Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar

AI funding momentum hasn’t slowed. From global infrastructure projects to nuanced questions about investor confidence, this week brought high-dollar commitments alongside critical reflections on where the money is flowing—and why.

The United Arab Emirates made headlines with a bold $1.4 trillion, 10-year commitment to invest in the United States, a move that reflects the centrality of AI and tech collaboration in long-term statecraft. Meanwhile, BlackRock’s joint initiative with Microsoft, NVIDIA, and xAI signals continued investor appetite for large-scale AI infrastructure, with $100 billion earmarked for global data centers and energy solutions.

Several firms are also reinforcing their US presence: Hyundai announced a $21 billion investment, Siemens followed with $10 billion, and Schneider Electric added another $700 million—all aimed at fortifying AI-driven manufacturing and operations amid ongoing trade policy uncertainty.

Vietnam’s small businesses are setting the tone in Asia-Pacific, where 44% named AI their top tech investment for 2024. Fractal Analytics’ $13.7 million investment into India’s first reasoning model and Germany’s €2.1 million seed round for enterprise AI search show how national AI goals are increasingly shaped by local strategies and use cases.

Yet, not all attention is on infrastructure. Thought leaders at Man Group and other investment firms raised flags about the sustainability of AI stock valuations. An AI model under a top-performing fund has been flashing warnings on mega-cap tech stocks, including Nvidia. Still, audiences from pharma to finance are assessing AI’s value not just in terms of returns, but in ethics and relevance, particularly when it comes to pharma’s future and the realities of Artificial General Intelligence claims.

As global interest in AI capital remains high, this week’s updates highlight a shift from novelty to operational depth. More investment—yes—but also more scrutiny.

Previous SwissCognitive AI Radar: New AI Investment Funds and Strategic Expansions.

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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New AI Investment Funds and Strategic Expansions – SwissCognitive AI Investment Radar

This week’s AI investment landscape sees bold financial commitments, expanding cloud infrastructure, and the growing influence of AI across industries. Oracle is set to invest £3.9 billion in the UK, alongside an additional $5 billion cloud expansion to strengthen AI adoption, signaling the company’s deep confidence in Britain’s AI future. Meanwhile, Saudi Arabia is launching a $40 billion AI fund, further establishing its role as a major player in the global AI race.

Microsoft’s AI investment strategy continues to gain momentum, earning an analyst upgrade as it builds out critical infrastructure. ARK Invest has joined a $403 million funding round for robotics firm Apptronik, highlighting investor enthusiasm for AI-powered automation. At the same time, Mirakl aims to push past $200 million in revenue with increased AI investments, showing how AI is reshaping business growth strategies.

In Asia, Thailand is attracting millions in AI data center investments, while Vietnam focuses on edge AI to compete in the global market. Azerbaijan is also setting its sights on AI by creating a strategy to attract foreign investment, positioning itself as an emerging tech hub.

AI’s role in finance and investment decision-making remains a focal point. National Grid Partners is committing $100 million to AI-driven energy solutions, while GapMinder Fund II is backing Romanian AI startup VoicePatrol, targeting real-time AI solutions for gaming. However, with AI’s growing influence, investors are warned about misinformation risks, reinforcing the need for well-vetted AI strategies.

With AI investments accelerating across industries, we continue to track how these financial commitments shape the broader technology and business landscape. Stay tuned for more insights in next week’s AI Investment Radar.

Previous SwissCognitive AI Radar: Major AI Funding Shifts.

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 New AI Investment Funds and Strategic Expansions – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Transforms EV Charging Networks”

People who drive gas-powered vehicles can lug a fuel can around if they ever run out while driving. For electric vehicle (EV) owners, it isn’t as easy. Many fear being stranded on the side of the road, which is why charging infrastructure is so important. However, chargers are often unreliable or outright out of order. Is artificial intelligence the solution?

Why EV Charging Networks Need an Overhaul

The current state of EV charging networks is less than ideal. Harvard Business School research revealed that charging stations are largely unreliable — and drivers are aware and dissatisfied. They can only successfully recharge using nonresidential stations an estimated 78% of the time, meaning one in five chargers in the United States don’t work. This makes them less reliable than the average gas pump.

Omar Asensio — the Harvard Business School fellow who led the study — said the main reason for this substandard reliability is that no one’s maintaining the stations. While these complex machines require extensive maintenance to keep the circuitry in peak shape, they are often neglected.

When electrical systems break down, equipment damage is not the only outcome. Potentially dangerous situations will occur unless companies perform electrical system maintenance regularly. Loose connections and fried circuits can ignite materials or shock users, causing injuries or death.

While the seemingly obvious solution is for drivers to recharge at home, people use home chargers just 10% of the time, according to one software company. Although modern batteries can reach hundreds of miles on a single charge, many people fear theirs will run out of power before they reach their destination, leaving them stranded. Besides, installation can be expensive, depending on their location and the type of at-home station they choose.

Companies Could Change EV Charging With AI

AI could help companies resolve the sector’s current charging challenges. For starters, it could autonomously manage loads, distributing power efficiently and safely among multiple stations. Reducing grid load — especially during peak hours — helps prevent EV charging equipment from damaging transmission lines, circuit breakers or transformers.

A study from the University of Michigan’s Transportation Research Institute proves this point. It states that large-scale, unmanaged EV charging could cause sudden current draw fluctuations, damaging the electrical grid. This inconsistency can lead to inefficient energy consumption, resulting in transformer strain. An outage is the likely outcome of accelerated equipment wear and energy waste.

Much of the U.S. power grid is already on its last legs. For instance, around 70% of the transmission lines are nearly three decades old, nearing their expected life span of 50 to 80 years. Minimizing strain with AI-powered smart load management can prevent outages while ensuring every battery is fully recharged.

A more comprehensive solution leverages predictive maintenance. Machine learning models can anticipate possible outcomes. They can use embedded, internet-enabled sensors to identify faults like a fried circuit or frayed wire. Maintenance teams would get real-time alerts, minimizing unplanned downtime.

AI could even improve battery health monitoring, maximizing charging efficiency. A research team from the United Kingdom’s Cambridge and Newcastle Universities discovered a machine learning method is 10 times more accurate than the current industry standard technique. It measures electrical pulses instead of tracking current and voltage during charge and discharge cycles. Improving EV battery reliability could transform the charging network’s layout.

Where would companies place new stations? With AI, they could analyze metrics like EV demand, travel frequency and location to determine where to build them. They could also optimize charging network design by plugging their budget, desired density and grid capacity into the algorithm.

Improving EV Charging Infrastructure With AI

Access to a reliable charging network is tightly intertwined with people’s opinions of EVs themselves — meaning companies can only make this mode of transportation more popular if they improve the reliability of the underlying infrastructure. AI is one of the few technologies that could help them fast-track this achievement.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Transforms EV Charging Networks erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Team – “Why AI Needs Global Collaboration – Call for Nomination”

Every morning, as we open our news feeds, we encounter the latest breakthroughs in Artificial Intelligence. New LLM models, emerging startups, record-breaking AI investments, and novel applications that push the boundaries of what’s possible. The competition among tech giants like OpenAI, Google, Meta, Microsoft, Nividia etc., alongside rising startups, has never been more intense, accelerating AI development at an unprecedented scale.

Here are just a few highlights from the past weeks:

• DeepSeek’s energy-efficient AI model triggered a significant shift in AI investments, causing stock declines, tech sell-offs, and a reevaluation of costly AI development strategies.
• xAI, Elon Musk’s AI venture, launched Grok-3, a model with over ten times the computing power of its predecessor.
• The New York Times is integrating AI tools into its newsroom for editing, summarizing, and writing tasks.
• The European Union announced a €50 billion investment to boost AI development and adoption across industries.
• Anthropic secured $6 billion in investments from Amazon and Google.
• Google unveiled an AI-powered “co-scientist” designed to accelerate biomedical research.

And this is just a small, randomly selected fraction of the developments in the field of AI that’s been happening globally since the beginning of the year.

The Critical Role of Collaboration in AI Development

With such high-speed advancements and large-scale AI adoption, our greatest responsibility is ensuring these developments serve humanity and society as a whole. Artificial Intelligence must be shaped through transparent communication, collaboration, and collective responsibility.

SwissCognitive has been committed to this mission since 2016, acting as a global AI facilitator—bridging knowledge gaps, fostering responsible AI adoption, and ensuring AI reaches its full potential as an economic booster.

One of our key initiatives to support this vision is the Global AI Ambassador Program, where AI leaders unite to spread knowledge and collaborate for the ethical, responsible, and transparent development of Artificial Intelligence.

Global AI Ambassador Program 2025 – A New Era of Collaboration

The Global AI Ambassador Program 2025 by SwissCognitive is designed to bring together  leading AI professionals across industries—fostering knowledge exchange, cross-sector innovation, and responsible AI governance.

This year, we are expanding the program on a larger scale than ever before. For the first time, we are introducing a peer-nominated selection process — ensuring that the most brilliant minds in AI are recognized and empowered to drive positive change.

Call for Nominations

Nominations are officially open until 28th of March 2025.
Unlike previous years, we have moved from self-nomination to a peer-nomination process, requiring two sponsors to nominate an AI expert.

We believe in the power of collaboration—because impactful AI leadership is stronger if we use our collective intelligence to shape the future together.

You can find all details, nomination criteria, and the application form at the link below.

“Ultimately, the global AI race will be won not by any one region alone, but through collaboration, knowledge-sharing, and a commitment to the responsible development and deployment of AI for the benefit of all.”

– Pascal Bornet Global AI Ambassador 2023, in the SwissCognitive AI Navigator 02/2024

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SwissCognitive Guest Blogger: Eleanor Wright – “Navigating the Adoption of AI by the Public Sector”

Perfectly positioned to transform government efficiency and public services, governments globally are investing heavily in AI. From the UK’s plan to ramp up AI adoption to the Emirati investment in project Stargate, no government wants to be left behind.

AI however has more to offer governments than transforming public services, and government contracts will accelerate AI companies to industry dominance.

The public sector adoption of AI will require infrastructure, expertise, and a risk appetite. Data centers will be built, and vast amounts of energy will be used. Beyond the financial and material investment, engineers will be needed to code and develop these systems, and government expertise will be required to procure and integrate AI into antiquated legacy systems.

AI, however, has more to offer governments than transforming public services, and governments have the power to transform the business of AI. By gatekeeping access to data and procuring long-term contracts, public sector contracts can rapidly accelerate AI companies into big businesses and deliver the capital needed to beat out the competition, enabling a new wave of incumbents.

This model of public sector procurement from the private sector, however, may not be in the best interest of the citizens and taxpayers who will ultimately fund these large contracts. As AI efficiency and capabilities develop and public sector jobs are replaced, the greater the dependency will be on these companies to maintain critical public services. Thus, it is fair to assume that a critical point will be reached where these companies become too big to fail. If public services become reliant on the capabilities and services of a handful of providers, the balance of power will shift.

This dependency however should not discourage the adoption of AI by the public sector, but shape how contracts are procured and the business model underpinning them. Whether it be public-private partnerships, state-owned or implementing a cooperative structure, the business models underlying the roll-out of AI into the public sector could determine how AI is procured and implemented.

Whilst state-owned assets or companies can be inefficient, open to political interference, and lack a drive for innovation, they offer public-focused interest. Capital saved can be reinvested into the impact of public services and jobs that will have been outsourced to the private sector can be internally generated.

In the same way, state-owned companies operate in the interest of the public, public-private partnerships and cooperative companies may represent a strong middle ground between purely public or privately sourced contracts. Public-private partnerships will limit the amount of control private companies exert, and cooperative companies could enable the development and procurement of AI systems that meet a common economic and social goal.

It should be noted however that neither public-private partnerships nor cooperatives are fully resilient against political or private interference. Decisionmakers will always be susceptible to desiring increased control and securing financial gain.

Finally, another alternative may be to implement an open-source procurement model. By procuring solely from companies utilising open-sourced base models, public service contracts built on open-source models could help mitigate incumbency dominance and level the playing field. These base models could even use university knowledge and expertise to drive and maintain innovation.

No matter how public service agencies and providers choose to procure and maintain AI contracts, the business model underpinning the procurement both internally and externally will heavily shape the future of AI. A carefully thought-out business model could provide a strategic advantage and deliver greater value to stakeholders.

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

Der Beitrag Navigating the Adoption of AI by the Public Sector erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Enables Swarm Robotics in the Supply Chain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar

We can all agree that this week, the spotlight was firmly on DeepSeek, whose budget-friendly AI model sent shockwaves through the market, triggering the largest single-day market cap loss in history for Nvidia. Investors reacted sharply, fearing reduced demand for high-end semiconductor chips. While the immediate sell-off was staggering, some experts argue that DeepSeek’s innovation could expand AI adoption rather than collapse the market, potentially opening up new investment opportunities rather than diminishing them.

Beyond the DeepSeek turmoil, Microsoft continues its aggressive AI strategy, committing $80 billion to cloud expansion, leveraging OpenAI’s technology to solidify Azure’s competitive edge. Meanwhile, Meta’s $65 billion AI expansion aims to scale its infrastructure with massive data center investments, signaling confidence in AI’s long-term role in the tech industry.

Venture capital activity remains strong, with SoftBank eyeing a major investment in robotics startup Skild AI, valued at $4 billion. The startup aims to develop an AI-powered “brain” for more agile and dexterous robots, further integrating AI into automation and real-world applications. In the AI data space, Turing has tripled its revenue to $300 million, demonstrating the growing demand for AI training data as more companies scale up their AI models.

Looking beyond big tech, geopolitical AI strategies continue to unfold. India faces challenges in AI infrastructure, with investors warning that a lack of GPUs and data centers could hinder its global competitiveness. Meanwhile, the U.S. is contemplating a $500 billion AI infrastructure initiative, dubbed the Stargate Project, though experts question its feasibility given the sheer scale and energy demands.

As the AI market rapidly evolves, investors are looking for ways to maximize the value of their AI investments, from optimizing AI integration to structuring data and equipping teams with language models. Pharma investors are also weighing AI’s long-term potential, balancing high expectations with the reality of AI adoption hurdles in healthcare.

Despite the ups and downs of the market, AI investment remains a dominant force, shaping industries and redefining long-term strategies. Stay tuned for next week!

Previous SwissCognitive AI Radar: Who’s Investing and Why in AI.

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 The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Dalith Steiger-Gablinger, Co-Founder SwissCognitive – “AI Takes Center Stage at Davos 2025: A SwissCognitive Perspective”

As the snow-capped peaks of Davos played host to the World Economic Forum 2025, the air was thick with excitement and a palpable sense of urgency. This year’s theme, “Collaboration for the Intelligent Age,” set the stage for intense discussions on artificial intelligence (AI) and its potential to reshape our world. As co-founders of SwissCognitive, Andy Fitze and I, Dalith Steiger, had the privilege of being flies on the wall at various public side events, soaking in the insights and debates that unfolded.

The buzz around AI was impossible to ignore, with sessions ranging from “Harnessing AI for Social Innovation” to “The Pulse of AI Innovation”. Clearly, the technology has moved beyond mere hype and into the realm of transformative force. As James Ong, one of the panellists, aptly put it, “We need to rethink the philosophy and the relationship between AI and human beings.” AI is not just a tool; it’s a paradigm shift that will redefine how we work, live, and interact with the world around us.”

“We need to rethink the philosophy and the relationship between AI and human beings.” James Ong, Founder and Director of Artificial Intelligence International Institute [AIII]

One of the most striking aspects of the discussions was the emphasis on collaboration. Gone are the days of siloed AI development. The consensus at Davos was clear: to harness the full potential of AI and ensure its benefits are widely distributed, we need unprecedented levels of cooperation between governments, businesses, and civil society.

Another discussion that deeply resonates with our vision at SwissCognitive is the AI discussion in avoiding the pitfalls of the digital divide, emphasising the need for AI to “lift all boats” rather than exacerbate existing inequalities. We strongly advocated for inclusive AI development.

The ethical implications of AI were another hot topic. The sentiment that we are not just building algorithms; we are shaping the future of humanity was echoed across multiple panels, with discussions ranging from AI’s impact on privacy to its potential to either mitigate or exacerbate climate change.

As we navigated the bustling streets of Davos, Andy and I found ourselves in impromptu discussions with fellow attendees. One of the enlightening discussions was while waiting for the Meta hot chocolate or queuing for the entrance of the Dome. One thing that was present through all our exchanges. People engaged openly, with respect and humour.

The energy was infectious, with everyone from startup founders to policymakers eager to share their perspectives on AI’s future. One conversation that stuck with us was with a young entrepreneur who’s using AI to tackle food waste in developing countries. It was a powerful reminder of AI’s potential to address some of our most pressing global challenges and SDGs.

The governance of AI emerged as a critical theme throughout the forum. With the rapid pace of AI development, there’s a growing recognition that our regulatory frameworks need to evolve just as quickly. The call for adaptive, agile governance structures was loud and clear. We shouldn’t govern 21st-century technology with 20th-century laws!

“We shouldn’t govern 21st-century technology with 20th-century laws!” during a Chatham rules debate

Perhaps the most stimulating discussions, however, centred around the potential of AI to complement human capabilities rather than replace them. AI should be seen as a co-pilot, not an autopilot. As advocates of collaboration between humans and AI, Andy and I were heartened to hear leaders from different sectors emphasise the importance of involving humans in development.

“AI should be seen as a co-pilot, not an autopilot.” during a Chatham rules debate

The Open Source Revolution: A Game-Changer in the Global AI Race

Another topic that consistently emerged in our conversations was the growing importance of open source in AI development. This trend is not just reshaping the technological landscape; it’s also challenging the traditional narrative of national AI supremacy.

The United States’ commitment to investing a staggering $500 billion in AI over the next three years is undoubtedly headline-grabbing. However, as Yann LeCun, VP & Chief AI Scientist at Meta, astutely pointed out during several discussions in Davos, the real story might be the rise of open-source models rather than any single nation’s dominance.

LeCun’s perspective is particularly illuminating: “To people who see the performance of DeepSeek and think: ‘China is surpassing the US in AI.’ You are reading this wrong. The correct reading is: ‘Open source models are surpassing proprietary ones.'”

“Open source LLM models are surpassing proprietary ones.” Yann LeCun, VP & Chief AI Scientist at Meta

This shift towards open source is democratising AI development on a global scale. LeCun explained that “DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta). They came up with new ideas and built them on top of other people’s work. Because their work is published and open source, everyone can profit from it. That is the power of open research and open source.”

Indeed, the open-source movement in AI is gaining momentum rapidly. Models like Llama 2, Mistral, and DeepSeek are not just matching but, in some cases, surpassing the capabilities of proprietary giants like GPT-4 and Google Gemini. This trend is reshaping the AI ecosystem, offering adaptability, cost-efficiency, and privacy compliance that many enterprises find increasingly attractive.

The implications of this shift are profound. While national investments like the U.S.’s $500 billion commitment are crucial, the collaborative nature of open-source development means that innovations can come from anywhere. This global pool of talent and ideas could potentially accelerate AI development far beyond what any single nation or company could achieve alone.

Moreover, the open source movement aligns with the growing calls for AI transparency and accountability. One tech executive at Davos noted, “We’re not just building algorithms; we’re shaping the future of humanity.” Open source development allows for greater scrutiny and collective problem-solving, potentially leading to safer and more ethical AI systems.

“We’re not just building algorithms; we’re shaping the future of humanity.” CEO during a Panel in Davos

As we reflect on the discussions at Davos, it’s clear that the future of AI is not just about who can invest the most money. It’s about fostering a global ecosystem of innovation, collaboration, and shared progress. The rise of open source in AI is not just a technological trend; it’s a paradigm shift that could redefine how we approach some of the world’s most pressing challenges.

In this new landscape, the winners will not necessarily be the nations or companies with the deepest pockets but those who can best harness the collective intelligence of the global AI community. As we move forward, it will be fascinating to see how this open-source revolution continues to shape the future of AI and, by extension, our world.

“In this new landscape, the winners will not necessarily be the nations or companies with the deepest pockets, but those who can best harness the collective intelligence of the global AI community.” Andy Fitze, Co-Founder SwissCognitive

As the forum drew to a close, we left Davos with a sense of cautious optimism. The challenges ahead are significant, but so too is the collective will to address them. The conversations made it clear that we are at a pivotal moment in the development of AI, and the decisions we make now will shape its trajectory for years to come. This future belongs to the young generations. We, the older generation, must be aware that every decision we make won’t affect us, as it will affect the younger generations! This responsibility is imperative!

As we return to our work at SwissCognitive, we’re more energised than ever to continue fostering dialogue and collaboration in AI. The insights gained at Davos will undoubtedly inform our efforts to build a future where AI truly lifts all boats, creating a rising tide of innovation and prosperity for all.

“We are the change we wanna see”, Yip Thy Diep Ta, Founder & CEO @ J3D.AI, House of Collaboration

In reflecting on our experience, Andy remarked, “The technical possibilities of AI are astounding, but it’s the human ingenuity in applying these technologies that will truly change the world.” I couldn’t agree more, adding, “AI has the power to amplify our human potential, but only if we approach its development with empathy, wisdom, and a commitment to inclusivity.“

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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

Der Beitrag 12 AI predictions for 2025 erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: forbes.com – “4 Questions To Design Your Personal Relationship With AI In 2025”

Artificial intelligence is bound to weave through the year that we have just entered. Over the past months it has become a constant companion for millions, on the desktop, the phone or both. The pace is accelerating.

Whether we want to recognize it or not, AI is steadily reshaping how we work, play, socialize, and think. From algorithmically driven movie suggestions on Netflix and deals on Amazon to ChatGTP for the creation, and editing of text and audio visuals; passing via ai powered dating to 24/7 companion technology. And that’s only the consumer-facing side of AI’s expanding fingerprint. Much more is going on behind the scenes. AI-powered decision making has been changing human lifes at mass scale for years, from human resource management to the attribution of social services, insurance schemes and legal systems. The tech free space is shrinking.

AI 2024

In the U.S., the generative AI market is projected to grow from $36.06 billion in 2024 to $356 billion by 2030, driven by applications in industries like healthcare, finance, and retail. And that’s just one piece of the worldwide giga business that generative AI represents. Globally 65% of organizations now use generative AI regularly, according to McKinsey. That’s double the percentage from just a year ago. In China up to 83% of business leaders actively use these tools.

Unfortunately there is no « free lunch ». Generative AI models consume massive amounts of energy. A single query to an advanced model like ChatGPT can use ten times the electricity of a standard Google search. Globally, data centers powering AI could double their energy demands by 2026. That makes their environmental footprint extensive.[…]

Read more: www.forbes.com

Der Beitrag 4 Questions To Design Your Personal Relationship With AI In 2025 erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.