What VCs really want: The five pillars for building a successful AI startup | CTech

As the AI investment landscape reaches greater maturity, investors are observing a decisive "shift from 'hype' to 'discipline'" in evaluation criteria. While AI undoubtedly offers what many describe as a "once-in-a-lifetime opportunity," investors are increasingly demanding evidence of "sustainable growth and proven profitability" rather than accepting promises based purely on technological potential.

In recent weeks, an in-depth survey conducted by CTech journalists among dozens of venture capital fund managers has sought to crack the code of the unique characteristics that define successful AI startups - the very traits investors look for when seeking the next big win. For entrepreneurs navigating this complex landscape, the message from leading venture capitalists is unambiguous: the AI revolution represents genuine transformation with enormous opportunities, but ultimate success will depend on mastering the same fundamental business principles that have always driven exceptional companies - outstanding people solving significant problems with sustainable competitive advantages and disciplined execution.

This evolution reflects a broader recognition that while the underlying technology may be revolutionary, the fundamental principles of successful business building remain constant. The startups that master all five critical pillars: 1. Exceptional teams with deep domain expertise; 2. Defensible competitive advantages that transcend underlying technology; 3. Operational discipline combined with efficient scaling; 4. Strategic market timing across transforming industries; 5. genuine AI-native value propositions - position themselves not merely for initial funding success but for long-term market dominance.

"AI is poised to change how people interact with products, with a shift toward conversational interfaces, personalization, and automation," explains Shelly Hod Moyal, Founding Partner at iAngels. This transformation, she notes, will be "perhaps as much or even more than mobile did," fundamentally redesigning entire workflows across professional services like law, finance, and healthcare.

The consensus among surveyed VCs is that investing in the AI era feels like both "a privilege and a challenge," necessitating a focus on specific, enduring characteristics that separate the future winners from the also-rans. Through extensive interviews with top-tier investors, five critical pillars have emerged that define what it takes for AI startups to not just attract investment, but to build the foundation for lasting success.

Despite the technological sophistication of modern AI, the most experienced investors maintain an unwavering focus on the human element. The survey reveals that early-stage investing, especially in AI, remains fundamentally "about people" - a principle that has guided successful venture capital for decades.

"The team is the single most important factor for our pre-seed and seed investments," states Adi Gozes, Managing Partner at AnD Ventures, who seeks "exceptional founders that have grit, charisma and the ability to move fast and adapt as markets evolve."

This people-first philosophy extends far beyond general leadership capabilities. Miri Fenton, Investor at Maverick Ventures Israel, highlights that as competitive landscapes change faster than ever, resilience, adaptation, strong vision, and hard work are the key traits her firm looks for in founders. She emphasizes that "the best founders attract talent, pivot fast, and sell early" - factors that directly impact valuation.

Horizon Capital's Managing Partners, Yaniv Jacobi and Lior Segal, have coined a term for what they consider essential: "Founder-market fit." They prioritize "Team first" in their valuation approach, specifically seeking founders with "intimate knowledge of the domain they're building for and a basis for insight-driven execution."

The most coveted founders combine technical excellence with deep domain expertise. Guy Yamen, Managing Partner at TPY Capital, actively seeks "founders with deep domain expertise who are applying AI to non-obvious, high-friction problems that aren't easily addressed by off-the-shelf solutions." These exceptional entrepreneurs, he notes, often build "differentiated products with minimal capital."

Israeli founders have earned particular recognition for their resourceful approach. Nadav Shimoni, Managing Director at Arkin Digital Health, highlights the advantage of Israeli entrepreneurs in "doing more with less, through scrappy product-market iteration and clever use of foundation models, proprietary models, and data streams."

Magenta Venture Partners, through Ran Levitzky, emphasizes backing teams that "combine technical excellence with sharp execution and commercial discipline," specifically looking for "product-led teams that move quickly and prioritize measurable business outcomes."

Perhaps most tellingly, J-Ventures' Oded Hermoni seeks founders who "know how to move fast with different tools and tech and know how to ask the AI the right questions" - essentially those who are "swimming in AI."

The ability to adapt and execute with unprecedented speed has become paramount. Yoni Heilbronn and Elad Ziklik from IL Ventures warn that "what's innovative today might be commoditized tomorrow," making teams that can "adapt fast, compound learning, and build defensibility beyond the model itself" absolutely crucial. Ori Striechman of Swish Ventures reinforces this urgency, stating that companies "must execute faster than ever before."

2. Redefining Competitive Advantage

The second critical pillar addresses a fundamental shift in how investors evaluate defensibility. The survey reveals a sobering reality: the "classic 'tech moat' is often missing or short-lived" in AI startups, as models and infrastructure rapidly commoditize.

This evolution has forced investors to seek new forms of competitive advantage, with unique data access emerging as the strongest available moat. Q Fund's Liav Ben Rubi and Dana Taigman Koren observe that "proprietary data often represents the strongest moat we see today," particularly for teams that partner with traditional players to gain "exclusive access to this data."

J-Ventures has developed specific criteria for evaluating data-driven advantages, seeking companies where the data is "exclusive or hard to replicate," "improves model performance as the company scales," and "creates increasing advantage over time."

Beyond data, successful AI companies are building defensibility through profound workflow transformation rather than simple automation. Eyal Redler of The Garage emphasizes that successful AI companies demonstrate a "clear understanding of how to integrate into enterprise workflows and deliver tangible ROI."

Square Peg has identified that "AI-native winners will reshape workflows rather than just automate tasks," positioning themselves as comprehensive platforms rather than point solutions. This fundamental transformation creates customer stickiness that extends far beyond feature adoption.

Some companies are discovering competitive advantages by combining AI with unique hardware solutions. Square Peg portfolio company Exodigo exemplifies this hybrid approach, "combining proprietary hardware with AI software for subsurface scanning, collecting data no one else has."

Proprietary intellectual property and algorithms remain critical differentiators even as base models commoditize. Shelly Hod Moyal points to companies like Oddity, which built "defensible value by integrating AI in a way that created unique outcomes" based on "core computer vision and deep learning IP."

Product-led growth and community building have also emerged as viable defensive strategies. Notable Capital highlights Streamlit and Neon for their "strong product-led growth or bottom-up adoption among technical users," creating network effects that compound over time.

3. Operational and Financial Mastery

The third pillar focuses on operational excellence, where AI presents both unprecedented opportunities for efficiency and significant new risks around cost management. The technology enables lean teams to achieve results that previously required much larger organizations, fundamentally altering traditional financial benchmarks.

Capital efficiency has become a defining metric. A lean team can now accomplish what used to require dozens of engineers, and what founders can achieve per dollar of capital - how fast they iterate, how much product they've built, and what traction they've generated.

Horizon Capital has established concrete performance expectations, anticipating that startups can "reach $1M in ARR with $1M in pre-seed investment within 12-18 months" - metrics that reflect AI's potential to dramatically accelerate traditional startup timelines.

However, AI introduces unique financial challenges, particularly around infrastructure costs. High compute expenses, GPU costs, and LLM API spending can rapidly erode margins if not carefully managed. TPY Capital specifically looks for businesses that can "reach or maintain 80%+ gross margins," either through aggressive optimization or by delivering sufficiently high-value outputs that justify premium pricing power.

Key1 Capital takes a particularly analytical approach to these challenges, scrutinizing infrastructure costs to ensure they don't erode margins as businesses scale. They emphasize "revenue per employee" as a crucial metric for gauging AI's impact on operational leverage, while also examining R&D spend efficiency to determine whether it translates into "meaningful product progress and differentiation."

Notable Capital, while comfortable with initially high costs of goods sold, insists on understanding "how margins improve over time" as companies achieve scale and optimization benefits.

The accelerated capabilities of AI have also created new expectations around product-market fit signals. AnD Ventures now expects to "see significant revenues pretty early on and even earlier than we would have seen before, since now it's much easier and quicker to deliver a product."

Several prominent funds are actively resisting inflated valuations. The Garage and Magenta Venture Partners prefer founders who focus on "business fundamentals as much as model performance," while J-Ventures emphasizes connecting valuations to "realistic exit scenarios, not just excitement or buzz."

4. Navigation in a Transforming Landscape

The fourth pillar addresses the critical challenge of market timing and strategic positioning in an era of unprecedented technological change. Successful AI startups must navigate not only current market conditions but also anticipate how rapidly evolving AI capabilities will reshape entire industries.

The most compelling opportunities lie in addressing genuine pain points within large, established markets. Flashpoint VC emphasizes that true transformation creates "new products and markets" rather than merely improving existing solutions incrementally.

Traditional and regulated industries have emerged as particularly attractive targets. These sectors are described as "ripe for transformation" with "large budgets and a lack of specific tooling." Despite inherent go-to-market challenges, funds like Maverick Ventures Israel believe "significant wins" can be achieved in regulated industries.

Q Fund identifies "immense" impact potential in sectors with traditionally thin margins, where AI can unlock substantial gains through predictive maintenance, process automation, or intelligent decision support. Industries including banking, insurance, healthcare, logistics, manufacturing, and defense are frequently cited as prime candidates for AI-driven disruption.

Many leading investors now conceptualize AI not as a distinct sector but as a "horizontal capability" that will penetrate virtually every industry vertical. Ran Levitzky of Magenta Venture Partners predicts that "the distinction between AI startups and non-AI startups will disappear entirely." Rotem Shacham of PSG Equity reinforces this perspective: "we cannot treat it as a standalone segment. Rather, we should ask ourselves how AI is impacting whichever company we are looking at, in any field."

This horizontal view creates both enormous opportunity and significant competitive risk. While the total addressable market is vast, startups must differentiate substantially beyond being mere "thin wrappers" on top of commoditized AI models. Generic solutions that are "easy to replicate," such as basic AI assistants, face intense competitive pressure and margin compression.

Israeli startups demonstrate particular strength in applied AI for cybersecurity, fintech, DevOps, and defensetech. The survey identifies strong exit potential in AI-enabled defensetech, enterprise copilots, AI-enhanced security platforms, AI infrastructure layers, and vertical AI solutions for regulated industries.

Physical AI - combining perception, decision-making, and interaction with the physical world - represents another compelling opportunity where Israeli startups excel. However, notable gaps remain in foundational LLM development due to capital and resource intensity requirements, and in product-led, UX-forward AI startups, where Israeli founders often prioritize technology development over user experience optimization.

5. The AI-Native Imperative

The fifth and final pillar distinguishes genuinely AI-native companies from those merely incorporating AI as additional features. True AI-native organizations embed artificial intelligence into their core strategy and product architecture from inception, creating businesses that fundamentally could not exist without AI capabilities.

Problem-solution fit becomes absolutely critical - there must be clear understanding of the specific problem being addressed and compelling evidence for why AI is essential to that solution rather than optional. Companies must demonstrate not merely technical proficiency but strategic intelligence in their AI application.

Key1 Capital focuses intensively on evaluating "how AI is being applied to improve those operations" and specifically "where it is driving impact - cost efficiency, gross margin, customer support, user acquisition." The fundamental question shifts from whether a company uses AI to how effectively it leverages AI to build sustainable competitive advantages and "how intelligently and strategically it does so."

Magenta Venture Partners seeks companies demonstrating "meaningful increase in ARR per employee, reflecting both operational leverage and disciplined execution." This metric captures both the efficiency gains achievable through AI implementation and the execution quality required to realize those theoretical gains in practice.

Speed has emerged as a critical competitive differentiator. Notable Capital emphasizes that "speed is often the key differentiator" in markets where competitive advantages can evaporate virtually overnight. This imperative requires continuous re-evaluation of technology stack and strategic direction in response to the rapid evolution of AI capabilities.

Global perspective matters particularly for Israeli startups, where success increasingly requires thinking beyond domestic markets from company inception, positioning for international scale from day one.

The ability to maintain agility and urgency in strategy becomes paramount. The most successful AI-native companies demonstrate capacity to continuously re-evaluate their technology stack and strategic approach in an exceptionally fast-changing environment.