Scaling Risks in the AI Era: Insights from Arkin Digital Health

The Challenge of Investing in Healthcare AI Startups

Artificial intelligence is revolutionizing industries worldwide, and healthcare is no exception. However, with rapid innovation comes heightened risk, especially for early-stage investors trying to navigate a volatile landscape. In CTech’s VC AI Survey, Nadav Shimoni, Managing Director at Arkin Digital Health, offers valuable insights into the specific financial and operational challenges investors face in this new AI-powered world. His reflections shine a light on the complexities of startup scalability, competition, and the true value AI brings to healthcare innovation.

the Original

Nadav Shimoni emphasizes that the biggest financial risk investors face today in AI healthcare startups is the inability to scale. With AI dramatically lowering the threshold for product development, the market is flooded with competitors, making it hard for any one company to stand out. Arkin Digital Health, founded in 2021, focuses on early-stage investments (pre-seed to Series A) in healthcare startups and is feeling AI’s impact at a solid “8 out of 10” level in terms of internal operations and decision-making.

AI tools now allow Arkin’s team to analyze companies faster, model investment scenarios, and enhance portfolio efficiency. However, Shimoni cautions against over-hyped “AI companies”—suggesting that nearly every startup today is using AI in some capacity, and the focus should instead be on how effectively they use it. The fund avoids vanity metrics and instead tracks user activation, operational efficiency, and real-world customer validation to measure a company’s potential.

When valuing AI startups without significant revenue, Arkin looks at market size, acquisition potential, and the founders’ capability to capture opportunity. The greatest financial hazards, aside from tech or compute costs, include entering oversaturated niches that lead to product commoditization.

Shimoni also notes that in Israel, AI innovation is thriving due to a lean, creative, and iterative culture. The country’s ability to build cost-effective, practical AI solutions—especially in healthcare—positions its startups for strong exits in the coming years. But the healthcare sector poses its own unique challenges: slow adoption rates, clinician burnout, and demand for tools that don’t disrupt workflows. AI’s ability to reduce administrative burden and support decision-making without increasing complexity will be key.

What Undercode Say:

The views shared by Nadav Shimoni provide a deeply pragmatic lens on investing in the AI era—particularly within the nuanced world of healthcare. Unlike the prevailing hype narratives, his emphasis isn’t on raw innovation or flashy demos, but sustainable differentiation and operational pragmatism. That framing is not only realistic—it’s essential.

Let’s break it down further.

1. Scaling vs. Surviving:

Shimoni’s core concern—that the low barrier to entry in AI development leads to commoditization—is extremely valid. Open-source tools, pre-trained models, and no-code platforms have democratized access, but also made it harder to maintain a competitive edge. Investors today must prioritize execution quality, go-to-market strategies, and user adoption over technical prowess alone.

2. AI Hype ≠ AI Value:

His rejection of the term “AI companies” is refreshing. In 2025, AI is an ingredient, not the entire dish. Founders and investors alike should be focused on relevance and ROI, not just sophistication. Startups that anchor their product’s value in real-world pain points—especially in mission-critical sectors like healthcare—are more likely to survive the shakeout.

3. Metrics That Matter:

Too often, investors get seduced by vanity metrics like “contracted revenue.” Shimoni’s focus on activation time, live user data, and sales cycle underscores a critical need for realism. These indicators show whether the product is not just sold—but actually used and loved. That’s what builds long-term value.

4. Financial Waste in the AI Boom:

Shimoni’s critique of bloated headcounts and inefficient spending during the past decade is spot-on. AI enables leaner ops. Startups should harness AI to streamline processes, minimize redundant roles, and speed up customer acquisition. Investors will increasingly favor companies that prove they can scale lean.

5. Israel’s Advantage:

The Israeli startup ecosystem excels in lean innovation. Its “do more with less” culture aligns perfectly with what’s needed in this AI rush. Israeli healthcare startups that build with clinical empathy, regulatory awareness, and a clear deployment path stand to capture significant market share—and exit opportunities.

6. The Healthcare Caveat:

Healthcare is notoriously slow to adopt tech, but AI may finally be the wedge that changes that. The current clinician burnout crisis is creating an appetite for smart, lightweight, and integrative tools. Shimoni is right to call for products that reduce friction rather than add more dashboards and logins.

Overall, this is a roadmap for investing wisely, not wildly, in the AI boom. Shimoni’s voice acts as a guidepost for those looking beyond the hype, focusing instead on companies that solve real problems with practical AI—and have the team, efficiency, and vision to stand out in a crowded field.

🔍 Fact Checker Results:

✅ AI adoption in Israeli healthcare is indeed accelerating, driven by burnout and staffing shortages.
✅ “Vanity metrics” like contracted revenue are widely criticized by VCs for inflating startup traction.
✅ The trend of using AI to reduce headcount and improve operational efficiency is growing across sectors.

📊 Prediction:

Within the next 3–5 years, many AI healthcare startups will fail due to commoditization, but those that combine clinical empathy, operational insight, and lean AI integration will dominate. Israeli startups, in particular, are poised for early exits and global partnerships—especially if they target administrative automation and decision-support tools over “big bang” AI platforms.

References:

Reported By: calcalistechcom_855d1ce0a4658cfe9b2b21b7
Extra Source Hub:
https://www.instagram.com
Wikipedia
OpenAi & Undercode AI

Image Source:

Unsplash
Undercode AI DI v2

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

💬 Whatsapp | 💬 Telegram

📢 Follow UndercodeNews & Stay Tuned:

𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin