In today's world of intensive technological development, where we hear about new breakthroughs in artificial intelligence every month, it's hard to imagine that this fascinating field has experienced periods of deep disappointment and drastic funding cuts. Yet the history of AI isn't just a string of spectacular successes – it's also a story of cycles of heightened enthusiasm followed by periods of stagnation and skepticism, known as "AI winters".
From euphoria to disappointment: Anatomy of the AI cycle
During my research into AI history, I've noticed that its development resembles a roller coaster – periods of rapid growth and enthusiasm are followed by declining interest and funding. This cyclical pattern, known as an "AI winter," first appeared in the 1970s and repeated in the 80s and 90s.
The term "AI winter" was coined in 1984 during the annual meeting of the American Association for Artificial Intelligence (AAAI). Two pioneers in the field – Roger Schank and Marvin Minsky – who had already experienced the first "winter" of the 70s, warned the research community that the contemporary enthusiasm for AI might spiral out of control, leading to inevitable disappointment.
This seasonal analogy perfectly captures the nature of AI development – after a period of intensive growth and "blossoming" comes a time of "freezing," when investments and interest significantly decline. However, just as in nature, the winter period doesn't mean complete stoppage – processes continue beneath the surface that may lead to another "spring" in the AI field..
The first AI winter (1974-1980): Disappointment with expert systems
Studying historical materials, I discovered that the first significant AI winter arrived in the mid-1970s. This period of disappointment followed a decade of intensive development and optimism sparked by the Dartmouth Conference in 1956, which formally initiated the field of AI. Particularly high hopes were placed on expert systems – computer programs designed to mimic the reasoning processes of human experts in various fields.
Expert systems like MYCIN (developed by Edward Shortliffe in 1976 for diagnosing bacterial infections), CADUCEUS (a system for diagnosing internal diseases capable of recognizing about 1,000 disease entities), and PUFF (a system for diagnosing lung diseases from 1979) initially generated enormous hope. These programs used knowledge bases and inference mechanisms to solve complex problems and make decisions.
However, enthusiasm quickly waned when it became apparent that these systems had serious limitations:
- Building and maintaining expert systems was extremely costly and time-consuming
- Systems struggled to adapt to new situations not included in their knowledge bases
- System operations were often opaque, making it difficult to understand and verify their decisions
- Lack of flexibility and learning ability limited their practical usefulness
These problems were particularly evident in contrast to initially inflated expectations. When reality checked these hopes, deep disappointment set in. This pattern – excessive optimism leading to disappointment – was later recognized as characteristic of AI development cycles.
The first AI winter was also the result of other factors:
- Criticism in the Lighthill Report (1973), which questioned the promises of artificial intelligence, leading to drastic cuts in research funding in Great Britain
- Reduction of funding by DARPA (Defense Advanced Research Projects Agency) for academic AI research in 1973-1974
- Frustration related to the Speech Understanding Research program at Carnegie Mellon University, which failed to meet expectations
Period of warming and the second AI winter (1987-1993/2000)
In my analyses, I noticed that after the first AI winter came a warming period in the 1980s, driven by the development of new technologies and approaches, such as neural networks. A significant influence was the emergence of the backpropagation algorithm, popularized by Geoffrey Hinton, which enabled effective training of multi-layer neural networks.
During this period, groundbreaking concepts also emerged, such as the Neocognitron developed by Kunihiko Fukushima in 1980, which was a precursor to today's convolutional neural networks. Another significant achievement was the NetTalk program created by Terrence Sejnowski and Charles Rosenberg in 1987, which learned to pronounce words in a manner similar to humans.
However, this renewed enthusiasm didn't last long. Between 1987-1993 (and according to some sources, even until 2000), a second AI winter occurred. The causes of this phenomenon were multiple:
- Sudden collapse of the specialized AI hardware market in 1987, when personal computers from Apple and IBM became powerful enough to replace more expensive Lisp machines
- Problems with maintaining and updating expert systems, which proved too costly and inflexible
- Drastic funding cuts by the Strategic Computing Initiative, as new DARPA leadership determined that AI wouldn't bring quick results
- Failure to achieve the ambitious goals of the Japanese Fifth Generation project, which was supposed to revolutionize computing
- Closure, bankruptcy, or acquisition of over 300 AI companies by the end of 1993
This second AI winter lasted longer and had more severe consequences for the industry than the first. Many companies and research institutions limited or completely halted work on AI, and the term "artificial intelligence" became so controversial that scientists began using alternative terms such as "machine learning" or "adaptive systems" to avoid negative associations.
Cyclical nature of AI development and the AI effect
Analyzing the history of artificial intelligence, I see a repeating pattern. New AI technologies initially generate enormous enthusiasm and unrealistic expectations. When these expectations aren't met in the anticipated timeframe, disappointment and declining interest follow. This cycle, known in technological marketing as the "hype cycle," perfectly describes the history of AI development.
Gartner Hype Cycle
In one of my earlier articles, "When admiration for artificial intelligence turns into a rut" I described an interesting phenomenon where once AI systems master skills previously thought to require human intelligence (like chess or image recognition), we stop viewing those skills as manifestations of true intelligence.
As Nick Bostrom aptly noted, "AI encompasses everything that amazes us at the moment, and when we cease to be impressed, we simply call it software." This effect contributes to the cyclical nature in how we perceive AI – achievements that initially inspire awe eventually become ordinary and cease to be recognized as "real" artificial intelligence.
Moravec's paradox in the context of AI winters
The phenomenon of cyclical AI development can be better understood through the lens of Moravec's paradox, which I explored in my article "Computers and people - the mystery of the Moravec paradox". This paradox points to a fundamental difference between human and machine intelligence – tasks difficult for humans (like complex calculations) are relatively easy for computers, while tasks simple for humans (like object recognition or spatial navigation) pose enormous challenges for machines.
This asymmetry partially explains why expectations for AI often go unmet – we strive to create systems that would mimic human abilities but fail to consider that human intelligence is the result of millions of years of evolution and relies on fundamentally different mechanisms than artificial systems. This discrepancy between expectations and technological possibilities contributes to the cyclical AI winters.
Moreover, Moravec's paradox sheds light on the problem of technology transfer from laboratories to practical applications. AI systems may excel at narrowly defined tasks in controlled environments but often fail when they must operate in a complex, unpredictable real world, leading to disappointment and declining interest.
From expert systems to machine learning
One of the key reasons why earlier approaches to AI, such as expert systems, led to winter periods was their fundamental architecture. These systems relied on manually coded rules and expert knowledge, making them rigid and difficult to adapt.
In my article "Machine Learning: How Computers Learn from Experience?" I discussed how the modern approach to AI focuses on systems that can learn from data rather than relying solely on programmed rules. This paradigm shift, initiated by pioneers like Arthur Samuel (who defined machine learning in 1959 as "the ability of computers to learn without being explicitly programmed"), proved crucial in overcoming the limitations that contributed to earlier AI winters.
The transition from expert systems to machine learning represented a fundamental change in approach to AI:
- Instead of manually coding knowledge, systems can automatically learn from data
- Instead of rigid rules, systems use statistical algorithms to detect patterns
- Instead of being limited to narrow domains, machine learning systems can be applied to a wide range of problems
This flexibility and adaptability have made modern AI systems more resilient to the problems that led to earlier winters, though they haven't completely eliminated the risk of cyclicality.
Today's AI landscape: Are we heading for another winter?
Recent years have brought unprecedented development in AI, especially in the area of generative artificial intelligence (GenAI). Language models like ChatGPT, Claude, and Gemini, image generators like DALL-E and Midjourney, and a range of other tools have revolutionized our understanding of AI capabilities. This new wave of AI technology, based on transformer architecture and deep learning, seems more resistant to the problems that led to earlier winters.
However, as an AI market observer, I notice some signs of cooling enthusiasm around AI. Analyzing sources from 2024/2025, I came across the statement: "The hype around artificial intelligence seems to be significantly less than just a year ago. The reason is prosaic – we've started trying to use AI in our daily work and often found that the results don't meet our expectations."
This observation aligns with Gartner's hype model, where a wave of excitement is followed by a moment of collision with reality and disappointment. Examples of contemporary disappointments include:
- Hallucinations of language models, where the returned answer is completely untrue
- Image generators creating culturally inappropriate or often even absurd content
- Problems with information accuracy, especially for specialized or rare data
However, today's AI landscape differs significantly from previous winter periods. Current AI systems are more advanced, flexible, and find real applications in many fields. Moreover, investments in AI are much larger and more diversified than in the past.
Differences between generative AI and AGI in the context of winter cycles
In my article "Generative AI vs General AI - Two Faces of Artificial Intelligence", I distinguished two main currents in contemporary AI: generative AI (GenAI), which creates new content based on existing data, and general AI (AGI), which aims to achieve human-level intelligence across a broad range of domains.
This division has important implications for a potential future AI winter:
- Generative AI already finds practical applications and generates real business value, which may protect it from drastic funding cuts
- At the same time, expectations for general AI may still be unrealistic, creating a risk of disappointment
It's worth noting that the costs of developing advanced AI models are enormous – training GPT-4 cost around $78 million, while Google's new Gemini Ultra model cost as much as $191 million. Despite these costs, investments in generative artificial intelligence are experiencing a boom. In 2023, the five largest technology companies (Amazon, Alphabet, Meta, Apple, and Microsoft) allocated a total of $223 billion to R&D, much of which may have been related to AI investments. In 2025, as many as 78% of companies plan to increase spending on generative AI, and we're talking about a figure of $307 billion!!!
This scale of investment suggests that even if some cooling of enthusiasm occurs, the AI industry is currently much more resistant to winter than in the past. However, history shows that cycles of enthusiasm and disappointment are a natural element in the development of breakthrough technologies.
Challenges and threats: Potential factors in a future AI winter
Based on my analysis of historical AI winters, I've identified potential factors that could lead to another cooling in this field:
Technical challenges
- Exhaustion of easy gains - Current AI successes rely largely on increasing model size and training data quantities. This approach may encounter scalability and efficiency limits
- Interpretability problems - Deep learning models often function as "black boxes," making it difficult to understand their decisions and potentially limiting their application in critical domains
- Energy constraints - Training and running large AI models requires enormous amounts of energy, which may become a significant limitation in the face of the climate crisis
Implementation problems
- Implementation gap - Despite impressive capabilities of AI models in the laboratory, their implementation in real business environments often encounters difficulties
- Integration problems - Incorporating AI systems into existing processes and IT infrastructure may be more complicated than initially appears
- Disparity between expectations and actual capabilities - Marketing promises for AI often exceed the technology's actual capabilities
Ethical and regulatory challenges
- Security and privacy issues - AI can be used to create disinformation, deepfakes, or violate privacy, which may lead to increased regulatory control
- Lack of social trust - If AI systems make decisions perceived as unfair or harmful, this may lead to a decline in trust and social acceptance
- Growing regulatory requirements - New regulations, such as the AI Act, may increase the costs and complexity of implementing AI systems
From Turing to digital oracle: Evolution of expectations for AI
Analyzing the cyclical nature of AI development, I notice an evolution in our expectations for this technology. In my article "The Turing Test - From Simple Concept to Human Intelligence", I described how initial conceptions of AI focused on mimicking human conversation. Over time, our expectations evolved toward systems capable of solving increasingly complex problems.
In another of my articles, "Digital oracle - between myth and reality of modern AI", I proposed an alternative view of AI – not as an "artificial human," but as a new kind of entity, a kind of digital oracle that makes predictions based on enormous amounts of data but lacks human consciousness or emotions.
This change in perspective may help avoid future AI winters because instead of unrealistic expectations about creating "true" intelligence, we focus on practical applications of AI systems as tools supporting human decisions and creativity.
In the article "How our understanding of AI is changing - from chess to prediction machines", I traced how our understanding of artificial intelligence evolved from perceiving it as a system capable of playing chess (once considered the pinnacle of intellectual capability) to the concept of "prediction machines" – systems that analyze data and predict future trends or outcomes.
This evolution reflects a deeper change in our understanding of the nature of intelligence and may lead to more realistic expectations for AI, which in turn may help mitigate cyclical fluctuations of enthusiasm and disappointment.
Conclusions and future perspectives
The history of AI winters provides valuable lessons for the current development of this field. Here are the key conclusions I've drawn from analyzing cyclicality in artificial intelligence development:
Realistic expectations
One of the main factors contributing to AI winters was unrealistic expectations. Instead of promising technology that "thinks like a human," we should clearly communicate the actual capabilities and limitations of AI systems. As examples from the past show, exaggerated promises lead to disappointment, even if the technology itself makes significant progress.
Focus on value
AI systems should solve real problems and deliver measurable value. Previous AI winters often occurred when technology was developed for its own sake, without a clear connection to practical applications. Today's GenAI systems seem better integrated with real business and social needs.
Balanced investments
History shows that rapid growth in AI investments often precedes a winter. A more balanced and long-term approach to funding can help avoid drastic boom and bust cycles. Today's AI investment landscape, though still dynamic, appears more diversified than in the past.
Interdisciplinary approach
Earlier AI winters often resulted from a narrow, technical approach to developing this technology. Including perspectives from various disciplines, such as cognitive science, philosophy, ethics, or social sciences, can lead to more balanced and resilient AI development.
Ethics and transparency
Building trust in AI through an ethical approach and transparency can help avoid public skepticism that might contribute to another winter. AI systems should be explainable, fair, and responsible to gain lasting social acceptance.
Is contemporary AI resistant to winter?
Analyzing current trends, I wonder if the contemporary wave of AI possesses characteristics that might make it more resistant to winter:
- Practical applications - Contemporary AI systems, especially in generative AI, find real applications in various industries, from marketing to healthcare
- Diversified investments - AI investments come from various sources, both public and private, reducing the risk of sudden funding halt
- Deeper integration with the economy - AI is increasingly integrated with core business processes, making it less susceptible to cuts during periods of economic uncertainty
- Greater ecosystem maturity - The contemporary AI ecosystem includes not only research but also extensive infrastructure, education, and support services
Nevertheless, history teaches us caution. Even the most promising technologies can experience periods of disappointment and reduced enthusiasm. The key to sustainable AI development is learning from past lessons and building systems that not only impress in the laboratory but also deliver real value in practice.
Summary: What have AI winters taught us?
The history of AI winters shows that the development of breakthrough technologies doesn't proceed linearly, but rather through cycles of enthusiasm, disappointment, and rediscovery. Each winter, though difficult for the industry, contributed to deeper reflection on the nature of artificial intelligence and led to the development of new approaches that ultimately moved the field forward.
The first AI winter in the 1970s revealed the limitations of rule-based systems and led to the development of more flexible approaches. The second winter in the 80s and 90s contributed to shifting emphasis from general AI to more specialized applications, such as machine learning.
Today's AI, though significantly more advanced than in the past, still faces challenges that could lead to cooling enthusiasm. However, lessons from the past give us tools for more conscious and sustainable development of this technology.
Ultimately, the cyclical nature of AI development need not be viewed as a problem, but rather as a natural process that all breakthrough technologies go through. Periods of cooling enthusiasm can lead to more realistic expectations, more solid technological and ethical foundations, and eventually to more mature and useful artificial intelligence.
As a person committed to democratizing knowledge about AI through my AIForEveryone.blog project, I believe that understanding these cycles is key to building lasting and positive societal engagement in the development of artificial intelligence. Preparing for the future, we must draw conclusions from the past to avoid repeating the same mistakes and together shape the future of AI that serves everyone.
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Note!
This article was developed with the support of Claude 3.7 Sonnet, an advanced AI language model. Claude not only helped with the organization and presentation of content but also translated the entire article from Polish to English. The article is based on reliable historical sources regarding the development of artificial intelligence and cycles of "AI winters." It maintains an objective approach to the topic, presenting both historical causes of cooling enthusiasm for AI and analysis of the contemporary landscape of this technology in terms of potential challenges and future perspectives. If you notice any translation errors or inaccuracies, please share your feedback in the comments section.
