.
The strong presence of Israeli VCs that are focused on startups at earlier stages is fueling the Israel AI ecosystem, together with the presence of international VCs that boost startups in growth stages, the two play a key role in supporting Israel to become an AI market-driver.
An ecosystem that stands out
Israel’s AI ecosystem stands out as a global innovator with its unique combination of applied AI focus, cross-industry expertise, and a thriving network of academia, startups, and MNCs. Supported by government initiatives, major MNCs, strong local VC network and good Incubation and venture studio programs – Israel’s AI startup ecosystem is well-positioned to continue its growth and leadership.
But there’s still a long way to go. Becoming a world leader in AI requires massive investments – both from the government and the private sector, a thriving ecosystem, and the ability to take risks when and where it is needed.
Talent acquisition continues to pose a significant challenge for Israel’s AI ecosystem, as the demand for skilled AI experts – developers and researchers, far exceeds supply. While academic research in AI is strong, increased funding and resources are necessary to sustain and strengthen Israel’s global leadership in the field.
Even during the past year, which has been one of the most challenging years for all of us, Israeli startups demonstrated high levels of commitment and delivery, no matter what. But these challenges also present opportunities, driving tighter collaboration between academia and industry to grow the next generation of AI experts. Moreover, the demand for AI solutions that address real-world issues positions Israel for continued innovation in applied AI, attracting both investors and strategic partners.
We’re in the early stages of a major computing platform shift, moving from general purpose computing to accelerated computing and AI – transforming $100T worth of industries – and now is the time to accelerate the local ecosystem’s efforts and position Israel as a world leader in AI.
ECOSYSTEM ANALYSIS
Companies
AI startups demonstrated greater resilience compared to non-AI counterparts
The number of active AI companies saw a remarkable increase of over 170%, rising from 783 companies in 2014 to 2170 in 2023, while the number of non-AI companies grew by only 12%. In 2018 the number of active non-AI companies reached its peak and then started to descend. At the same time, active AI companies continued to grow, reaching a peak in 2022.
AI acceleration already started in 2014
While the number of newly founded startups is declining in the past 10 years, the number of newly founded AI startups rose between 2014 to 2019. This suggests that the surge in AI startup activity began well before the launch of Large Language Models (LLM) and Generative AI technologies. Moreover, these technologies did not drive new startup foundation. Although final figures for the latest year is not final as startup activity continues to be revealed, since 2019 AI startups are following a general trend of decline in the number of new startups established.
Funding
Consistent increase in funding for AI companies between 2018-2021
Both AI and non-AI startups experienced substantial funding growth through 2021, with AI startups demonstrating a more consistent upward trend. Non-AI startups peaked in funding that year, driven by exceptionally high valuations. Despite a decline in funding for both sectors after 2021, AI startups have shown resilience, indicating sustained interest and innovation potential within the AI domain.
Though non-AI startups secured a greater number of funding rounds over the years, the trends are largely parallel. Given that AI startups represent only 30% of the Israeli ecosystem, their funding achievements underscore the significant role AI plays in attracting capital and driving growth in the tech sector.
Share of AI funding higher than share of AI companies the ecosystem
The proportion of funding allocated to AI companies is higher than their representation in the Israeli ecosystem. As shown in the chart below, both the share of private funding rounds and the total funding amount consistently exceed the share of active AI companies, indicated by the black dashed line. In 2024, although the AI funding share dropped to 30%, the share of AI rounds remained above 40%.
AI companies annual average share
%
Active Tech Companies
%
Funding Amount
%
Funding Rounds
Israel's tech ecosystem is more AI-oriented than U.S. and Europe
Since 2018, Israel’s AI investment share has typically been 3-4 times higher than Europe and the U.S. However, the recent surge in AI funding in the U.S., driven largely by substantial investments in companies like OpenAI, alongside a slight decline in Israel’s AI funding share, has narrowed the gap between the geo-zones, especially in the share of total funding amount. Preliminary data for 2024 shows this trend continuing. For the first time in recent years, the U.S. leads AI investment, mainly due to high volume mega deals.
Advantage of AI startups in securing first funding
A strong indicator of a startup’s success is its ability to secure funding. Divided into yearly cohorts, the chart below illustrates the percentage of startups that managed to raise capital within two years of their foundation. While the data may be subject to bias, as startups that secure funding are more likely to be discovered regardless of their classification, a distinct trend emerges. Until 2021, AI startups consistently outperformed their non-AI counterparts, with a gap of at least 10% favoring AI-driven ventures.
Only 24% of AI companies have yet to secure equity funding
A key indicator of success when comparing AI and non-AI companies is the proportion that managed to raise equity funding. Among companies founded from 2014 onward, there’s a stark contrast: 43% of non-AI companies were unable to secure any equity funding, compared to only 24% of AI companies. Furthermore, AI companies held a 10% edge in the share of companies that achieved more than two funding rounds.
Notable AI startups
Next Insurance develops insurance solutions for small businesses, offering simple, affordable coverage tailored to the needs of each class of business. Next Insurance offers policies that are easy to buy, with online access to services such as certificates of insurance and additional insured coverage with no extra fees.
Via is aiming to enhance public mobility systems and optimize networks of various transportation modes worldwide, including shuttles, buses, accessible vehicles, school buses, and autonomous vehicles.
Trax specializes in providing image-recognition technology and market data services for tier-one manufacturers. Trax enables in-store execution controls and the ability to unlock revenue opportunities at all points of sale. The company’s technology can be integrated as a plug-in with existing merchandising or retail execution systems.
Here are some notable AI companies:
Exits
AI Exits Outpace Ecosystem Share Post-2021
Until 2020, the share of exits among AI companies was relatively low compared to their representation in the ecosystem. This trend shifted in 2021, likely due to increased funding and the maturation of companies founded around 2018, which were still in early stages at that time.
Since 2021, however, the total number of exits has declined for both AI and non-AI companies. Yet, by September 2024, despite fewer exits than in 2023, the total exit value has nearly doubled, reaching $7.2 billion for non-AI companies and $2.9 billion for AI companies.
Israeli AI acquisitions by foreign MNCs outpace non-AI
To gauge the relative strength of Israel’s AI ecosystem, an analysis was conducted on the number of AI companies acquired each year since 2014 by top AI multinationals. This list includes NVIDIA, Google, Microsoft, Meta, Apple, Amazon, IBM, and Intel. By the end of 2023, for every four American AI companies acquired by these MNCs, one Israeli AI company was also acquired. This ratio is even more favorable when comparing Israeli to European AI companies. In contrast, the acquisition ratio between Israeli and American non-AI companies is about 1:11. This indicates that while Israel’s broader tech ecosystem is remarkable for a country of its size, the interest from leading MNCs in Israeli AI companies is particularly strong.
Notable exits by AI companies
BioCatch develops Behavioral Biometrics which analyzes an online user’s physical and cognitive digital behavior to protect individuals and their assets. Its mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust and ease seamlessly co-exist.
Run:AI is bridging the gap between data science and computing infrastructure by creating a high-performance compute virtualization layer for deep learning, speeding the training of neural network models and enabling the development of large AI models.
Chorus.ai is the provider of a conversation intelligence solution for sales teams. The company’s solution is designed to improve both the salesperson’s selling skills and the manager’s ability to manage, based on data showing the specific factors that have the greatest impact on the outcome of sales conversations.
Here are some notable exits of AI Companies:
SECTOR ANALYSIS
Cross Sector Comparison
This chart represents the total funding figures between 2018- 2024, and the companies distribution for each sector, broken down by AI & non-AI. The funding distribution between the groups varied significantly across primary sectors. Sectors like retail platforms, agriculture & food tech are more AI dominant, while for automotive non-AI funding is more dominant.
Health tech
Kimberly Powell
VP Healthcare at NVIDIA
AI in Healthcare
Artificial intelligence (AI) plays a significant role in healthcare, with diverse applications in areas such as medical imaging, electronic health records, robotics, drug discovery, and clinical trials. While these applications hold immense promise for improving healthcare outcomes and efficiency, there are several challenges that need to be addressed for successful adoption, including data privacy concerns, data scarcity, business model complexities, long development cycles in drug discovery, and navigating the regulatory landscape.
Key Areas of AI Impact in Healthcare
Medical Imaging and Computer Vision
AI is being used to improve the quality and accuracy of medical imaging, assisting in anomaly detection, workflow optimization, and quality assurance in various imaging modalities such as X-rays, MRIs, CT scans, and PET scans. AI is also being used to help healthcare professionals make faster diagnoses of medical conditions like strokes.
Electronic Health Records (EHR) and Natural Language Processing (NLP)
AI, particularly large language models (LLMs) and natural language processing (NLP), is being used to transcribe, summarize, and format patient-clinician conversations for integration into EHR systems. This reduces the documentation workload on healthcare providers. LLMs are also being developed to act as reasoning agents to help make informed decisions, streamlining administrative tasks and potentially enhancing clinical decision-making.
Robotics and Assistive Technologies
AI is being integrated into assistive robotic systems to provide patient care, particularly for elderly individuals at home. By combining computer vision and NLP, these robots can enhance safety and monitoring capabilities for elderly patients.
Pharmaceuticals and Drug Discovery
AI is being used in genomics and drug discovery to analyze DNA, protein, and chemical sequences to discover new drugs. Generative AI models can simulate and predict new proteins or chemical compounds, helping scientists explore a wider range of potential therapies. AI is also being used to predict the properties and effects of new drug molecules, which can enhance the safety and efficacy of potential treatments. AI can help anticipate issues like toxicity and manufacturability early in the development process, saving time and costs.
Clinical Trial Optimization
AI models are being used to improve the design of clinical trials and select appropriate patient populations. This can help lower the high failure rates in clinical trials. AI is also being used to improve the predictive accuracy of trial outcomes, potentially accelerating the development of new therapies.
Challenges of Implementing AI in Healthcare
Data Scarcity and Privacy Concerns: Obtaining sufficient and diverse healthcare data for training AI models is challenging due to privacy regulations and the sensitivity of patient information. This data scarcity can make it difficult to train robust and generalizable AI models. Healthcare data is constantly evolving, which requires regular updates to AI models, adding complexity. Federated learning, which trains AI models within the data environments of health organizations without transferring sensitive patient information, is being used to address data scarcity and privacy concerns.
Business Model Challenges: The complex reimbursement structures and insurance coding systems, especially in the United States, make it difficult to develop a sustainable business model for AI in healthcare. Many AI solutions struggle to demonstrate a clear return on investment, hindering widespread adoption. Some companies are adopting platform-based approaches to simplify adoption for healthcare providers. Others are working towards securing reimbursable status for their algorithms.
Long Development Cycles in Drug Discovery: AI-driven drug discovery often faces long development timelines, taking years or more for a new drug to go through clinical trials and enter the market. This can make it difficult to maintain investor interest and financial support. To manage these challenges, biotech companies often partner with larger pharmaceutical companies to secure funding, share development risks, and facilitate clinical trials.
Regulatory Navigation and Market Entry: Navigating the complex regulatory environment in healthcare, including obtaining FDA clearance for medical devices, can be challenging and time-consuming. This can delay market entry for new AI technologies. Organizations like NVIDIA are providing guidance and support to startups to help them understand and achieve regulatory approvals.
Funding for AI in health tech surpasses non-AI counterparts since 2021
In 2018 and 2019, total funding in the health tech sector was relatively balanced between AI and non-AI companies and in 2020 more funding went to non-AI companies. However, from 2021 onward, AI companies began to surpass their non-AI counterparts in terms of private funding, indicating a potential shift toward AI in health tech, particularly in digital healthcare (explored later in this report). The number of rounds for AI companies increased between 2018 and 2022 but dropped in 2023.
AI funding share in health tech is well beyond its share of health tech companies
The proportion of funding directed toward AI companies in healthcare surpasses their representation within the sector, mirroring a trend seen across the broader ecosystem. As illustrated in the chart below, both the share of private funding rounds and total funding amounts consistently exceed the percentage of active AI companies, represented by the black dashed line. Notably, in 2024 funding was split evenly between AI and non-AI companies, with each receiving approximately 50% of total funding.
AI companies annual average share
%
Active Tech Companies
%
Funding Amount
%
Funding Rounds
The share of AI exits in health tech is relatively low except for a peak in 2021
The chart below illustrates the relationship between active AI companies in healthcare (represented by the black dotted line) and the exit ratio of AI companies in a given year. As shown, until 2021 the exit ratio remained well below the representation of AI healthcare companies in the ecosystem. However, this changed dramatically during the 2021 hype period. Post 2021, the exit share dropped back below overall company share.
Digital health tech leads in applying AI and in AI funding share
The healthcare ecosystem is divided into three primary sub-sectors: Digital Healthcare, Medical Devices, and Pharma & Biotechnology, with Digital Healthcare being the largest. When analyzing the share of AI investment in each sub-sector, it becomes evident that Digital Healthcare leads both in the proportion of active AI companies (averaging 58% annually) and in AI investment figures, which are significantly higher than in the other sub-sectors. As illustrated in the chart below, across all three sub-sectors, as well as the overall ecosystem, the proportion of funding rounds and total investment directed toward AI companies exceeds their representation within the ecosystem.
Notable AI startups in health tech
Ultima Genomics is focused on driving the scale of genomic information to enable unprecedented advances in biology and improvements in human health. The company’s new sequencing architecture lowers the costs of sequencing to help overcome the tradeoffs that scientists and clinicians are forced to make between the breadth, depth, and frequency with which they use genomic information.
K Health is data-driven digital primary care platform using the power of shared medical knowledge to get everyone access to better, affordable healthcare. Their platform uses artificial intelligence to help people understand how those with similar symptoms and conditions were diagnosed and treated. Its platform consolidates the knowledge of thousands of doctors and billions of clinical data insights to get people trusted information and treatment fast.
Immunai is developing a proprietary data set of multi-omic, immune-centric peripheral tissue analyses. The company’s advanced analytics and data-visualization interface allow researchers to comprehensively view and interrogate data and to derive novel conclusions on the underlying mechanisms of disease.
Here are some notable Health Tech & Life Sciences AI companies:
Notable exits of health tech AI companies
Zebra Medical Vision develops an imaging analytics platform that allows healthcare institutions to identify patients at risk of disease and offer preventative treatment pathways to improve patient care. The company’s AI solutions analyze millions of clinical imaging data points in real time, detecting various medical indications.
DiA Imagin Analysis is a provider of AI-powered ultrasound analysis software that solves the two main challenges ultrasound users are struggling with today: how to capture the right image and how to analyze it correctly. DiA’s technology is based on advanced pattern recognition, deep learning and machine learning algorithms that automatically imitate the way the human eye detects image borders and identifies motion.
Pulsenmores at-home prenatal ultrasound for remote assessment of fetal well-being empowers women to proactively manage their pregnancies in collaboration with their healthcare providers. The technology aims to enhance the availability of care, improve resource management, and increase patient satisfaction.
Here are some notable Health Tech & Life Science AI exits:
Robotics
Amit Goel
Director of Robotics and Edge Computing at NVIDIA
AI in Robotics
Today, AI is already at the forefront of the robotics industry, with a significant portion of robotics applications advancing through the adoption of AI technologies. Robotics in various embodiments such as mobile robots, robotic arms, and humanoid robots are being applied across various fields, such as industrial technologies, agriculture, and healthcare. The following are some examples for AI applications in robotics:
Key Areas of AI Impact in Robotics
Autonomous Navigation in Healthcare Facilities
AI enables mobile robots to autonomously plan their paths, avoid obstacles, and understand their environments. This is particularly relevant in logistics and manufacturing, where robots operate in complex environments.
Logistics and Industrial Automation
Robots with varied degrees of freedom, advanced perception and manipulation capabilities are used for tasks like palletization, depalletization, and object sorting. In logistics, robots handle a wide variety of items, each differing in size, shape, and weight, which would be challenging without AI’s ability to adapt.
AI has accelerated the use of robotics in logistics, as it allows robots to handle a diverse range of objects. AI enables robots to perform tasks such as picking, placing, and sorting objects in industrial settings.
Agriculture
AI-powered robots are being used across the agricultural process, including planting, harvesting, and weed removal. These robots handle specific tasks like picking fruits, and AI plays a crucial role in optimizing these processes for different crops.
Most robots today are designed to perform a single specialized task, such as cherry-picking or planting seeds. The complexity and uniqueness of succeeding in even the smallest task, necessitate access to rich datasets from multiple sources (e.g., video, sound, text) that also incorporate an understanding of physics, climate, and other variables. This demands vast amounts of data, which may not always be available. This is actually the first challenge AI is facing, integrating with robotics.
The second challenge with AI & robotics, (and unlike many other AI applications), is that robots operate in physical environments where precision is paramount. For example, when deploying robots in factories, business owners require near 100% accuracy. This emphasizes the need for extensive, complex field testing, which often involves coordinating thousands of robots in a single physical space, driving up costs and resource demands.
In order to solve the related challenges in robotics, three computers are required:
AI Model Trainer: This computer is designed to train large models using the complex inputs mentioned earlier, producing sophisticated outputs that can be applied across various environments.
Omniverse Simulator: Physically-based, scalable simulation platform that enable:
- Accessible testing environment, allowing developers to run experiments that bridge the gap between simulation and real hardware, drastically reducing the need for costly field tests.
- Generating vast synthetic datasets to fill in missing information for specific robotic tasks, mitigating data scarcity issues.
- Learning within the simulation environment, allowing AI models to acquire multiple skills before being deployed in the real world.
What Does the Future Hold?
The convergence of AI and generative AI has the potential to overcome many of these challenges. As we move toward a new frontier in robotics, the transition from single-purpose to general-purpose robots is becoming a reality. This shift could help address global labor shortages in manufacturing, filling vacancies with advanced robotic solutions, ultimately boosting global GDP.
While still on the horizon, future robots may become a more integral part of everyday life. Currently, working with robots requires specialized skills, but as they gain more intelligence, robots will become more accessible and widely adopted. This will unlock tremendous potential for integrating robotics into everyday tasks, transforming how people interact with technology.
AI is Dominating the Robotics Industry
The robotics sector is relatively small within the Israeli ecosystem, and it’s clearly overshadowed by AI. Investment data comparing non-AI and AI companies reveals a consistent trend: AI companies not only closed more funding rounds nearly every year, but they also secured significantly more capital between 2019 and 2022. In 2023 there was a slowdown but by mid-2024, it seems the momentum is returning to AI, with $83 million raised in 11 rounds, compared to just $24 million across 5 rounds for robotics non-AI companies. This is noteworthy given that robotics is a hardware-driven industry, offering a glimpse into how the future might evolve. More information about the robotics industry as a whole can be found in the published spotlight here.
Robotics AI funding share is the highest of all sectors
AI’s dominance in the robotics sector is evident when examining key metrics: the number of active companies, funding rounds, and total investment share. Despite a dip in 2023, AI regained its strong position the following year, capturing 69% of funding rounds and securing 78% of the total invested capital.
AI companies annual average share
%
Active Tech Companies
%
Funding Amount
%
Funding Rounds
AI robotics dominates the industrial technologies sector
Robotics is a unique field, as it often serves diverse objectives across various sectors. The three primary sectors where robotics companies operate are industrial technologies, agriculture and food, and health tech. While AI plays a prominent role in industrial technologies in terms of the number of companies involved, when it comes to funding figures, its impact is even more pronounced in agriculture and food technologies.
Notable AI robotics startups
Fabric is a logistics platform that aims to make on-demand eCommerce possible, profitable, and sustainable for retailers while powering retailers’ unique offerings. The company builds multi-tenant and private networks of automated micro-fulfillment centers that position automation physically close to end customers.
Inuitive designs powerful multi-core processor integrated circuits that serve as vision processors for smartphones, augmented reality and virtual reality devices, as well as robots.
Beewise offers robotic bee hives that operate with minimal human intervention by utilizing AI-powered precision robotics. The company’s solution automates all beekeeping activities in order to increase yield, reduce colony loss, and eliminate the use of chemical pesticides. Beewise is a comprehensive solution that aims to keep bees alive in spite of all the stressors and challenges they face, allowing them to thrive, pollinate, and produce honey.
Here are some notable Robotics AI companies:
Notable exits of robotics AI companies
Kryon is a pioneer in automated process discovery whose robotic process automation (RPA) offerings allow organizations to put powerful, AI-driven attended and unattended RPA bots to work to generate fast ROI. Kryons process discovery technologies enable companies to automatically identify repetitive work processes, visually map the main path and variants of any given process, evaluate the suitability of the process for automation, and instantly generate workflows.
Ecoppia offers robotic cleaning solutions designed to keep PV modules clean and at peak performance year-round. The company’s water-free, fully automated robotic solutions use advanced business intelligence capabilities to prevent soiling losses and potential damage.
Airtouch Solar developed a solution for utility-scale photovoltaic panel cleaning. Airtouch Solar’s solution is an autonomous water-less cleaning robot that removes heavy soiling particles. Airtouch utilizes microfiber wipes and wind-blowing technology with the aim of improving cleaning results.
Here are some notable Robotics Tech Science AI exits:
CLIMATE TECH
Dion Harris
Director of Product, Accelerated Computing at NVIDIA
AI in Climate Tech
There is an exciting potential for applying artificial intelligence (AI) in climate technology. AI can be used to address various climate challenges, including improving weather forecasting, monitoring deforestation, optimizing farming practices, preventing wildfires, and promoting renewable energy. The obstacles to AI adoption in climate tech include data silos, lack of high-resolution data, and the need for powerful computing resources. There are efforts to overcome these challenges through digital twin technology, accelerated computing, and hybrid AI models. These solutions are leading to more accurate climate simulations and allowing for more effective climate action.
Key Areas of AI Impact in Climate Tech
Climate and Weather Modeling
AI can be used to accelerate climate and weather models, which can improve the speed, accuracy, and efficiency of these models. This can be helpful for industries such as agriculture, logistics, and disaster recovery. AI and digital twins can also be used to simulate weather systems and other climate-related phenomena, which can provide more accurate forecasts and planning tools.
Geospatial Monitoring and Detection
AI can be used for image classification and detection in environmental monitoring. For example, AI can help detect illegal deforestation in the Amazon by identifying new road formations, enabling early interventions, and reducing illegal deforestation.
Agritech and Sustainable Farming
AI can be used to optimize conditions such as temperature, humidity, and energy use in indoor farming and bug farming, which can ensure sustainable production. AI can also be used to improve resource management in agriculture, such as water, energy, and land, making farming more sustainable and efficient. Finally, AI can be used for optimizing genetic discovery and development activities for new biopesticide development.
Wildfire Detection and Prevention
AI, combined with geospatial and ground-based technologies, can be used to predict and prevent wildfires.
Renewable Energy
AI can help optimize energy production, manage grids, and improve efficiency in renewable energy sources like solar and wind.
Broader Sustainable Technologies
AI can be broadly applied across different climate tech sectors, including start-ups and large enterprises, to create sustainable technologies in agriculture, weather systems, energy, and environmental monitoring.
Challenges of Implementing AI in Climate Tech
Climate data is fragmented, coming from sources like satellites and historical datasets (e.g., ERA5), making it hard to centralize for AI training. Digital twin technology helps address these data silos by integrating various datasets into a federated model, providing a unified view of the climate system. Projects like Earth 2 and Project Destiny aim to aggregate this data for easier AI and simulation use.
High-resolution datasets are crucial for accurate AI climate models, but large-scale simulations are required to produce them. Companies like NVIDIA use GPU-optimized models (e.g., WARF, ICON, IMPASSE) and platforms like Grace Hopper to accelerate simulations, creating kilometer-scale, high-fidelity data efficiently.
AI models depend on data from complex and computationally intensive numerical weather models, which NVIDIA has accelerated using GPU-based systems. This allows these models to run faster, generating the high-resolution data needed for AI training and improving weather prediction accuracy.
The Earth’s climate system is complex, involving multiple interacting components like the ocean and atmosphere. Digital twins and data federation models integrate diverse datasets into a unified system, enabling more accurate AI-driven climate simulations by capturing these interrelations.
Predicting long-term climate changes is challenging due to limited historical data. A hybrid AI approach, combining data-driven and physics-based models, addresses this by incorporating physical laws like thermodynamics, making predictions more accurate even with incomplete datasets.
High-resolution climate models require supercomputing resources that are often inaccessible, especially in developing countries. Platforms like NVIDIA’s Omniverse help democratize access to climate data and models, enabling collaboration and sharing, even among organizations with fewer resources.
Validating AI climate models is difficult due to changing conditions. Hindcasting—using historical data to test models’ accuracy—helps gauge prediction reliability by comparing results with known outcomes.
Many climate datasets are low-resolution, limiting actionable insights, especially for disaster preparedness in vulnerable regions. AI-based techniques, like those used by platforms like Corrdiff, enhance dataset resolution, enabling local governments to make more informed decisions.
AI Companies Lack Dominance in Climate Tech Funding
When comparing climate tech companies between AI and non-AI segments, it’s evident that non-AI has consistently dominated since 2018, both in terms of the number of funding rounds and total private investment. In years of increased funding, non-AI companies benefited more significantly, whereas in years of decline, AI companies experienced sharper drops in funding. This trend has persisted into 2024.
Low funding is due to fewer startups applying AI in this sector, but the relative share of funding is still higher
AI’s share of active companies in climate tech is the lowest of all analyzed sectors in this report, represented by the black dotted line. However, similar to the other sectors, its share of total funding has consistently surpassed this share, a trend that continues into 2024.
AI companies annual average share
%
Active Tech Companies
%
Funding Amount
%
Funding Rounds
AI as it relates to climate tech dominates the mobility sector
Like robotics, climate impact is applicable to multiple sector. Companies working in this space span multiple tech industries, each addressing the climate crisis in its own way. AI’s influence is most pronounced in the automotive and mobility technologies sector, with energy tech following as the second most AI-driven field, despite having the lowest absolute funding figures.
Notable AI startups in climate tech
Augury develops AI and IoT-based solutions for monitoring machine and process health in industrial settings. Their technology aims to improve machine reliability by providing insights into machine performance and potential issues.
Tomorrow.io is a weather technology company that offers a hyper-local weather forecasting platform for businesses and individuals. The company’s platform uses a combination of proprietary technology, advanced modeling techniques, and machine learning to provide accurate weather information for specific locations in real time.
New BrightSource Energy offers advanced forecasting and optimization capabilities along with expert systems and artificial intelligence to help harness and manage renewable energy resources, energy storage, and the future grid and to meet the worlds growing clean energy needs.
Here are some notable Climate Tech AI companies:
Notable exits of climate tech AI companies
Prospera Technologies is focused on solving some of the greatest challenges in the agriculture industry. The company creates technology that combines machine learning, computer vision, and practical know-how to enable growers to make more informed, efficient, and scientific decisions. Prospera’s goal is to drive better, more sustainable food production worldwide.
Autofleet provides solutions designed to optimize operations for fleets and mobility operators, enabling the enhancement of existing operations and the initiation of new on-demand passenger and logistics services. The company’s platform helps fleets maximize their utilization and revenues, reducing downtime and introducing new mobility services.
EcoPlant offers a software solution that utilizes AI technology to optimize the performance of compressed air systems. By connecting to controllers and sensors, it is able to continuously monitor and control the system, reducing energy waste and improving maintenance.
Here are some notable Climate Tech Science AI exits:
Methodology Notes
About Us
Startup Nation Central is a free-acting NGO providing global solution seekers frictionless access to Israel’s bold and impatient innovators to help tackle the world’s most pressing challenges.
Deeply immersed in the Israeli technology ecosystem, we provide a platform that nurtures business growth, influences policies, and cultivates partnerships with corporations, governments, investors, and NGOs to fortify Israel’s economy and society.
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This report was composed by Eran Igelnik, Business Data Analyst; and Einat Ben Ari, Head of Data and Insights.
Report Editor: Yariv Lotan, VP of Digital Products, Development, Data and Insights.