Exclusive data from dealigence, recently commissioned by Battery Ventures and HSBC, found that one in every 22 early-stage Israeli startups is now led by a repeat founder, the highest ratio in the world. Israel’s serial founders account for nearly 10 percent of all early-stage, serial founder-led companies globally, despite the country representing less than 0.1 percent of the world’s population.

The Next Phase of the Startup Nation

Israel’s repeat entrepreneurs are also scaling. Among companies with between $50 million and $200 million in total funding, Israel ranks second worldwide, with one in every 7.7 companies in this group led by a repeat entrepreneur.

This density of experience has real economic impact. Over the last five years, Israeli serial founders have created more than $75 billion in exits, according to dealigence¹.

These entrepreneurs are, in our view, a special breed, and we feel they’re particularly suited to succeed in today’s fast-moving tech environment, now marked by huge disruption created by AI. While the bulk of our investments continue to go to first-time founders, we realize that serial entrepreneurs often have a leg up because they have already navigated fundraising, scaled teams across borders, and managed growth through volatility.

That repetition isn’t redundancy; it’s refinement. Every successful company, in some form or another, seeds the next one. Every founder who scales a business reinvests time, capital, and conviction back into the system. The result is a flywheel of talent and trust that now defines Israel’s competitive edge.

And crucially, this is not a story of serial founders replacing first-time ones. The opposite is true. The two groups reinforce each other, forming a self-sustaining loop of innovation. First-time founders are standing on the shoulders of those who came before them. The presence of experienced entrepreneurs accelerates learning for the next wave of builders, helping first-timers move faster, avoid common pitfalls, and aim higher from day one. We feel this dynamic is pivotal in making Israel’s startup ecosystem more mature and its companies more ambitious with each generation.

Why the Model Works

Several forces make Israel unusually good at turning experience into momentum.

A steady cadence of exits keeps liquidity and learning circulating. Each outcome adds new mentors, angels, and experienced operators to the mix, deepening the country’s collective expertise. Then there’s the national bias toward urgency and iteration, meaning decisions get made quickly, progress beats perfection, and execution often outruns deliberation. And perhaps most important, founders here don’t fear failure. It’s treated as a stage in the journey, not the end of one. Lessons learned from a first company often power the success of the second or third.

The result is a system that’s fast-moving and deeply interconnected. Founders learn quickly, capital recycles rapidly, and talent stays in the ecosystem rather than leaving it.

The Compounding Dynamic

In venture, experience compounds just like returns. Founders who have built before tend to raise faster, hire smarter, and navigate scaling challenges with greater precision. They’ve seen the cycles, they know when to hold steady and when to sprint.

At Battery, we have made 20 investments to date into Israeli companies founded by at least one serial entrepreneur—companies in industries ranging from cybersecurity to fintech to semiconductors to HR software and more. Relationships are the lifeblood of our business, and it’s clear that Israeli founders know this, too: They feed their knowledge, networks, and opportunities back into the ecosystem. First-time entrepreneurs bring fresh ideas and bold risk-taking, repeat founders bring structure and context. Together, they create one of the most efficient and resilient innovation networks in the world.

That balance shows up in the market data. Israel’s repeat founders accounted for more than 7 percent of all global early-growth companies led by serial entrepreneurs, outpacing countries with ten times its population. And in a period of global uncertainty, Israeli serial founders continued to raise large rounds, a sign that investors are rewarding proven execution over noise.

At Battery, we’ve seen this dynamic firsthand. We continue to back new entrepreneurs as well as second-time founders who are leveraging prior experience to build even more ambitious, globally minded companies, a reflection of how far Israel’s startup ecosystem has come and how much further it can go.

Download PDF

Loading...

Taking too long?

Reload document

| Open in new tab

The post Israel: The Land of Milk, Honey, and Serial Entrepreneurs appeared first on Battery Ventures.

The speed of execution has never been higher, but the craft of building well is being rewritten in real time. Earlier this month at Battery’s Future of Product dinner, we brought together a dozen product leaders to discuss how the evolution of the PM role is unfolding across startups and enterprises alike. Below are seven shifts that surfaced:

1. From writing-first to building-first product culture

Many large incumbents are trying to unlearn their traditional “writing-heavy” cultures built on PRDs and structured rituals. To move at startup speed, they’re adopting a build-first, show-don’t-tell culture. PMs are now expected to prototype, validate and iterate ideas using tools like V0, Lovable, Cursor—not just write documents.

Gamma’s CEO Grant Lee recently shared an inside look into how his product team operates on Lenny’s Podcast—what once took weeks of planning can now happen before lunch. That pace changes everything about how PMs operate.

2. Engineering is no longer the bottleneck

For years, engineering bandwidth was the limiting factor in product velocity. With AI-assisted coding tools, that bottleneck has shifted to surface a new one: clarity. Engineers can now build and ship features faster than PMs can plan, and in some cases, faster than users can absorb.

As one product leader noted, customers are now asking them to slow down feature launches, frustrated that the UI seems to change every week. The greatest risk for product teams today isn’t moving too slowly; it’s moving fast in the wrong direction.

3. Everyone’s a product builder

Figma’s CEO Dylan Field recently noted, “We’re all product builders, and some of us are specialized in our particular area.” As roles overlap, the traditional Engineering–Product–Design (EPD) boundaries are dissolving. PMs are designing and prototyping independently; designers are thinking more deeply about what and why to build; and engineers are engaging directly in user discovery and validation. Design, in many ways, has become the connective tissue—translating speed into coherence.

4. Product strategy is expanding beyond the build

AI startups are finding it harder than ever to rise above a sea of sameness. As differentiation becomes tougher, PMs are being pulled deeper into pricing, packaging and positioning—areas historically owned by go-to-market teams.

Given their proximity to customer value, workflow design and user outcomes, PMs often have the best context for deciding how a product should be priced, how value should be communicated and what users will actually pay for. In many AI-native companies, like Unify*, product marketing now reports directly to product—a reflection of how tightly product strategy and narrative are intertwined.

5. FDEs and agent PMs are turning AI into ROI

One of the more interesting developments that surfaced is how startups are actually helping enterprises build with AI.

In this AI wave, everyone from startups to large enterprises is an early adopter. Many large enterprises have built AI “centers of excellence” to map and prioritize use cases, but they often lack the technical depth to actually build and deploy agentic applications.

This gap has led to the rise of the FDE (Forward Deployed Engineer) and Agent PM models, in which startups like Ramp embed dedicated pods inside enterprise customers to take a use case from definition to prototype to production and ROI. It’s a services-heavy, but ROI-driven, approach that delivers tangible outcomes fast. Over time, these partnerships are expected to evolve into more productized, self-serve models as enterprise maturity increases.

6. Redefining product success metrics for agentic apps

Traditional metrics like DAU, MAU and session time were built for UI-based products, but agentic apps often have no UI and aim to complete work autonomously. Success is shifting toward outcome-based measures such as workflow completion rates or automation accuracy. In short, “time spent” is giving way to “work done,” marking a fundamental shift in how product success and user value are defined.

One product leader noted that following the launch of AI agents, overall weekly active users declined, but engagement among power users has surged. These power users are often Admins who frequently log in to fine-tune configurations, audit agent logs and build new agentic workflows.

7. AI Evals are becoming part of PM discipline

Because AI systems are inherently non-deterministic, evaluations—once an engineering concern—is now a core part of the PM’s role. PMs are designing evaluation frameworks that measure how well models or agents perform against business goals, setting thresholds for accuracy, reliability and quality.

This “AI evaluation” capability is emerging as a new pillar of the PM’s craft—one that blends experimentation, analytics and product judgment to ensure model behavior aligns with intended outcomes.

The next frontier of product management

As AI accelerates every part of the software development cycle, roles are converging in service of one shared goal: building products that truly work for users. The PMs who thrive in this new era won’t measure their impact in features shipped or issues resolved, but in the clarity, coherence and taste they bring to what’s built—and what’s left out.

The post The Role of a Product Manager in the Age of AI appeared first on Battery Ventures.

As anyone following today’s tech headlines knows, a lot has changed for the chip giant, unfortunately. Intel is widely acknowledged to have missed the mobile-computing revolution and, more recently, ceded the silicon limelight to Nvidia, the GPU pioneer whose chips are now fueling the current AI boom (and whose market value is nearly 30 times Intel’s.) Intel’s star has fallen so much that there’s been serious discussion that the company could get acquired by another large tech company or even be taken private by investors. Recently, the Wall Street Journal reported the company could be split, with its operations taken over by rivals TSMC and Broadcom.

How did this happen? Here, I’ll explore some of the lessons Intel’s shifting fortunes could hold for AI leaders trying to navigate the current technology landscape, where silicon continues to drive so much innovation and technology cycles are moving faster than ever.

The main problem: complacency

Intel’s woes have a host of causes, but to me the chief one is complacency. The company’s original, founder-driven mentality – which had propelled it to dominate the CPU market with over 90% share in PCs and servers – began to shift a couple of decades ago. Under former CEO Paul Otellini’s sales-driven leadership, Intel expanded its business significantly but missed critical emerging markets, including gaming (Nvidia’s former sweet spot), mobile computing and then cloud services. While Intel focused on protecting its existing markets, Nvidia spotted the graphics-processing unit (GPU) opportunity—first in gaming, then crypto, and now AI.

Similarly, Amazon Web Services turned “small” cloud services into a $27B+ quarterly revenue stream. Even Microsoft, Intel’s longtime “Wintel” partner, innovated out of its old PC-centric business model to become a major player in cloud and quantum computing and now AI. Intel, instead, focused on protecting its existing business.

So how can today’s startup founders stay nimble and avoid getting caught up in the complacency trap?

1. Stay in “founder mode.”

Y Combinator Founder Paul Graham made news last year with his essay extolling the virtues of what he called “founder mode.” According to Graham, successful tech-company founders should stay engaged in all aspects of their business—what might be called micro-managing—instead of handing over the reins to professional managers (who operate in “manager mode”, hiring good people and delegating work to them.)

Intel, like many large companies, morphed from a founder-led business—it was started by Gordon Moore and Robert Noyce in 1968, with Grove as its third employee—to one run by executives more focused on management and sales. Paul Otellini ran the company from 2005 to 2013 as the first non-engineer to serve as CEO. While he managed a lucrative partnership with Apple, he also misread the size of the mobile-computing market and the fact that less-powerful, “good enough” chips could power the next generation of computing.

Similarly, today’s startups can’t afford to dismiss adjacent markets or emerging use cases, particularly with the length of technology cycles shortening. Our portfolio company Databricks* is a great example. The company’s evolution from a business that developed the Apache Spark open-source processing system, focused on big data workloads, into one that made 1) software-defined data warehouses, and now 2) comprehensive AI/ML platforms, demonstrates the importance of constant reinvention while maintaining core strengths.

Interestingly, Databricks had seven original founders, all academics at the University of California at Berkeley. One of them, Ali Ghodsi, still runs the company today.

Other notable companies have engineered similar transformations. As mentioned, Microsoft successfully transitioned from the PC to the cloud era and then to AI; the company’s biggest source of revenue is currently its “intelligent cloud” business segment. Apple went from being known for candy-colored computers to changing the world with the iPhone, while Netflix morphed from a DVD movie-rental service into a major producer of award-winning movies and TV shows.

2. Stay close to the customer

I think the Intel story also demonstrates that product innovation requires customer proximity. Intel grew distant from end users over time, focusing on selling its products to system-integrator go-betweens instead of more directly to customers. Its foundries essentially sold chips to Intel’s internal PC division, cutting the company off from real market feedback about its products.

Nvidia took the opposite approach: The company doesn’t just build chips, but creates complete solutions based on customer needs. I see Nvidia and its charismatic CEO, Huang, constantly engaging with customers at events. He’s constantly collecting data about how machine-learning workflows evolve and building both hardware and software to meet those needs—even though he’s already running a business that had $60.9 billion in revenue in its fiscal year ending in January 2024.

Nvidia owns around 90% of the GPU market, but Huan clearly wants to understand trends in AI applications and use cases while thinking ahead about what architectural decisions will create the next opportunities for his company.

3. Stay product-driven

Maintaining a product-driven culture inside a company is always crucial for innovation, and no company exemplifies that better than Intel. For years, the company was an undisputed industry leader and employed some of the smartest engineers in the industry. Under Grove, it operated under a mantra of “productive paranoia”: constantly anticipating potential market threats and using that fear to help drive innovation quickly and seize new opportunities before they’re apparent to everyone else.

Grove dramatically shifted Intel’s direction from dynamic random-access memory (DRAM) chips to microprocessors in the mid-1980s, for example, which represented a huge and risky shift for the company. But Grove’s intuition, like Huang’s with his graphics chips, was correct. Years later, in my view, under subsequent CEOs, Intel’s leadership became increasingly focused on sales and market protection rather than innovation.

Today’s AI upstarts can learn from these decades-old product shifts. I firmly believe that most of the value from AI will come at the application and middleware layer, and that raising hundreds of millions (or billions) of dollars to compete at the AI foundation layer with much larger tech companies (Microsoft, Facebook) is a losing game. Getting as close as possible to your end customer, and seeing how they’re deriving value from your specific application, is the way to go.

The next few years will determine which AI companies can learn from Intel’s experience and which will repeat its mistakes. For founders building in this space, the message is clear: Your current success is just the beginning. Stay hungry, stay close to your customers, and never stop innovating. The market won’t wait for those who don’t.

The post Only the Paranoid Survive: Lessons from Intel and Andy Grove for Today’s AI Startups appeared first on Battery Ventures.

The post Decoding the Black Box of Software R&D Spend appeared first on Battery Ventures.