AI Funding Highlights – SwissCognitive AI Investment Radar

This week’s AI investment landscape has been defined by diverging strategies, capital flows, and a widening discussion around equity, access, and economic consequence. On one side, the U.S. and EU are outlining ambitious visions for leadership. While the Stargate initiative pushes scale and speed, the EU’s dual strategy of financial commitment and regulatory positioning is placing ethical trust at the heart of its long game.

At the institutional level, signals of maturity are surfacing. Stanford’s AI Index highlighted pressure points shaping enterprise tech strategy, while BCG’s IT Spending Pulse underlined a shift: budgets are recalibrating as generative AI moves from novelty to core capability. Large investors are responding in kind—Bay Area-based SignalFire closed a $1 billion fund focused solely on applied AI companies, and Microsoft’s AI alliance with MSCI emphasizes the financial sector’s shift to AI-informed strategies.

From a regional angle, the Gates Foundation is betting $7.5 million on Rwanda as a launch point for AI scaling hubs in health, agriculture, and education. Canada attracted a CAD$150 million investment from Siemens for a global AI R&D center focused on battery production, while Italy’s Axyon AI secured €4.3 million for financial forecasting, and Ukraine’s QurieGen raised €2.2 million for AI-driven cancer drug R&D.

Meanwhile, a different class of firms is recalibrating customer interaction models. Arta Finance unveiled a suite of AI agents for portfolio insight, and startups skipping traditional funding stages—especially in Europe—signal a shift toward faster, more efficient capital strategies. But UNCTAD’s report reminds us that AI’s projected $4.8 trillion global impact comes with significant risks: unless addressed, the gap between early adopters and the rest could deepen.

This week’s updates confirm that the race is no longer about who adopts AI—it’s about how, and at what cost.

Previous SwissCognitive AI Radar: From Mega Rounds to Market Ripples .

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

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SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “Fortifying the Future: Ensuring Secure and Reliable AI”

As AI becomes further integrated into various domains, from infrastructure to defence, ensuring its robustness will become a matter of national security. An AI system managing power grids, security apparatus, or financial networks could present a single point of failure if compromised or manipulated. Historical incidents, such as the Stuxnet cyberweapon, illustrate the physical and cyber damage that can be inflicted. When considering AI’s complexity, the potential for a cascade of both physical and digital harm increases dramatically.

As such, we should ask: How do we fortify AI?

AI systems must be designed to withstand attacks. From decentralisation to layering, these systems should be constructed so that control points can seamlessly enter and exit the loop without disabling the broader system. Thus, building redundancy and backup at various control points within the AI systems. For example, suppose a sensor or a group of sensors is deemed to have failed or been corrupted. In that case, the broader system must be capable of automatically readjusting to stop utilising data and intelligence gathered from said sensors.

Another strategy for strengthening AI systems involves simulating data poisoning attacks and training AI systems to detect such threats. By teaching the systems to recognise and respond to attacks or failures, they can automatically reconfigure without the need for human intervention. If an AI can learn to identify tainted data, such as statistical anomalies or inconsistent patterns, it could flag or quarantine suspect inputs. This approach leans heavily on machine learning’s strengths: pattern recognition and adaptability. However, it’s not a failsafe; adversaries could evolve their attacks to more closely mimic legitimate data, so the training would need to be dynamic, constantly updating to match new threat profiles.

Maintaining a human in the loop to enable oversight and override is considered one of the most crucial elements in the rollout of AI in various industries. Allowing humans to oversee AI decision-making and restricting autonomy can prevent potentially harmful actions taken by these systems. Whilst critical in the early stages of AI deployment as capabilities scale and evolve, there may come a point where human oversight inhibits these systems and, in itself, causes more harm than good.

Finally, AI sovereignty may prove to be the most critical element in ensuring companies and governments fully control essential algorithms and hardware powering their operations. Without this control, these systems could be vulnerable to foreign interference, including cyberattacks, espionage, or sabotage. As the use of AI increases, the sovereignty of AI systems and their components will become increasingly important. At its core, AI sovereignty is about control, whether exercised by governments, corporations, or individuals. Through the control of data, infrastructure, and decision-making power, those who build and deploy AI systems and sensors gain control of AI.

Fortification will involve integrating resilience, adaptability, and sovereignty into AI’s DNA, ensuring it is not only intelligent but also resilient and unbreakable. It can provide technological advantages, but it may also expose systems to disruption and vulnerability exploitation. As organisations race to harness AI’s potential, the question looms: Will AI enable organisations to gain a strategic advantage, or will it undermine the very systems it was designed to strengthen?

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

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SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “A New Era of Intelligent Robots”

Imagine a world where humanoid robots cook for you, care for your loved ones, and streamline your workday – all powered by AI smarter than ever before. The global AI in robotics market, projected to surpass $124 Billion by 2030, is set to make this vision a reality. As the capabilities of AI evolve, these machines will become our companions, caregivers, and coworkers, they’ll make mobility more affordable, transform access to services, and redefine the value of human effort.

From Amazon’s fleet of 750,000 warehouse robots to Tesla’s ambitions to build 10,000 humanoid Optimus robots this year, the age of robots is upon us. Dependent on sensors and actuation systems to navigate and interact with the physical environment, this new age of robotics hinges on the developments of AI, designed to mimic and learn from its biological makers. Equipping these robots with intelligence, engineers working across various domains of expertise, utilise AI to enable vision, natural language processing, sound processing, pressure sensing, and more.

Beyond sensing, AI also enables robots to reason, adapt, and learn, using approaches including—but not limited to—reinforcement learning, neural networks, and Bayesian networks. These models and methods enable robots to assess risks and determine actions, and by learning from experience, robots can adapt to new tasks and environments. Thus, AI enables robots to perceive, act, learn, and adapt, allowing them to perform tasks with greater autonomy and precision.

However, integrating AI into robotics isn’t seamless, it comes with hurdles. Robots struggle with real-time processing delays, adapting to messy unpredictable environments, squeezing efficiency from limited hardware, and understanding human quirks like vague commands or gestures. These challenges constrain capabilities and the pace at which robots enter and dominate markets.

So, how can these challenges be addressed?

Some developments in addressing these challenges include:

1. Parallel computing

Parallel computing involves dividing larger tasks into smaller, independent tasks that can be processed simultaneously rather than sequentially. This enables increased computational efficiency, reduced latency, and improved cost efficiency. In robotics, parallel computing allows robots to process inputs from LIDAR, radar, and cameras simultaneously, enabling them to navigate environments more effectively and efficiently.

2. Transfer learning

Transfer learning leverages pre-trained models to solve new, but similar, problems. In this approach, a model trained on one task or dataset is reused and fine-tuned for a related task. For example, in machine vision for defect detection in manufacturing, fine-tuning a pre-trained model on a smaller dataset of images allows it to quickly adapt to detect specific defects, such as cracks or dents, without needing to train a model from scratch.

3. Self-calibrating AI

Self-calibrating refers to AI systems that autonomously adjust their parameters, models, or processes to maintain optimal performance without manual intervention. In robotics, self-calibrating AI enables robots to adapt to changes in their environment, hardware, or tasks, ensuring they operate with optimized accuracy and efficiency over time.

4. Federated learning

Federated learning is a technique that enables AI systems to learn from distributed data sources whilst ensuring privacy and security. It allows AI to collaboratively train a shared model without transferring sensitive data, preserving privacy and reducing reliance on centralised storage. For example, delivery robots use federated learning to optimise pathfinding without sending raw data, such as sensor inputs or location, to a central server. Instead, they locally update their models and share improvements, preserving both privacy and security.

These developments indicate a key focus on efficiency, adaptability, and learning – all of which are essential for the continued evolution of robotics in complex, real-world environments. Additionally, these advancements contribute to a future where robots collaborate with humans, leveraging their ability to learn from experience and improve over time.

So, what’s next for AI in Robotics?

Just as AI agents are taking over the digital realm, they are about to flood robotics too. AI agents embedded in robotics will supercharge the autonomy and flexibility of robots, enabling them to communicate with humans and even interpret intentions by analysing gestures and potentially emotional cues. Crucial to human-robot interactions, AI agents may prove highly effective in assisted care, hospitality, and other service industries.

Additionally, as technologies like federated learning and edge computing evolve, robots will share knowledge without compromising privacy or relying on centralised data. This will improve scalability and efficiency by reducing the need for costly centralised storage and processing, and enable additional robots to integrate rapidly into existing networks.

So, where does this leave us?

Although there are abundant market opportunities for AI in robotics, the pace at which different markets adopt robotics will vary; with AI being a key factor driving this adoption. Crucial for overcoming challenges related to autonomy, adaptability, and decision-making, AI will empower robots to perform tasks once considered too complex or risky for automation. As AI continues to evolve, it will not only raise important concerns about safety, ethics, and integration but help address them; ensuring robots can work seamlessly alongside humans and contribute to a more productive future.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

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$100B for AI Chips, $40B for AI Bets – SwissCognitive AI Investment Radar

AI investment shows no signs of slowing, with capital flowing across semiconductors, cloud AI, financial AI, and responsible AI initiatives. This week, TSMC is preparing a staggering $100 billion investment in U.S. chip production, reinforcing the U.S. AI supply chain. Meanwhile, Anthropic’s valuation tripled to $61.5 billion, after securing $3.5 billion in funding to keep pace with OpenAI and DeepSeek.

The private sector’s AI appetite remains insatiable. Blackstone’s Jonathan Gray emphasized AI’s dominance in global investment trends, while Guggenheim and billionaire investors assembled a $40 billion AI investment pool to fuel finance, sports, and media innovation. Meanwhile, Canva’s AI report revealed that 94% of marketers have now integrated AI into their operations, marking a fundamental shift in business strategy.

The global AI race is also drawing government interest. The European Commission announced a €200 billion mobilization for AI investments, alongside France’s €109 billion push, as President Macron aims to position Europe as a heavyweight in AI development. Across the globe, China’s Honor pledged $10 billion to AI investment, deepening ties with Google for a global expansion.

The infrastructure for AI applications continues to scale rapidly. DoiT announced a $250 million fund dedicated to AI-driven cloud operations, while Shinhan Securities backed Lambda Labs with a $9.3 million investment to advance NVIDIA GPU-powered AI cloud services. Meanwhile, Accenture is doubling down on AI decision intelligence, backing Aaru to improve AI-powered behavioral simulations.

Beyond the corporate sphere, responsible AI investments are gaining traction. Chinese firms are increasing spending on ethical AI as part of a broader strategy to align AI governance with innovation. Meanwhile, Blackstone committed $300 million to AI-driven Insurtech, supporting AI-powered safety solutions in insurance.

With tech giants, startups, and governments all placing massive bets on AI, the sector’s financial landscape is evolving faster than ever. Investors are watching closely as AI’s long-term ROI takes center stage.

How will the capital influx shape AI’s next phase? The coming months will bring more answers.

Previous SwissCognitive AI Radar: AI Expansion and This Week’s Top Investments.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

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By Martina Fuchs

Credits: The Female Quotient / The Frequency School – “First Song Ever to Unite Women from All Countries Using Sound Healing Frequencies”

The Frequency School – in partnership with The Female Quotient – launched “195”, the first song in history featuring women from all of the world’s 195 countries using sound healing frequencies and the 528 Hz ‘Love Frequency’ in a bid to ignite a global movement and raise awareness about gender equality across various industries including technology.

The Frequency School co-founded by Grammy-nominated and multi-platinum music producer Maejor, Kingsley Maduka, Brandon Lee, Aaron Dawson and Martina Fuchs premiered this visionary, powerful and universal campaign at a side-event in Davos, Switzerland which took place during the first day of the 55th Annual Meeting of the World Economic Forum (WEF).

Using the sounds of birds and the heartbeat as instrumental elements, “195” aims to raise the world’s frequency, elevate humanity, and trigger a positive impact by uniting and empowering women worldwide.

One woman in every one of the world’s 195 countries recognized by the United Nations said one word: “EQUALITY” in her national language or native tongue.

Martina Fuchs, business journalist and executive producer of the 195 women song, said:

“It has always been my dream to produce the first song in history featuring every country on the planet. Our vision was to unite 195 women from all walks of life from around the globe in this pioneering and groundbreaking initiative to advocate for gender equality and the rights of women and girls, and to help people struggling with mental health issues. Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world.”

U.S. Grammy-nominated and multi-platinum music producer Maejor who produced the song using sound healing frequencies said:

“We chose to use 528 Hz for the track which is often referred to as the ‘love frequency’, or the frequency of transformation and miracles. It provides a powerful energetic foundation for creating an environment where equality can flourish. By resonating with a frequency linked to unconditional love, we wanted to promote more respect and fairness for women, as well as deep inner healing and a state of peace. The transformative vibration of 528 Hz can inspire positive action and empathy and people to act more kindly and inclusively.”

According to scientific studies and music theory, Solfeggio frequencies, ranging from 174 Hz to 963 Hz, offer unique sound patterns that promote relaxation, stress relief, and overall well-being. These frequencies have been shown to positively impact mental, emotional, and physical health by generating vibrations that help achieve a state of calm and balance of the mind, body and spirit.

Miriam Moriati, President of the Kiribati Rotaract Youth Club and a Women and Youth representative for OARS (Ocean Alliance for Resilience and Sustainability), said:

“I’m from Kiribati, a small island nation in the Pacific, where our highest point is just 3 meters above sea level. Our women in Kiribati are vulnerable due to gender equality not being part of our culture and traditions. They are often the first to be affected by crises and the last to recover. Being part of this initiative to support women on an international stage is an incredible honor. Opportunities to represent our small country are rare, and I am grateful for this platform to amplify the voices of Kiribati women.”

Dalith Steiger, Co-Founder of SwissCognitive and the voice of Israel in the song said:

“Equality is not just an ideal—it’s our collective responsibility. The lack of gender equality still remains a huge challenge in the technology space that we need to tackle. I’m convinced that AI can support us to unbias the bias! I am grateful to be part of this global women empowerment initiative to raise awareness across various sectors.”

A new white paper by IMD in collaboration with Microsoft Switzerland and EqualVoice released at the WEF on Thursdy provides in-depth analysis and guidance for organizations to mitigate against risks and use AI responsibly.

More than 80% of the world’s organizations are expected to be using GenAI tools in production environments by 2026, and the productivity gains will likely boost global GDP by $7tn in the next decade. Yet even as innovation races ahead, important questions need to be asked about the potential risks.

Read the full report “Mind the Gap Addressing the risk of bias in Generative AI”.

The “195” song is planned to be submitted for a Guinness World Records for the title: “Most nationalities to contribute vocals to a musical recording (single song)”.

@Brandon Lee Shelley Zalis The Female Quotient The Frequency School

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AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar

The UK government is making a $17 billion commitment to AI, setting the stage for large-scale adoption with its AI Opportunities Action Plan. Meanwhile, Microsoft has confirmed a staggering £65.1 billion AI infrastructure investment, reinforcing the tech industry’s reliance on expanding AI data centers. In the U.S., Amazon is allocating $11 billion toward cloud and AI infrastructure in Georgia, further cementing its role as a key player in AI development.

The private sector is also making significant moves. Blackstone’s $300 million investment into AI data company DDN positions the firm at the forefront of AI-driven data storage and analytics. Meanwhile, Singapore secures a $7 billion Micron investment to strengthen its role in the AI supply chain. In the automotive industry, Hyundai is investing $16.6 billion to integrate AI into electric vehicle production, signaling a shift in manufacturing strategies.

Retail and consumer brands are also embracing AI, with spending projected to rise by 52% in 2025. A Honeywell survey reveals that over 80% of U.S. retailers plan to expand AI investments to improve customer experience and operational efficiency. However, while enterprises are willing to invest up to $250 million in generative AI, questions about return on investment persist.

AI is increasingly shaping global markets, not just as a technological tool but as a key driver of economic strategy. Whether through national policies, corporate spending, or AI-driven supply chains, investments in AI are becoming a defining force for the future of business and innovation.

Stay tuned for next week’s AI investment updates.

Previous SwissCognitive AI Radar: AI Investment Opportunities Worldwide.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: techtarget.com – “What Are Autonomous AI Agents and Which Vendors Offer Them?”

Autonomous artificial intelligence (AI) agents are intelligent systems that can perform tasks for a user or system without human intervention. They’re a specific type of intelligent agent characterized by their ability to operate independently, make decisions and take actions without requiring ongoing human guidance.

A normal software agent is a goal-oriented program that reacts to its environment in limited autonomous ways to perform a function for an end user or other program. Intelligent agents are typically more advanced, can perceive their environment, process data and make decisions with some level of adaptability. Autonomous Artificial Intelligence agents, by comparison, are designed to operate independently with a higher level of adaptability to enable them to make more complex decisions with little to no human influence.

Agents can typically activate and run themselves without input from human users. They can also be used to initiate or monitor other programs and applications. Autonomous AI agents typically use large language models (LLMs) and external sources like websites or databases. They can also continuously improve using self-learning techniques. Autonomous agents can operate in dynamic environments, making them ideal for complex tasks like enterprise customer service.

Agent-based computing and modeling have existed for decades, but with recent innovations in generative AI, researchers, vendors and hobbyists are building more autonomous AI agents. While these efforts are still in their early stages, the long-term goal is to enhance efficiency, streamline workflows and advance processes. For example, autonomous Artificial Intelligence agents could be used in tandem with robotic process automation (RPA) bots to execute simple tasks and eventually collaborate on whole processes.[…]

Read more: www.techtarget.com

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Copyright: cio.com – “12 AI predictions for 2025”

This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.

Generative AI has seen faster and more widespread adoption than any other technology today, with many companies already seeing ROI and scaling up use cases into wide adoption.

Vendors are adding gen AI across the board to enterprise software products, and AI developers haven’t been idle this year either. We’ve also seen the emergence of agentic AI, multi-modal AI, reasoning AI, and open-source AI projects that rival those of the biggest commercial vendors.

According to a Bank of America survey of global research analysts and strategists released in September, 2024 was the year of ROI determination, and 2025 will be the year of enterprise AI adoption.

“Over the next five to 10 years, BofA Global Research expects gen AI to catalyze an evolution in corporate efficiency and productivity that may transform the global economy, as well as our lives,” says Vanessa Cook, content strategist for Bank of America Institute.

Small language models and edge computing

Most of the attention this year and last has been on the big language models —  specifically on ChatGPT in its various permutations, as well as competitors like Anthropic’s Claude and Meta’s Llama models. But for many business use cases, LLMs are overkill and are too expensive, and too slow, for practical use.

“Looking ahead to 2025, I expect small language models, specifically custom models, to become a more common solution for many businesses,” says Andrew Rabinovich, head of AI and ML at Upwork. LLMs aren’t just expensive, they’re also very broad, and not always relevant to specific industries, he says.

“Smaller models, on the other hand, are more tailored, allowing businesses to create AI systems that are precise, efficient, robust, and built around their unique needs,” he adds.[…]

Read more: www.cio.com

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Copyright: informationweek.com – “AI and the War Against Plastic Waste”

Plastic pollution is easy to visualize given that many rivers are choked with such waste and the oceans are littered with it. The Great Pacific Garbage Patch, a massive collection of plastic and other debris, is an infamous result of plastics proliferation. Even if you don’t live near a body of water to see the problem firsthand, you’re unlikely to walk far without seeing some piece of plastic crushed underfoot. But untangling this problem is anything but easy.  

Enter artificial intelligence, which is being applied to many complex problems that include plastics pollution. InformationWeek spoke to research scientists and startup founders about why plastics waste is such a complicated challenge and how they use AI in their work.

The Plastics Problem

Plastic is ubiquitous today as food packaging, clothing, medical devices, cars, and so much more rely on this material. “Since 1950, nearly 10 billion metric tons of plastic has been produced, and over half of that was just in the last 20 years. So, it’s been this extremely prolific growth in production and use. It’s partially due to just the absolute versatility of plastic,” Chase Brewster, project scientist at Benioff Ocean Science Laboratory, a center for marine conservation at the University of California, Santa Barbara, says.

Plastic isn’t biodegradable and recycling is imperfect. As more plastic is produced and more of it is wasted, much of that waste ends up back in the environment, polluting land and water as it breaks down into microplastics and nanoplastics.

Even when plastic products end up at waste management facilities, processing them is not simple. “A lot of people think of plastic as just plastic,” Bradley Sutliff, a former National Institute of Standards and Technology (NIST) researcher, says.[…]

Read more: www.informationweek.com

Der Beitrag AI and the War Against Plastic Waste erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: informationweek.com – “Reducing the Environmental Impact of Artificial Intelligence (AI)”

By adopting energy-efficient architectures, optimizing AI models for performance, and pushing for cloud providers to embrace renewable energy, businesses can help reduce the carbon footprint of their AI solutions.

Artificial intelligence is reshaping our world. Its transformative power fuels innovation across industries — delivering new value to organizations and consumers alike. As the proliferation of AI accelerates, people are starting to ask important questions: How does AI impact the environment? And furthermore, how do we keep pushing for progress without leaving a heavy carbon footprint on the planet? 

AI’s Eco Impact

Artificial intelligence software runs in data centers that consume large amounts of energy and often cause significant carbon emissions. According to Bloomberg, there are more than 7,000 data centers worldwide. Collectively, they can consume as much power annually as the entire electricity production of Australia or Italy. The growing use of AI will further increase this already substantial energy consumption of data centers. 

The use of AI can be separated into two main tasks: training and inferencing. During training, AI models learn from vast amounts of data that can take months depending on data complexity and volume. Once an AI model has been trained, it consumes energy each time it generates a new response or “inference.” The International Energy Agency (IEA) has reported a ChatGPT inquiry requires up to 10 times the electricity of a Google search to respond to a typical request. This energy consumption adds up and can quickly surpass the energy used for training.

The WEF estimates training comprises about 20% of an AI model’s overall energy use across its lifespan, while inferencing makes up the remaining 80%.[…]

Read more: www.informationweek.com

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