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Thinking of Buying C3.ai Stock? Here Are 2 Red Flags to Consider.

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C3.ai (NYSE: AI) is one of the most talked-about artificial intelligence (AI) stocks on the market today. With a platform purpose-built for enterprise customers, early traction in generative AI, and expanding partnerships with cloud and consulting giants, the company checks many of the right boxes for investors looking to gain exposure to the AI megatrend.

However, before getting swept up in the narrative, it’s worth pausing to look beneath the surface. While the company is making the right strategic moves, it’s still early — and the numbers reveal a business that has a lot more to prove.

This article will cover two red flags to keep in mind.

Image source: Getty Images.

C3.ai has carved out a unique position as a pure-play enterprise AI platform company. It doesn’t build flashy consumer chatbots. Instead, it helps large organizations deploy AI across real-world operations — from supply chains to energy grids to battlefield logistics.

Using the C3 Agentic AI Platform, a company can quickly develop and implement AI in its operations or leverage C3 AI Applications for prebuilt applications in sectors like energy, defense, and manufacturing. Later on, enterprises can deploy C3 Generative AI to create AI agents.

By focusing on prebuilt agents and vertical-specific tools, C3.ai aims to simplify deployment and shorten the time from pilot to production. Moreover, it’s moving toward a consumption-based pricing model, allowing customers to start small and scale their usage over time — a shift that aligns incentives and could smooth out the adoption process.

In short, there’s a solid case for optimism about C3.ai’s long-term potential, especially as large enterprises’ adoption of AI picks up.

Like most growth companies, C3.ai incurs significant cash expenditures as it invests in platform development and customer acquisition. The company has been unprofitable since its inception in 2009, with accumulated losses totaling $1.4 billion as of April 30, 2025.

That’s despite years of riding a major AI tailwind. It guided for non-GAAP (adjusted) loss from operations to be around $100 million in fiscal year 26, ending April 30, 2026. While it ended the year with $743 million in cash and equivalents, that cushion could shrink quickly if the current pace of losses continues.

It’s not uncommon for high-growth software companies to operate at a loss for years — Amazon and Salesforce are examples. However, the issue is that C3.ai’s growth hasn’t kept pace with spending. For instance, it guided the fiscal year 2026 revenue growth rate to be between 15% and 25% — solid, but nothing to shout about.

The silver lining here is that growth has slowly accelerated (averaging above 20%) over the last five quarters, suggesting that the company could deliver at the higher end of its guidance.

Additionally, the AI company signed 264 agreements in fiscal year 2025, representing a 38% year-over-year increase. Given that there’s usually a time lag between signing agreements and revenue flowing in, investors may see better growth rates in the coming quarters.

The bottom line is that C3.ai is spending like a hypergrowth company but growing like a mature one. It needs to either accelerate top-line growth or rein in operating losses — ideally both.

When C3.ai went public, it was one of the few public companies offering a full-stack enterprise AI platform. That’s no longer the case.

Today, C3.ai faces pressure from multiple directions. On one side, big tech companies, such as Microsoft, are embedding AI into Azure and its entire software stack. Similarly, Google Cloud and AWS are investing heavily in AI infrastructure and developer tools. These firms not only have more capital, but they also already have established customer relationships.

Besides, smaller but fast-moving start-ups are building narrow, agentic AI tools for sales, logistics, customer service, and more, many of which are easier to implement and priced more flexibly. Even C3.ai’s closest peer, Palantir, has stepped up its generative AI strategy with its Artificial Intelligence Platform (AIP) — gaining traction in both government and commercial markets.

To stay relevant, C3.ai must continue to solve complex customer problems in core verticals such as defense, energy, and industrial manufacturing. If not, it risks being relegated to a niche role — or worse, being left behind as newer solutions become the standard.

In short, it’s no longer enough for the company to have a head start. It must continue to deepen its moat or risk losing its competitive edge in the long term.

C3.ai is doing many things right. It’s focused on enterprise AI rather than the overcrowded consumer AI market, is early in a large market, and is investing heavily in its future.

Still, the question is whether the company can scale quickly enough to become the gold standard in its key verticals and, along the way, reduce its losses to deliver massive profits.

Until then, an investment in C3.ai stock remains highly speculative.

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Thinking of Buying C3.ai Stock? Here Are 2 Red Flags to Consider. was originally published by The Motley Fool



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New Empirical Research Report on AI-Driven Drug Repurposing

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AI-Driven Drug Repurposing Solutions Market

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Standigm Inc.

Berg LLC

Valo Health Inc.

twoXAR Pharmaceuticals Inc.

Evaxion Biotech A/S

Lantern Pharma Inc.

Exscientia plc

Verge Genomics Inc.

Deep Genomics Inc.

Owkin Inc.

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AI Research

AI in health care could save lives and money − but change won’t happen overnight

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Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.



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Report shows China outpacing the US and EU in AI research

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Governments now face the reality that falling behind in AI capability could have serious geopolitical consequences, warns a new research report.

AI is increasingly viewed as a strategic asset rather than a technological development, and new research suggests China is now leading the global AI race.

A report titled ‘DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI’, authored by Daniel Hook, CEO of Digital Science, highlights how China’s AI research output has grown to surpass that of the US, the EU and the UK combined.

According to data from Dimensions, a primary global research database, China now accounts for over 40% of worldwide citation attention in AI-related studies. Instead of focusing solely on academic output, the report points to China’s dominance in AI-related patents.

In some indicators, China is outpacing the US tenfold in patent filings and company-affiliated research, signalling its capacity to convert academic work into tangible innovation.

Hook’s analysis covers AI research trends from 2000 to 2024, showing global AI publication volumes rising from just under 10,000 papers in 2000 to 60,000 in 2024.

However, China’s influence has steadily expanded since 2018, while the EU and the US have seen relative declines. The UK has largely maintained its position.

Clarivate, another analytics firm, reported similar findings, noting nearly 900,000 AI research papers produced in China in 2024, triple the figure from 2015.

Hook notes that governments increasingly view AI alongside energy or military power as a matter of national security. Instead of treating AI as a neutral technology, there is growing awareness that a lack of AI capability could have serious economic, political and social consequences.

The report suggests that understanding AI’s geopolitical implications has become essential for national policy.

Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!



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