# What’s happening to AI startups after the 2024 boom?

Just a year ago, it seemed like the world of artificial intelligence was experiencing a golden age. Dozens of new AI startups were emerging every week: some promised to forever change office work, others — to rewrite the rules of business, and some — to simplify life for every person on the planet. It felt like simply adding the word "AI" to a project could instantly attract millions of dollars in investment.\
But the beginning of 2025 painted a very different picture. Somewhere between the headlines about new models and polished presentations, reality started to falter. The flow of money slowed. Startups began shutting down. Investors became more cautious.\
What happened? And what lies ahead for AI startups?

## It all started with a gold rush

Understanding the situation begins with what happened in 2024.\
It truly was a year of madness. Huge language models became available to everyone. Companies raced to integrate neural networks into their products. Ambitious plans were announced at the governmental level: from national AI development programs to integrating the technology into courts, hospitals, and schools.\
This wave gave rise to hundreds of new projects. Often they were driven by talented people. Sometimes — by those who simply knew how to catch the moment.\
But alongside this excitement, another reality emerged: many startups hastily built solutions without checking whether the market actually needed their technology. Some created yet another tool for writing emails; others — assistants for resume building. There were too many such products, and users quickly grew tired.\
When the novelty wore off, it turned out that not every AI application was truly necessary.

![Image from kitrum.com](img/failure-rate-1024x762.jpg "Image from kitrum.com")

Image from [kitrum.com](https://kitrum.com/blog/why-do-ai-startups-fail-5-lessons-learned-from-startup-failures/)

## When technology outpaced reality

In 2025, what was bound to happen sooner or later finally did: the market started cooling down. Investors who, just half a year earlier, were signing checks without fully reading pitch decks, began asking uncomfortable questions: How many real customers do you have? How are you planning to make money? What is the cost of implementation?\
It turned out that creating a prototype based on ready-made models was one thing, but building a sustainable business model was quite another. AI products demanded constant updates, investments in infrastructure, and solutions to legal issues related to data.\
Some startups folded without even reaching a second product version. Others quietly sold off for modest sums or went underground, trying to find a new niche.

But to say that the market is dead would be a huge mistake.\
Amid the chaos, another trend began to emerge: those who had been building technologies to solve real problems — not just to ride the hype — started to survive.\
Companies like Anthropic, which focused on system safety, became especially in demand among financial and healthcare clients.\
Perplexity AI proved that high-quality information search is far from a solved problem — it's a vast field for innovation.\
Young teams working on open models, such as Mistral AI, attracted a whole community of developers eager to grow new products together.\
The surviving AI startups became quieter. Less buzz, more real-world cases. They focused on narrow tasks: automating specific processes, smart data handling, and building efficient personal assistants for specific industries.

### How the rules changed for investors

Investors evolved too.\
They no longer want stories about how a startup will "change everything forever." They are looking for projects that can demonstrate tangible results — even if it's small but steady growth.\
It became fashionable not to promise revolutions, but to calmly say, "We automate accounting data processing for small businesses and earned $500,000 in six months."\
Those who can deal with reality became easier to spot. These are the startups now getting funded, hiring people, and growing — perhaps not at the dizzying speeds of last year, but much more confidently.

### New trends are emerging on the horizon

Universal AI products are giving way to niche solutions: for healthcare, legal services, construction. There is growing demand for systems that can act independently — not just respond to questions but truly perform tasks from start to finish.\
Finally, the issue of regulation is becoming more pressing. Businesses now understand that solutions must be built with data protection, ethics, and algorithm transparency in mind. Those who ignore this quickly lose client trust.

## What awaits AI in 2025: New trends

While some startups are shutting down, others are adapting to the new market expectations. And in 2025, clear trends are already visible that will shape the future of AI.

1. **The Era of AI Agents**\
   The main trend of 2025 is the shift from "smart assistants" to autonomous AI agents.\
   These are no longer just chatbots or text generators. Agents can plan tasks, make decisions, and execute them from start to finish without human intervention. And not just in experimental settings, but in real business operations — like marketing, CRM management, or internal process automation.\
   (You can read more about AI agents' potential in the latest review by The Cymes: *[What Are AI Agents and How They Are Changing Business Processes](https://thecymes.com/article/smart-fast-and-indispensable-why-ai-agents-are-the-future)*.)
2. **A Shift from "Text and Images" to Actions**\
   It's no longer enough just to generate text, images, or code. In 2025, there’s a growing demand for AI solutions that can act: manage workflows, take initiative, and interact with systems without constant user prompts.
3. **Specialization Over Universality**\
   Universal models are moving to the background. Startups offering narrow but deep solutions for specific industries — healthcare, finance, logistics, law — are winning.\
   This allows them to quickly demonstrate real product value — and makes it easier to attract and retain customers.
4. **The Growing Importance of Trust and Ethics**\
   Issues of privacy, data reliability, and algorithm transparency are no longer just about reputation — they are becoming critical business factors.\
   Companies are increasingly choosing solutions that proactively address ethical standards and offer data security guarantees.

![Image by Pexels](img/pexels-goumbik-577210-3-.jpg "Image by Pexels")

Image by Pexels

## What’s next?

The AI market is not disappearing.\
The early years’ naivety — the belief that neural networks would solve all humanity’s problems — is fading. A new philosophy is taking hold: AI is just a tool. A powerful, promising tool — but one that requires conscious, careful use.\
For startups, this means one thing: it’s no longer enough to win with hype alone. Those who can identify real problems and build simple, reliable solutions for them will succeed.\
And perhaps, that’s the best thing that could have happened to the industry after the 2024 boom.

After the 2024 AI startup boom, the market is undergoing a major shift. Hype is fading, investors are demanding real results, and only startups solving real-world problems are thriving. Discover why the era of AI agents, industry-specific solutions, and ethical AI is reshaping the future — and what it means for startups and investors in 2025.

What’s Happening to AI Startups After the 2024 Boom?

# **Has data outpaced human cognition?** 

Despite promising clarity, some systems leave IT teams wading through terabytes of noise. Artificial intelligence can be a competitive advantage, but it can also pose threats like privacy violations and biased algorithms. Business leaders and IT managers should keep AI-driven data growth productive rather than paralyzing.

## [](<>)**Defining Data Overload in an AI-Driven Environment**

Data overload occurs when the volume, velocity or variety of incoming information exceeds a team’s ability to file, interpret and act on it. For modern knowledge workers, this “default state” erodes productivity because time shifts from decision-making to low-value sorting tasks.

AI compounds the problem in two ways.

1. **Lower storage cost:** Cheap cloud capacity incentivizes saving everything — even when retention policies are vague.
2. **Acceleration of generation:** Tools such as customer-experience platforms, log analytics and generative AI agents continuously create new records, summaries and synthetic tasks.

The result is a vicious cycle where more data feeds AI models — producing excess derivative information. Without rigorous curation, the signal-to-noise ratio collapses, leaving decision-makers hunting through even thicker haystacks for the same few needles.

## [](<>)**Why Leaders Must Act Now**

Recent figures expose the gap between ambition and readiness. An OpenText survey showed that [96% of executives](https://blogs.opentext.com/todays-biggest-information-management-challenges/) believe information management is crucial, yet 70% admit they struggle to implement it. Unchecked overload has tangible psychological, physical and cognitive costs — spanning individual, organizational and societal levels.

Ethical risk also exists. AI systems inherit bias from training data — creating unfair outcomes in domains such as criminal justice risk scores or loan approvals. Ensuring algorithmic decisions remain fair and auditable is a nontrivial engineering challenge. For example, in capital markets, algorithmic-trading engines analyze millions of data points in real time to complete high-speed trades far beyond human capability. Transparency and control are mandatory.

Regulators and talent markets in the U.S. are tightening the screws. In September 2024, the Federal Trade Commission’s Operation AI Comply took [enforcement action against five companies](https://www.ftc.gov/news-events/news/press-releases/2024/09/ftc-announces-crackdown-deceptive-ai-claims-schemes) for deceptive AI claims — exaggerated marketing or sloppy data handling can violate Section 5 of the FTC Act. Locally, New York City’s Local Law 144 — in effect since July 2023 — authorizes civil penalties of [as much as $1,500 per violation](https://natlawreview.com/article/nyc-s-local-law-144-and-final-regulations-regulation-ai-driven-hiring-tools-united) for employers that use AI-driven hiring tools without an annual bias audit.

On the talent side, a 2024 Workday study found that [four in five U.S. employees](https://www.weforum.org/stories/2024/01/why-there-is-an-ai-trust-gap-in-the-workplace/) say their company hasn’t shared specific guidance on responsible AI use — widening the internal trust gap and stalling adoption.

## [](<>)**Add Intelligence — Not Chaos**

More than [20,000 custom GPT bots](https://inbargroup.com/how-business-owners-can-leverage-ai/) are in use across businesses. While they can answer tricky questions in seconds, an uncontrolled flood of bots quickly fragments knowledge and duplicates effort.

To keep things tidy, IT teams should maintain one central registry, standardize prompt templates with built-in governance tags, track usage and retire quietly. Review metrics regularly and decommission bots that nobody queries before they multiply silos. Success becomes evident when analysts spend less time cleaning data, duplicate data sources shrink and answers to priority questions arrive faster. Data that does not move these indicators is noise — eliminate it.

## [](<>)**Preconditions for Sustainable AI Adoption**

It helps to understand the foundational capabilities every organization needs before implementing specific controls.

* Map data sources, owners, classifications and retention rules.
* Implement bias testing, versioning and rollback procedures.
* Involve security, data science, compliance and line-of-business leaders in one steering committee.

Leaders often resort to tooling to curb overload, but changing the process is the best defense. After establishing the preconditions, consider these four action areas.

### [](<>)**1. Enforce Intent-Based Data Collection**

Blind accumulation breeds noise. Adopt purpose-limiting ingestion — each pipeline must document the reasons for collecting the data, its decision window and an expiration date. Do not keep data that lacks a defined business purpose.

### [](<>)**2. Automate the First Pass With AI Agents**

Offload routine triage — deduplication, PII masking and topic tagging — to lightweight AI copilots. This agent can act as the infinite interns TDWI described, freeing senior analysts for synthesis rather than sorting.

Having completed low-level filtering, higher-order reasoning still needs human oversight. The following measures keep that task scalable.

1. **Tiered dashboards:** Present indicators by criticality, not by data source count.
2. **Narrative alerts:** Combine metrics with plain-language content, empowering executives to grasp implications quickly.
3. **Feedback loops:** Record which alerts the agent ignored or escalated and retrain if necessary to improve relevance.

### [](<>)**3. Institute Data “Red Teams” for Ethical Review**

Because AI models [can reinforce hidden biases](https://rehack.com/ai/development-of-artificial-intelligence/), create rotating review squads that probe outputs under adversarial scenarios — especially for hiring, credit or sentencing applications. Document findings in the model registry and require sign-off before promotion to production.

### [](<>)**4. Rightsize Retention and Access**

Legal hold aside, operational data can lose value fast. Use temperature tiers — hot for 30 days, warm for 180 days, cold for archival. Automate the deletion of expired data and revoke stale user entitlements monthly. Fewer rows mean quicker queries and smaller risks.

## [](<>)**From Liability to Leverage — Turning AI Data Surges Into Strategic Advantages**

AI will continue to amplify data volume, but overload is not inevitable. Leaders can convert weaknesses into opportunities by treating information as a governed asset — intentionally collected, filtered by intelligent agents and retired when its value drops. The organizations that master disciplined data hygiene today will position themselves to unlock AI opportunities tomorrow instead of becoming buried in digital debris.

What can you do to avoid data overload? More in our latest article!

How to Avoid Data Overload in the Age of AI 

# Artificial intelligence in psychotherapy: a new frontier or a threat to humanity?

Psychotherapy, for a long time considered exclusively the sphere of human interaction, is transforming today under the influence of the rapid development of AI. Chatbots, self-help apps, and remote counselling platforms based on machine learning algorithms are slowly entering our lives, promising new opportunities in mental health. But how useful are these innovations, and what risks do they carry?

## Accessibility and personalization: The benefits of AI in psychotherapy

One of the main benefits of AI in psychotherapy is accessibility. Conventional therapy is often limited by geographic barriers, high cost, and lack of skilled professionals. AI tools, by contrast, can provide 24/7 support tailored to a user's individual needs at a significantly lower cost, and in some cases at no cost.

Apps using cognitive behavioral therapy (CBT) have shown effectiveness in combating depression and anxiety disorders by offering structured programs and personalized tasks. For example, [Woebot](https://spectrum.ieee.org/woebot) Health provides a CBT-based chatbot that helps users deal with negative thoughts and emotions. Users interact with Woebot via text messages, receiving real-time support and recommendations.

Another example is [Wysa](https://www.wysa.com/for-individuals), which offers an AI assistant for support in everyday life. The app provides more than 200 digital self-help tools, including mindfulness exercises, breathing practices, and relaxation techniques. Wysa also offers the ability to communicate with qualified professionals for additional support.

### Specialist support: AI as an assistant, not a replacement

AI can help psychotherapists in their work. Algorithms are able to analyze large amounts of data, identifying patterns in patient behavior that may be overlooked by humans. This allows specialists to more accurately diagnose problems, develop personalized treatment plans, and track progress. In addition, AI can automate routine tasks such as record-keeping and reporting, freeing up time for deeper patient interactions.

Spring Health uses AI to personalize psychotherapeutic care. Their platform analyzes patient data to find the most appropriate treatments and specialists. This helps to reduce waiting times and increase the effectiveness of therapy.

### Empathy as a non-programmable quantity

But for all the efficiency of algorithms, the key question remains: can AI replace human understanding and empathy? Psychotherapy is not just the application of techniques, but the process of establishing a deep, trusting connection between two people.

AI can recognize the tone of speech, choose the right exercise, but it does not "feel" pain, doubt, or guilt. It doesn't interpret silence, it doesn't pick up semitones, it doesn't make a genuine connection. As psychotherapist Laurie Gottlieb said, "a person's presence and attention is something that heals, not an algorithm".

Philosophically, the question here is: can psychotherapy without a person be truly therapy, or does it become only an improved form of self-help?

## Risks: privacy, addiction, and the illusion of help

AI in psychotherapy is associated with several dangers:

1. Copyright/IP Policy. Psychotherapeutic sessions contain sensitive information, and leaks or breaches of systems can be devastating. For example, in 2023, there was a large-scale scandal with data leakage from the BetterHelp application - more than 7 million users were at risk.
2. Dependence on technology. If a person begins to rely solely on a chatbot, this can weaken their motivation to develop communication skills, self-reflection, and real interaction. "Pattern psychotherapy" appears, where depth is replaced by speed.
3. Security illusion. Some users may perceive AI as a complete replacement for a therapist without realizing the limitations of the technology. But AI has no right to diagnose, it is not responsible - it can be dangerous in crises.

## The future of psychotherapy

The future of psychotherapy likely lies in a symbiosis of human experience and artificial intelligence capabilities. AI will be used to increase access to care and make treatments more effective, while maintaining the centrality of the human therapist and their capacity for empathy and understanding.

It is important to develop ethical standards and regulations for the use of AI in psychotherapy to ensure the safety and effectiveness of such tools. More research also needs to be done to assess the long-term effects of AI use in mental health.

Ultimately, artificial intelligence can be a powerful tool in the psychotherapy arsenal, but it should not replace the human interaction that underpins effective treatment.

What are the pros and cons of AI in psychotherapy?

AI against anxiety: how GPT models are becoming next-generation therapists

# The use of Big Data in business strategy and marketing

Big data has been a game-changer in the current fast-paced business world, transforming the very nature of how businesses operate and engage with their customers. Businesses suffer from an overflow of data that comes from multiple sources, and knowing how to analyze and interpret the data effectively stands out as one of the most important competitive advantages. This article will analyze how big data affects business strategy and marketing in the modern context. It reveals how organizations can leverage them to fuel growth, foster innovation, and improve their market understanding.

## Examining the elements of Big Data

To define it, big data refers to a high volume of both structured and unstructured data collected within a short period. This includes social media interactions, online comments and reviews, and the purchase history of customers. The difficulty lies in the fact that there is an endless ocean of information, and extracting meaningful data from it lies at the heart of the matter. Advanced analytics allows for pulling complex, multi-faceted data sets in their raw form and preparing them for use in problem-solving. These procedures facilitate effective decision-making and process streamlining and enhance insight into consumer behavior.

There are different stages involved in big data analysis, starting from data gathering to a set of dedicated processes. Company sources include, but are not limited to, internal sales figures and client files, market research documents, and social media and public database platforms. These data points are stored in sophisticated data management systems, which collect and segment the information. This is where the organization turns data into information, identify, and predict trends and actions for the future and not just the past.

![Image from Pexels](img/imgonline-com-ua-dexifizqqsj7ddmft.jpg "Image from Pexels")

Image from Pexels

## The importance of Big Data within business

Business strategies shift dramatically when big data is involved, as it offers a holistic view a company uses in shaping its policies. A firm that can harness it is likely to develop a phenomenal level of competitive advantage through customer and strategic knowledge. Such is the case with Amazon, the company uses big data for customer purchase trend analysis. Based on this study, Amazon makes shopping suggestions that optimize the customers' experience while significantly boosting company revenue. Because of this level of flexibility, Amazon has grown into the world's largest online merchant.

Operational efficiency experiences significant improvement when big data is applied. Companies gain the ability to pinpoint workflow shortcomings and resource distribution problems within their supply chains. Walmart uses big data analytics to fine-tune its supply chain functions by forecasting demand through sales data analysis. Retail giant Walmart uses big data to enhance product stocking while minimizing waste and thereby increasing profits. Through its ability to forecast consumer demands, Walmart ensures its shelves remain fully stocked with the intended products whenever needed.

Big data has a substantial impact on customer support as an essential operational area. Companies that understand customer behavior and preferences can design their marketing tactics and product offerings to address particular customer requirements. Netflix uses viewing data to deliver personalized content recommendations to its users. Personalized experiences drive customers to develop stronger brand relationships, which results in higher repeat purchases and enhanced brand loyalty. Netflix's user behavior tracking has led to enhanced user satisfaction and refined content development strategies through strategic investment in viewer-preferred programming.

![Image from Pexels](img/imgonline-com-ua-dexif3bvbyrxcct08.jpg "Image from Pexels")

Image from Pexels

Abundant data availability creates a conducive environment for innovation. Through constant monitoring of consumer demands and market trends, businesses are able to find opportunities for new service offerings and product development. Nike uses big data to study customer opinions and buying habits which enables them to develop new products and modify their product lines according to consumer demands. Nike adopts a proactive strategy that keeps them updated in today's competitive landscape. Nike uses real-time data analysis to develop products that connect with their customers and maintain their leadership position in the athletic apparel market.

Big data gives organizations the ability to predict and stop risks through its analytical tools. Through the use of predictive analytics together with real-time tracking systems, organizations can detect potential threats like fraud or market volatility before they develop into critical issues. PayPal is a case in point, as it employs big data analysis to monitor transactions in real time, identifying suspicious patterns that could indicate fraudulent activity. This capability to look ahead not only protects business interests but also ensures stability in operations. Using data to make decisions about risks, PayPal builds trust with its clients, further improving its reputation as a secure payment system.

## Augmenting analytical capabilities with Big Data

The future of big data in business looks bright. As companies become more aware of its significance, they will seek out new sources of data and new methods of acquiring it. Emerging technologies like the Internet of Things (IoT) hold great potential for gathering real-time data that can be leveraged to enhance analytical capability. For instance, smart home devices collect data on user behavior, which can be analyzed to improve product offerings and customer experiences. Philips Hue is one such firm that uses data from smart lighting systems to gain insights into usage patterns and preferences, allowing for better product design and customer engagement.

Live analysis is to alter how customers engage. The ability to track customer behavior in the moment allows companies to engage with consumers on a deeper level. For example, if a consumer is browsing products on a website, a company can have them receive tailored offers or be assisted by a live support feature. Not only does this facilitate a more consumer-oriented experience, but it also encourages more interaction with the brand. Big-box stores like Target have been able to use real-time data to deliver personalized offers to consumers according to their purchasing patterns, resulting in increased sales and customer satisfaction.

Machine learning and artificial intelligence are increasingly being associated with big data analytics, forming the potential for smarter decision-making. With these technologies, firms can make processing of data automatic, allowing improved insights with accuracy. For example, Starbucks uses AI to read customer buying habits and preferences and uses this to inform product development and advertising campaigns. By predicting which products will be successes among specific groups or geographies, Starbucks can adjust its products and marketing campaigns accordingly, thus enhancing customer satisfaction and revenue.

![Image from Pexels](img/imgonline-com-ua-dexifsqz0ntysznn3.jpg "Image from Pexels")

Predictive analytics represents another exciting frontier for big data. By reviewing historical data, companies can anticipate future consumer trends and industry directions. An airline can utilize prior flight trends to strategize how to most effectively sell specific routes at the right times, optimizing their efforts. This feature allows companies to be competitive and allocate resources appropriately. Airlines, for instance, utilize predictive analysis to forecast air demand, adjusting fares and capacity to maximize profit.

Personalization is increasingly a hallmark of today's customer interactions, and big data drives the change. Customers increasingly expect experiences to be tailored to their preferences. For instance, Spotify uses data intelligence to create personalized customer playlists according to their listening patterns. This not only drives user satisfaction but brand loyalty as well. By continuously refining its algorithms, Spotify ensures that listeners are exposed to new music pertinent to their interests, retaining them on the platform.

## Final thoughts

Big data is naturally revolutionizing how businesses operate and strategize. It enables firms to make informed decisions, automate processes, and deliver tailored services that enhance customer satisfaction. With advancing technology, big data's strategic use will become inevitable for those businesses that hope to thrive within a competitive scenario. By seizing the potential of big data, organizations create fresh avenues for growth and innovation, ensuring their future sustainability in a rapidly changing business environment. The journey of utilizing big data is not a technological pursuit but recognizing the value of information and using it to establish strong customer relationships and drive business success. As more organizations adopt data-driven business, the future of business will continue to evolve, and big data will be an essential component of future success.


Learn how companies leverage data analytics to enhance customer experiences, improve operational efficiency, mitigate risks, and drive innovation, creating sustainable competitive advantages in today's data-driven marketplace.

The role of big data in modern business strategies

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