Titan Capital Partners: In AI, access beats product | CTech

In the race to back the next generation of category-defining software companies, Titan Capital Partners isn’t betting solely on superior code or flashy demos. For Founder & Managing Partner, Ben Topor, the real competitive edge lies in something harder to copy: access to the right people, markets, and opportunities before anyone else gets there.

“Software is more competitive than ever,” he said. “Access - not just product - will decide who wins.”

You can learn more in the interview below.

Fund ID Name and Title: Ben Topor, Founder & Managing Partner Fund Name: Titan Capital Partners Founding Team: Firm Partners - Ben Topor, Omer Schloss Founding Year: 2021 Investment Stage: From early-growth to late-stage Investment Sectors: AI, Fintech, Cyber, Mobility, Healthcare, Gaming, Foodtech. everything software and internet

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year - specifically in terms of the day-to-day work of the fund's partners and team members?

I’d rate it a 6. AI hasn’t transformed the core of what we do-sourcing, judging, and winning deals still hinges on human insight, access, and pattern recognition. But it has meaningfully improved our internal efficiency. We’re using AI for quicker company research, faster diligence on technical product claims, and generating early drafts of investment memos. It's reduced the time-to-first-insight across our workflow. That said, it’s more of an augmentation than a revolution, at least for now. In funds like ours, where we’re often running point on complex or backing founders in category-defining markets, AI helps keep our edge sharper-but it doesn’t replace judgment, strategy, or network.

Overall, AI has become indispensable, enhancing not only the efficiency of our operations but also the quality and strategic value of the work performed by our partners and team.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

Yes - Titan has had three notable exits over the past 24 months involving companies that successfully embedded AI into their workflows, though not always as the headline product. A good example is eToro, where AI played a supporting role in optimizing back-office operations, from fraud detection to personalization engines and user engagement algorithms. What these companies shared wasn’t necessarily a core AI product, but rather a pragmatic and high-leverage use of AI to enhance operational efficiency, user experience, and margin structure. In each case, AI was a means to deepen competitive advantage

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

Yes, but not because we throw away our framework. We still apply the framework: partnering with the most disruptive tech companies in fast-growing software and internet sectors. The key difference is the volatility and fragility of AI moats. In traditional software, moats often come from integration depth or distribution. In AI, you're often dealing with thin wrappers on foundation models, so we have to work harder to validate whether the the firm’s strategic positioning is actually defensible and if the startup can survive the competition intensity of the early stages. Many AI startups look promising on a demo-few survive the go-to-market and margin test. In that respect, having global pattern recognition and analytical rigor is an edge we lean on.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

The fundamentals still apply-CAC payback, gross margin, retention, net revenue expansion. But for AI companies, we overlay three AI-specific KPIs:

The ones we back tend to show early signs of product-market symmetry and long-term proprietary architecture and benefit from our value creation support to accelerate defensibility.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

We typically invest in growth stages where companies have more than $10M of sales and valuation is more straight forward. In case we examine earlier stage companies and in AI startups, traditional valuation multiples (like ARR-based) often don’t apply because revenue is limited. So instead, we think in terms of option value-what would the company be worth if they win the race?

It also gives us flexibility to invest in both primary and secondary rounds, depending on where the opportunity lies.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

While most investors focus on infrastructure costs and model dependency when evaluating AI startups, we believe the deeper financial risks are more subtle. One such risk is value compression through overdelivery. Many AI products demo so well that they unintentionally erode their own pricing power. When buyers see immediate value from a prompt or UI interaction, they often perceive the solution as a thin wrapper around a public model and resist enterprise pricing-regardless of how much engineering or workflow depth sits beneath the surface. Ironically, the better the early experience, the harder it can be to justify large contract sizes.

Another overlooked risk lies in organizational misalignment. AI tools frequently gain traction among individual users or technical teams, but lack an executive budget sponsor. Without an owner, these products are vulnerable to non-renewal, not because they don’t work, but because they live outside formal budget processes. And even when adoption is strong, many AI companies fail to stand out in a narrative-saturated market. The “AI copilot for X” story has become so crowded that buyers struggle to differentiate between solutions. This slows down sales velocity and drives up CAC-a financial drag that compounds quickly. As a global fund, we sometimes help founders shape the narrative and refine positioning for U.S. and public-market buyers early - especially when the category gets crowded.

Do you focus on particular subdomains within AI?

As growth investors, we don’t focus on the subdomain or the underlying technology-we focus on the function it serves. Whether it’s machine learning, NLP, or computer vision is secondary. What matters to us is: does it create leverage for the customer? Does it unlock a new workflow, compress time-to-value, or defend a core system?

That’s why we’re equally interested in AI infrastructure and application software as long as the product solves a real problem and shows signs of distribution or defensibility. The only area we typically avoid is training foundational models from scratch - which doesn't align with how we think about risk/returns or product-market fit. We’re comfortable underwriting high-risk, high-reward profiles in these areas.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

We believe AI represents the next major infrastructure shift, on par with mobile and cloud. But unlike those prior waves, AI’s impact is deeply uneven across industries. Some sectors are poised to see immediate and transformational effects, while others will take longer to absorb the change.

At Titan, we’ve leaned heavily into Vertical SaaS, where AI isn’t just hype-it’s already reshaping workflows and unlocking real economic value:

We’re especially drawn to “boring” workflows-procurement, underwriting, claims-because they tend to hide the deepest moats.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

Israel has always excelled where deep tech meets real-world urgency, and we’re now seeing that converge in some very high-potential AI niches.

Two in particular stand out:

These are not trends-they’re rails. And Israeli teams excel at building rails. That’s where we believe the largest M&A and IPO outcomes will come from-and where Titan is spending time as a growth investor.

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

Israel has built a strong global reputation in cybersecurity and core infrastructure but ironically, that success has narrowed the lens. Most AI startups here still default to building for technical buyers: devs, infra teams, security leaders.

The big gap? AI applied to the messy, low-glamour business problems-procurement, logistics, HR operations, claims processing. These are unsexy domains, but they’re full of trapped value, fragmented data, and poor tooling-perfect terrain for AI-native SaaS. Yet most Israeli founders shy away from these because they aren’t “deep tech enough.”

Another missing layer is distribution thinking. Too many teams build for performance benchmarks or model elegance, but ignore the hardest part: winning the workflow.

We’re looking for founders who start with a wedge - an urgent pain, a daily tool, a non-obvious user-and use AI to earn their way into broader system control. Our edge as a fund is knowing how to help these teams go building a tool and building a platform.