9 REAL AI trends to watch in 2025

The Gartner Hype Cycle shows the maturity and adoption timeline of AI technologies:

Timeline (phases):

• Innovation Trigger: New tech emerges but lacks practical use.
• Peak of Inflated Expectations: Overhyped with limited success.
• Trough of Disillusionment: Interest wanes as tech fails to deliver.
• Slope of Enlightenment: Practical use cases emerge.
• Plateau of Productivity: Tech becomes mainstream and widely adopted.

Plateau timing:

The color-coded symbols show when a technology will reach mainstream adoption:

• White: <2 years
• Blue: 2–5 years
• Dark Blue: 5–10 years
• Orange: >10 years
• Red X: Obsolete before adoption.

How to interpret Gartner’s Hype Cycle for Artificial Intelligence?

The tech position shows current hype, while the color indicates time to maturity. For example, Generative AI is in its disillusionment phase and will reach pa plateau in 2–5 years.

Technologies in similar positions on the timeline can still reach the Plateau of Productivity at different speeds. This is because the “time to plateau” (color-coded symbols) reflects factors like complexity, market readiness, and adoption barriers, which differ even for places closely on the timeline.

For example, AI Engineering and Edge AI are both near the “Peak of Inflated Expectations,” but:

• AI Engineering has a 5–10 year (dark blue) time to plateau, suggesting it has a longer journey to make due to challenges in standardization or integration.
• Edge AI has a 2–5 year (blue) time to plateau, likely because it already has practical use cases and is closer to widespread adoption.

infuture.institute’s Trend Map is a tool that tracks 81 trends across 7 megatrends, and examines them across four timeframes:

1. 1. NOW = trends happening now
2. 2. NEW = trends to happen in 1-5 years (2025-2030)
3. 3. NEXT = trends to happen in 5-15 years (2030-2045)
4. 4. BEYOND = trends to happen in 15+ years (2045 and beyond)

I’m focusing on the NOW segment for this article.

You might also want to check that viral 2024’s article on AI trends. The predictions mentioned them are still relevant, but looking at them know, I can see some are too general or haven’t yet happened. That’s why, this time, I did my best to narrow it down to the actual upcoming trends for the 12 months ahead.

AI software is rapidly transforming our world, and this trend is only going to accelerate in the years to come.

Let’s dive in into the future of artificial intelligence with our guide to the top 26* AI trends poised to revolutionize 2024.

*The tremendous popularity this article got on Medium (over 75k views, 45k reads, 5k claps, and 140+ comments) inspired us to do even more research, and double the initial number of AI trends mentioned. So, here’s an updated version of it.

From the rise of generative AI to BYOAI and AI legislation, discover how it’s shaping the world around us.

1. Generative AI: The most disruptive AI trend of the decade

Generative AI (GenAI) is a type of artificial intelligence that can generate new creative content, such as text, code, scripts, musical pieces, emails, letters, etc. GenAI models are trained on massive amounts of data, and they are able to learn patterns in the data and use those patterns to generate new outputs

Almost all images in this article were generated using Bing’s built-in Chat GPT-4 & DALL-E 3. This entire text was written with the help of Google’s Bard and Chat GPT-3.

Generative AI won’t replace writers and graphic designers (DALL-E 3 still can’t get the words right in the images it generates); however, it dramatically speeds up the entire process by generating images and text, rephrasing, making it shorter, longer, or simpler, and by fact- and grammar-checking it.

The trend of generative artificial intelligence speeding up work applies to any job and activity. It offers the potential to automate tasks, boost productivity, reduce costs, and offer new growth opportunities. 

That’s why the widespread availability of AI content-creation tools that democratize access to information and skills makes it one of the most disruptive trends of this decade.

At Pragmatic Coders, we’ve harnessed the power of generative AI in our own AI-based Market Research Tool, which quickly validates business ideas and conducts comprehensive market research. This tool provides detailed market analysis, competitor insights, and potential revenue opportunities, showcasing how generative AI can transform traditional processes.

AI trends report by Gartner predicts: by 2026, the adoption of generative AI is expected to skyrocket, with over 80% of enterprises incorporating generative AI APIs, models, and applications into their operations, up from less than 5% currently.

2. Augmented working, BYOAI & Shadow AI

BYOAI (Bring Your Own Artificial Intelligence) is a new workplace trend where employees bring their own AI tools and applications to work. The increasing availability of affordable and easy-to-use AI tools and the growing demand for AI skills in the workforce drives this trend. Forrester reports that 60% of workers will utilize their own AI to perform tasks.

There are many benefits to BYOAI, including increased productivity and innovation, improved employee satisfaction, and reduced costs. 

While BYOAI is a great opportunity for workers, it might easily get out of control.  

Shadow AI, also known as Shadow IT for AI, refers to using artificial intelligence applications and tools within an organization without explicit knowledge or oversight from the IT department. 

It poses several risks, such as: 

• Data privacy and security breaches: Unsanctioned AI tools may not have the same protections as official ones, so sensitive information can be stolen or lost. 
• Compliance violations: Similarly, these tools might not follow important regulations, which could lead to legal trouble.

3. Open source AI

The 2023’s generative AI boom was mostly driven by the proprietary models of OpenAI.

However, many organizations are now adopting open-source models, such as GPT-J.

Open-source models are more transparent, flexible, customizable, and cost-effective than proprietary models. 

While it doesn’t mean that proprietary models will be soon gone, the future leaves more space for open-source solutions, with 85% of enterprises incorporating open-source AI models into their tech stacks, according to Forrester.

4. AI risk hallucination policy

While GenAI is a powerful tool, it also has the potential to produce false outputs that look as if they might be true. These false outputs are known as hallucinations. 

As GenAI becomes more widely used, there is a growing concern about the risk of hallucinations, and the demand for insurance coverage will increase. 

The market for AI risk hallucination insurance is still in its early stages, but it is expected to grow rapidly in the coming years. According to one of Forrester’s AI predictions for 2024, a major insurer will offer a specific AI risk hallucination policy. […] In fact, hallucination insurance will be a big money maker in 2024.

5. AI coding

According to Gartner, by 2028, three out of four enterprise software engineers will use AI helpers to write code. Just to compare: in early 2023, less than one out of ten software engineers used these helpers.

Why trending?

Artificial intelligence helps developers in various ways, such as:

• Automation of repetitive tasks (code generation, documentation formatting, application testing),
• Optimization of creative processes,
• Improving code quality,
• Support problem-solving.

With AI enhancing the software development process so much, you should assume that everyone around you has already started to use AI tools to boost their productivity and time to market. 

Soon, if not already, using AI coding tools will be a standard practice. Those who don’t embrace them in time will soon fall behind their competitors. 

6. AI TRiSM

AI TRiSM stands for Artificial Intelligence Trust, Risk, and Security Management. It is a framework that helps organizations manage the risks of developing and deploying AI models. 

AI TRiSM addresses five key areas:

1. Explainability: AI TRiSM helps organizations understand how their AI models make decisions and identify potential biases.
2. ModelOps: AI models need to be managed and maintained just like any other software system. AI TRiSM provides tools and processes for automating and monitoring the lifecycle of AI models.
3. Data anomaly detection: AI models are trained on data; if the data is incorrect, the outputs won’t be satisfying, too. AI TRiSM helps organizations identify and address data anomalies that could lead to errors in AI models.
4. Adversarial attack resistance: AI TRiSM provides tools and techniques for defending against adversarial attacks.
5. Data protection: AI models often contain sensitive personal data. AI TRiSM helps organizations comply with data privacy regulations and protect the privacy of individuals.

AI TRiSM is becoming increasingly important as organizations adopt more AI. According to insights by Gartner, by 2026, companies that use AI TRiSM to manage their AI systems will make better decisions by removing 80% of inaccurate or fake data.

7. Intelligent apps & AI for personalization

Read any of our last few articles on fintech predictions, the future of banking, or digital health trends for 2024, and you’ll see the word “personalization” cropping up there all the time. 

No wonder: the rise of AI is transforming the way we interact with technology, and this is especially evident in the realm of personalization. 

As we can read in Gartner’s report, by 2026, a third of all new apps will use AI to create personalized and adaptive user interfaces. This is a significant increase from today’s numbers, where only about 5% of apps use AI in this way.

Why trending?

By leveraging AI algorithms to analyze user data and preferences, intelligent apps can tailor content, recommendations, and user experiences to each individual user. 

AI-powered personalization has a huge impact on user engagement and conversion rates. For example, a study by McKinsey found that companies that excel at personalization generate 40% more revenue from those activities than average players.

This is because personalized recommendations align more closely with a user’s interests, making them more likely to click on and purchase a product.

8. Quantum AI

The marriage of quantum computing and AI, known as quantum AI, is a rapidly emerging field that opens up many possibilities. The global Quantum AI market is expected to reach USD 1.8 billion by 2030, growing at a CAGR of 34.1%. 

Quantum computers can provide the computational power to train and run complex AI models, while AI algorithms can optimize and utilize quantum resources efficiently.

This synergistic relationship has the potential to revolutionize areas such as:

• Financial modeling and risk assessment: Quantum AI can analyze vast amounts of financial data to identify patterns and predict market movements, improving risk management and investment strategies.
• Drug discovery and development: With quantum algorithms, scientists will be able to optimize drug design and simulate molecular interactions to speed up the discovery of new and effective therapies.
• Artificial General Intelligence (AGI): Quantum AI could play a crucial role in achieving yet hypothetical artificial general intelligence (AGI), the ability of machines to perform any intellectual task that a human can.

9. AI Legislation

As artificial intelligence becomes increasingly sophisticated and integrated into our lives, there is a growing need for legislation to govern its development and use. 

AI can be used for a wide range of positive and negative purposes, and it is important to have laws in place to ensure that it is used responsibly and ethically.

EU AI Act

The European Union is leading the way in AI legislation, with the European Commission proposing the Artificial Intelligence Act in 2021. This proposed regulation would be the first global framework for AI governance. The EU AI Act will likely be adopted in early 2024 before the June 2024 European Parliament elections.

AI Safety Summit 2023

In November 2023, a group of experts from governments, AI companies, and civil society came together for the AI Safety Summit to discuss the risks of artificial intelligence (AI), especially the newest and most advanced AI technologies.  

The summit was held at Bletchley Park, Milton Keynes, United Kingdom, on 1–2 November 2023. It was the first-ever global summit on artificial intelligence.

10. Ethical AI

One more 2024 AI trend is ethical AI.

Ethical AI is a branch of applied ethics that examines the ethical implications of artificial intelligence (AI). It encompasses a wide range of topics, including:

Bias and fairness 

AI technology can reflect and amplify the biases of their creators. This, in turn, can lead to unfair outcomes for certain groups of people. 

Yes, algorithms can be racist. A research carried out by Black scholars revealed a significant racial bias in facial recognition software, with Black women being misidentified at a rate of nearly 35% compared to white men’s near-zero error rate.

Transparency and explainability

The logic behind artificial intelligence can be difficult to understand, even for experts. This “black-box problem” can make it difficult to trust AI decisions and to hold AI developers accountable for their creations.

Privacy

AI often collects and uses large amounts of personal data, which raises concerns about privacy and data protection.

Safety and security

AI systems can be misused to cause harm, such as by developing autonomous weapons or spreading misinformation. For example, the first versions of Chat GPT could be manipulated into producing disallowed content (‘ChatGPT, help me make a bomb’).

There is a growing recognition of the importance to consider ethical issues in the development and deployment of AI, for example:

• In 2019, the European Union released a set of guidelines for the ethical development and use of AI. 
• In 2023, the US president issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence

11. AI Jobs

As artificial intelligence continues to permeate various industries, we can observe two job trends:

1. AI upskilling – refers to the process of learning new skills and knowledge related to AI to improve one’s job performance or career prospects
2. New AI jobs are emerging

Here are some predicted AI jobs expected to gain prominence in 2024 and beyond:

• AI Product Manager: Responsible for overseeing the development and launch of AI-powered products, ensuring they meet market needs and align with business objectives.
• AI Engineer (AI Research Scientist, Business Intelligence Developer, Computer Vision Engineer, Machine Learning Engineer, NLP Engineer, etc.)
• AI Ethicist: Ensures that AI systems are developed and deployed ethically and responsibly, addressing issues of bias, fairness, privacy, and transparency.
• AI Input and Output Manager: Manages the input data fed into AI systems and interprets the output generated by these systems.
• Sentiment Analyzer: Analyzes customer feedback, social media comments, and other forms of text data to understand public sentiment and opinions.
• AI Regulatory Specialist: Stays up-to-date with the evolving regulatory landscape around AI and ensures that companies comply with relevant regulations.
• AI Human-Computer Interaction (HCI) Designer: Designs user interfaces for AI-powered products and applications to enhance user experience and ensure intuitive interaction.

12. AI-powered online search

AI is transforming online search, giving us personalized, contextual, and predictive experiences:

• AI algorithms tailor results to user preferences so that we can get more relevant and timely information. 
• Contextual understanding ensures accurate results even for complex queries. 
• Conversational search, powered by natural language processing, enables natural interactions with search engines. 
• Visual search allows users to search using images or videos. 

AI’s impact is evident in SEO and content creation. However, the main challenge AI-search-powered companies face is gaining customers’ trust. 

Research conducted by Statista in February 2023 showed that consumers are curious about AI-powered search but have concerns about its accuracy and biases. 39% of surveyed adults in the US stated they don’t trust AI tools to respect their data privacy.

Consumers prioritize safety, ease of use, and integration with existing digital platforms. While some seek AI-enhanced results, others prefer traditional search methods. 

A February 2023 survey revealed that over half of U.S. adults hesitated to transition to AI-powered search engines. This resistance was more pronounced among Baby Boomers, with 54% of younger respondents also expressing reluctance. Conversely, Millennials showed a greater openness to AI-powered search, with 40% indicating a willingness to switch.

13. AI in customer service

Lastly, to conclude our artificial intelligence predictions, let’s look at The State of AI in Customer Service: 2023 Report by Intercom to see how AI trends are predicted to change customer service.

1. Companies are investing more in AI for customer service.

Customer service leaders are excited about AI’s potential and plan to invest more in it in the coming years. In fact, 69% of support leaders say they will invest more in AI in the year ahead.

2. AI will make customer service jobs better, not replace them.

AI will not replace human customer service representatives but will make their jobs easier and more efficient. Over three-quarters (78%) of support leaders expect AI to transform customer support careers in the next five years.

3. AI can help companies save money and improve efficiency.

Adding AI and automation to your customer service toolkit can help you save money and improve efficiency. At a time when business resilience is more important than ever, 66% of support leaders are excited about using AI and automation to increase the efficiency of their teams in the year ahead.

4. AI can give companies a competitive edge in customer service.

Customer experience is a key differentiator in today’s market, and AI can help companies provide better customer service and give them a competitive edge. In fact, 73% of support leaders believe customers will expect AI-assisted customer service in the next five years.

5. There is a gap between what customer service leaders vs. customer service representatives know about AI.

While over two-thirds of support leaders are confident that customers are ready to interact with an AI chatbot, less than half of support practitioners feel the same.

Customer service leaders are optimistic about using artificial intelligence, but consumers aren’t that eager to use chatbots (see this research by Gartner). This casts doubt on the near future of AI in customer service.

14. AI’s Environmental Impact

The research conducted by Cornell University to assess the energy usage of the biggest LLMs (like Chat GPT-3 for the time of the research) revealed they demand significant energy – comparable to the annual consumption of… about 200 average Germans.

The environmental impact is by no means notable. Here are a few examples, as detailed by Earth.org.

• A substantial carbon footprint is produced due to energy-intensive processes in AI model training. This leads to increased global greenhouse gas (GHG) emissions. GPT-3’s training alone resulted in nearly 500 tonnes of CO2 emissions.
• E-waste (the disposal and environmental impact of electronic devices and hardware used to develop, train, and run AI systems) containing harmful chemicals poses a further environmental risk.
• The indirect impact of AI, such as the threat to natural ecosystems (animals) from driverless vehicles or drones, also raises concerns​​​​.

However, despite its challenges, AI presents innovative avenues for environmental preservation and climate action. According to NPR, AI is being increasingly used in climate change mitigation efforts.

• AI-driven satellite analysis helps detect methane emissions, a significant contributor to global warming.
• AI can detect forest fires early, preventing them from escalating into megafires.
• AI assists in planning controlled burns, aiding burn managers in decision-making.
• Lastly, AI is revolutionizing green tech mining by locating critical minerals like lithium and cobalt more efficiently, crucial for climate solutions like solar panels and electric vehicles.

According to Statista, using AI for environmental applications is actually predicted to reduce GHG emissions worldwide. North America and Europe are the leaders here. By 2030, their emissions are expected to drop by 6.1% and 4.9%, respectively.

Using artificial intelligence for sustainable environmental applications can also boost employment and economic growth. East Asia could see its workforce expand by 2.5% in 2030 thanks to AI-powered environmental jobs, adding around 25.1 million new positions. Europe is poised to reap the largest economic benefits from AI sustainability applications, potentially increasing its GDP by 5.4% in 2030.

The critical issue, however, is the lack of transparency on AI’s environmental impact, where the complexity of AI systems obscures their ecological footprint. Solutions lie in developing energy-efficient AI hardware and algorithms, along with promoting a culture of transparency and accountability. Ethical AI design standards and precise government regulations are essential for sustainable AI development​.

15. Peace AI

AI is a double-edged sword in any area, but it seems especially visible when it comes to world peace (or its lack).

Artificial intelligence can be used to create or escalate conflicts. See autonomous lethal drones, cyber warfare, deep fakes (such as a 2022 deep fake video of Ukrainian President Volodymyr Zelenskyy surrendering) and misinformation, automated surveillance, or AI-enhanced propaganda. However, it can also serve as a peacekeeping tool.

Here, AI capabilities extend to:

Large-scale digital dialogues: The United Nations Department of Political and Peacebuilding Affairs (UN DPPA) has utilized AI-assisted digital dialogues in Yemen and Libya to advance inclusivity in peace processes. AI-powered tools facilitated large-scale consultations in local dialects and languages and allowed real-time analysis and segmentation based on demographic interests​.

Early warning of mass violence: AI can analyze online patterns of disinformation, hate speech, and propaganda to identify early warning signs of mass violence and pursue targeted interventions​.

Monitoring cease-fire violations: Non-weaponized autonomous drones using AI have been employed to monitor lines of contact and cease-fire violations. This technology aids in reducing harm to peacekeepers and units on the ground. AI helps process the vast amount of data collected by drones and satellite imagery, which is essential for monitoring cease-fires, observing disarmament, and identifying war crimes​.

16. AI-supported problem-solving and decision-making

One of the emerging AI trends that is currently in the piloting phase is the use of sophisticated decision-making algorithms. We may see a more widespread adoption of these algorithms in 2024 and beyond.

According to a recent article in Nokia Thought Leadership, “highly effective decision-support algorithms” are emerging as a transformative tool for navigating complex decision-making scenarios. These machine learning-powered AI systems can examine a wide range of potential options and narrow them down to a more manageable shortlist based on specified criteria.

Examples of how AI can be used for decision-making and problem-solving are numerous and rapidly expanding. Many of them have to do with back office automation. Here are a few notable applications:

• Healthcare: Analyze patient data to identify potential health risks, recommend personalized treatment plans, and monitor patient progress. Check: Healthcare technology trends for 2024 and beyond
• Finance: Detect fraudulent transactions, assess investment risks, and optimize investment strategies. Check: Top 6 fintech trends for 2024 and beyond
• Manufacturing: Optimize production processes, predict maintenance needs, and automate quality control.
• Supply chain management: Optimize shipping routes, manage inventory levels, and predict demand fluctuations.
• Customer service: Automate customer support interactions, provide personalized recommendations, and resolve customer issues more efficiently.

17. Lawsuits against AI companies

The increase in lawsuits against AI companies, particularly in the realm of generative AI, is a significant trend that has been developing over recent years.

James Grimmelmann, a law professor, predicted that 2024 would be pivotal for AI-related lawsuits, suggesting a possible financial impact on generative AI developers​.

The data and expert insights from multiple sources shed light on this evolving legal landscape:

Growing Number of Lawsuits

There has been a noticeable rise in lawsuits related to generative AI, challenging issues like privacy, consumer safety, and intellectual property protection. These lawsuits vary widely in their legal basis, encompassing claims of copyright infringement, invasion of privacy, and more.

Significant AI cases in 2023

• In June and July 2023, several federal class action lawsuits were filed against major AI developers like OpenAI and Google, alleging violations of privacy and property rights​.
• A notable case, Andersen v. Stability AI Ltd., involved artists alleging that Stability AI scrapped billions of copyrighted images for training their models​.
• In February 2023, Getty Images filed a lawsuit against Stability AI, asserting claims of copyright and trademark infringement​.
• The New York Times filed a lawsuit against OpenAI in December 2023, claiming the misuse of their articles for training AI, impacting traditional reporting​.

Industry and legal experts’ views

There are concerns that applying copyright law to AI might stifle AI development and creation, potentially creating a system favoring well-funded companies​​​.

Legal efforts to adapt copyright law to AI and the rising trend in litigation might lead to a shift in how AI is developed and used, with the outcomes of lawsuits potentially setting new precedents.

18. 2024 US presidential election & AI threat

As the 2024 United States presidential election approaches, concerns are mounting about the potential impact of artificial intelligence on the democratic process.

The rise of AI-powered disinformation and deepfakes has raised alarms among experts who worry about the potential for manipulation and erosion of public trust in elections.

To give you a gist of how AI is posed to play a significant role in campaigning, voter targeting, and election administration, I’ve summarized a discussion panel held by the Brennan Center For Justice, where experts examined critical questions about AI. (The full discussion is available here. I’ve included timestamps if you wish to learn more about a specific topic).

1. Imitation threat and harassment (07:06 – 09:20): The dangers of AI in elections include imitation threats like deepfakes and phishing attacks, which could compromise the credibility of election offices. AI can also flood election offices with fake requests to obstruct election officials’ work.
2. AI-driven voter suppression and misinformation (11:31 – 13:43): AI’s ability to reshape cyber attacks and create deepfakes poses a risk of spreading misinformation and suppressing votes, especially targeting vulnerable groups.
3. Malicious uses of AI in public opinion (14:04 – 16:25): AI can mess with public opinions, like in 2017 when bots sent over a million fake messages to the FCC about net neutrality. The evolving sophistication of AI makes it harder to distinguish between real and AI-generated content.
4. Election subversion in the AI era (31:25 – 33:14): Concerns are raised about AI amplifying false narratives around elections, creating fake election websites, and manipulating voter registration and purges.
5. 💡 Safeguarding measures (10:15 – 11:11): Protective strategies against AI threats include implementing resilient systems like multifactor authentication, paper backups for electronic voting systems, and increased resources for election offices.

19. AI, cybercrime & the social engineering threat

Social engineering is a psychological manipulation technique that exploits human error or weakness to gain private information, access, or valuables. And, unfortunately, this practice has become much easier and widespread with the rise of AI.

Companies that use AI are mostly concerned about protecting their data from hackers. Over half (51%) of the companies surveyed by Stanford University said they are taking steps to prevent cybersecurity risk. No wonder the worth of AI in cyber security is forecast to increase to 46.3 billion U.S. dollars by 2027 (in comparison to over ten billion U.S. dollars in 2020).

AI cybercrime is a huge iceberg, with spear phishing (phishing attack that uses personalized emails or messages to trick victims into clicking on malicious links or opening infected attachments), harpoon whaling (a type of spear phishing attack that targets high-level executives or other high-value individuals), virtual kidnapping (scammers use social media to claim they have kidnapped a loved one and demand a ransom), or BEC (Business Email Comprise: scammers pretend to be from companies you trust to get you to send money)  just at its tip.

The social engineering threat will be even bigger in the years to come. According to Trend MicroSecurity Predictions for 2024 report, in 2024, voice cloning, already being a powerful tool for identity theft, will become the primary tactic employed in targeted scams.

20. AI for psychotherapy

The global behavioral therapy market is expected to experience phenomenal growth, reaching $308.8 billion by 2032, with a substantial compound annual growth rate (CAGR) of 8.1% from 2023 to 2032.

Digital technologies and platforms are being increasingly adopted as one of the market opportunities. Here, AI has the potential to address the large gap in the availability of mental health professionals. However, it also comes with some limitations.

The following are some key areas where AI is making an impact, according to articles for the British Association of Counselling and Psychotherapy & Psychology Today:

• Enhanced diagnostic and treatment options: AI can boost diagnostic precision and treatment effectiveness by analyzing patient communication patterns and keeping psychiatrists informed about the latest research and therapies.
• Hybrid care models: AI is trending towards a supportive role in psychotherapy, complementing human therapists for more efficient, cost-effective care. However, essential oversight by qualified mental healthcare professionals is necessary.
• Therapeutic limitations: Multiple studies show that the most important factor for effective therapy is the strong connection between therapist and client, built on empathy, body language, and positive support. These are the areas where AI will never outperform humans (?).
• Ethical concerns: AI brings significant ethical and legal issues, including concerns about patient privacy, informed consent, or regulation compliance (like HIPAA).
• Potential in manualized therapies: AI shows promise in aiding manualized therapies like CBT, but its effectiveness in fostering a therapeutic alliance or dealing with complex therapy aspects needs more study.

21. Loneliness & emotional dependency on AI

There’s a growing trend where AI, through sophisticated virtual assistants and companion robots, is fulfilling roles traditionally occupied by friends or family. These AI entities, designed to engage in meaningful conversations and exhibit empathetic behaviors, are becoming sources of companionship for many, especially among those experiencing social isolation.

Sociable robots like ElliQ and Paro have been developed to provide companionship, especially to the elderly. A study by the National Institute of Health reported that social robots could reduce feelings of loneliness in older adults, demonstrating their potential as companions. These AI-powered robots can converse, remind users of medications, and provide emotional support.

Perhaps most intriguingly, there are instances where individuals develop romantic feelings towards AI entities. Instances of people developing romantic feelings for AI, such as Replika AI, are seen in the context of virtual characters in games or interactive platforms. There have even been real-life cases reported in the media where individuals have formed romantic relationships with AI entities or robots. For example, a man in China ‘married’ a robot he built himself.

22. AI influencers

Source: Google Trends

The influencer marketing landscape is rapidly evolving with the advent of AI influencers, a trend underscored by the fact that over 52% of the total US population currently follows a cyber-creator on Instagram​.

​AI influencers offer several benefits: they are highly efficient, capable of continuous presence without the need for breaks, and their content is driven by data insights, ensuring alignment with audience desires​​.

However, they also come with drawbacks. Since AI influencers can’t genuinely “feel” or “experience” life, their ability to form deep, empathetic connections with certain audience segments is limited. Additionally, there’s a trust challenge with AI influencers as they might be perceived as impersonal or inauthentic, especially by older generations.

Examples of AI influencers include:

• Miquela: A virtual 19-year-old robot living in LA with 2.6 million followers who has collaborated with brands like Prada, Dior, and Calvin Klein.
• Shudu: The world’s first digital supermodel and an AI influencer, with striking AI-generated photos, amassing 241K followers​​.
• Imma: A Tokyo-based virtual influencer interested in fashion, art, and film, with nearly 400K followers and her own brand​​.
• Ion Göttlich: An AI-generated biker part of the Instagram cycling community, boasting 77K followers and sharing cycling content with humor​​.
• Lu do Magalu: A Brazilian virtual influencer with a remarkable 6.7 million followers, generating $552 million in 2019 alone​​.

23. China’s race for AI supremacy

China’s rise in the field of artificial intelligence (AI) is a striking example of technological evolution and strategic growth. In its 2023 Artificial Intelligence: in-depth market analysis, Statista names it one of the seven key AI predictions.

With its AI market valued at about RMB 150 billion (US$23.196 billion) in 2021 and projections to reach RMB 400 billion (US$61.855 billion) by 2025, China’s journey in AI began post-1970s economic reforms emphasizing science and technology.

Initially lagging behind Western nations, China, since 2006, has methodically crafted a national AI agenda consisting of three stages (benchmarks for 2020, 2025, and 2030) aiming to become a global AI leader by 2030 and bolster its AI industry’s worth to over 1 trillion RMB​.

Central to this strategy is the government’s collaboration with key companies like Baidu, Tencent, Alibaba, SenseTime, and iFlytek, each leading development in specialized AI sectors such as facial recognition, software/hardware, and voice intelligence. This rapid development has profound socio-economic, military, and political impacts, reshaping industries like agriculture, transportation, and manufacturing.

However, this growth brings challenges, including potential labor market disruptions, ethical dilemmas, and privacy concerns, necessitating careful navigation and regulation​.

24. Artificial Emotional Intelligence

Artificial Emotional Intelligence (AEI) integrates the essence of emotional engineering, human-computer interaction, and emotional computing into AI systems, enabling machines to recognize, interpret, and respond to human emotions. It’s a rapidly expanding field, with the global AEI market projected to grow by 21.5% from 2023 to 2030​.

In practice, AEI involves emotion recognition, generation, and enhancement, and its applications are diverse and impactful. 

• In marketing, Emotion AI, like Realeyes, analyzes audience engagement to enhance ad campaigns’ effectiveness.
• Call centers utilize it to match customer emotions with the most suitable agents, improving resolution rates​.
• In education, platforms like Vedantu use facial analysis software to optimize e-learning content based on student engagement and emotional responses.
• AEI also advances mental healthcare by enabling more accurate diagnoses through emotional cue analysis.

These applications underscore the potential of AEI in creating a new business ecosystem, although they also highlight the need for infrastructure and ethical considerations​.

25. Growth in AI hardware and software

Not surprisingly, we will experience growth in AI-driven software and hardware. What’s also not surprising is how tremendous the growth will be.

AI software: The sales of AI software are expected to grow significantly by 2025. North America will have the biggest market share and the fastest growth, with sales increasing to over $50 billion in 2025, followed by Asia Pacific and Europe.

AI hardware: The sales of AI-powered hardware will skyrocket in the coming years, with revenue jumping to a projected $235 billion by 2025.

The biggest segment in the market for AI-powered hardware is expected to be composed of products like central processing units (CPUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and system-on-a-chip (SoC) accelerators. This category is anticipated to reach a value of approximately $171 billion by 2025. The following segments are graphic processing units (GPUs; $54.52 billion), storage devices ($6.35 billion), and network products ($2.54 billion).

26. Self-driving AI labs

Self-driving labs (SDLabs) combine artificial intelligence and robotics to automate the process of scientific experimentation. In contrast to LLMs (large language models), SDLabs are optimized for efficiency with small dataset inputs and don’t necessitate the expense of extensive algorithm training and fine-tuning.

What is the concept of SDLabs revolutionary? They can perform a wide range of tasks, from designing and executing experiments to analyzing data and making predictions:

• SDLabs can continuously learn from the data they collect.
• SDLabs will perform experiments much faster than humans.
• SDLabs might use AI algorithms to decide what experiments to conduct and the best way to do it.

The development of SDLabs is still in its early stages, but the technology has the potential to transform the way we do science, specifically in two areas: pharmaceutical discovery (developing new, individualized medications and therapies more quickly and efficiently) and chemicals (SDLabs can be used to design and synthesize new materials with desired properties).

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