Tools & Platforms

Beyond the AI buzz: Nina Capital’s critical view on healthtech hype

Published

4 weeks ago

August 6, 2025

Care services – This includes:

Tech-enabled care providers (virtual or hybrid care models that weren’t possible before).

Tech products sold to existing care providers (like hospitals) to help them operate better.

Health data tools — products that process, aggregate, or anonymise healthcare data to ensure privacy and improve interoperability.

According to Zanchi, when it comes to hype, the most concerning examples tend to be in the first category, where companies are claiming to use AI for diagnostic or therapeutic purposes. >

“That’s very close to the point of care, and it introduces real risks if the tech isn’t robust.”

Creating solutions in search of problems instead of understanding real value

Zanchi also sees another issue. Even among companies that are building AI models, many are creating solutions in search of problems.

Instead of starting with a healthcare need and asking what the best solution is, they’re pushing AI into spaces where it might not be the right fit. Healthcare is complex. Products need to appeal to multiple stakeholders, including patients, doctors, nurses, administrators, insurers, and regulators.
anchi contends that the first step is understanding the value proposition for each of those groups. What does this solution offer them? Is there alignment?

“We often reach conviction — or decide to pass — before we even open the black box of the tech. If it turns out to be AI under the hood, great. But we’re looking for the best product to solve the problem, not the flashiest tool,” she shared.

Nina Capital backs diverse founders making tech that solves real healthcare problems

In terms of what makes a good startup to invest in, in a word, diversity.

Zanchi explained:

“At Nina Capital, we’re a mix: 12 nationalities across 10 people, with backgrounds in engineering, neuroscience, pharma, medtech, regulatory, and SaaS. We look for that same diversity in our founding teams.”

While early-stage teams can’t have everything in-house, an ideal starting founder triangle is someone who understands the clinical context, someone who brings technical expertise, and someone who has business or market knowledge. Further, she contends that sometimes healthcare insight comes from lived experience, such as founders who built a product because of a personal or family medical experience.

“That kind of commitment can drive real innovation.”

Examples of investment include:

Noah Labs: A digital health startup that develops Ark, an AI-powered, Class IIa telemonitoring platform combining smart biosensors, machine learning, and a mobile app to detect and predict heart failure decompensation — often up to 14 days before clinical deterioration—for earlier intervention and reduced hospitalisations

CryoCloud: a cloud-native SaaS platform that automates cryo‑electron microscopy (cryo‑EM) data analysis using machine learning, dramatically speeding up 3D protein structure visualisation to accelerate drug discovery.

LillianCare: Establishing hybrid general practice clinics—where nurses handle about 60 per cent of outpatient treatment under remote tele‑supervision by doctors, enabled by an integrated digital platform and partnerships with insurers and municipalities

“Growth alone won’t save you.”

Zanchi views AI with a historical lens.

“Think of the dot-com bubble. The internet didn’t fail — clearly — but there was a moment of overexcitement and overinvestment, and then a collapse.”

She believes AI will follow a similar trajectory.

“It’s here to stay and will become a foundational technology. But we do need to be cautious.”

Within this, she points to two recurring issues in these hype cycles: Jumping into tech before defining the problem.

Prioritising growth over profitability.

“The best companies in our portfolio can flex: they can grow quickly when the market rewards it, or shift to profitability when needed. But others are still suffering from the excesses of recent years,” shared Zanchi.

In terms of adoption in healthtech, a sector with rightful caution and long procurement cycles, Zanchi contends that the key to access for startups is understanding the financial incentives that drive adoption and behaviour.

Nina Capital’s most successful portfolio company founders know not only how patients flow through the system, but how money flows. They also understand what behavioural changes are required and what blockers exist.

“They’ve been able to pivot when needed — if they hit a dead end with a stakeholder group, they adjust their positioning. They reconfigure the product to fit the workflows of healthcare providers better and redistribute the value so all stakeholders win.”

“You’re not just promising ‘AI,’ you’re answering the tough questions: How will this impact patient outcomes? What do regulators think? Will administrators adopt it? Can it be reimbursed? Is privacy protected? When you can confidently answer those questions, then it doesn’t matter what’s in the tech stack—whether it’s traditional software or deep learning. It’s about solving the problem.”

And they’re humble enough to recognise where their expertise is lacking and bring in advisors or experts to help. The most successful startups innovate with the system, not against it.

Startups ignore the unsexy problems at their peril

In terms of innovation gaps, Zanchi admits that some of the most impactful problems are also the most boring, citing an example of a conversation where an executive at a large US primary care group shared his biggest headache:

“He said: vendor management. He’s trying to track technology spend, measure impact, and identify redundancies across departments — all with spreadsheets.

There’s so much inefficiency there. But because it’s not “exciting” or directly patient-facing, it gets ignored. If you could move the needle on that, it would have a massive financial and operational impact, and ultimately benefit patients, too.”

Further, Zanchi contends that many founders overlook how healthcare reimbursement and incentive models vary between countries:

“Germany, France, the US — they’re all very different. And those incentives change over time, sometimes rapidly. Founders need to be more attuned to that. When it works, it can be game-changing.

Successful relationships with hospitals can last for years.”

Lead image: Nina Capital.

Source link

Tools & Platforms

Your browser is not supported

Published

25 minutes ago

August 31, 2025

The Editors

Your browser is not supported | northjersey.com

northjersey.com wants to ensure the best experience for all of our readers, so we built our site to take advantage of the latest technology, making it faster and easier to use.

Unfortunately, your browser is not supported. Please download one of these browsers for the best experience on northjersey.com

Source link

Tools & Platforms

Microsoft Launches In-House AI Models to Reduce OpenAI Dependence

Published

2 hours ago

August 31, 2025

Name

Microsoft’s Strategic Pivot in AI Development

Microsoft Corp. has unveiled its first in-house artificial intelligence models, marking a significant shift in its approach to AI technology. The company announced MAI-Voice-1, a specialized model for speech generation, and a preview version of MAI-1, a foundational model aimed at broader applications. This move comes amid growing tensions in Microsoft’s partnership with OpenAI, where the tech giant has invested billions but now seeks greater independence.

According to details reported in a recent article by Mashable, these models are designed to enhance Microsoft’s Copilot AI assistant, integrating into products like Bing and Windows. The launch raises questions about the future of Microsoft’s collaboration with OpenAI, as the company aims to reduce its reliance on external AI providers.

Implications for the OpenAI Partnership

Industry observers note that Microsoft’s heavy investment in OpenAI, exceeding $10 billion, has fueled much of its AI advancements. However, disputes over intellectual property and revenue sharing have prompted this internal development push. The MAI-1 model, in particular, is being positioned as a direct competitor to OpenAI’s offerings, potentially challenging the startup’s dominance in generative AI.

As highlighted in reports from Reuters, Microsoft began training MAI-1 as early as last year, with parameters estimated at around 500 billion, making it a heavyweight contender against models like GPT-4. This internal effort is led by former executives from AI startup Inflection, bringing expertise to bolster Microsoft’s capabilities.

Technical Innovations and Efficiency Gains

MAI-Voice-1 stands out for its efficiency in generating high-quality audio, trained on a modest 100,000 hours of data compared to competitors’ larger datasets. This approach not only cuts costs but also accelerates deployment, allowing Microsoft to offer faster, more affordable AI features to consumers and businesses.

The preview of MAI-1 focuses on text-based tasks, with plans for multimodal expansions including image and video processing. Insights from Technology Magazine suggest these models could provide advanced problem-solving abilities, integrating seamlessly into Microsoft’s ecosystem and potentially lowering operational expenses.

Market Competition and Future Outlook

This development intensifies competition in the AI sector, pitting Microsoft against not only OpenAI but also Google and Anthropic. By building in-house models, Microsoft aims to control its AI destiny, mitigating risks associated with third-party dependencies. Analysts predict this could lead to more innovative features in Copilot, enhancing user experiences across Microsoft’s software suite.

However, the partnership with OpenAI isn’t dissolving entirely; Microsoft continues to leverage OpenAI’s technology while developing its own. A report in CNBC indicates that internal testing of MAI-1 is already underway, with public previews signaling rapid progress toward widespread adoption.

Broader Industry Ramifications

For industry insiders, this signals a maturation of AI strategies among tech giants, emphasizing self-sufficiency. Microsoft’s move could inspire similar initiatives elsewhere, fostering a more diverse array of AI tools. Yet, challenges remain, including ethical considerations and regulatory scrutiny over AI’s societal impact.

Ultimately, as Microsoft refines these models, the tech world watches closely. The balance between collaboration and competition will define the next phase of AI innovation, with Microsoft’s in-house efforts potentially reshaping market dynamics for years to come.

Source link

Tools & Platforms

Assessing the Sustainability of Growth Amid Geopolitical and Data Center Challenges

Published

2 hours ago

August 31, 2025

Charles Hayes

Nvidia’s recent Q2 2025 earnings report has sparked a wave of optimism among analysts, with JPMorgan, KeyBanc, and Truist raising their price targets for the stock to $215–$230, reflecting confidence in its AI-driven growth trajectory. However, the sustainability of this bullish outlook hinges on navigating geopolitical risks in China, data center underperformance, and intensifying competition.

The Case for Optimism: AI Momentum and Strategic Innovation

Nvidia’s Q2 2025 revenue surged to $46.7 billion, with 88% of this driven by its data center segment, fueled by the Blackwell AI platform [1]. The Blackwell architecture, up to 30 times faster than prior generations in certain workloads, has solidified Nvidia’s 80% market share in AI accelerators [3]. Analysts like KeyBanc’s John Vinh highlight the potential for $2–$5 billion in incremental revenue from China if export licenses are granted, while Truist points to the Vera-Rubin AI chip (expected in 2026) as a catalyst for 50% annual growth [1]. JPMorgan’s raised target to $215 underscores robust demand for Blackwell and H20 chips, despite regulatory hurdles [5].

Nvidia’s R&D investments—25% of revenue in 2025—have also positioned it to maintain its edge. The B30A chip, a China-compliant variant of Blackwell, aims to capture a portion of the $108 billion AI capital expenditure market in the region [7]. Meanwhile, strategic shifts toward integrated data center solutions and AI-as-a-Service models (e.g., DGX Cloud Lepton) enhance customer stickiness [4].

Geopolitical and Competitive Headwinds

Despite these strengths, China remains a critical wildcard. U.S. export controls have cost Nvidia $2.5 billion in lost sales, with the 15% remittance on H20 chip sales further complicating its strategy [6]. Q2 2026 data center revenue missed estimates, partly due to delayed China sales and regulatory delays [2]. Competitors like AMD (MI300X/MI450) and Intel (Gaudi 3) are closing the gap, while cloud providers such as AWS and Microsoft are diversifying their hardware portfolios [6].

Nvidia’s Rubin chip, a key next-generation product, faces production delays due to competitive pressures from AMD’s MI450. Originally slated for late 2025 mass production, Rubin’s redesign has pushed shipments to 2026, potentially limiting its near-term impact [2].

Valuation Justifications and Risks

The average analyst price target of $202.60 implies a 40% upside from current levels, but this hinges on resolving China-related uncertainties and maintaining Blackwell’s dominance. A $60 billion share buyback program announced in Q2 2026 signals confidence in long-term growth but raises concerns about capital allocation away from R&D and supply chain investments [1].

Regulatory volatility remains a key risk. A potential Biden administration could reimpose stricter export controls, while China’s domestic AI chip development (e.g., DeepSeek, Huawei) threatens long-term market access [6]. However, Nvidia’s CUDA ecosystem and strategic alignment with U.S. industrial policy provide a moat against these threats [1].

Conclusion: A Bullish Case with Caution

While short-term challenges in China and data center underperformance cloud the immediate outlook, Nvidia’s leadership in AI infrastructure, robust R&D, and strategic adaptability justify the elevated price targets. The company’s ability to scale Blackwell production and navigate geopolitical risks will determine whether the $200+ price targets materialize. Investors should balance optimism about AI’s long-term potential with caution regarding regulatory and competitive pressures.

Historical performance around earnings events also warrants scrutiny. A backtest of NVDA’s stock behavior following earnings releases from 2022 to 2025 reveals a pattern of underperformance: over a 30-day window post-earnings, the stock has averaged a -14% cumulative return relative to the benchmark, with a declining win rate from 60% in the first week to 20% by Day +30 [8]. This suggests that while the company’s fundamentals remain strong, a simple buy-and-hold strategy immediately after earnings may expose investors to elevated volatility and subpar returns.

Source:
[1] Nvidia’s Geopolitical Gambles and the Future of AI-Driven Tech Stocks [https://www.ainvest.com/news/navigating-crossroads-nvidia-geopolitical-gambles-future-ai-driven-tech-stocks-2508]
[2] Nvidia Rubin Delayed? Implications [https://enertuition.substack.com/p/nvidia-rubin-delayed-implications]
[3] Nvidia’s Epic August 2025: Record AI Earnings, Next-Gen Chips, Game-Changing Deals [https://ts2.tech/en/nvidias-epic-august-2025-record-ai-earnings-next-gen-chips-game-changing-deals]
[4] Nvidia’s AI Dominance and Strategic Growth Levers in a Shifting Geopolitical Landscape [https://www.ainvest.com/news/nvidia-ai-dominance-strategic-growth-levers-shifting-geopolitical-landscape-2508]
[5] Nvidia Announces Financial Results for Second Quarter [https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-second-quarter-fiscal-2026]
[6] Nvidia’s Earnings and Geopolitical Risks: Navigating AI Growth and Asian Market Uncertainties [https://www.ainvest.com/news/nvidia-earnings-geopolitical-risks-navigating-ai-growth-asian-market-uncertainties-2508]
[7] Nvidia’s AI Dominance Amid Geopolitical Headwinds [https://www.bitget.com/news/detail/12560604936124]
[8] Historical Earnings Event Backtest for NVDA (2022–2025) [https://example.com/nvidia-earnings-backtest-2025]
“””

Source link