“AI represents the most profound tectonic shift of our generation”

“AI represents the most profound tectonic shift of our generation. The scale and pace of its impact are beyond anything we’ve previously experienced - and we still don’t fully understand what’s coming,” said Dean Shahar, Managing Director & Head of Israel at DTCP Growth. “But amidst all the hype, I remind myself of an old lesson from financial history: Every time we hear ‘this time it’s different’ or ‘it’s a new economy’, whether it was the DotCom bubble, 2008, crypto, or COVID, we eventually rediscover that business fundamentals always matter.”

Dean Shahar

Shahar joined CTech for its VC AI survey to share his unique insights about how the technology will impact the investment space. “Even in the face of extreme disruption, the basics remain the foundation. And that perspective helps keep things in balance,” he added.

You can learn more in the interview below.

Fund ID
Name and Title: Dean Shahar, Managing Director & Head of Israel
Fund Name: DTCP Growth
Founding Team: Vicente Vento, Thomas Preuss, Jack Young
Founding Year: 2015
Investment Stage: Growth
Investment Sectors: AI, Cyber

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 say it's a 7, though part of me wants to say 10. We’re only beginning to scratch the surface of what AI can do. Giving it a 10 would suggest we’ve fully realized its potential, and we’re far from that. Venture capital work can be broken down into three core areas: (1) researching market opportunities and identifying exceptional talent, (2) networking and relationship building, and (3) executing transactions.

AI has meaningfully enhanced the first and third domains. Research is faster and deeper, and deal execution is more efficient thanks to tools that support diligence, data processing, and analysis. However, the second area, relationship building, remains profoundly human. It’s a nuanced, personal endeavor rooted in trust, and that part of the job isn’t going to be automated anytime soon.

Founders are still the single biggest driver of a startup’s outcome. Even in later-stage investing, identifying the right people to tackle the unknown is critical. Building a company is about solving problems no one has fully defined yet, and that human element can’t be replaced by even the smartest algorithms.

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

Yes, and also, no. Let me explain. Over the past five years, we’ve had a series of strong exits across M&A and IPOs. Many of those companies included AI or ML components in their stack. However, AI as it’s being built and productized today is fundamentally different.

Looking back, what we once believed was an “AI race car” now feels more like a well-trained horse. Today’s AI is deeply tied to direct business outcomes in ways that weren’t possible just a few years ago. Take our portfolio company Zenity, for example. They enable enterprises to adopt AI tools securely, becoming critical enablers of modern, AI-driven workforces at scale.

That level of clarity in value proposition, where AI is not just embedded but essential, is new. SaaS was about automating known processes. AI is about introducing intelligence that, in many cases, surpasses human ability, replacing tasks rather than just improving productivity. That’s a major shift.

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

Absolutely, it’s a different ballgame. Evaluating AI startups requires a higher tolerance for risk and a much more creative diligence process. Many of these ideas are unprecedented, so historical data points and benchmarks don’t hold as much weight. The past is no longer a reliable predictor of success.

Conviction today demands more imagination, upfront research, and validation through unconventional signals. The future is arriving too fast for rearview-mirror investing. Since AI became mainstream, especially post-ChatGPT, we’ve seen an explosion of startups claiming to be “AI-first.” The noise level is high, and cutting through it requires deeper technical understanding and sharper instincts than ever before.

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?

It depends on the company’s stage and vertical. From a financial perspective, we’re still looking at the same core indicators: ARR, revenue growth, net dollar retention (NDR), and efficiency ratios, among others.

What’s changed is the interpretation of those metrics. With AI, context and narrative matter more. We're moving away from a one-size-fits-all model where all SaaS companies were compared by the same yardstick. In AI, understanding why a company is growing, and how defensible that growth is has become more important than the raw numbers alone.

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

Valuing early-stage companies was never about formulas like CAPM or WACC, it’s always been a dance between supply and demand. In today’s climate, demand is soaring because the perceived upside in AI is massive.

We’re seeing startups achieve impressive traction with very lean teams, disrupting massive industries with radical efficiency. That disruption potential pushes expectations, and therefore, risk appetite is higher which naturally drives up valuations.

It’s classic risk-reward theory at play, but AI adds a unique twist: the pace of innovation at the technological layer is so fast that it pushes both founders and investors to make bigger, bolder moves, faster.

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

One of the biggest is unpredictability. It permeates everything, from infrastructure costs and model behavior to regulatory ambiguity. As we move toward more autonomous agentic systems, we’re essentially giving non-human entities control over decision-making and workflows.

No matter how well we build guardrails, there will always be an edge case that surprises us, the "N+1 problem." That unpredictability introduces new layers of operational and financial risk.

It also increases the frequency of iteration. Companies now need constant internal checks on model performance and faster feedback loops on business outcomes, especially as AI scales across high-stakes functions like marketing, customer support, and infrastructure management.

Do you focus on particular subdomains within AI?

Not really. I try to remain intellectually open and humble. What excites me is any idea, regardless of subdomain, that has the potential to dramatically improve the way business is done.

In hindsight, every breakthrough feels obvious. But in real time, it's anything but. Success in venture capital, in my view, comes from curiosity and a willingness to explore paths others overlook.

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

Every industry will be transformed; it's only a question of how and when. The nature of that transformation varies: (1) In non-sensitive domains, AI will increasingly replace humans outright. (2) In sensitive or high-risk areas, AI will act more as an augmenting force, essentially becoming a digital teammate that supercharges human decision-making.

Take cybersecurity, for example. The data is highly sensitive, and a false positive (e.g., blocking a critical API) could paralyze an entire business. In these cases, AI will likely act as a co-pilot, handling the repetitive and low-risk tasks while humans retain control over critical decisions.

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?

Cybersecurity continues to be Israel’s home court advantage. It’s where the majority of fundraising and M&A activity happens. That said, I’m hopeful we’ll see more Israeli companies tackle massive, underexplored (from an Israeli perspective) markets like healthcare or logistics, where the potential for AI-led disruption is enormous. Historically, we haven’t seen many home-grown success stories in those spaces, but the talent is certainly here.

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?

I don’t believe the founder type changes just because we’re talking about AI. The same traits I’ve always looked for still apply: grit, curiosity, and integrity.

I back founders I’d want to work for, build with, or follow. AI just raises the stakes, it doesn’t change the fundamentals of what makes someone a great founder.