It is a fair question. A few years ago, building an internal tool meant a product manager, a designer, several engineers, a roadmap, infrastructure, QA, security review and months of work. Today, a small technical team with Cursor, Claude Code, Codex, Lovable, Replit, Supabase and a weekend can produce something that looks surprisingly close to a commercial SaaS product. The demo gap has collapsed.

That is changing the psychology of software buying. For a buyer, the default question used to be “which vendor should we buy?” Now it is increasingly “why should we buy at all?” But this does not mean SaaS is dead. It means weak SaaS is exposed. The AI era forces founders to answer a deeper question: what are you really selling? Are you selling a feature that an internal team can recreate with an AI coding assistant, or are you selling a workflow, an operating model, domain intelligence, implementation muscle, governance, maintenance and compounding learning over time?

That distinction will determine which startups survive the new build vs. buy conversation.

The new build vs. buy question

The classic build vs. buy framework was relatively simple. Build when the capability was core, differentiated or strategically sensitive. Buy when speed, cost, vendor expertise or standardization mattered more. AI breaks that clean separation.

A company can buy a foundation model, use an orchestration layer, connect it to internal data, build custom agents, deploy them into a proprietary workflow, monitor them with third-party tooling and train employees to operate them. Is that build or buy? Increasingly, it is both. The right question is no longer “should we build or buy?” It is “which layers should we own, and which layers should we rent?”

Most companies should not build their own foundation models. They probably should not build their own model hosting layer, vector database, speech API, OCR engine, generic agent framework or evaluation tooling. These layers are either too capital intensive, too fast-moving or too commoditized. But companies do need to own what makes their business different: proprietary data, operational context, decision rules, customer relationships, escalation paths, quality thresholds, regulatory judgment and the institutional knowledge that lives in the heads of their best employees.

That is where the new software opportunity sits. The vendor that wins is not necessarily the one that says “don’t build.” It is the one that helps the customer decide what is worth owning and what is better outsourced.

The prototype is cheap. The system is not.

AI has made the first version dramatically cheaper. It has not removed the cost of maintenance, reliability, security, workflow integration, data quality, permissions, auditability, model changes, user adoption and organizational accountability.

This is where many internal AI builds fail. The business sees a magical demo, approves a pilot, gets early enthusiasm and then discovers that the real work starts after launch. Who owns the system when the model changes? Who updates the prompts? Who maintains integrations when Salesforce, NetSuite, SAP or an internal database changes? Who handles edge cases? Who is responsible when the system gives a confident but wrong answer? Who retrains new users? Who makes sure the system improves from feedback rather than decays into another brittle internal tool?

In the AI era, building is easier, but owning is harder.

One Remagine Ventures portfolio founder building enterprise tools put it to me bluntly: “Companies can try to do what we do, allocate 30 engineers and take a few months to try to crack it. Or they can spend $100,000 to $200,000 a year with us and get all the bells and whistles and new features shipped weekly.”

That is the new sales argument. Not “you cannot build this.” They can build something. The argument is “do you really want to own the full lifecycle of this problem?” For many customers, the honest answer will be no.

The token bill is coming due

There is another reason the build vs. buy conversation is changing: AI costs are becoming visible. In the early phase of the AI adoption wave, many companies encouraged employees to use as much AI as possible. More prompts meant more experimentation. More tokens meant more learning. “Tokenmaxxing” became a kind of productivity theatre.

Now the bill is arriving.

Companies that were pushing employees to maximize AI usage are starting to ask them to minimize waste. The New York Times recently covered tech workers moving from maxing out AI use to trying to reduce token consumption. The Economist has written about companies scrambling to curtail soaring AI costs as agentic workflows drive up usage. ComputerWeekly’s interview with GitLab CIO Manu Narayan captured the shift well: GitLab is deliberately avoiding tokenmaxxing because it can drive the wrong behaviour. The goal is not more context in and more context out. The goal is business impact.

This matters for founders because the perceived cost of “we’ll just build it ourselves” is changing. A prototype built by an internal team may look cheap when measured in engineering time. It looks less cheap when you include model usage, retries, context windows, evaluation runs, monitoring, security reviews, data cleanup, tool subscriptions, staff training, failed experiments and the management time required to keep the thing alive.

AI did not make software free. It changed the cost structure. Some costs moved from headcount to inference. Some moved from upfront development to ongoing usage. Some moved from vendors to internal coordination. Some are still hidden until the pilot becomes production.

For buyers, the question is not simply “can our team build this?” It is “can we run this safely, cheaply and reliably at scale?” For founders, this is where the opportunity lies. A credible vendor can turn unpredictable experimentation cost into a predictable operating cost.

Jevons paradox for software

There is a useful economic concept here: Jevons paradox. In the 19th century, William Stanley Jevons observed that as steam engines became more efficient, coal consumption did not fall. It rose, because cheaper energy expanded the number of economically viable uses for coal.

The same may happen with software and AI. If AI makes software cheaper to create, we should not assume the world will need less software. We may need much more of it. Tasks that never justified a custom application before will suddenly become software-addressable. Workflows that were too small, too local, too weird or too manual for a traditional SaaS company can now support a purpose-built AI system.

This is particularly important for the traditional economy. The next wave of software may not look like another horizontal SaaS platform sold to every enterprise. It may look like thousands of vertical and workflow-specific products that make previously uneconomic software categories viable. AI does not just lower the cost of building. It expands the surface area of what is worth building.

That is Jevons paradox for enterprise software.

The ICP is moving into the traditional economy

AI changes the ICP for many software companies. Historically, the best early customers for new software were often tech-forward enterprises, high-growth startups or innovation teams inside large companies. These buyers had budget, urgency and a willingness to experiment. They also had one drawback: they were often capable of building internally.

In the AI era, the most attractive ICP may shift away from the obvious technology buyer and toward the traditional economy: manufacturers, logistics providers, construction companies, industrial distributors, healthcare operators, insurance companies, finance teams, legal operations and mid-market businesses with messy but valuable workflows.

These companies have large pools of work that were never properly digitized. They still run on PDFs, spreadsheets, emails, phone calls, legacy systems, tribal knowledge and “ask Sarah, she knows how it works.” They have real operational pain, but historically the software market did not serve them well because the workflows were too bespoke and the implementation effort was too high.

AI changes the economics. A mid-market manufacturer may not need a generic AI assistant. It may need an agent that helps sales engineers turn messy customer requirements into a quote. It may need a system that reads supplier emails and updates procurement workflows. It may need AI to triage warranty claims, generate compliance documentation, analyze machine downtime, recommend spare parts or help a plant manager understand why throughput dropped on line three last week.

These are not glamorous workflows, but they are where a lot of the economy actually operates. The best customers may not be the ones asking for “AI transformation.” They may be the ones saying: “We are drowning in manual work, our best people are bottlenecks and our current software does not understand how our business really runs.”

The new startup risk: becoming a one-off artifact

Not every AI product deserves to exist as a company. If the product produces a one-off artifact, it is vulnerable. A report, a summary, a simple dashboard, a generated email, a deck, a workflow script or a chatbot over a small knowledge base can be useful, but these are increasingly easy to reproduce.

The more your product looks like a prompt, the more likely it is to be replaced by a prompt.

Durable AI companies will need to create continuous value. They will need to learn from usage, accumulate workflow-specific context, improve decisions over time and embed into processes that matter. A good test for founders is this: if a customer used your product for six months and then cancelled, would they lose something that had compounded inside your system?

If the answer is no, you may not have a product moat. You may have a feature.

How founders should qualify build risk

Founders should qualify prospects differently now. It is not enough to ask whether the customer has pain and budget. You need to understand their internal build capacity, their maintenance appetite and their cost discipline around AI.

The first question is who inside the company believes they can build this. If the answer is a strong internal AI team with budget and executive support, your sale will be harder. If the answer is “our CTO said maybe someone could hack it together,” your real competition may be inertia, not internal build.

The second question is who will own the system after the prototype. Many internal builds die because ownership is unclear. A demo can be everyone’s favourite project, but a production system needs an owner, a budget, a security model and a roadmap.

The third question is how costly it is to be wrong. The higher the cost of errors, the more buyers should value monitoring, governance, audit trails, human review and accountability. This is especially true in regulated industries or workflows touching money, safety, compliance, customer commitments or sensitive data.

The fourth question is how messy the workflow is. Messiness is bad for generic tools but good for startups that can productize domain expertise. If the workflow is full of exceptions, tacit knowledge, edge cases and handoffs, there may be a valuable company hiding inside the complexity.

The fifth question is whether the product improves with repeated use. If the system gets better as it observes decisions, corrections, exceptions and outcomes, you have the foundation for compounding value. If it resets to zero every time the user opens a new chat, the value is easier to copy.

The sixth question is whether there is a measurable business outcome. “Productivity” is too vague. Reduced quote turnaround time, fewer rejected claims, lower downtime, faster collections, shorter onboarding, fewer support escalations and better conversion are easier to buy.

The seventh question is whether the customer understands the real cost of building. Engineering hours are only one line item. Token usage, evaluation, monitoring, security, maintenance, reliability, data quality and change management all belong in the calculation.

The best ICP is not simply “companies that cannot build.” It is companies where building is tempting but incomplete. They can create a prototype, but they lack the domain depth, maintenance capacity, workflow integration, governance and feedback loops to make it valuable in production.

What founders should sell instead

The old SaaS sales motion sold seats, features, dashboards and efficiency. The AI-native sales motion needs to sell something closer to operational leverage.

That means founders should emphasize workflow ownership, not just task completion. They should sell domain-specific judgment, not generic intelligence. They should integrate into existing systems, not create another destination app. They should speak about time to value, not model sophistication. They should make governance and quality control part of the product, not a security appendix at the end of the sales process. They should show continuous learning, not static output. Most importantly, they should sell business outcomes, not “AI transformation.”

This is especially true in traditional industries. A manufacturer does not care that your product uses the latest model. They care that orders are processed faster, fewer mistakes reach the factory floor, downtime is reduced and their best people are no longer stuck answering repetitive questions.

The founder’s job is to make AI boring enough to be trusted and useful enough to become indispensable.

Why traditional industries may produce the next wave

A lot of AI innovation so far has been concentrated in obvious knowledge-work categories: coding, content creation, sales, customer support, research, design, legal drafting and analytics. These are large markets, but they are also crowded. Many buyers already have internal champions experimenting with tools.

The underappreciated opportunity may sit in the less glamorous parts of the economy: industrial operations, field services, logistics, food production, compliance-heavy back offices, insurance operations, procurement and the long tail of mid-market companies that never had software built precisely for them.

These industries have attractive characteristics for founders. Pain is measurable. Workflows are repetitive but not simple. Data is proprietary but underused. Labor shortages are real. Existing software is often old and fragmented. Internal AI teams are usually limited. ROI can be tied to revenue, cost, risk or speed.

This is not a call to build “AI for manufacturing” as a generic category. The winning companies will likely start much narrower: AI for quoting custom metal parts, AI for warranty analysis in heavy equipment, AI for procurement exception handling, AI for construction submittals, AI for compliance documentation in food manufacturing or AI for collections in B2B distribution.

The narrower the wedge, the easier it is to understand the workflow better than both the customer’s internal team and the horizontal AI platforms.

What this means for venture

For investors, the build vs. buy debate changes diligence. It is no longer enough to ask whether the company uses AI or whether the product is technically impressive. We need to ask whether the startup is solving a problem that survives the customer’s internal build instinct.

The best AI companies will likely share a few traits. They will be close to the workflow. They will have a painful, specific ICP. They will own feedback loops that improve the product. They will integrate into systems of record. They will sell measurable outcomes. They will understand the buyer’s operational constraints. They will turn unpredictable experimentation into a reliable operating system.

The companies most at risk will be horizontal wrappers that produce generic artifacts without accumulating context or ownership. They may grow fast on curiosity, but churn when customers realize they can recreate 80 percent of the value themselves.

In the AI era, “can they build it?” is the wrong question. Of course they can build something. The better question is: can they keep it working, make it better, control the costs and turn it into an operational advantage?

That is where founders should build. And that is where customers should buy.

On one hand, software has never felt more magical. A solo founder, and increasingly even a non-technical one, can use AI tools to prototype, design, code, test, deploy and iterate an entire product in days. GitHub’s research on Copilot showed developers completing a coding task 55% faster with AI assistance, and Stack Overflow’s 2025 Developer Survey found that 84% of respondents are already using or planning to use AI tools in their development process.

On the other hand, the bar for building a venture-scale company has rarely been higher.

It is easier than ever to create an app. It is harder than ever to make anyone care. It is easier than ever to write code. It is harder than ever to build something defensible. It is easier than ever to launch. It is harder than ever to stand out when thousands of other founders can launch something similar at roughly the same time.

This is the paradox VCs are wrestling with in 2026. The tools are better, talent is more leveraged, the market is bigger, and yet the signal is noisier, capital is more concentrated, and the definition of “exceptional” keeps moving.

AI made software easier. It did not make company-building easier.

AI coding tools are real. They are not just a productivity narrative. Every founder now has access to what feels like a small engineering team in a browser tab. The distance between idea and demo has collapsed.

But that superpower is available to everyone.

That changes the venture equation. If everyone can build faster, then speed of building alone stops being a moat. The question shifts from “can this team build?” to “can this team build something that compounds?”

That means investors are spending more time asking harder questions. Is there proprietary data? Is there deep workflow insight? Is the founder solving a painful enough problem? Is the product embedded in a customer’s day-to-day behavior? Can the company acquire users efficiently? Can it retain them? Does the AI improve with usage? Is there technical IP or domain expertise that cannot be replicated by a prompt and a weekend?

The more powerful the tools become, the more important taste, insight and distribution become.

When everyone sees the same opportunity

Another challenge for VCs is that AI makes certain problems visible to everyone at the same time.

Take agent reliability, monitoring of agent outputs, AI security, enterprise copilots, code review, AI customer support, synthetic data, vertical AI assistants or workflow automation. Once a new platform shift becomes obvious, multiple talented teams often start companies around the same problem within months of each other.

Sometimes that is a sign of a real market. If several smart founders are independently attacking the same pain point, there is probably something there. But it also creates a difficult question for investors: how many of these companies can actually become large outcomes?

In some categories, the market may support one or two major winners and a long tail of smaller companies. In others, the feature may get absorbed by incumbents, cloud providers, model companies or larger platforms. And when the underlying technology is not proprietary, the investor has to ask whether the company is building a product, a feature, or simply a temporary interface on top of someone else’s model.

This is where execution and go-to-market become decisive.

If there is no obvious technical moat, the company that raises the most capital, hires the strongest team, signs the best early design partners and gets in front of customers fastest can gain an advantage. Venture capital can become a form of kingmaking. The best-funded company does not always win, but funding can help create the conditions for winning: better talent, more enterprise credibility, faster sales coverage, stronger partnerships and more time to iterate.

That creates another uncomfortable dynamic for VCs. The decision is not only “is this a good company?” It is also “is this the team that can win this market?” and, sometimes, “will our capital help make them the winner?”

This is especially hard at seed, when evidence is incomplete. Several companies can have similar decks, similar demos, similar customer conversations and similar visions. The real differentiation may come down to founder quality, speed of learning, depth of customer insight, credibility with buyers, and the ability to recruit exceptional people before the market becomes crowded.

In a world where many products can be copied, the company itself becomes the moat. The team, the speed, the customer intimacy, the distribution, the brand and the ability to keep compounding are what separate the eventual winner from the many credible attempts.

The SaaS playbook is being rewritten in real time

For the past 15 years, venture investors had a fairly well-understood SaaS playbook. Find a painful workflow. Build a cloud product. Sell to an ICP. Track ARR, retention, CAC payback, gross margin and expansion. The best companies could become category leaders with strong net revenue retention and high gross margins.

That playbook is not dead, but it is under pressure.

Carta’s 2025 data shows the contradiction clearly. Series A deal count on Carta was down 18% year-over-year in Q2 2025 and cash raised fell 23%, even as median Series A valuations hit a new high of $47.9 million. In other words, the market is not closed. It is selective. [2]

The result is strange: you can meet a very good SaaS company today, with revenue, customers and a competent team, and still worry that it may not be “venture obvious” enough.

That phrase, “venture obvious”, is part of the problem. In a capital-rich consensus market, companies often need to look inevitable before they are fundable. Growth needs to be faster. The market needs to feel larger. The wedge needs to be sharper. The AI story needs to be credible. The team needs to have a right to win.

A few years ago, a horizontal SaaS company growing steadily could attract strong investor interest. Today, unless it is growing very fast, has a clear AI-native advantage, or owns a valuable niche with real depth, it risks being seen as “good, but not enough”.

That is brutal for founders. It is also challenging for investors. The talk about SaaSpocalypse isn’t helping investors get more confident, either.

Capital is concentrating at every level

The venture market is not behaving like one market. It is behaving like several markets at once. Just in Q1 2026, over $300 billion VC dollars were deployed into startups globally. The graph below shows what this means in context. But while the total amount deployed has catapulted, the number of deals is actually declining. So less companies are attracting more capital.

At the fund level, capital is flowing to a small number of established platforms also. PitchBook-NVCA reported that in Q1 2026, 73.1% of capital committed went to just five VC firms. In the same quarter, experienced managers captured more than 90% of capital raised. This had a pretty massive impact on the number of venture capital funds. It dropped from 1,609 in 2022 to 537 in 2026.

At the company level, capital is also concentrating in fewer companies and sectors. In Q1 2026, PitchBook-NVCA reported that $195.6 billion was invested in just five companies. The same report said 88.8% of Q1 deal value went to AI and machine learning companies, while 42.5% of deal count involved an AI startup.

Globally, the OECD found that AI companies attracted 61% of all venture investment in 2025, or $258.7 billion out of $427.1 billion. The United States alone accounted for about 75% of global AI VC deal value.

This is why the market feels so strange. The headline numbers can look euphoric while the lived experience for many founders and funds feels constrained. Mega-rounds make the market look liquid. Emerging managers struggle to raise. AI labs and infrastructure companies attract enormous capital. Good non-AI companies can find themselves fighting for attention. Silicon Valley absorbs more of the narrative and the dollars, while founders outside the main hubs have to work harder to be seen.

The average does not describe the market anymore. The median probably does not either. Venture is increasingly defined by the outliers.

The allocation problem for VCs

For VCs, this creates a real allocation dilemma.

If you ignore AI, you risk missing the most important platform shift in technology. If you chase every AI deal, you risk paying inflated prices for companies with shallow moats. If you only back companies with revenue, you may miss the deeply technical teams building breakthrough infrastructure. If you only back deep tech, you may miss the consumer and application-layer companies that turn new capabilities into daily behavior.

The classic venture tension has become more acute: should you back what is obviously working, or what is still non-consensus but could matter more?

There is also a portfolio construction question. In a market where capital concentrates around perceived winners, the temptation is to pile into consensus deals. But consensus often comes with high prices, compressed ownership and crowded cap tables. The alternative is to invest earlier, with more uncertainty, before the market has decided. That is emotionally harder, but potentially more rewarding.

The challenge is that “early” has changed too. A pre-seed company today can have a polished product, paying customers, AI-generated demos, a global waitlist and a team of two. At the same time, some of those signals are easier to manufacture than ever. The demo can be misleading. The revenue can be pilot-heavy. The product can be a thin layer on top of someone else’s model. The growth can come from novelty rather than retention.

For investors, diligence has to go deeper. The question is not just “does it work?” It is “will this still matter in 18 months?”

Growth or depth

In this market, I increasingly look for one of two things: unusual growth or unusual depth.

The first is speed. Some companies grow so quickly that they become impossible to ignore. They find a wedge, capture demand, and show week-over-week or month-over-month momentum that suggests something unusual is happening. In consumer, that might mean retention and organic sharing. In B2B, it might mean fast revenue, expansion and clear ROI. In AI, it might mean usage that compounds because the product becomes more useful with more data or more workflow integration.

The second is depth. Some companies have a technical insight, domain expertise, proprietary data, regulatory advantage or workflow intimacy that is genuinely hard to copy. They may not look explosive on day one, but they are building from a foundation that can become defensible over time.

The hardest companies to fund today are the ones in the middle: not growing fast enough to stand out, not deep enough to be defensible, not cheap enough to be obvious, and not differentiated enough to overcome investor hesitation.

That does not mean they are bad companies. Many may become profitable, durable, valuable businesses. But venture capital is not designed to fund every good business. It is designed to find the few companies that can return the fund.

That distinction matters more now.

The opportunity is still enormous

Despite all of this, I remain optimistic.

In fact, the confusion itself may be the opportunity. When markets become noisy, conviction matters more. When capital crowds into obvious categories, overlooked areas can become attractive. When tools commoditize execution, founders with deep taste, insight and lived experience become more valuable. When everyone can build, knowing what to build matters more.

At Remagine Ventures, we continue to get excited by founding teams that combine ambition with depth. We are especially interested in Israeli founders globally using AI to reimagine how people spend their time and money across entertainment, gaming, media, commerce and consumer behavior.

We like founders who understand a market from the inside. Founders with a non-obvious wedge. Founders who can move fast, but are not just chasing the trend of the week. Founders who have a reason to win beyond “we can build this now with AI”.

This market is confusing because two things are true at once. It has never been easier to start. It has rarely been harder to break through.

For founders, that means the bar is higher. For investors, it means the job is harder. But for the very best teams, the opportunity remains as exciting as ever.

The next great companies are still being formed. They may just look different from the last generation. They may be smaller, faster, more technical, more creative, more global and more AI-native from day one.

And that is exactly what makes this moment worth paying attention to.

In this post, I’me taking a deep dive into why Israel has become the ultimate testing ground for next-generation security, the capital flooding into the space, and where the biggest opportunities lie for founders and investors alike.

The Numbers: Mega-Rounds and Blockbuster Exits

Israel’s defense industry was born out of survival. Facing daily security threats from hostile states and terrorist groups, the country was forced to build technological independence — and that necessity produced a structural advantage rarely seen elsewhere: tight collaboration between the military, academia, and industry that translates theoretical ideas into operational systems at remarkable speed.

Israel’s defense industry is now exporting that combat-proven technology and operational experience to the world. Driven by conflicts from the Middle East to Ukraine, global demand for next-generation, cheaper, faster-to-produce weapon systems has skyrocketed, propelling Israeli defense exports to a record $14.7 billion in 2024.

The venture capital flowing into Israeli defense tech is staggering. Early-stage innovation is still thriving, but we’re now seeing growth rounds and exits that rival traditional enterprise SaaS:

• Massive funding rounds: Secretive battlefield-AI startup Airis Labs raised $60 million during wartime. Kela, building an operating system to integrate modern military systems, is raising $200 million at a $1 billion valuation. Shifters raised a $10.2 million seed round to build autonomous battlefield robots for high-risk environments.
• Blockbuster M&A: The exit market is red hot. In 2026, U.S. giant Motorola Solutions acquired Israeli counter-drone pioneer D-Fend for $1.5 billion. Offensive cyber intelligence firm Paragon was acquired by AE Industrial Partners for up to $900 million, and military software firm Omnisys was bought by Ondas for $200 million.

The Entry of Anduril and U.S. Giants

Global aerospace and defense giants have long recognized Israel’s talent pool. Now, a new breed of defense tech giant is making more aggressive moves.

Anduril Industries, the Silicon Valley juggernaut recently valued at $61 billion, is in advanced talks with Israeli officials to establish a local R&D and operations hub. Founded by Palmer Luckey, Anduril is reportedly shortlisting military veterans to lead its Israeli operations, eyeing both organic growth and local acquisitions to fuel its international expansion and future IPO ambitions. Its arrival would be a major validation of Israel’s talent pool, and is likely to spark more M&A activity as U.S. primes look to absorb battle-tested Israeli IP.

Specialist Funds vs. The Dual-Use Appeal

The capital landscape is evolving to match this demand. Dedicated, specialist entities are emerging, like the Kinetica defense-tech fund, Aurelius Capital, and the government-backed MAFAT for Startups, which fast-tracks companies into IDF procurement pipelines.

But one of the most interesting trends is the influx of non-specialist, generalist VCs entering the space through “dual-use” deep tech. Because many traditional funds have mandates restricting investment in kinetic weapons, they’re pouring capital into dual-use applications: technologies built for the commercial sector that also carry vital defense applications. Areas like AI-powered underground mapping from Exodigo, autonomous maritime navigation from Orca AI, and satellite weather intelligence from Tomorrow.io are bridging that gap, letting generalist VCs capitalize on defense budgets without violating their ESG constraints.

Opportunities for Startups

For founders looking to build in defense and national security, several massive greenfield opportunities stand out, areas where military budgets are increasingly being allocated (aka necessity is the mother of invention):

1. AI & Autonomous Systems: Militaries want to remove humans from the line of fire, driving immense demand for autonomous drone swarms, loitering munitions (such as those pioneered by UVision), and unmanned ground vehicles (UGVs) built for complex urban warfare and tunnel operations.
2. Counter-UAS (Unmanned Aerial Systems): As drones come to dominate modern warfare, the shield matters as much as the sword. Startups developing AI-powered electro-optical tracking, spoofing defences, and non-kinetic cyber takeover systems (like D-Fend which recently sold for $1.5 billion) are in high demand from both military and civilian infrastructure buyers.
3. Intelligence, Fusion & Electronic Warfare: The modern battlefield is flooded with data. Opportunities exist for startups building platforms that turn unstructured video into real-time intelligence (like Senai), map the invisible electromagnetic spectrum (like Tenna Systems), or create hardened, AI-optimized communications that survive in GPS-denied environments.
4. Human Resilience and Medical Tech: Defence tech isn’t just hardware and software, it’s about the warfighter. Startups drawing on academic research to build remote cardiopulmonary sensors, new PTSD treatments, and advanced camouflage nano-materials are uncovering a lucrative intersection between med-tech and defence.
5. Space & Maritime Domain Awareness: The innovation race is rapidly expanding beyond land and air. In the maritime sector, startups like Orca AI and Skana Robotics are developing AI computer vision and autonomous surface and underwater vessels to protect naval operations and infrastructure. Simultaneously, the space frontier offers massive opportunities for sovereign intelligence, with companies like Remondo building high-resolution, low-cost microsatellites and Tomorrow.io deploying proprietary radar constellations for global tactical awareness

Israel’s defence tech ecosystem is no longer just a quiet frontline of national security, it’s a global venture capital powerhouse. For startups that can navigate the complexities of government procurement and dual-use commercialisation, the potential for scale has never been greater.

The original version of #Firgun was published on Linkedin to 8,100+ subscribers. Sign up for free to get the next edition in your inbox.

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, the public debate was dominated by deep skepticism around the newly signed U.S.-Iran 14-point memorandum of understanding, which critics argue defers the hard questions on nuclear enrichment, ballistic missiles and Iran’s regional proxies, while exposing visible friction between Prime Minister Netanyahu and President Trump. Yet against the backdrop of geopolitical uncertainty, security pressures and market volatility, Israel’s tech engine continued to show resilience: high-tech reportedly accounted for roughly half of the country’s economic growth over the past year, Tel Aviv held its position as the world’s fourth-largest startup ecosystem, and Dream’s $260M round at a $3B valuation underscored how tightly AI, cybersecurity and national security are now intertwined.

In the wider tech world, the defining story was Anthropic being forced to disable Fable 5 and Mythos 5 outside the U.S. after a sudden U.S. national-security export-control directive, turning AI sovereignty from policy talk into platform risk; SpaceX is reportedly buying Anysphere / Cursor for $60 billion… OpenAI hired former Google / Gemini co-lead Noam Shazeer to lead architecture research ahead of its IPO; Z.ai’s GLM-5.2 emerged as a new open-weights model to watch; Midjourney surprised the market with its first hardware product, an ultrasound-based full-body scanner which will roll out as a network of Spas; Amazon Web Services (AWS) launched new AI agent tools while Microsoft’s Azure reportedly continued benefiting from ByteDance’s $1B+ annual AI spend; Databricks said annualised revenue hit $6.9B, up more than 80%, while AI-agent usage pressures margins; Odyssey raised $310M for Hollywood-grade world models with Amazon and AWS among the backers; CuspAI reportedly raised $400M for AI materials discover. General Intuition was reportedly raising $300M from Jeff Bezos and others; Accenture pushed deeper into cyber with a $4.18B OT-security package including Dragos, runZero and NetRise;; Fox Corporation reportedly agreed to acquire Roku for $22B; Snap Inc. unveiled new Specs AR glasses and the stock took a 20% hit; Waymo recalled nearly 4,000 robotaxis after construction-zone incidents; Google rolled out Android 17 and Gemini-powered Pixel updates; France announced €655M in AI investments and a Mistral-powered public-services chatbot; Finally, PayPal Ventures is reportedly shutting down after $850M in investments.

QUOTE OF THE WEEK

“The purpose of knowledge is not knowledge itself, but what it leads us to do.” — The Lubavitcher Rebbe

NEW FUNDING DEALS

• Congratulations Shalev Hulio and team Dream on your $260M series C at a $3 billion valuation to addresses national cyber systems, governments and national infrastructure companies – nuclear reactors, gas, oil, water and electricity threats.
• Well done Zohar Alon and team NewCore on coming out of stealth with a $50M series A (as well as a previously undisclosed $16M seed round) to create the identity management system for the new era!
• Way to go Itai Hirsch and team Turnout on your $35M series A for your AI-powered consumer advocacy service!
• Kudos David Priev and team Limitless Labs on your $20M series A to scale your Agentic Physical AI for precision manufacturing!
• Mabruk Shahaf G. and team Mars Security on coming out of stealth with $8.5M seed funding raised to date for your autonomous threat hunting and detection engineering platform!
• Good stuff Barak Sternberg and team Tenet Security on your $6M seed round for your runtime protection for autonomous systems accessing sensitive data and workflows!
• Big congrats Ben Fuxbruner and team Kimba on coming out of stealth with a $5.4M seed round to elevating health and sleep through AI and scent technology!

EXITS

• Well done Rom Carmel ? and team Apono on the acquisition by 1Password for a reported $250M-$300M for your AI-powered, cloud-native Privileged Access Management (PAM) platform that eliminates standing privileges by enforcing Just-in-Time (JIT) and Just-Enough access.
• Congrats Itzik Alvas and team Entro Security on the acquisition by SailPoint for a reported $200M to secure non-human identities, API keys, secrets, and credentials across cloud and on-premises environments!
• Well done Michael Grosman and team NextVision on selling $200M worth of shares for your stabilised camera for drones!
• Way to go Eran Sarouk and team GMT – Advanced Financial Services on the acquisition by Western Union for an estimated $70M to scale your international and local money transferring services!

NEW FUNDS

• Congrats Aviel Ichaï and team monday.com on announcing Monday Ventures a new $200M corporate fund to invest in Israeli AI startups!

LINKS FOR YOUR BROWSER

VC CAFE/ REMAGINE VENTURES/ ISRAEL VC

• The Power of Small Tickets: When “Getting In” Matters More Than the Model – Ownership matters in venture. But in a market where capital is concentrating into fewer companies, access may matter even more.
• Israeli Startup M&A in H1 2026: Strategic Buyers Are Back – From Apple, Nvidia and Cisco to Mobileye, Fireblocks and Cyera, the first half of 2026 shows that Israeli tech remains high on the global M&A agenda.
• The Modern GTM Stack for AI-Native Startups – Why the winning 2026 motion is a signal system, not a bigger sales machine.

ISRAEL

• Tel Aviv ranked world’s No. 4 startup hub with ecosystem value of $250B – Startup Genome’s annual global report says Tel Aviv’s ecosystem value surged 162% from its previous ranking, driven by AI, big data, analytics, cyber and life sciences, placing the city behind only Silicon Valley, New York and London
• The most promising Israeli startups for 2026 according to Globes – Majestic Labs ai takes the top spot followed by Quantum Art and Port.io.
• Sovereign AI – Israel has a New AI directorate

GLOBAL

• Americans and AI 2026: Chatbots, Smart Devices and Views on Impact – new report by Pew Research Center
• Future of the Web 2026: AI Brand Visibility Research – based on research from 2,000 decision-makers and consumers.
• Y Combinator’s Winter 2026 batch is its most technically complex cohort yet — here’s what it signals about physical AI

MEDIA OF THE WEEK

Israel’s energy tech landscape and report was added to my 2026 Israeli startup landscape collection on VC Cafe. (source)

The International VCs backing Israeli tech so far in 2026 – by Israel VC (source)

Who are the VC investors backing israeli healthtech, medtech, biotech and medical devices in 2026? Another infographic by Israel VC (source)

Interesting website – the capex investment in AI vs. revenue (source) – so far it hasn’t paid off…

What’s next in wellness? the 2026 scouting report by FITT Group (source)

Pharma should have produced dozens of iconic software companies by now. Instead it’s produced one: Veeva ($3B+ rev). The AI in pharma market map by Sapphire Ventures (source)

Along the same lines MMC Ventures published the AI x Bio market map – where AI is being used for drug discovery (source)

I’ve shared this map by Battery Ventures before, but thought it might be worth re-sharing as the GTM for AI-native startups is changing rapidly. The AI-native GTM stack (source)

More Americans are using chatbots, and some are adopting AI summaries and smart speakers. But views about AI and how fast it’s advancing tilt negative – even for younger adults

The Winter 2026 Y Combinator batch is the most technically complex yet by CB Insights (source – see links section)

That’s all for this week. Thank you for tuning in. There’s a lot to celebrate in our “small” community! Keep on Creating!

Shabbat shalom,

Eze Vidra

AI-native companies increasingly sell into a different reality. The buyer may test the product before speaking to anyone, run a proof of concept in a few days, and arrive at the first call with an informed view of the product’s quality, integration effort and likely ROI.

That changes the job of GTM. The goal is not simply to create more meetings. It is to identify real intent earlier, help a prospect reach value quickly, and turn product usage into a stronger sales and expansion motion. In many cases, the decisive moment is a 30-day pilot with clear success criteria, not a long sequence of human hand-offs.

Signals and Data Enrichment

The old model began with a list. The modern one begins with a signal: a relevant hire, new funding, a technology change, a visit from a target account, a sharp rise in product usage, or public evidence that a company has the problem you solve.

Clay is the best-known example of the new enrichment layer: it lets teams combine multiple data sources, web research and workflows rather than accept the limits of one database. Apollo remains a pragmatic option for contact data and basic outreach. Common Room is useful once you have enough channels and community or product activity to turn into actionable buyer signals.

The key idea is a data waterfall. Rather than relying on one provider for every email, job title or technology tag, query several in sequence and keep the best verified answer. That is useful. Buying every possible intent-data package before you know your triggers are not.

What this means for a founder: Choose three to five signals that reliably precede a good conversation. A list of 200 relevant accounts is more valuable than 2,000 generic names.

Outbound and Inbound Orchestration

This is the noisiest layer of the stack. Tools such as Apollo and Amplemarket make outreach more assistive: they help identify accounts, prepare research, draft messages and coordinate follow-up. Agentic offerings such as Artisan and 11x are aiming at a more autonomous model, from prospecting to meeting booking.

Both models can be useful, but they are not interchangeable. An autonomous agent works best when the audience, trigger, offer and message are already well understood. It is not a substitute for discovering them. Handing an unproven message to an agent merely automates your uncertainty.

Inbound is a better early use case for autonomy. Qualifying a form fill, answering routine questions, routing a lead, suggesting a meeting time and preparing an account brief are structured tasks with immediate feedback. The cost of delay is high and the brand risk is lower.

What this means for a founder: Let AI remove research and first-draft work. Keep a human close to strategic accounts, senior buyers and new messages until you know they earn a response.

Content, SEO and Distribution

AI has made publishing cheap. It has also made undifferentiated content close to worthless.

Every company can now produce a competent article, a week of social posts and a set of SEO pages. The output often looks polished but says nothing a buyer could not find elsewhere. That is not a content engine. It is a treadmill.

Use tools such as Claude for drafting and repurposing, Ahrefs for search research and AI-visibility monitoring, and Beehiiv or a similar platform when an owned audience matters. But begin with one original input: a customer insight, product benchmark, non-consensus opinion, data set, or story from the field. AI can multiply that input across formats; it cannot manufacture the underlying point of view.

What this means for a founder: Your moat is not publishing velocity. It is a perspective that compounds because your company sees something others do not.

Sales Engagement and CRM

Your CRM should be the company’s memory, not a reporting chore for a future sales team.

HubSpot is often the sensible default for an early sales-led motion because it gives a small team CRM, marketing and customer context in one place. Attio is compelling when the business does not fit a clean, linear pipeline: product-led expansion, partnerships, developer relationships, or usage-based accounts with multiple stakeholders.

The practical distinction is not “legacy” versus “AI-native.” It is whether the CRM contains enough trusted context for AI to do useful work. An agent cannot prioritise an account, produce a useful brief or recommend a next step when key information is scattered across Slack, email, product analytics and a founder’s memory.

What this means for a founder: Pick a simple source of truth early. Do not choose Salesforce because it looks grown-up, and do not customise any CRM until the motion is repeatable.

Customer Success, Expansion and Retention

For an AI company, customer success increasingly begins in the product data.

The most useful signals are rarely vanity metrics. Look for whether a customer has reached the high-value workflow, added teammates, expanded use cases, increased usage steadily, or hit a recurring point of friction. Those are the events that should trigger onboarding, support, a renewal conversation or a commercial expansion.

Vitally can help a lean team turn health scores into action. Intercom’s Fin is a strong example of where an AI agent can take on routine support, letting humans focus on difficult cases and strategic customers. But a health score is only useful if it changes a behaviour: alert an owner, trigger a playbook, or surface an expansion opportunity.

What this means for a founder: Instrument the journey to durable value before hiring a large customer-success function. Product usage is not a dashboard metric; it is a commercial signal.

Analytics and Attribution

Traditional attribution separated marketing, sales and product. That has always been an artificial division, and it is especially misleading when a buyer discovers you, tries the product, invites colleagues and only then starts a sales conversation.

PostHog is a sensible seed-stage choice because it brings product analytics, session replay, experimentation and feature flags closer together. Later, a warehouse stack may make sense. At the beginning, the goal is not perfect multi-touch attribution. It is being able to answer basic questions: which accounts activate, which actions predict conversion, where do pilots stall, and what usage predicts expansion?

What this means for a founder: Start with a data model, not a dashboard. What you instrument now determines which decisions you can make a year from now.

The Assistive-versus-Autonomous Line

The most useful debate is not whether agents replace SDRs. It is which work is structured enough for autonomy and which work still benefits from human judgment.

Full automation is already credible for enrichment, account research, inbound qualification, meeting scheduling, data hygiene, routine support and first drafts. These are repetitive workflows where speed matters and errors are relatively easy to catch.

Human judgment still wins in complex discovery, pricing, objection handling, strategic relationships and brand voice. The reason is not sentimentality about salespeople. It is that these moments require context, judgment and occasionally the ability to say something unexpected.

The hybrid model is therefore not an interim compromise. It is the best operating model for most seed and Series A companies: agents prepare, qualify, route and follow up; people make the consequential calls.

The Mistakes to Avoid

• Over-tooling before product-market fit. A 12-tool stack does not compensate for an unclear ICP, a weak proof point or an untested message.
• Buying “all in one” too soon. Consolidation is valuable after you understand your operating system. Before that, it can lock you into mediocre workflows and hide what is actually broken.
• Automating outbound without review. An agent can scale relevance, but it can also scale a stale message or an awkward tone directly into the inboxes that matter.
• Confusing activity with pipeline. More sequences, content and dashboards do not create demand. Relevance, timing, proof and follow-through do.

A Lean Stack for Seed Stage

Battery ventures published the modern AI native GTM landscape capturing the transitional move from manual labor to agentic AI.

For a seed-stage B2B AI company, I would keep the stack deliberately small:

• Clay + Apollo for enrichment, contact data and targeted outreach.
• HubSpot Starter or Attio as the account and relationship system of record.
• PostHog for the product signals that should shape acquisition, pilots and expansion.
• Claude, Search Console and Ahrefs for research, original-content production and measurement.
• Intercom with Fin or Vitally only when support or customer complexity is sufficient to justify a dedicated layer.

I would not add a fully autonomous AI SDR platform until the company has one repeatable outbound play, clear qualification criteria and a message that founders are proud to send themselves.

The Stack Will Keep Changing

Over the next few years, individual categories will blur. Enrichment will move into CRM, support agents will become onboarding and expansion agents, and product usage will become a more important GTM signal than a form fill.

The durable advantage will not be owning the newest tool. It will be building a system that connects signals, product value and human judgment faster than competitors can.

For founders building GTM infrastructure, AI agents or AI-native companies with an unusual path to market, Remagine Ventures would love to hear from you. We invest early in founders using AI to reimagine how people spend their time, attention and money.

According to the Israel Innovation Authority’s State of High-Tech 2026 report, Israeli high-tech generated approximately $84 billion in exits in 2025, alongside $85 billion in exports and nearly $15 billion in fundraising. High-tech output grew by 8.2%, and the sector accounted for 58% of Israel’s total exports.

Given that backdrop, the natural question entering 2026 was whether Israeli M&A would slow down after such an exceptional year.

So far, the answer is: not really.

The first half of the year has already produced a long list of strategic acquisitions involving Israeli startups. The buyers include some of the world’s most important technology companies: Apple, Nvidia, Cisco, Palo Alto Networks, CrowdStrike, ServiceNow, Motorola Solutions, Medtronic, PayPal, Akamai, SailPoint, 1Password, Western Union, Nuvei and others.

But the more interesting story is not just that global companies are still buying Israeli startups.

It is that Israeli M&A in 2026 is becoming more layered:

1. International acquirers are buying Israeli technology to fill strategic product gaps.
2. Multinationals that already have R&D activity in Israel are doubling down through additional acquisitions.
3. Israeli and Israeli-rooted companies are increasingly acquiring other Israeli startups.

That third category, the “blue-and-white” M&A story, may be one of the clearest signs of ecosystem maturity.

The biggest Israeli startup M&A deals of 2026 so far

The largest reported transactions in 2026 so far span fintech, defense tech, AI, semiconductors, robotics, medtech and cyber.

This is not 2021-style froth. The market is more selective. But that is precisely what makes the 2026 list interesting. Buyers are not simply acquiring growth stories. They are buying strategic capabilities.

What are acquirers buying?

The 2026 list shows clear demand for several categories where Israeli startups have historically been strong — and where global strategic buyers now have urgent needs.

The common thread is that acquirers are not buying “nice-to-have” products.

They are buying capabilities they believe they need in order to compete.

AI is not a sector. It is the acquisition rationale.

One of the clearest patterns in the 2026 M&A data is that “AI” is no longer just a category. It is the logic behind many transactions.

Some deals are obviously AI-related, such as Nvidia acquiring Illumex, ServiceNow buying Traceloop and Pyramid Analytics, and Apple acquiring Q.ai.

But AI also appears indirectly across the list.

In cybersecurity, AI is changing the threat model. The rise of agents, automated workflows and non-human identities is pushing buyers toward companies that can secure machine-to-machine interactions, enterprise browsers, data access, permissions and automated response.

That explains why companies like Cisco, Palo Alto Networks, SailPoint, 1Password, Akamai, Check Point, Cyera, Torq and Silverfort are all active around AI-era security surfaces.

In commerce, AI is changing how products are discovered and purchased. PayPal’s acquisition of Cymbio points toward a world where retail infrastructure needs to work inside AI assistants, search interfaces and emerging agentic commerce platforms.

In infrastructure, AI is changing the hardware stack. The acquisitions of DustPhotonics and Teramount point to the growing importance of photonics, optical connectivity and data movement in AI data centers.

AI is not one line in the table. It is the connective tissue across the table.

Cyber is still Israel’s main M&A engine

Cybersecurity remains the most obvious export category for Israeli technology.

The 2026 acquirer list includes:

The interesting thing is that this is not simply another year of classic cyber consolidation.

The categories are shifting toward:

• non-human identity
• AI-agent security
• browser security
• data governance
• automated response
• API security
• enterprise access control

That is a strong signal for founders and investors. The next cyber wave is being shaped by AI adoption, and Israeli startups are already being acquired in the emerging subcategories.

Defense tech and autonomy are now mainstream

The acquisition of D-Fend Solutions by Motorola Solutions for $1.5 billion is one of the most important deals of the year.

Counter-drone technology has moved from a specialist defense niche to a mainstream requirement for governments, airports, law enforcement, critical infrastructure and military organizations.

Other deals point in the same direction:

The convergence of drones, robotics, autonomy, defense software and AI is becoming one of the defining technology themes of the next decade.

Israel has a natural advantage in this area because of its defense ecosystem, engineering talent and operational urgency. But the category is no longer local or niche. It is global.

Semiconductors and photonics are having a moment

For years, Israeli tech was often described through the lens of software: cyber, SaaS, fintech, gaming, adtech and enterprise software.

The 2026 M&A list shows that the “harder” layers of technology are becoming strategic again.

This matters because AI infrastructure is not only about GPUs.

As AI clusters scale, data movement becomes a bottleneck. The physical movement of data between chips, servers and data centers is now a core part of the AI infrastructure stack.

That creates demand for optical connectivity, photonics, advanced semiconductors and networking infrastructure.

This also connects to one of the themes in the Israel Innovation Authority’s 2026 report: the renewed importance of deep tech, defense tech, space, quantum and AI infrastructure in the next generation of Israeli companies.

In a world where software moats are being compressed by AI, deep technical infrastructure may become more attractive again.

The “blue-and-white” M&A story

One of the most interesting patterns in the 2026 data is not just that global strategic buyers are active in Israel. It is that Israeli and Israeli-rooted companies are also becoming more active acquirers of Israeli startups.

This matters because it points to ecosystem maturity.

In earlier phases of the Startup Nation, the classic exit narrative was simple: build in Israel, sell to a US or European technology giant, become that company’s local R&D center.

That model still exists, and it remains important. But the 2026 data shows a more layered picture.

Israeli category leaders are now using M&A to consolidate talent, product capabilities and strategic wedges.

This is a healthy sign.

A mature ecosystem should not only produce startups that get acquired by foreign multinationals. It should also produce local champions that acquire teams, absorb technical capabilities and build larger platforms from within the ecosystem.

The rise of blue-and-white M&A also creates a local recycling mechanism. Founders, employees, capital and expertise do not simply leave the ecosystem after an exit. They often stay connected to it, either through the acquiring company’s Israeli operations or through the next generation of founders, operators and angel investors.

Are acquisitions still a route to multinational R&D in Israel?

Another question is whether foreign acquirers are using Israeli M&A to enter the market for the first time, or whether they are doubling down on existing Israel R&D operations.

The answer is both.

A public-source review of the 2026 acquirer list suggests that at least 40% of the international strategic acquirers already had a meaningful Israel R&D or technology operation before making their 2026 acquisition.

This group includes companies such as Cisco, Nvidia, Palo Alto Networks, Apple, PayPal, Medtronic, Akamai, ServiceNow, CrowdStrike, Nuvei, Ondas and Motorola Solutions.

That matters.

It means M&A is not only an entry mechanism. For many multinationals, Israel is already a strategic product and engineering base, and acquisitions are a way to keep feeding that base with new technology and talent.

At the same time, the list also includes buyers for whom the 2026 transaction appears to be a first or early Israeli foothold. 1Password’s acquisition of Apono was reported as its first acquisition in Israel and a basis for establishing a development center. Guardant Health’s acquisition of MetaSight was described as creating its first R&D center outside the United States.

That dual pattern is one of the defining features of Israeli M&A:

This is the real story of H1 2026. Israeli M&A is not only about exits. It is about where global technology companies choose to build, and where Israeli category leaders choose to deepen their advantage.

The macro context: strength, but also warning signs

The Israel Innovation Authority’s 2026 report gives important context for the M&A data.

On the positive side, Israeli high-tech returned to growth in 2025. The sector generated approximately $85 billion in exports, $84 billion in exits, and nearly $15 billion in fundraising. High-tech output grew by 8.2%, reached NIS 352 billion, and accounted for 18.3% of Israel’s GDP. The sector also accounted for 58% of Israel’s exports.

This is a remarkable level of resilience given the security, political and economic environment.

But the report also highlights structural concerns.

For the first time in more than a decade, the number of R&D employees in Israeli high-tech declined. The share of private Israeli high-tech company employees based in Israel has also continued to fall, from 69% in 2019 to 62% by March 2026.

That is the tension at the heart of Israeli tech today.

The ecosystem is producing valuable companies and attracting strategic buyers. But more activity, management and R&D are also moving abroad.

M&A can strengthen Israel as a global R&D hub when acquirers keep and grow local teams. But it can also accelerate the movement of decision-making abroad if local operations are not preserved and scaled.

That makes the blue-and-white M&A trend even more important. When Israeli companies acquire locally, the center of gravity is more likely to remain in the ecosystem.

What does this mean for founders?

For founders, the 2026 M&A data offers several practical lessons.

The companies being acquired in 2026 are not just “interesting startups”. Many own a strategic wedge. That is the bar in the current market.

What does this mean for investors?

For investors, the data reinforces several themes.

Cyber is still strong, but the next generation of cyber is shifting toward AI-era surfaces: non-human identity, agent security, browser control, data governance and automated response.

Deep tech is becoming more investable as the AI infrastructure buildout creates demand for semiconductors, photonics, robotics, autonomy and defense technologies.

Fintech is not dead, but the strongest exits are likely to come from infrastructure, compliance, payments, crypto rails, embedded finance and agentic commerce rather than consumer financial apps alone.

And perhaps most importantly: M&A remains a core path to liquidity in Israeli venture. The IPO window may reopen selectively, but the acquisition market is already active. Strategic buyers are still willing to pay for Israeli technology when it solves a strategic problem.

The bottom line

After a record-breaking 2025, 2026 is already showing that Israeli startup M&A remains active, strategic and globally relevant.

But the nature of the market is changing. This is not a broad, easy exit environment. It is a selective market where buyers are looking for specific capabilities: AI infrastructure, cyber for the agentic era, defense and autonomy, photonics, fintech rails, medtech, data and workflow automation.

The acquirer list is also a map of global demand. Apple, Nvidia, Cisco, Palo Alto Networks, CrowdStrike, Motorola Solutions, Medtronic, PayPal and others are showing what they believe matters. At the same time, Israeli companies like Mobileye, Fireblocks, Cyera, Torq, Check Point, eToro and Elbit are showing that Israel is not only a source of acquisition targets. It is also producing acquirers.

That is the more important signal. Israeli M&A in 2026 is not just about exits.

It is about the next structure of the ecosystem: foreign multinationals doubling down, new buyers entering Israel, and local champions consolidating from within.