AI’s Next Frontier: Why Human-to-Human Work Could Become the Ultimate Luxury

Artificial intelligence is no longer just a futuristic buzzword—it’s actively reshaping industries, redefining job roles, and forcing investors to rethink their strategies. While some sectors are poised to see massive productivity boosts from AI integration, others face an existential shake-up. According to Moshe Zilberstein, General Partner at Next47, the value of human-to-human interaction will actually increase in the AI era, transforming certain professions into premium services.

This perspective comes from CTech’s VC AI Survey, where Zilberstein shared deep insights into how AI is shifting the global economic and employment landscape, and why Next47 is betting big on startups with “AI DNA.” His analysis goes far beyond the hype—touching on productivity gains, industry disruptions, investment strategies, and the unique role of Israeli tech in optimizing AI infrastructure for the future.

the Original

Moshe Zilberstein of Next47 believes AI will fundamentally transform both white- and blue-collar sectors, but in very different ways. For white-collar workers, AI will act as a powerful productivity enhancer rather than a job killer—streamlining workflows, summarizing technical documents, and providing faster market intelligence. In blue-collar industries, however, the changes could be far more disruptive, with AI-powered robotics replacing human labor in areas like logistics, agriculture, and manufacturing.

He predicts that tasks currently considered “too expensive to automate” will soon become cost-effective, accelerating the adoption of robotics and automation. Yet, paradoxically, human-to-human work—especially in fields like hospitality, education, and healthcare—will gain new value as technology elevates rather than replaces personal interaction.

Next47 has already embedded AI deeply into its own operations, from due diligence to market research. The fund treats AI as the primary investment lens and is backing companies with “AI DNA” rather than those merely layering AI onto existing models. One notable exit was Deepscale, an AI perception company acquired by Tesla.

When evaluating AI startups, Zilberstein looks for defensible moats beyond just access to models, focusing on differentiated data, vertical integration, and proprietary workflows. Key performance indicators include traditional metrics like market size and margins, alongside AI-specific ones such as inference cost per transaction, accuracy, and latency.

Valuations of early-stage AI startups are approached globally, with high premiums accepted for teams and tech that offer asymmetric opportunities. However, risks exist—such as overreliance on generalized models, shallow competitive moats, and vendor concentration.

While Next47 sees massive disruption potential in generative AI, it also invests in infrastructure layers like data storage, optimization, and secure model deployment. In Israel, the strength lies in AI optimization rather than foundational models, with companies like Vast Data leading in efficiency and scalability. The biggest gap in Israel’s AI ecosystem, according to Zilberstein, is a lack of national-scale shared computing infrastructure, something he believes requires public-private collaboration.

What Undercode Say:

The AI conversation often swings between extremes—either utopian visions of limitless productivity or dystopian fears of mass unemployment. Zilberstein’s perspective offers a more nuanced view: AI is neither universally destructive nor universally beneficial. It’s context-dependent.

His framing of “human-to-human work as a premium” is particularly compelling. As automation handles more mechanical, repetitive, or data-heavy tasks, the scarcity and value of genuine human connection will rise. This has clear implications for industries like hospitality, mental health, coaching, and education—sectors where emotional intelligence is not just a nice-to-have, but the core product. In the AI economy, kindness, trust, and empathy may become monetizable assets.

For white-collar sectors, AI’s role as an augmentation tool could usher in an era of “cognitive leverage.” Imagine lawyers drafting contracts in minutes, analysts processing complex data in seconds, or product designers iterating ideas in real time with generative tools. The real winners will be those who can combine domain expertise with AI fluency to multiply their output.

For blue-collar roles, however, the outlook is more unsettling. AI-powered robotics are advancing faster than labor market retraining programs. This creates a looming risk of structural unemployment in certain sectors unless governments and companies proactively invest in reskilling initiatives. A “robot tax” or universal basic income could become part of the policy debate sooner than expected.

From an investment standpoint, Zilberstein’s “AI DNA” filter is spot on. Many so-called AI startups are simply reselling or repackaging existing models without a defensible moat. As foundational models become commoditized, the differentiator will be unique datasets, proprietary processes, and integration into high-value workflows. In this environment, being “AI-adjacent” is not enough—you need to be AI-native.

His warning about shallow moats is particularly important. The tech history books are full of startups that skyrocketed in valuation only to crash when competitors gained equal access to the same tools. In AI, infrastructure dependency is a double-edged sword: it accelerates development but can strangle profitability if licensing costs spike or access is restricted.

On Israel’s role, the focus on optimization over foundational models is strategically sound. Competing with U.S. or Chinese giants in model training is resource-intensive and economically risky. By focusing on efficiency—reducing compute costs, improving inference speed, enhancing model deployment—Israel can position itself as an indispensable enabler of global AI adoption.

The gap Zilberstein points out—shared national compute infrastructure—cannot be overstated. Without it, Israel risks losing its edge in AI R\&D to countries with greater computational resources. This mirrors the early days of the internet when access to high-speed infrastructure determined which nations became tech leaders.

In short, Zilberstein’s vision suggests that the AI revolution will reward those who can blend human uniqueness with machine precision, while punishing those who fail to adapt their value proposition to a world where algorithms handle most of the legwork.

🔍 Fact Checker Results

✅ AI-powered robotics are already replacing human labor in certain logistics and agricultural roles.
✅ Commoditization of foundational models is an acknowledged challenge in AI investing.
✅ Israel’s comparative advantage lies in AI optimization and infrastructure efficiency.

📊 Prediction

In the next five years, human-facing industries like healthcare, hospitality, and education will see a surge in demand for premium, personalized services, while blue-collar sectors without reskilling programs will face accelerated job displacement. Startups with proprietary AI workflows, unique data assets, and strong infrastructure strategies will dominate the investment landscape, especially in markets like Israel that focus on optimization rather than model creation.