For most of the last decade, the reaction from generalist investors was predictable: fascinating science, huge market, impossible timelines. Quantum was the sort of thing that governments, universities, corporate labs and patient deep tech funds could afford to care about. Traditional VC, especially early-stage VC, largely stayed on the sidelines.

That may be starting to change.

In the past 18 months, quantum has moved from “interesting research frontier” to a more investable strategic category. The change is not because a useful, fault-tolerant quantum computer has suddenly arrived. It hasn’t. The change is that the industry is starting to look less like a collection of lab experiments and more like an emerging stack: hardware, control systems, error correction, compilers, quantum software, security, sensing, networking, cloud access, and eventually applications.

The latest signal came from Europe. In one week only, four quantum computing startups raised a number of mega rounds: Oxford Quantum Circuits (OQC) raised a $350 million Series C, France’s Quobly announced a €115 million Series A, and Finland’s IQM upsized its PIPE financing to $146 million ahead of a public-market transaction. The headline number in some reports was close to $1 billion of quantum funding in a week, though that should be treated carefully, as some coverage appears to count OQC and Oxford Quantum Circuits separately when they are the same company.

Even with that caveat, the direction of travel is clear: quantum is attracting serious capital again.

At the same time, McKinsey’s 2026 Quantum Technology Monitor argues that quantum has reached a “commercial tipping point,” with more than 300 organizations globally engaging with quantum computing and over $1 billion in quantum-computing revenue generated worldwide in 2025. The U.S., France, the U.K., the EU and China are all treating quantum as a strategic technology. Public funding, defence procurement, national security concerns and private capital are converging.

The question for venture investors is no longer whether quantum is scientifically important. It is whether now is the right time to invest.

The venture case for quantum

The strongest argument for quantum is simple: if it works, it changes what is computationally possible.

Quantum computers are not “faster laptops.” They are a different model of computation. Their promise is to solve certain classes of problems that are extremely difficult, expensive or impossible for classical computers to solve efficiently. That could matter in drug discovery, chemistry, materials science, logistics, financial risk, machine learning, cryptography and national security.

That “if it works” is doing a lot of work.

Quantum remains technically hard. Error rates are high. Scaling is difficult. Timelines are uncertain. Hardware approaches are fragmented: superconducting qubits, trapped ions, neutral atoms, photonics, silicon spin qubits, topological qubits and others. It is still unclear which architectures will win, or whether the future will be heterogeneous.

But this is also why the venture opportunity exists.

When a technology is obvious, derisked and close to revenue, it is usually too late for venture-scale returns. The role of venture is to fund the uncomfortable middle: after the science has become credible, but before the market has fully formed.

Quantum may now be entering that middle.

Several things have changed:

First, the technical roadmaps are getting more credible. Google’s Willow chip showed progress in quantum error correction. IBM has published a path toward fault-tolerant systems by the end of the decade. Microsoft, despite continued scientific debate around its topological approach, is also pushing toward a 2029 target. Startups such as Quantinuum, IonQ, PsiQuantum, IQM, OQC, QuEra, Alice & Bob and others are increasingly judged not only on papers, but on systems, customers, deployments and integration.

Second, quantum is becoming part of the AI infrastructure conversation. AI has reminded the market that compute is not a solved problem. As AI models become more expensive, energy-intensive and constrained by hardware bottlenecks, the search for new computing paradigms becomes more urgent. Quantum will not replace GPUs, but hybrid quantum-classical systems could become part of the future high-performance computing stack.

Third, governments are behaving like strategic customers. Quantum has implications for national security, encryption, defence, sensing and sovereign computing capacity. This matters for venture because government capital can extend runway, validate roadmaps and create early demand in markets that are too early for normal enterprise budgets.

Fourth, the investable surface area is expanding. Investors no longer need to bet only on “the company that builds the quantum computer.” There are opportunities in picks and shovels: control systems, cryogenics, lasers, photonics, chip design, error mitigation, software abstraction layers, compilers, developer tools, post-quantum security and vertical applications.

That is where Israel becomes interesting.

Why Israel is a good place to look for Quantum

Israel is unlikely to outspend the U.S., China or Europe on national quantum infrastructure. But Israel has rarely won by outspending larger markets. It wins when deep technical talent, military-grade problem solving, strong academia, entrepreneurial urgency and global capital collide.

Quantum fits that pattern.

The Israeli quantum ecosystem is still small, but it is unusually dense. A 2025 map by Earth & Beyond Ventures and Deloitte Catalyst identified nine core Israeli quantum-computing startups that had collectively raised around $650 million, including more than $300 million raised in 2025 alone. That is a meaningful amount of capital for a country of Israel’s size.

Israel has already produced globally relevant companies in quantum control, software, photonics, trapped ions, error mitigation and neutral atoms. The ecosystem has academic depth, national urgency, engineering talent and a growing investor base. It also has a history of turning constraints into advantage. The emerging Israeli stack includes:

Quantum Machines – building control and orchestration systems for quantum processors. This is a classic Israeli wedge: not necessarily owning the whole machine, but controlling a critical layer of the stack.

Classiq – building quantum software and tools that help developers design quantum algorithms and applications without needing to work at the hardware level.

Quantum Source – developing photonic quantum computers, a route that could potentially offer advantages in scaling and data-center integration.

Quantum Art – building trapped-ion quantum systems, with ambitions to deliver full-stack quantum computing.

Qedma – focused on error suppression and error mitigation, one of the hardest and most commercially relevant problems in making quantum useful.

QuamCore – working on scalable superconducting quantum architecture.

Q-Factor – a newer entrant building neutral-atom quantum computing, backed by investors including NFX, TPY Capital, Intel Capital and others.

Quantum Transistors – pursuing diamond-based and photonic approaches to quantum processors.

This matters because Israel is not producing only one kind of quantum company. The ecosystem spans hardware, software, photonics, control, error correction and architecture. That diversity is important because, at this stage, nobody knows which approach will dominate.

Israel also has several structural advantages.

1. Academic depth. Quantum companies are often born out of physics, electrical engineering, computer science and materials science departments, not from weekend hackathons. Israel has strong research institutions, including the Technion, Weizmann Institute, Hebrew University, Tel Aviv University and Bar-Ilan University, with a history of turning deep science into startups.
2. National security relevance. Quantum sensing, secure communications, cryptography and high-performance computing are not abstract commercial categories. They are strategic capabilities. Israel’s defense and intelligence ecosystem has historically created talent and urgency in areas where technical advantage matters.
3. Israel’s semiconductor and systems heritage. Quantum is not just physics. It requires electronics, control systems, software, materials, packaging, lasers, cryogenics and manufacturing know-how. Israel has decades of experience in chips, communications, cyber and systems engineering. That combination is useful in quantum, where the bottleneck is increasingly shifting from theory to engineering.
4. Founder mindset. Quantum founders need scientific credibility, but they also need commercial aggression. The best Israeli startups tend to be good at turning technical depth into global go-to-market motion. That will be critical in quantum, where early customers may be in the U.S., Europe, Japan and government-backed research programs.
5. The presence of specialist capital.

A small ecosystem can become powerful when it has repeat founders, repeat investors and repeat technical talent. Israel’s quantum market is not there yet, but the ingredients are visible.

What should investors look for?

For early-stage investors, the most interesting quantum companies will likely share a few traits:

• A wedge into today’s market. The company should not depend entirely on a 2035 quantum breakthrough. It should have a path to sell something useful earlier: control systems, developer tools, simulation, security, benchmarking, optimization, sensing, or infrastructure for quantum labs and enterprises.
• A credible technical founder-market fit. In quantum, technical depth is not optional. The team needs to be world-class scientifically, but also capable of productizing complexity.
• A clear architecture bet. Founders should be able to explain why their approach can scale, what the key technical bottlenecks are, and how they will prove progress over time.
• A non-dilutive capital strategy. The best quantum startups will often combine venture money with government grants, research partnerships and strategic capital. That is not a weakness. In deep tech, it can be a feature.
• A commercial bridge. Even if the big market is years away, there should be a path to near-term engagement with customers: pilots, paid research, hardware access, cloud deployments, licensing or strategic partnerships.
• A defensible layer. In AI software, moats can erode quickly. In quantum, defensibility can come from patents, know-how, hardware integration, scientific talent, manufacturing process, data, partnerships and long technical roadmaps.

Quantum will not be a quick flip. It will require patient capital, technical humility and a willingness to back teams before the market is fully legible.

But that is exactly where venture is supposed to operate.

The next computing platform will not be built overnight. It may not even look like the platforms we know today. But if quantum becomes part of the future compute stack, the best time to build positions may be before the category becomes obvious.

Is now the right time?

Quantum is not a normal SaaS category. It does not fit neatly into the standard pre-seed checklist of MVP, early users, fast iteration and short sales cycles. Like many other deeptech investments, the best quantum companies may require longer timelines, more non-dilutive capital, deeper technical diligence and more patience than the average venture-backed startup.

That makes quantum a poor fit for tourist capital. But it can be a strong fit for investors who understand three things.

First, the market will not arrive all at once. The first venture-scale returns may not come from the final universal quantum computer. They may come from enabling layers: software tools, control systems, integration platforms, error mitigation, simulation, quantum-safe security, sensing and hybrid quantum-classical workflows.

Second, the customer set is different. Early customers may be governments, national labs, cloud providers, pharma companies, banks, defense contractors, semiconductor companies and industrial giants. Selling into that world requires credibility, partnerships and patience.

Third, the timing of infrastructure markets is strange. The best time to invest is usually before the market is obvious, but after the enabling conditions are in place. In quantum, those enabling conditions are increasingly visible: large public programs, more credible technical roadmaps, serious private rounds, early enterprise budgets, and growing geopolitical urgency.

The venture question is not “will quantum be huge?” It probably will. The better question is: “Which parts of the quantum stack can become valuable companies before fault-tolerant quantum computing is fully mature?”

At Remagine Ventures, we’ve been mapping this space closely, including through IsraelVC, where we recently charted the named funds behind ten Israeli agentic AI startups that raised in the last few months (more on that below). The exercise crystallised something we’d been feeling in deal flow for a while: “agentic AI” is not one category. It’s at least two, and they have almost nothing in common as investments.

Infrastructure vs. Vertical: two very different bets

Agent infrastructure is the plumbing: runtimes, sandboxes, orchestration layers, identity and permissions, observability, evaluation, and security/control planes that make it possible to run an agent in a production environment without it doing something catastrophic. This is the “picks and shovels” layer. It’s technically hard, often defensible through deep systems work, and it sells to other AI builders as much as to enterprises.

Vertical AI agents are the applications: agents that do a job, like triaging support tickets, running procurement workflows, doing compliance review, managing revenue operations, or writing code in a specific framework. This is where the outcomes live, and increasingly where the capital is concentrating. Vertical agents account for roughly half of 2026 agentic AI deal volume and an even larger share of dollars deployed, with the busiest verticals being cybersecurity, healthcare operations, finance ops, procurement, compliance, and customer-facing support.

The infrastructure layer is smaller in capital terms but disproportionately active. Investors are chasing the execution and control layers that make agents safe enough to deploy in regulated or high-stakes environments. That’s a tell: the market has moved from “can we build an agent” to “can we trust an agent,” and that question is an infrastructure problem before it’s a product problem.

For founders, this distinction matters enormously when you’re raising. Infrastructure pitches need to prove technical depth and a path to being the standard that vertical builders depend on. Vertical pitches need to prove workflow ownership, data moats, and that you’re not just a thin wrapper that the underlying model provider could absorb in a quarter. Investors have sharpened their filters accordingly: proprietary data, novel architecture, and deep integration into a customer’s workflow are now the line between a credible pitch and “a prompt with a UI.”

The three problems every agentic AI founder eventually hits

Whichever side of the infra/vertical line you sit on, there are structural challenges in this category that are different in kind, not just degree, from the SaaS era most of us grew up investing in.

1. Token costs are quietly eating the business model. This is the one that catches founders off guard, because it doesn’t show up until you have real usage. AI features are compressing gross margins from the 80-90% SaaS founders are used to down to 50-60%, because every agent action is a metered compute event, not a marginal-cost-zero software interaction. Goldman Sachs projects token consumption could grow roughly 24x, to around 120 quadrillion tokens a month, between 2026 and 2030.

And critically, falling per-token prices have not translated into falling bills, because usage intensity (longer contexts, more agent steps, more autonomous loops) is growing faster than prices are dropping. Several companies have reported blowing through their entire annual token budget in a single quarter. Aaron Levie’s recent post on this, token costs are becoming one of the hottest issues in AI economics captures exactly the dynamic we’re seeing across our portfolio: usage growth outpacing unit economics improvement, and founders discovering that “more usage” isn’t automatically “more margin.”

As tokens take on a significant amount of the cost of any given workflow, then companies will inevitably want to ensure that their dollars go into the most efficient use of tokens for the particular job at hand.

Aaron Levie, Box CEO

The startups we find most compelling right now are the ones treating cost governance and pricing architecture as a product problem from day one, not a finance afterthought to solve post-PMF. Marking up tokens is a race to the bottom; the winners are the ones who design around consumption rather than reselling it.

2. Non-determinism breaks the QA playbook. Traditional software is deterministic: same input, same output, testable, debuggable, repeatable. Agents are not. The same prompt, the same workflow, run twice, can produce two different action sequences, and in a multi-step agentic chain, small probabilistic deviations compound. This isn’t a bug to be fixed; it’s the nature of the technology, and it means founders building in this space need fundamentally different approaches to evaluation, monitoring, guardrails, and rollback than anything in the previous software generation’s toolkit. We’d argue this is the reason the “control plane” sub-category of infrastructure (evals, observability, agent monitoring) has become one of the most actively funded corners of the market: enterprises will not deploy what they cannot predict, audit, or roll back.

3. Security is a different shape of problem entirely. Giving an autonomous system the ability to take actions (call APIs, move money, touch customer data, write and execute code) opens an attack surface that didn’t exist in the chatbot era. Prompt injection, data exfiltration through tool use, identity and permission sprawl across agent fleets, and the simple fact that an agent can be socially engineered the way a human employee can: these are not edge cases, they’re the central design challenge. It’s no surprise that some of the sharpest recent rounds we’ve tracked are security-for-agents plays (Onyx Security’s $35M Series A with Conviction, building on an earlier Cyberstarts seed, is a good regional example). Expect “agent security” to do for this wave what “cloud security” did for the SaaS wave, except compressed into a fraction of the time.

Where we’re spending our time

As an early-stage fund with deep Israeli roots and a thesis around founders building from “the edge” (gaming, consumer, and now agentic systems), we’re drawn to teams that treat these three challenges (cost, non-determinism, security) not as risks to be managed around the edges, but as the actual product surface area. The founders who are going to build enduring companies here are the ones designing for unpredictable, metered, adversarial environments from the first commit, not retrofitting governance once an enterprise customer asks an uncomfortable question in a security review.

We mapped 32 named funds across just ten recent Israeli agentic AI raises, from Team8 and Glilot to Insight Partners, Index Ventures, and Lightspeed, which tells you how fast capital is moving into this space.

See the full breakdown on IsraelVC

The most interesting part is not only the size of the IPO. It is the story SpaceX is telling investors. SpaceX is no longer being positioned simply as a launch company, or even as Starlink. It is being pitched as a platform for opening entirely new markets: global connectivity, satellite infrastructure and, perhaps most futuristically, AI compute in space. Reuters reported that SpaceX’s IPO roadshow described a potential $23 trillion opportunity around space-based AI initiatives, including data centres and internet infrastructure in orbit.

That may sound like science fiction, but it is becoming a serious engineering conversation. Google’s Project Suncatcher is exploring solar-powered satellite constellations equipped with TPUs and connected by free-space optical links, effectively asking whether large-scale machine learning infrastructure could one day be built in space.

The appeal is obvious. Terrestrial data centres are increasingly constrained by power, cooling, water, land, grid connections and permitting. In orbit, the theoretical advantages are significant: abundant solar energy, less competition for physical space, and the ability to use radiative cooling rather than rely on scarce water or air-based cooling infrastructure. Recent academic work on orbital AI data centres explores architectures powered solely by solar energy, using radiative cooling and shielding to manage heat and radiation.

It is important not to oversimplify this. Space is not magically “cold” in a way that solves every thermal problem. Hardware still needs to survive radiation, micrometeoroids, launch constraints, maintenance challenges and communications bottlenecks. But the direction of travel is clear: as AI infrastructure becomes one of the world’s largest physical buildouts, space is entering the conversation not as fantasy, but as a possible extension of the compute layer.

In other words, space is cool again because it is becoming economically relevant.

It is no longer only about planting flags. It is about solving Earth’s problems from above.

Israel already had its moonshot moment

For Israel, the emotional starting point of the modern space-tech story is SpaceIL and Beresheet.

In 2019, Beresheet became Israel’s first lunar mission and the first attempt by a private company to land on the Moon. The spacecraft launched on a SpaceX Falcon 9, achieved lunar orbit, and was ultimately lost during its landing attempt in April 2019. NASA described the mission as an “incredible accomplishment” and noted that Beresheet carried a small laser retroreflector as part of the mission.

Beresheet did not complete a soft landing, but it did something arguably more important: it changed the perception of what a small country and a small private team could attempt. A country of fewer than 10 million people reached lunar orbit with a privately led mission. That matters.

The follow-up mission, Beresheet 2, has faced funding challenges, but the original Beresheet mission remains a powerful symbol for Israel’s space ecosystem: ambitious, resourceful, technically bold and willing to attempt something that only a handful of countries had ever tried.

From national projects to commercial space

Historically, Israel’s space capabilities were built around national security: satellites, intelligence, communications, aerospace and defence. That foundation is now turning into a broader commercial ecosystem.

StarBurst Aerospace, an incubator and accelerator facilitating connections between space startups and corporates included 120 startups in its mapping of the Israeli space tech ecosystem published in November 2025.

The key forces shaping Israel’s SpaceTech ecosystem:

• Dual-use is becoming the default. Defence and commercial space are increasingly converging, from propulsion and resilient infrastructure to ISR, communications and geospatial intelligence.

• Universities are powering the pipeline. Institutions like the Technion, Tel Aviv University and Ben-Gurion University are helping turn advanced research into practical space-tech applications.

• The ecosystem is spreading beyond Tel Aviv. Emerging clusters in Haifa, Be’er Sheva and other hubs are making Israeli SpaceTech more distributed and nationally rooted.

• Capital is starting to deepen. More local funds, corporate strategics and international VCs are paying attention as Israeli space startups mature from R&D projects into investable companies.

This is exactly where Israel tends to be strong: when deep technical capability, defence urgency, software talent and entrepreneurial speed collide.

The use cases: why space tech is no longer niche

A useful way to understand the breadth of the space economy is the a16z Space Market Map, which breaks the sector into launch, satellites, manufacturing, software, logistics, servicing, resources, exploration, habitation and in-space manufacturing. The important point is that “space tech” is not one market. It is an emerging stack: infrastructure, hardware, software, data and services, with multiple entry points for startups.

Space tech is becoming a horizontal layer across multiple industries.

Defence and national security are the most obvious. Satellites are critical for intelligence, surveillance, missile warning, resilient communications, navigation and battlefield awareness. In an era of contested airspace, drone warfare and geopolitical instability, sovereign space capability is becoming strategic infrastructure.

Geospatial intelligence is another major category. Earth observation can help monitor crops, infrastructure, shipping, borders, construction, climate risk, emissions and natural disasters. Israeli companies already have strong capabilities in computer vision, AI, sensing and data analytics — all highly relevant to extracting value from satellite data.

Connectivity remains one of the biggest commercial opportunities. Starlink showed the world what a space-based connectivity layer can look like at scale. Israeli companies such as hiSky and BeetleSat / NSLComm sit in this broader satellite communications opportunity.

Climate and infrastructure monitoring is becoming increasingly important. Satellite-based systems can detect methane leaks, water leaks, soil conditions, infrastructure stress and environmental changes. This is not just “nice to have” ESG reporting; it can become core infrastructure for energy companies, utilities, insurers and governments.

Space-based computing is perhaps the newest frontier. Companies such as Ramon.Space, which builds space-resilient computing systems, sit directly in the path of a future where satellites are not just sensors or relays, but compute nodes.

Mining and resources are also moving from science fiction to early-stage commercial exploration. The Moon and asteroids may not be near-term commercial markets for most startups, but the technologies developed for space resource utilisation can have applications on Earth too: energy, materials, robotics, sensing and industrial automation.

And then there is science and manufacturing: microgravity experiments, pharma research, advanced materials, optics, quantum, robotics and semiconductors. Some of these markets may take longer to mature, but they are exactly the kind of hard technical domains where Israel can punch above its weight.

The investors are showing up

The capital stack around Israeli space tech is still early, but it is becoming more structured. Earth & Beyond Ventures launched with $125 million to invest in Israeli space and deep-tech startups, with backers including strategic corporates and the Israel Innovation Authority.

Beyond dedicated space funds, the investor base now includes Israeli VCs, international deep-tech and climate investors, corporate strategics, defence-linked capital and government-backed non-dilutive funding.

Why the timing matters

For years, the dominant venture theme was bits over atoms. Software ate the world because it scaled faster, carried higher margins and required less capital than building in the physical world.

But AI is changing that equation.

As code becomes easier and cheaper to produce, pure software moats are becoming harder to defend. Distribution, proprietary data, workflow ownership, regulatory positioning and deep technical complexity matter more. The companies that win may increasingly be those that combine software speed with real-world defensibility.

That shift could benefit categories that combine software with atoms: robotics, defence, energy, semiconductors, advanced manufacturing and also space tech.

Not because space suddenly became easy. Quite the opposite. The difficulty is part of the moat. Hardware constraints, regulation, mission-critical customers, proprietary sensor data, aerospace-grade reliability and long sales cycles are not bugs in this market; for the right team, they are sources of defensibility.

Israel is well suited to that kind of company formation. The country is unlikely to compete with SpaceX on launch, and it does not need to. The more natural opportunity is in the enabling layers: resilient compute, optics, sensors, cyber, AI models, satellite payloads, geospatial intelligence, mission software, defence applications and data infrastructure.

Many of the most interesting Israeli space-tech companies may not look like “space companies” at first. They may look like climate intelligence companies, defence software companies, robotics companies, semiconductor companies or advanced data platforms.

That is what makes the category more investable now. Space is moving from national prestige project to commercial infrastructure.

From moonshot to market

SpaceIL gave Israel its symbolic moonshot. Beresheet did not complete the soft landing, but it proved that a small country, a private team and a resource-constrained ecosystem could reach lunar orbit and capture the imagination of an entire generation.

The next phase is different. It is less about proving that Israel can get to the Moon, and more about proving that Israeli startups can build the infrastructure, data layers and mission-critical technologies that the space economy will depend on.

SpaceX may become the public-market moment that wakes up global investors to the size of the opportunity. But the Israeli opportunity is more focused: build the picks and shovels for the next era of space, from Earth observation and defence to climate, communications, compute and resource exploration.

In a world where software is becoming easier to create, the frontier may shift back toward companies that can combine software speed with hard technical depth. Space tech sits exactly at that intersection.

For Israel, that makes it a sector worth watching closely.

Can you believe it’s June already?

The original version of #FIRGUN was published on LinkedIn to over 8,000 subscribers.

Firgun (pronounced ‘feer-goon’) is an Israeli term describing genuine, unselfish joy in someone else’s success… It’s basically the opposite of “schadenfreude”. Every week since March 2020, we celebrate the wins of our startup community. Read more about why Firgun matters. If this was forwarded to you and you’d like to subscribe, previous editions are on VC Cafe.

This week in Israel was a reminder of the strange duality of the market right now: geopolitical uncertainty and layoffs on one side, and remarkable investor appetite on the other. Israeli tech companies raised more than $1.1 billion in a matter of days… You’ll find the full breakdown in the funding section below. Exits continued as well, driven by a hot defence tech market, where Israeli battle-tested tech is at a premium. Israel’s tech sector is under pressure, but capital is still flowing to companies solving urgent problems in AI infrastructure, cyber, defence, automation and resilience.

The wider tech world this week was dominated by mega-rounds (more capital concentration), AI infrastructure news, and the public-market reopening. Ramp raised $750 million at a $44 billion valuation, Supabase raised $500 million at a $10.5 billion valuation as AI coding tools drive demand for backend infrastructure, Impulse Space raised $500 million, Flourish AI Labs raised $500 million to build low-power “brain-like” AI, Helion, the Sam Altman-backed Fusion Energy Startup, raised $465 million at a $15.5 billion valuation, Generalist raised $400 million for robotics, Suno raised more than $400 million, and AlphaSense raised $350 million. The platform wars also intensified: OpenAI models and Codex became generally available on Amazon Bedrock, Microsoft pushed deeper into agentic computing with Scout (an always on personal agent), MAI models and Project Solara (a new agent platform), while NVIDIA and Microsoft unveiled RTX Spark PCs for local AI agents. Apple also quietly moved into the agent economy, approving Poke as the first AI agent on Messages for Business, while its App Store ecosystem hit $1.4 trillion in 2025 billings and sales, with consumer AI apps growing faster than the broader app economy. On the infrastructure side, Cloudflare acquired VoidZero (acquired by Cloudflare), the company behind Vite and related JavaScript tooling, reinforcing how strategic developer infrastructure has become in the AI-native web. And on the frontier AI side, Anthropic said more than 80% of code merged into its own codebase is now authored by Claude, while the FT reported the company is helping the NSA deploy Mythos for offensive cyber operations, a sign that frontier models are moving from productivity tools into national-security infrastructure. Meanwhile, the IPO window continued to warm up, with Quantinuum raising $1.68 billion in its Nasdaq debut and SpaceX preparing what could become the largest IPO ever (raising $75 billion at over $1.75 Trillion valuation). The takeaway: AI is no longer just a product cycle: it is reorganising cloud, chips, developer tools, robotics, energy, defence, consumer interfaces and capital markets. NVIDIA also directly challenged Intel and AMD by unveiling its new “M1X” central processing unit (CPU) for personal computers, a piece of silicon co-developed alongside Microsoft as hyperscalers aggressively rush to build local, on-device AI architectures.

QUOTE OF THE WEEK

“Not all those who wander are lost.” — J.R.R. Tolkien

NEW FUNDING ROUNDS

• Big congrats Ido Susan and team DriveNets on securing a $410M series D at $8.5 billion valuation to allow Telcos and AI developers to build, scale, and manage their networks like modern cloud data centres!
• Way to go Yotam Segev and team Cyera on completing a $300M round at $12 billion valuation (just months after your previous $400M round in January!) to continue scaling your comprehensive Data Security Posture Management (DSPM) and Data Loss Prevention (DLP) solution!
• Well done Ariel Assaraf and team Coralogix on your $200M series F to obliterate slow, costly, and outdated observability with AI!
• Mazel tov Erez Freibach and team ZutaCore on raising a $100M series C for your direct-to-chip liquid cooling designed for high-performance computing (HPC) and AI workloads!
• Kudos Itai Ben-Zaken and team Honeycomb Insurance on securing a $40M series C to reinvent real estate insurance!
• Kol hakavod Gal Rimon and team Centrical on raising $39M series D to measure frontline employee performance with AI!
• Thumbs up Ofer Ballin and team Shifters on coming out of stealth with $10.2M seed round to build autonomous ground robotic systems and AI software platforms for hazardous environments!
• Congratulations Eyal Ben Ezra and team Willow on coming out of stealth with $7M in funding for an AI agent control layer in enterprises!
• Good stuff Dan Bendler ?? and team Offroad on coming out of stealth with a $7M seed round to build an AI identity security team for the modern enterprise!
• Nice job Noam Tenne and team Sky Valley Ambient Computing on securing a $2M pre-seed round to make websites adaptive by default!

EXITS

• Big congrats Zohar H. and team D-Fend Solutions on the $1.5 billion acquisition by Motorola Solutions for your counter-drone technology for critical infrastructure!
• Way to go Ronnen Lovinger and team DustPhotonics on completing the $954M acquisition by Credo Technology Group, LLC for your silicon photonics technology for data centres and AI applications!

LINKS FOR YOUR BROWSER

VC CAFE/ REMAGINE VENTURES/ ISRAEL VC

• Israeli High-Tech 2026: Stronger, Bigger and More Vulnerable Than It Looks – a recap of the IIA state of Israeli tech 2026 report
• Capital Is Concentrating: What 13 Israeli $100M+ Rounds Tell Us About the 2026 Market – almost $4 billion were raised by 13 companies in the past 3 months alone. We’re seeing a huge concentration of capital in tech globally, and there are real implications on graduation rates from seed to series A and onwards.
• Consumer AI Is Back. Retention Will Decide Who Wins – AI is invigorating consumer tech, and Israeli B2C startups are no exception. What are the top categories?

ISRAEL

• Israeli High-Tech Annual Report 2026 – by the Israel Innovation Authority ???? ???????
• Most people across 36 countries have negative views of Israel and little confidence in Netanyahu – A bit depressing, but some bright spots, new research by Pew Research Center
• The $400 Drone Bomb and the $19B Defense Export Boom – “The contrast is absurd. Israel exported $19.2 billion worth of military technology last year, an all-time record. Meanwhile, Israeli soldiers in Lebanon are defending themselves with fishing nets against drones that cost less than a night at a standard Tel Aviv hotel” – by Amir Mizroch

GLOBAL

• The InfraRed report on the state of AI infrastructure in 2026 by Redpoint
• A functional taxonomy of world models – by Fei-Fei Li
• The state of agentic eCommerce by Bain & Company
• Bonus – the case for AI datacentres in space (easier to cool, free solar energy, no space constraint, cheaper to send payload to space)

MEDIA OF THE WEEK

Did you know that most of the Growth in Israeli High-Tech Output in 2025 Came from Hardware? (source)

How capital is concentrating in Israeli tech and expands from traditional SaaS to more specialised sectors (source)

Take this ranking with a grain of salt (because we’re missing China) but nevertheless, Israel ranked #3 globally in innovation according to StartupBlink (source)

Visual code generation landscape by Andreessen Horowitz (source)

The semiconductors supply chain chokepoints

It’s easier than ever to vibe code an app, but it’s equally hard to sell it.

Where do the ultra-rich choose to live in 2026?

Stripe Atlas data suggests solo-founder companies are being created at a faster rate than companies with multiple founders, across both AI and non-AI categories (source).

That’s all for this week, thank you for reading! There’s a lot to celebrate in our “small” community! Keep on creating!

Eze Vidra

That makes sense. Enterprise budgets are large, ROI is easier to explain, and the buyer has a spreadsheet.

But while the market has been obsessing over enterprise AI, something equally important has been happening on the consumer side: AI has quietly become a daily habit.

Hundreds of millions of consumers are already using AI assistants, image generators, AI video tools, AI music apps, coding tools, study companions, shopping assistants and character bots. According to Menlo Ventures, more than 60% of U.S. adults used AI in the first half of 2025, while consumer AI spend was still only around $12 billion, with just a small percentage of users paying. That gap between adoption and monetisation is exactly where the opportunity sits.

A16Z’s latest ranking of the top 100 generative AI consumer apps tells a similar story. ChatGPT remains the dominant consumer AI product, reportedly reaching around 900 million weekly active users, but the category is no longer just about one assistant. Gemini, Claude, Grok, Perplexity, Character.AI, creative tools, coding apps, AI video platforms and emerging agents are all training consumers to expect software that understands intent, generates output, remembers context and increasingly acts on their behalf.

This is the part that matters for founders: consumer AI is not one category. It is a new interface layer for every consumer category.

Dating is being rebuilt around better matching, coaching, trust and intent. Shopping is moving from search and comparison to delegation. Gaming is shifting from static content pipelines to AI-assisted studios, generative worlds and live personalisation. Entertainment is moving from passive consumption to creation, remixing and synthetic media. Personal assistants are becoming context layers across work, life and commerce.

In other words, the next generation of consumer startups will not simply “add AI” to existing apps. They will re-architect the experience around AI from first principles.

AI changes the interface. It does not remove the laws of consumer investing.

Consumer investing has always been unforgiving. The best companies look obvious in hindsight, but early on they often look messy, weird or too niche. The market does not care about your narrative. It cares whether people use the product, tell their friends, pay for it and keep coming back.

AI does not change that.

If anything, it makes the bar higher. AI makes it easier to build, easier to launch and easier to create something that looks impressive for five minutes. That means attention gets more expensive and novelty decays faster.

For AI consumer startups, I care much less about the first demo and much more about the following:

• What does the D1, D7 and D30 retention curve look like?
• Are the best cohorts improving or deteriorating?
• Is there a natural frequency of use?
• Are users generating, consuming or transacting repeatedly?
• Is there evidence of willingness to pay?
• Does paid acquisition work, or is ROAS negative once the AI costs are included?
• Are users inviting others, sharing outputs or creating organic distribution?
• Does the product get better with more context?
• Is this a feature, a workflow, a network or a habit?

The most dangerous trap in consumer AI is confusing intensity of reaction with retention. A user can be amazed once and never return. They can post a screenshot and churn. They can join the Discord and never activate. They can generate 20 images in one evening and never pay.

That is not a consumer company. That is entertainment.

The best AI consumer startups create a loop. The user does something, gets value, leaves behind context or content, and has a reason to return. Over time, the product should know more, personalise better, produce better outputs, or become more useful because of the user’s history.

The AI magic moment matters. But the cohort curve matters more.

The LLMs are now consumer platforms

The large model companies are not just infrastructure providers anymore. They are consumer platforms.

ChatGPT, Gemini, Claude, Perplexity, Grok and Meta AI are becoming new front doors to the internet. For many users, the LLM is where they start a task: searching, comparing, writing, planning, shopping, learning, coding or creating.

That has major implications for founders.

First, LLMs are becoming distribution layers. If consumers begin product discovery, travel planning, content creation or shopping inside an assistant, the assistant becomes a gatekeeper. Founders used to optimise for SEO, app stores, social feeds and paid acquisition. Now they also need to think about how AI assistants discover, recommend and execute.

Second, the model labs are moving up the stack. OpenAI is building shopping, memory, images, voice, video and agents. Google can connect Gemini to Search, Android, YouTube, Maps and Workspace. Meta can distribute AI through WhatsApp, Instagram, Messenger and Facebook. These companies are not neutral suppliers. They are increasingly competitors.

That kills a lot of thin wrappers.

If your product is just a generic chat interface on top of a model, you are exposed. If your feature can be copied into ChatGPT, Gemini or Claude with better distribution and lower cost, it probably will be.

But LLMs also create the opening. They teach consumers new behaviours. They normalise AI interfaces. They expand what people are willing to delegate to software. And they make vertical, opinionated products more valuable where the horizontal assistant is too generic.

The startup opportunity is not to build a worse ChatGPT. It is to own a specific context, workflow, community, content layer, transaction, data asset or habit that compounds over time.

The consumer AI categories that matter

AI is touching every consumer category, but not every category will produce venture-scale outcomes. I would focus on the places where AI changes either frequency, creation, personalisation, transaction or identity.

Gaming: creation gets cheaper, discovery gets harder

Gaming is one of the most interesting categories because AI changes both the supply side and the operating layer.

It is reducing the cost of prototyping, concept art, dialogue, localisation, QA, asset generation, analytics and live operations. Smaller teams can move faster. Studios can test more ideas. UGC can become more powerful. AI NPCs and adaptive gameplay can create more personalised experiences.

But this also creates a problem. If AI makes it easier to create games, it increases the number of games. The bottleneck moves from production to distribution.

That is why I am more interested in AI that improves iteration, retention and monetisation than in generic “prompt-to-game” demos. In gaming, the hard questions are familiar: are players coming back, are sessions getting longer, are cohorts improving, is LTV expanding, does the game have a content loop, and can paid acquisition work?

Israeli examples are relevant here because Israel has deep gaming DNA. Playo.ai is working on prompt-to-playable gaming. Keewano is building AI intelligence for gaming and consumer app teams. The real opportunity is not AI as a gimmick, but AI as an operating advantage: faster testing, better live ops, smarter segmentation and improved retention.

Entertainment and creators: output is abundant, taste becomes scarce

AI video, voice, music, avatars and editing tools are turning more people into creators. That is exciting, but it also creates a new problem: if everyone can produce content, content itself becomes less scarce.

Taste, workflow and distribution become more important.

The best creator AI tools do not just generate output. They help users make something good, make it faster, adapt it to a platform, learn from performance and repeat the workflow. A creator tool with weak retention is usually just a toy. A creator tool with repeat usage, paid conversion and organic sharing can become infrastructure.

This is where companies like Lightricks, D-ID, Hour One, Munch and Decart are relevant from Israel. They each touch a different part of the generative media stack: editing, avatars, synthetic video, short-form repurposing and real-time generative media. Dazl is adjacent in AI-assisted application creation, moving from prompt to usable product.

This category is tough, but continues to attract investment. Just this week, Suno announced a $400M series D, in February Runway raised $315M series E and ElevenLabs raised $500M series D at $11 billion valuation. All three offer both B2B and B2C products that have become part of the infrastructure layer for companies and consumers alike. Whether you’re creating a gaming studio or Youtube videos, you’ll need music, video editing and voice over.

The investor question in this category is not “is the output impressive?” It is “does the product become part of the creator’s workflow?”

Shopping and commerce: from search to delegation

Shopping is moving from search to delegation.

The old consumer journey was keyword search, tabs, reviews, price comparison and checkout. The AI-native journey is more conversational: “Find me the best carry-on bag for a three-day work trip, under £250, that fits European cabin restrictions and does not look cheap.” The assistant asks questions, narrows the options and may eventually transact.

OpenAI reversed course when it comes to agentic shopping. It shut down its Checkout product shortly after it launched. But it’s not the end of agentic commerce. Consumer habits are changing fast and this changes who owns demand. Retailers and brands used to optimise for search, marketplaces and social platforms. Increasingly, they will need to be visible to agents and recommendation systems.

ZyG is an interesting Israeli example, not because it is a classic consumer app, but because it sits inside the consumer commerce value chain. Founded by ironSource alumni, ZyG is building an AI-powered commerce engine for product growth and DTC scale. That reflects a broader point: some of the best consumer AI companies may be B2B2C. They may power the brands, stores, creators and platforms that reach the end consumer.

In commerce, I would look hard at ROAS, contribution margin, payback periods and whether AI improves conversion enough to justify the cost. “AI shopping assistant” is not enough. The question is whether it changes purchase behaviour.

Dating and social: AI can help, but trust is the product

Dating is a good example of where AI is both promising and dangerous.

Swipe fatigue is real. Users want better matches, less wasted time and more authentic connection. AI can help with profile quality, safety, intent, matching, coaching and event-based discovery.

But dating is also one of the most trust-sensitive consumer categories. Too much automation can make the experience feel fake. AI-written messages, AI-enhanced profiles and synthetic personas can easily make the problem worse.

The winners will not be the apps that automate dating. They will be the products that use AI to improve confidence, trust and connection.

Social is hard to penetrate and scale. Instagram, Snap, X are nearly two decades in the making. Newer social networks are either too niche (and therefore less VC-backed) or fade quickly (remember Clubhouse, the buzzy audio social network? Or BAYC?)

To succeed in social and dating, the metrics here are not just matches or messages sent. They are repeat engagement, successful conversations, offline conversion, safety outcomes and whether users feel less exhausted by the process.

In consumer AI, automation is not always the product. Sometimes the product is trust.

Personal assistants: horizontal interfaces, vertical habits

The horizontal assistants are already huge. That is obvious.

But the existence of ChatGPT or Gemini does not mean every vertical assistant dies. It means the vertical assistant has to be much more specific. A good example is Town, which just raised $55M series A. Town built a personal AI assistant that works across the tools you already use: email, calendar, Slack, docs, WhatsApp, desktop, web. It learns how you work and starts proactively pitching in. Not because you configured it just right, but because it actually understands you and how you work.

A great AI travel product should know your family constraints, budget, food preferences and tolerance for chaos. A great parenting assistant should understand age, safety and context. A great finance assistant should know your actual spending and goals. A great health or wellness companion needs trust and continuity. A great consumer productivity tool should reduce friction in a recurring workflow.

The question is not whether a horizontal model can technically answer the same prompt. It probably can. The question is whether the vertical product has context, habit, data, UX and trust that make it meaningfully better.

That is the narrow path for consumer AI assistants. The goal in this category, is to build a Jarvis-AI type of assistant we all know from Iron-Man.

Consumer hardware: the surprising return of atoms

One emerging area that has become more investable as software moats get eroded by AI is consumer hardware.

For the past decade, many VCs were allergic to consumer hardware. It was capital intensive, operationally complex, lower margin and hard to scale. Software looked cleaner. But AI is changing that equation. If software features are easier to copy, the physical object can become part of the moat.

I am seeing more founders building around smart wearables, specialised headphones, desktop games, connected collectibles, toys, music instruments and other physical products that combine AI, sensors, community and software. Some of these products are gaining real traction and attracting venture funding because they offer something a pure app often struggles to create: presence, ritual, identity and daily usage. Case in point, digital table top startup Board, just raised $20M series A led by USV.

The bar is still high. Hardware does not get a free pass because it has AI inside it. The investor questions are different but equally unforgiving: are people pre-ordering, paying, using it repeatedly, sharing it, upgrading it, buying accessories, joining the community, and telling others about it? What are the gross margins? What is the supply chain risk? Is the AI layer essential or cosmetic? Does the product become better with data, context and software over time?

The most interesting consumer hardware companies are not gadgets. They are software-enabled consumer products where the physical object creates habit, defensibility and emotional attachment. In a world where many apps can be cloned quickly, that may become more valuable again.

What the best founders are getting right

The strongest AI consumer founders I see are not selling “AI”. They are selling a better consumer outcome.

They start with a real behaviour. They know the natural frequency of use. They understand the wedge. They can explain why the user comes back. They care about onboarding, activation and cohort retention. They know their CAC, even if early. They know whether users are willing to pay, and what the gross margin looks like after inference costs.

AI is a means to an end. It can be helpful in accelerating time to market, fine tuning GTM and making more with less. But it doesn’t replace the need for product-market-fit. Creativity and originality are still at the heart of successful consumer products, even if they rely on AI heavily.

They are also honest about distribution. In consumer, “we will go viral” is not a strategy. The best teams understand whether growth will come from creator-led distribution, UGC, referrals, SEO/GEO, app stores, paid acquisition, influencers, communities, partnerships or embedded workflows.

Most importantly, they understand that AI should improve the loop. Better recommendations, better outputs, better personalisation, better timing, better conversion, better retention. If AI only improves the demo, it is not enough.

What I would avoid

I would be cautious with five types of consumer AI startups.

1. Thin wrappers with no retention. If the product has no workflow, no context and no reason to return, the model labs will absorb it.
2. Novelty products. Fun once is not enough. I’ve seen ‘personal robots’ that could just be an app on your phone.
3. Products with expensive usage and weak monetisation. AI can create bad gross margins very quickly if users generate a lot and pay little.
4. Apps that rely entirely on paid acquisition before proving retention. You can buy downloads. You cannot buy love.
5. Products where automation damages the core experience. In dating, social, kids, wellness and companionship, the human element is often the product.

The Israeli and investor lens

Israel is usually discussed in AI through cybersecurity, chips, infrastructure and enterprise software. That misses part of the picture.

There is a real Israeli opportunity in consumer AI, especially around gaming, entertainment, commerce, creative tools and AI-native product creation. These are areas where Israel already has talent, global distribution experience and founder muscle memory from gaming, adtech, mobile apps and commerce.

Some will be AI-native studios. Some will be creator tools. Some will power gaming teams. Some will sit inside commerce infrastructure. Some will sell to consumers directly. Others will sell to the companies that reach consumers.

There is also a renewed opening for Israeli founders building AI-enabled consumer hardware, from gaming peripherals and smart wearables to connected toys, music devices and collectibles, especially where the hardware creates a defensible daily ritual and the software layer compounds over time. A recent example, Israeli startup Musical Beings has raised 20x their target in a recent Kickstarter campaign.

Another example, that also happens to be in music, is Heavys – the first headphones designed for Heavy Metal. With over 20,000 units sold, the company found a real niche and is able to compete with giants.

For investors, the bar should be simple but unforgiving:

• Retention
• Engagement.
• Cohort analysis
• Willingness to pay
• Organic distribution
• ROAS if you are using paid acquisition
• Why this gets better with scale
• Why this does not become a feature for another product

Consumer AI is back, but the playbook is not “AI everywhere”. The playbook is still consumer investing: behaviour, frequency, emotion, distribution and monetisation.

The difference is that AI changes what is possible inside the product.

The companies that win will not be the ones with the most impressive demo. They will be the ones that turn AI into a habit.

Israeli founders: If you’re building in consumer AI, reach out to us at Remagine Ventures. We’re always happy to chat and provide constructive feedback.

That is only partially true.

What is really happening is more nuanced: capital has become significantly more concentrated. It is not flowing evenly across the ecosystem, and it is certainly not rewarding “AI wrapper” stories indiscriminately. But for a small number of companies sitting in the critical layers of the AI economy, the checks are still very large.

In the past three months alone, from March to early June 2026, 13 Israeli and Israeli-founded startups raised rounds of $100 million or more. Together, these companies raised approximately $3.41 billion.

The rounds were not concentrated in generic SaaS. They were clustered around AI infrastructure, data security, networking, observability, defense tech, healthcare AI, quantum computing, cloud optimization, machine identity and data center cooling. In other words, the capital is flowing into the picks and shovels of the AI era, the trust layer around enterprise adoption, and the physical infrastructure required to keep the whole system running.

This is not a broad-based boom. It is a flight to category leaders.

The $100M+ Club: March–June 2026

Total: approximately $3.41B across 13 companies
Average round size: approximately $262M

What the Mega-Rounds Tell Us

The Israeli tech market has split. On one side, late-stage category leaders with strong traction, real customers, strategic relevance and a credible path to becoming large independent companies are able to raise large amounts of capital. On the other side, many early and growth-stage startups are still facing a much harder fundraising environment, especially in crowded software categories where AI is compressing differentiation.

This is the K-shaped venture market in action.

The companies at the top of the K are benefiting from three tailwinds at once:

1. The global AI infrastructure buildout
2. Enterprise urgency around security, data and automation
3. Strategic investor appetite for companies that can become foundational platforms

The companies at the bottom of the K are discovering that “we use AI” is no longer enough. AI has moved from novelty to table stakes. Investors are asking harder questions: What is the durable moat? What is the distribution advantage? Why does this need to be a venture-backed company? What happens when the model providers move into your market?

Israel’s AI Infrastructure Moment

The most striking feature of the list is how much of the capital is tied, directly or indirectly, to AI infrastructure.

Vast Data’s $1 billion round is the most obvious example. The company sits at the intersection of data, storage and AI workloads. As model training and inference become more data-hungry, the infrastructure layer becomes more valuable. The same is true for DriveNets, which is building networking infrastructure for the AI era, and ZutaCore, which tackles one of the least glamorous but most important bottlenecks in AI: cooling the data centers that power it.

ScaleOps is another example of the same pattern. AI has made compute both more strategic and more expensive. Companies are under pressure to do more with limited GPU and cloud capacity. The winners in this category are not just reducing cost; they are helping enterprises make AI economically viable at scale.

This is an important shift for Israeli tech.

Historically, Israel’s biggest technology outcomes have often come from cyber, semiconductors, enterprise software and deep technical domains. AI infrastructure brings those strengths together. It rewards systems thinking, technical depth, military-grade engineering cultures and global go-to-market ambition. Those are areas where Israeli founders have historically punched above their weight.

Cybersecurity Is Becoming AI Security

Cyber remains central, but the category itself is changing.

Cyera’s $300 million round at a reported $12 billion valuation is a clear signal that data security is becoming one of the most important layers in enterprise AI adoption. AI systems are only as safe as the data they can access. As enterprises connect LLMs and agents to internal systems, the old perimeter-based security model becomes less relevant. The key questions become: Where is sensitive data? Who or what can access it? Can we enforce policies across humans, machines and agents?

Oasis Security points to the same trend from another angle. Machine identities already outnumber human identities in many enterprises, and AI agents will accelerate that imbalance. The more software acts autonomously, the more important it becomes to govern non-human identities, permissions and access.

This is why Israeli cyber continues to attract large rounds. The category is not static. It keeps expanding into adjacent layers of the technology stack. Cloud security became data security. Data security is becoming AI security. Identity is becoming machine identity. SOC automation is becoming agentic security operations.

The underlying theme is trust.

Enterprises want to deploy AI, but they cannot do it blindly. The companies that make AI adoption safer, more governable and more compliant are becoming critical infrastructure.

Defence Tech Has Entered the Mainstream

Kela’s reported $200 million round is another sign of a broader change: defence tech has moved from a niche VC category to a mainstream strategic priority.

This is not just an Israeli story. Across the US and Europe, governments are increasing defence budgets, modernising procurement, and looking for software-first solutions in areas like autonomy, command and control, cybersecurity, drones, logistics and battlefield intelligence. Israel, for obvious reasons, has a unique density of founders with relevant operational experience.

The question for Israeli defense tech startups is whether they can translate local urgency into global scale.

That requires more than technical capability. It requires procurement expertise, regulatory fluency, long sales cycles, trusted partnerships, and often a US or European market entry strategy. The opportunity is enormous, but the bar is also high. Defense tech companies need to be both startup-fast and institutionally credible.

AI Is Moving from Demos to Deployment

Wonderful, Aidoc, Decart and Tomorrow.io represent different versions of the same broader shift: AI is moving from impressive demonstrations to production-grade systems.

Wonderful is building enterprise AI agents. Aidoc is applying AI in clinical workflows, where the cost of failure is high and adoption depends on trust, validation and integration. Decart is pushing into real-time AI infrastructure and world models. Tomorrow.io combines weather intelligence, satellites and AI to deliver operational resilience.

These are very different companies, but they have one thing in common: they are not just selling a chatbot interface.

They are applying AI to domains where accuracy, latency, reliability, data, workflow integration and domain expertise matter. That is where venture-scale AI companies are more likely to emerge. The value is not in the model alone. It is in the system around the model.

This distinction matters because the market is becoming flooded with thin AI applications. Many of them can generate early revenue. Far fewer can build durable advantage.

The Israeli companies raising the largest rounds are not necessarily the ones with the flashiest demos. They are the ones addressing painful, expensive, mission-critical problems.

The Return of Deep Tech

Quantum Art’s $140 million round is a reminder that not all of the action is in near-term enterprise software.

Quantum remains a technically difficult and commercially uncertain category, but the strategic importance is obvious. Countries, corporations and investors are racing to secure positions in the next generation of computing. Israel has a strong academic and defense-adjacent talent base, making it a credible player in areas like quantum computing, photonics, sensors and advanced materials.

The same deep tech logic applies to data center cooling and AI infrastructure. The next wave of software will increasingly depend on hardware, energy, cooling, networking and specialized compute. The clean separation between “software” and “infrastructure” is starting to break down.

For investors, this means the venture playbook is also changing. The best opportunities may require more technical diligence, longer timelines, more capital intensity and stronger strategic partnerships. But they may also produce companies with deeper moats than yet another workflow automation tool.

The Market Is Strong, But Not Easy

The $3.41 billion raised by these 13 companies is a positive signal for Israeli tech. It shows that global investors remain willing to back Israeli founders at scale, even in a more selective funding environment.

But it should not be mistaken for a broad recovery.

Many startups are still struggling to raise. Many software categories are under pressure. Many growth-stage investors are reserving capital for only the clearest winners. The IPO window remains selective, M&A is uneven, and the path from Seed to Series A has arguably become harder, not easier.

The mega-rounds tell us where conviction is highest. They do not tell us that capital is widely available.

For founders, the lesson is not to “raise bigger.” It is to build something that earns the right to raise bigger.

That means solving a large and urgent problem. Showing real usage or revenue momentum. Building a credible wedge into a massive market. Demonstrating why AI makes the company stronger rather than more vulnerable. And, perhaps most importantly, proving that the company can become strategically important to customers, not just interesting to investors.

What It Means for Israeli Tech

There are three practical takeaways from this recent wave of mega-rounds.

First, Israel’s core strengths are becoming more relevant, not less. Cyber, infrastructure, data, AI, defense, healthcare and deep tech are all areas where Israeli founders have a credible global edge.

Second, the AI boom is not only about model companies. Some of the most valuable companies will be built around the infrastructure, security, data and workflow layers that make AI usable in the real world.

Third, capital concentration will define the next phase of the market. The strongest companies will continue to raise large rounds. Everyone else will need to be more disciplined, more capital efficient and more precise about their category, positioning and go-to-market.

The market has not closed. It has become more demanding.

What can we expect going forward

The signal from the past few months is clear: Israeli tech is adapting to the AI shift. As software becomes easier to build, the market is rewarding companies with deeper technical moats — AI infrastructure, chips, networking, cooling, robotics, cyber, space, healthcare and defense. That was once a “hard tech” thesis. In 2026, it is quickly becoming consensus.

Recent Israeli exits and mega-rounds in semiconductors, photonics, drones, RF defense, data center cooling and AI networking show that the ecosystem has both the founders and the capital to compete in the layers that matter most. The next wave of Israeli winners may not look like the SaaS companies of the last decade. They may be the companies powering the AI economy, securing it, cooling it, connecting it and defending it.

Capital is still here. It is just choosing its targets much more carefully.