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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AI Market Adjustments and Billion-Dollar Bets – SwissCognitive AI Investment Radar

The AI investment landscape remains as active as ever, with Alphabet announcing a staggering $75 billion commitment to AI infrastructure. This substantial bet highlights Big Tech’s continued push into AI dominance, following Meta’s $240 billion market value surge as investors back its open-source AI approach. Meanwhile, SoftBank is doubling down on AI, unveiling a joint venture with OpenAI in Japan and backing a $230 million investment into Indian AI startup Krutrim as the country accelerates its AI ambitions.

While these headline moves dominate discussions, AI hype versus reality is becoming a growing concern for investors. Early Nvidia investor Jonathan Cohen warns about “AI washing,” with companies inflating their AI capabilities to attract capital. This skepticism extends to financial institutions, where banks are ramping up AI investments, yet most are focused on incremental gains rather than disruptive overhauls.

Across the Atlantic, European AI startups raised $8 billion in 2024, setting the stage for the upcoming Artificial Intelligence Action Summit in Paris, where global leaders will debate AI’s role in economic growth. In fintech, AI-powered tools are attracting fresh funding, with Jump securing $20 million to develop AI-driven financial advisory solutions, while Marlin Equity Partners takes a majority stake in Napier AI, reinforcing AI’s role in financial crime prevention.

For investors looking at the financial impact of AI, new data suggests that mid-sized businesses can break even on AI investments within 9.5 months, achieving a 281% ROI in just three years. Yet, with Alphabet set to report earnings soon, investors are keen to scrutinize its AI-related capital expenditures, questioning whether such massive spending will translate into real returns.

Finally, China-based DeepSeek remains a wildcard in the AI trade, following its disruptive AI model that rattled the market last week. Whether its breakthrough is a game-changer or an overhyped anomaly remains a key debate among industry watchers.

As AI investments continue to shape global industries, we’ll be tracking the key shifts, opportunities, and market reactions in next week’s AI Investment Radar.

Previous SwissCognitive AI Radar: The AI Market Shake-Up: Where the Investments Are Headed.

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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Who’s Investing and Why in AI – SwissCognitive AI Investment Radar

From ByteDance’s $12 billion AI chip investment in 2025, aiming to strengthen its infrastructure, to Trump’s announcement of up to $500 billion in private sector investments, the focus on AI-driven infrastructure continues to expand across industries and geographies.

The UK’s AI sector has been attracting £200 million per day in private investment, highlighting strong confidence in its long-term growth potential. Meanwhile, China’s newly established $8.2 billion AI investment fund comes as a direct response to tightened US trade controls, signaling a strategic push to build domestic AI capabilities amid geopolitical challenges. On a different note, Capgemini’s research finds that 67% of businesses in Singapore see AI as their top investment priority for 2025.

Private firms are also positioning themselves for growth, with Nvidia set to invest over $500 million in a new AI research facility in Israel, aiming to accelerate advancements in AI technologies. Similarly, DDN’s $300 million funding from Blackstone is positioning the company as a leader in AI storage solutions, building on its established presence in high-performance computing.

However, despite the influx of investments, many companies are grappling with the ROI puzzle of AI, as leaders weigh the balance between potential and profitability. Some businesses are even considering selling non-core assets to fund AI projects, underscoring the financial pressures that come with large-scale adoption.

As AI investments continue to shape the global economy, staying informed about emerging trends and strategic priorities will be critical for stakeholders looking to navigate this rapidly evolving space. Join us next week too!

Previous SwissCognitive AI Radar: AI in Corporate Budgets and National Strategies.

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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Copyright: htworld.co.uk – “The EU’s Artificial Intelligence (AI) Act: The First Of Its Kind”

The EU has introduced new legislation on AI, the EU AI Act, which lays the foundation for the regulation of, and responsible development of, AI across all industries within the EU.

The Act was published in the Official Journal of the EU on 13 July 2024 and is due to enter into force on 2 August 2024.

While it will be the first of its kind to come into effect globally, it seems Colorado in the US is not far behind, being the first US state to recently pass comprehensive legislation on the issue.

This article will look at what the EU AI Act says, how it categorises AI systems, what is prohibited under the Act and what is deemed high risk.

While the Act will be relevant to many industries, this article will briefly consider some of the implications for Medtech specifically and will also touch on how the Act compares with Colorado’s equivalent.

What the EU AI Act says

How the Act categorises AI systems?

The Act classifies AI according to its risk:

• Unacceptable risk: Unacceptable risk is prohibited (e.g. manipulative AI and social scoring systems);
• High risk: Most of the Act addresses high risk AI systems, which are regulated;
• Limited risk: A smaller section of the Act addresses limited risk AI systems, which will be subject to lighter transparency requirements (e.g. developers/deployers must ensure that end-users are aware they are interacting with AI); and
• Minimal risk: Minimal risk is unregulated (includes various AI applications such as video games and spam filters)

What systems are prohibited?

According to the Act (quoting https://artificialintelligenceact.eu/high-level-summary/), the following types of Artificial Intelligence systems are prohibited, those:

• deploying subliminal, manipulative, or deceptive techniques to distort behaviour and impair informed decision-making, causing significant harm.
• exploiting vulnerabilities related to age, disability, or socio-economic circumstances to distort behaviour, causing significant harm.
• biometric categorisation systems inferring sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labelling or filtering of lawfully acquired biometric datasets or when law enforcement categorises biometric data.
• social scoring, i.e., evaluating or classifying individuals or groups based on social behaviour or personal traits, causing detrimental or unfavourable treatment of those people.[…]

Read more: www.htworld.co.uk

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Copyright: venturebeat.com – “Can We Please Stop Talking About Replacing Employees With AI?”

An online retailer recently underwent an AI transformation after it realized it no longer needed to employ an expensive local workforce to provide customer support. They split their customer support between AI bots serving as the first tier of support and an offshore team to which AI could escalate calls, functioning as a second tier of support. Its operational costs dropped precipitously, but so did the quality of service and sales.

This is just one example of the trendiest conversation in every boardroom, event and trade conference. More than anything, executives want to know when they can finally replace employees who require benefits, vacations, mental health programs, promotions and professional development and replace them with an army of AI bots. And we need to talk about this.

Handling Today’s Threatscape at Machine Scale

The chopping block includes roles like customer support, software developers, copywriters and content creators, marketing managers, forklift operators, drivers and more. The latest edition to this extinction list is no less than the CEO, says The New York Times. I’m less concerned about this, though, because we CEOs are still the ones deciding who gets replaced by AI.

Let’s augment, not replace

On behalf of all CEOs, I’ll admit that 75 to 90% of our day-to-day work is fully automatable by AI. Every task that involves collecting information, analyzing it and recommending decisions to maximize outcomes, AI can do better than a human CEO. Then there is the remaining 10 to 25%, and it’s crucial and unique to who a CEO is as a leader. It includes empathy, accountability, vision and inspiration to name a few.[…]

Read more: www.venturebeat.com

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Copyright: theglobeandmail.com – “AI Outperforms Humans in Analysis, Enhances Investor Behavior”

The next generation of robo-advisers might veer into the world of short-term trading and stock-picking.

A recent study by researchers from the University of Chicago has shown that AI, specifically large language models that have been trained on vast amounts of text and can generate natural-sounding responses, can analyze financial statements as well as, if not better than, human analysts. But before you ask ChatGPT to suggest your next trade, there’s more to the story.

The researchers provided GPT-4 with financial statements – with company names and identifying details removed – and asked it to predict the direction of future earnings. The results showed that GPT-4 outperformed human analysts with more accurate predictions of earnings changes. Moreover, trading strategies based on GPT-4′s predictions yielded higher returns than those based on other models.

So, what does this mean for the future of financial analysis? In the short term, AI’s ability to process vast amounts of data and generate insights quickly and accurately is undeniably going to have an impact on the financial services industry. Ultimately, for investors, AI will strengthen the argument for a passive investing approach.

AI can analyze trends, compute key financial ratios, and provide narrative insights about a company’s future performance faster than humans. Professionals will add AI co-pilots to their tool kits, much as software developers are already.

However, the long-term implications for retail investors are more nuanced. As more firms adopt AI technologies, the initial edge provided by AI will diminish. Just as high-frequency trading firms, who rely on speed for a trading advantage, competed with one another to reduce the time it took for their orders to reach an exchange (one high-frequency trading firm once paid US$14-million for a field next to the Chicago Mercantile Exchange so they could put up antennae to route their orders one microsecond faster), so too will AI-enhanced financial analyses develop into an arms race.

In other words, if an edge exists, the market will devour it until no sustainable advantage remains.[…]

Read more: www.theglobeandmail.com

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Copyright: bmmagazine.co.uk – “Bosch’s €2 billion gamble: Investing in employee retraining amid the AI revolution”

What is ‘quiet hiring?’ – Organizations that invest in reskilling and upskilling can fortify their workforces for the coming seismic changes wrought by technology, globalization, and markets.

Not long ago, Bosch announced a staggering plan to invest €2bn in retraining a portion of its 400,000 employees. As Europe’s largest car parts supplier, Bosch aimed to mitigate further job losses as the automotive industry transitions from traditional combustion engines to electric vehicles. The issue extends far beyond car-making.

McKinsey & Company forecasts that by 2030, one in 16 workers – totaling over 100 million across eight economies – may need to change occupations. This underscores the pressing need for reskilling and upskilling initiatives, driven primarily by rapid technological advancements automating jobs and generating demand for new skills.

Additionally, globalization and shifting market dynamics necessitate workers to adjust to new industries and roles. This interconnectedness has boosted trade, communication, and mobility across borders, often resulting in the outsourcing of jobs to countries with lower labor costs, displacing or rendering jobs in traditional sectors obsolete.

This process is also driven by shifts that occur within markets over time, due to changes in consumer preferences or regulatory overhauls. Tasks within industries tend to become more complex as new procedures, tools, and regulations emerge.

In the financial services industry, the proliferation of complex financial products like collateralized debt obligations (CDOs) and credit default swaps (CDS) has heightened the complexity of risk management. Assessing credit, market, and liquidity risk for these instruments poses unique challenges, demanding specialized knowledge and skills from risk managers. Continuous learning and skill development are essential for these professionals to remain relevant in their field, a necessity that extends beyond banking.[…]

Read more: www.bmmagazine.co.uk

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Copyirght: cepr.org – “Copyright Policy Options For Generative Artificial Intelligence”

For ‘small’ AI models (trained using an identifiable corpus of content), it shows that giving content owners full copyright protection leads to higher investments in both content quality and AI model quality. For larger models, there is a trade-off between the benefits of training data access against the risk of harm to content owners. Policymakers should take these into account and craft copyright rules that promote both flourishing creative ecosystems and cutting-edge artificial intelligence.

In recent years, powerful generative artificial intelligence (AI) models have emerged, including large language models like ChatGPT, which can produce human-like text outputs from prompts, and image generation models like DALL-E, which creates images from text descriptions. While there is a broad debate regarding various economic issues associated with such models, from the environmental impact of power consumption (Abeliansky et al. 2023) to more standard liability issues (Kretschmer et al. 2023), a more recent flashpoint for discussion surrounds copyright protections. This is because training data used to build these AI models often include copyrighted content like books, articles, and online media. Should AI companies have to license and pay for the copyrighted data used to train their models? Or does such usage fall under fair use provisions? (See Samuelson 2023, for an excellent overview).

This issue received new prominence as a lawsuit was filed by a leading content provider against a leading AI provider. In 2023, the New York Times (NYT) filed a lawsuit alleging that OpenAI had used the newspaper’s copyrighted content to train its GPT large language models without permission. As evidence, the NYT demonstrated that both ChatGPT (created by OpenAI) and Bing Chat (which licenses GPT from OpenAI) were able to reproduce some NYT articles nearly verbatim when prompted in certain ways.

The NYT argued this showed the models were trained on its copyrighted material. It asked the court to prevent OpenAI from using models trained on NYT content and requested statutory damages for the alleged copyright infringement.[…]

Read more: www.cepr.org

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SwissCognitive Guest Blogger: Sandeep Saharan, Assistant Professor, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University, Vice President, Woxsen University and Dr. Hemachandran Kannan, Professor and Director, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

In the dynamic and always changing realm of digital marketing, maintaining a competitive edge is essential for achieving favorable outcomes. With the increasing connectivity and engagement of customers on social media platforms, companies are using data analytics to optimize their marketing tactics. The amalgamation of data analytics with social media marketing is influencing the trajectory of advertising and enabling organizations to execute more precise, customized, and impactful campaigns compared to previous practices. This article aims to examine the interdependent association between data analytics and social media marketing, elucidate the advantages it presents, and elucidate how enterprises might exploit this convergence to stimulate expansion and involvement.

The Rise of Social Media Marketing: The pervasive use of social media has become an indispensable component of our everyday existence. Social media platforms provide as a means of establishing connections with friends, engaging with preferred companies, and exploring novel goods and services. Consequently, social media platforms have undergone significant transformations, becoming as influential marketing tools that provide unique capabilities in terms of audience reach and engagement. The substantial number of people present on social media platforms such as LinkedIn, Twitter, Instagram, Facebook, and TikTok renders them very advantageous environments for companies to establish connections with their intended target demographics. Nevertheless, the primary obstacle is in differentiating oneself within the densely populated digital environment and providing material that effectively connects with people. Data analytics plays a crucial role in this context. The field of data analytics encompasses the processes of gathering, scrutinizing, and interpreting data in order to arrive at well-informed and rational decision-making. Within the realm of social media marketing, the use of data analytics empowers firms to get valuable information pertaining to user behavior, preferences, and prevailing trends. These insights possess significant value in the customization of marketing tactics that are not only efficient but also relevant and current.

The Data-Driven Approach to Social Media Marketing

The era of just depending on intuition or subjective estimations to develop marketing initiatives has become obsolete. The use of data-driven methodologies in social media marketing is significantly reshaping the business. Here’s how it works:

Audience Insights

Data analytics tools provide firms a plethora of information pertaining to their social media audience. This encompasses several factors such as demographic characteristics, geographical location, areas of interest, and even real-time indicators of user involvement. With this acquired information, marketers has the ability to generate content that effectively communicates with their intended target audience.

Content Optimization

The examination of various content formats, such as photographs, videos, infographics, and articles, enables organizations to enhance their content strategy. Data analysis allows marketers to get insights into audience preferences and identify successful strategies, allowing them to concentrate their efforts on effective approaches.

Timing and Frequency

The use of data analytics may facilitate the identification of optimal time periods for content posting, hence maximizing exposure and interaction. Additionally, it may provide valuable information about the optimal frequency of content sharing, ensuring that the audience is neither overwhelmed or alienated.

Competitor Analysis

Businesses may obtain a competitive advantage by closely monitoring the social media activity and engagement metrics of their rivals. Data analytics
technologies have the capability to identify deficiencies in the market, prospects for
distinctiveness, and domains in which rivals are succeeding.

Campaign Performance

The evaluation of marketing campaigns’ effectiveness heavily relies on the monitoring of essential performance metrics, often referred to as key performance indicators (KPIs). These KPIs include conversion rates, click-through rates, and return on investment (ROI). The use of data analytics allows the continuous monitoring and modification of campaigns in real-time, with the aim of optimizing outcomes.

The Tools of the Trade

Data Analytics for Social Media: In order to optimize the use of data analytics within the context of social media marketing, enterprises depend on a diverse array of tools and platforms.

Here are some of the key players in the field:

1. Google Analytics

This multifunctional instrument offers valuable insights on the volume of website traffic that originates from various social media networks. The use of this tool aids organizations in monitoring conversions, analyzing user behavior, and assessing the influence of social media on website efficacy.

2. Facebook Insights

Facebook Insights provides a detailed analysis of page performance, audience demographics, and engagement analytics for companies who have a presence on the Facebook platform. The use of this instrument is crucial for enhancing the effectiveness of Facebook marketing endeavors.

3. Hootsuite

Hootsuite is a comprehensive social media management software that facilitates companies in the scheduling of posts, monitoring of social media discussions, and analysis of performance data across various social networks.

4. Sprout Social

Sprout Social provides a comprehensive range of social media management solutions including analytics, publishing, and interaction functionalities. This analysis offers significant perspectives on the demographics of the audience and their patterns of involvement.

5. Buffer

Buffer streamlines the procedure of arranging and disseminating social media content. Additionally, it provides analytics tools to assess the efficacy of content across several platforms.

6. Google Analytics 360

Google Analytics 360 offers enhanced analytics and data integration functionalities to cater to the needs of bigger organizations, enabling them to get a more thorough assessment of their marketing effectiveness.

Benefits of the Convergence

Why Data Analytics Matters: The amalgamation of data analytics with social media marketing presents several advantageous outcomes for firms:

Targeted Advertising

The use of data analytics enables organizations to effectively segment their audience and implement precise advertising strategies. This practice not only
enhances the relevancy of advertisements
but also optimizes the efficiency of ad expenditure.

Content Personalization

By comprehending user preferences and behavior, organizations have the ability to provide customized content that effectively connects with specific users, hence enhancing user engagement and conversion rates.

Improved ROI

Marketing initiatives that are informed by data are more probable to provide a greater return on investment. Marketers has the ability to enhance resource allocation efficiency and improve campaigns by using real-time data.

Real-time Insights

Data analytics offers timely and valuable information into the success of campaigns. This enables marketers to promptly modify their strategies and take advantage of developing trends or possibilities.

Competitive Advantage

Companies that use data analytics in their social media marketing strategies have a competitive advantage by proactively anticipating industry trends, understanding customer preferences, and adapting to market dynamics.

Leveraging Data for Social Media Success

Best Practices

In order to properly use the potential of data analytics in the realm of social media marketing, organizations are advised to adhere to the following set of recommended practices:

Set Clear Objectives

Establishing clear and well-defined objectives for social media marketing endeavors is crucial. These objectives may include several aims, including but not limited to augmenting brand recognition, generating higher volumes of online visitors, or enhancing sales performance. It is essential that data analytics be aligned with these stated goals.

Choose the Right Metrics

The primary focus should be on the key performance indicators (KPIs) that are most important to your organization. These metrics could consist of engagement rates, conversion rates, click-through rates, and consumer acquisition costs.

Use Multiple Data Sources

Integrate data from many sources, including social media platforms, customer relationship management (CRM) system, and website analytics, to provide a holistic perspective of your target audience and organizational performance.

Invest in Training

It is essential to ensure that the marketing staff has comprehensive training in the proficient use of data analytics solutions. The acquisition of knowledge and the enhancement of skills are vital in the context of this swiftly progressing domain.

A/B Testing

Conduct many experiments using diverse content, posting schedules, and advertising formats in order to ascertain the most effective means of engaging with your target audience. A/B testing enables the refinement of plans via the use of insights derived from data analysis.

Monitor and Adapt

It is important to consistently evaluate the efficacy of one’s social media efforts and remain flexible in response to evolving trends and shifts in audience behavior.

Data Privacy Compliance: Throughout the process of collecting and using client data, it is crucial to safeguard user privacy and comply with data protection standards.

Conclusion

The Future of Social Media Marketing: The combination of data analytics and social media marketing is revolutionizing the way in which businesses establish relationships with their target consumers. In today’s world characterized by an abundance of data, the ability to analyze and respond effectively to data is a valuable skill that can have a significant impact on the success or failure of a marketing endeavor. With the continuous advancement of technology, it is anticipated that more advanced data analytics tools and methodologies would emerge, hence augmenting the capabilities of social media marketing. The future trajectory of marketing is dependent on the ability of individuals to utilize data effectively in order to provide customized, pertinent, and influential information to their designated demographic. By implementing this convergence, businesses can position themselves strategically for success in the era of digitalization, when data is the most important asset for attaining marketing excellence.

About the Authors:

Sandeep Saharan is Assistant Professor at AI Research Center, Department of AI and Analytics, School of Business, Woxsen University. He received his Bachelor of Technology degree in Computer Science and Engineering from M.D. University, and then Master of Engineering degree in Computer Science and Engineering from Thapar University. He is pursuing Ph.D. with the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology. He has published research articles in reputed international journals as well as in international conferences, such as, IEEE Transactions on Intelligent Transportation Systems, Future Generation Computer Systems, Computer Communications, Applied Mathematics and Information Sciences, and IEEE Globecom. His research interests are in the areas of evolutionary optimization, game theory, and intelligent transportation system. He is an active member of various organizations, such as, IEEE, ACM, and CSI.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

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Andy Fitze, Co-Founder of SwissCognitive, World-Leading AI Network
Copyright: inside-it.ch – Andy’s AI Almanac: Technology Trilogy Engineer – die Köche der Zukunft

Let’s start from the beginning. We humans still believe that we understand non-linear processes. But in reality, we don’t. We can’t even get a bathtub halfway to a comfortable bathing temperature after the hundredth time. We are not made for things that we cannot grasp linearly. We are so overwhelmed by a simple weather forecast that we usually don’t even know what the weather is going to be like after reading the weather report. Intuition doesn’t want to enter our heads.

This leads me to 3 conclusions:

1. Things are complex, and we have simplified them so that we can understand and apply them. And that’s a good thing otherwise we’d all be overwhelmed. Anyone who goes skiing in winter, for example, knows this. There are the 3×3 rules of avalanche safety. That’s how it’s understood and it’s practical, but the reality is much more complex. So complex that we don’t understand it and can’t process it, especially not in the terrain at minus 20 degrees.
2. We deduce the future from experience, even and especially with highly volatile systems. We call this intuition, but what we mean is “competent behaviour in the face of complete cluelessness”. We tend to behave in this way when complexity is involved, perhaps in order to create a certain logic. For example, we believe that the next logical step after assisted driving is autonomous driving. In other words, we describe this change as “one step”. In reality, the technology is 1000 times more complex in this single step. When Steve Ballmer made fun of Apple in 2007, Microsoft sold millions of smartphones a year, and Apple none. Today, Apple sells that amount in one day.
3. We like to forget so badly we are practically world champions in it. Individual and social amnesia, so to speak. Before boarding the plane, we check our boarding pass to remember our seat number, but as soon as we get on the plane, we have forgotten everything.

And now we are surprised about AI, as if we had forgotten that calculators, Wikipedia, Google search, Excel, smartphones and even our kitchen stove have long surpassed us.

Wake up! With our experience, logic, and knowledge, we will hardly grasp technological developments, far less predict them.

We need to engage more intensively with technology. Much more!

What we see today with AI and its rapid development dazzles us. We are amazed, stunned and therefore blind to see what is coming.

My prognosis:

1. AI applied directly will boost productivity.
2. AI applied in technologies will define new markets.
3. AI merged with multiple technologies will change the foundations of our world.

We are already experiencing the first one today. The second one is also already advancing in the B2B sector. But the third one will be the most exciting. For example, the combination of AI, crypto, and blockchain will fundamentally change the trade of all assets in this world. And AI, biotech, and quantum will completely alter our understanding of ourselves, nature, and the healthcare industry.

For the first time in history, we will be able to answer complex questions with complex systems. I admit that I am very excited. This is beyond our current understanding.

Therefore, I suggest a new professional category: Technology Trilogy Engineer – chefs who understand how to rediscover technology recipes.

Original article: www.inside-it.ch

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