The era of infinite competition for cloud-based AI models is fading due to astronomical costs and user disappointment. We are entering an era of infrastructure and practical, life-integrated AI services.
Imagine this: You wake up and tell your smartphone AI, “Summarize the important work emails that came in overnight and draft replies for the ones I need to answer immediately.” Moments later, perfectly phrased emails appear on your screen. You feel like you have your own highly capable personal assistant. Up to this point, this is the future we’ve seen countless times in the news and have even begun to experience ourselves.
However, behind this magical scenario hides a massive bill we often don’t see. To process your single question, massive data centers—the size of several football fields located in distant deserts or coastal areas—run countless computer chips that emit staggering amounts of heat. To cool them, precious water from drought-stricken regions is drawn in volumes enough to fill swimming pools [The end of the cloud-based LLM gold rush | Hacker News](https://news.ycombinator.com/item?id=48527817). Every time you ask for a question or a summary, it’s as if an invisible meter in a luxury taxi is running at a terrifying speed.
For the past two years, tech companies worldwide, led by Silicon Valley, have been caught in a ‘Gold Rush’—a frantic race to see who can build the smartest and largest AI. But recently, the atmosphere of this feverish festival has become noticeably subdued. Analysis is prevailing that the craze for cloud-based (large central servers connected to the internet) Large Language Models has reached its tail end. What exactly is happening in the AI industry?
Why It Matters
The primary reasons are the vast gap between people’s ‘expectations’ and reality, and the unsustainable ‘costs.’ According to one case study analyzing traffic for a specific AI model, user access skyrocketed 25 times in just three months in early 2025, reaching over 170,000 sessions per month by April. This is the equivalent of a small neighborhood store suddenly being swarmed by thousands of customers a day. However, after this moment of explosive curiosity passed, foot traffic naturally dwindled and stabilized at a flat level [25x Growth in LLM Traffic in 3 Months | daydream](https://www.withdaydream.com/library/case-studies/openart-llm).
Why did people leave? To general users without deep technical backgrounds, AI was marketed as a ‘magical genie that solves everything instantly.’ People firmly believed this miraculous tool would do their work for them and boost productivity immensely. Ultimately, however, the promised magic was not fully realized. As the bubble began to burst, people faced the painful realization that they were already paying expensive monthly cloud server fees and AI ‘token’ costs (the billing unit for how AI recognizes and generates text) [China’s OpenClaw Boom Is a Gold Rush for AI Companies | WIRED](https://www.wired.com/story/china-is-going-all-in-on-openclaw/).
Consequently, the blind competition of the past two years—focused on mindlessly increasing model size (number of parameters, similar to the number of brain cells) and pouring in vast amounts of data—has ended. Now, the industry’s eyes are turning away from the flashy magic show and toward the sturdy infrastructure and tools that actually make AI run: the ‘picks and shovels’ [What LLMs and the Gold Rush Have in Common](https://www.linkedin.com/pulse/what-llms-gold-rush-have-common-salesforce-cjhce).
The Explainer
To understand this situation accurately, we need to look at two important metaphors.
Metaphor 1: The Vending Machine vs. the Star Chef (The Economics of AI)
First, we need to understand Large Language Models (LLMs), the AI that communicates like a human by learning from vast amounts of text. This technology is built on ‘Transformer’ (an AI structure that identifies relationships between words in a sentence) developed by Google in 2017, and is created by training on billions of text and content data points [Large Language Models (LLMs) with Google AI | Google Cloud](https://cloud.google.com/ai/llms).
Simply put, the way an LLM is maintained is completely different from a typical computer program [The Unattainable Economics of LLMs: Why the AI Race May Collapse...](https://www.linkedin.com/pulse/unattainable-economics-llms-why-ai-race-may-collapse-pierre-jean-wtpkf). The photo editing apps or word processors we use every day on our smartphones are like ‘vending machines.’ For a company, it costs a lot of money to design a great vending machine and build it in a factory, but once it’s installed on the street, it costs almost nothing extra whether 100 people use it or 10,000. Only the monthly electricity bill remains.
In contrast, current cloud-based LLMs are like hiring a dedicated ‘star chef’ from a high-end restaurant to cater to every single user’s taste. Whether you ask a light question like “What’s the weather like today?” or a complex one like “Explain the theory of relativity to an elementary student,” the AI chef fully activates its massive brain to cook up a new dish (sentence) from scratch every time. In other words, it’s not a one-and-done creation; it’s a structure where enormous power and computing costs are burned in real-time at the data center every time a user utilizes the service. The more it’s used, the more the company faces an unbearable cost bomb—a so-called ‘unsustainable cost structure’ [The Unattainable Economics of LLMs: Why the AI Race May Collapse...](https://www.linkedin.com/pulse/unattainable-economics-llms-why-ai-race-may-collapse-pierre-jean-wtpkf).
Metaphor 2: The 1849 Gold Rush and Blue Jeans
By way of analogy, today’s AI market is very similar to California in 1849. When gold was discovered there, hundreds of thousands of people rushed to the mines dreaming of striking it rich. This period is known as the ‘Gold Rush.’ But who actually made the most stable and significant amount of money during this craze? It wasn’t the miners trying to dig gold out of the muddy water every day; it was the merchants who sold those miners durable ‘blue jeans’ that could withstand hard labor and the ‘picks and shovels’ to dig the hard ground.
The AI market is following this exact formula. Everyone rushed to be the first to mine the massive internal AI models (gold), but as seen in the case of the podcast platform Spotify, the side seeing the true benefit is different. Spotify holds the vast audio data (the vein of gold) that people listen to every day, and AI developers have formed a symbiotic relationship where they come with massive capital and sophisticated algorithms (picks and shovels) to propose deals to analyze this data [Audio Is the New Dataset: Inside the LLM Gold Rush for Podcasts...](https://www.francescatabor.com/articles/2025/7/22/audio-is-the-new-dataset-inside-the-llm-gold-rush-for-podcasts).
Where We Stand
Despite these limitations of cost and efficiency, AI development hasn’t stopped completely. So, how is the current AI industry landscape laid out?
The AI we use most familiarly today is still cloud-based LLMs. Giants like OpenAI’s GPT series, Anthropic’s Claude, and Google’s Gemini belong here. They live inside incredibly powerful servers owned by big tech companies [Local LLMs vs. Cloud AI: Which Should You Choose?](https://arsturn.com/blog/local-llms-vs-cloud-ai-the-ultimate-showdown).
From an enterprise customer’s perspective, cloud AI is still a quite attractive option. This is because they can immediately build an AI system that tens of thousands of employees can use as long as they have an internet connection, without having to directly buy supercomputers costing billions of won. In other words, it has the advantage of being able to easily scale up and down as needed without initial facility investment costs [How 3 Breakthrough LLM Technologies Are... - Peter's Pick](https://peterspick.co.kr/en/how-3-breakthrough-llm-technologies-are-revolutionizing-enterprise-ai-infrastructure-in-2025/).
At the same time, these massive models are becoming smarter. Moving beyond chatbot levels that simply exchanged text, they have evolved into independent assistant agents equipped with multi-modal functions—seeing photos with their eyes and hearing voices with their ears. Furthermore, while in the past they were indirectly taught “this is a good answer” (RLHF method), they are now evolving with the latest technology called Direct Preference Optimization (DPO)—a technique where AI directly and safely learns the answers people prefer, amidst the strong regulatory flow of the European Union (EU) [What is Large Language Models (LLM) - Top Use Cases, Datasets, Future](https://www.shaip.com/blog/a-guide-large-language-model-llm/).
However, a massive company has emerged to crack the winner-takes-all structure of the cloud. It’s NVIDIA, often called the greatest beneficiary of the AI craze. NVIDIA, which exclusively supplied the chipsets that act as the brains of AI, recently declared that it would not stop at selling the ‘picks’ of hardware. They are shaking up the market by announcing ‘Foundation Model as a Service,’ which helps companies safely build their own customized AI models using internal secret data that cannot be leaked externally ["$NVDA will not stop at selling picks & shovels for the LLM gold......](https://twitter.com/DrJimFan/status/1661783178854674438).
What’s Next
What will remain once this bubble has cleared? Experts agree that the era of competing on massive model size has finally ended, and ‘The AI Product Era’—where AI is incorporated as a useful tool in daily life—has arrived. Just as the steam engine or the internet did in human history, now that the massive bricks of AI models have been fired, it’s time to stack those bricks and build useful buildings that actually change our lives [The End of the LLMs Gold Rush, The Start of the AI Product... | Medium](https://medium.com/@bytestobusiness/the-end-of-the-llm-gold-rush-the-start-of-the-ai-product-era-baf5441f3547).
The three most prominent features of this new era are as follows:
1. AI Entering My Phone and Computer (The Rise of Local LLMs) The anxiety of having to access a massive cloud server via the internet every time, and not knowing if my private questions or a company’s confidential data will be stored on a central server. Plus, the murderous cloud usage fees companies face every month. To solve all these problems, an alternative called ‘Local LLM’ is growing rapidly. Local LLM refers to AI that runs directly inside the laptop or smartphone you use every day, without an internet connection, rather than on the central servers of Google or OpenAI.
Recently, various solutions have been pouring into the market, from apps that run on-device (processing on the device itself) in mobile environments (iOS, Android) while perfectly protecting privacy, to local AI tools that developers can handle directly on their own computers [Ollama vs vLLM vs LM Studio: Best Way to Run LLMs Locally in 2026?](https://www.glukhov.org/llm-hosting/comparisons/hosting-llms-ollama-localai-jan-lmstudio-vllm-comparison/). In particular, there is a huge demand for ‘Uncensored’ local models that provide free answers without being tied to the rigid ethical answer guidelines set by large corporations. Models that possess strong reasoning capabilities while running smoothly even on general graphics card environments are appearing one after another, establishing themselves as the strongest competitors to existing cloud AI [Best Uncensored Local LLMs (And Why You Might Want...) | InsiderLLM](https://insiderllm.com/guides/best-uncensored-local-llms/).
2. Drastic Dieting (The Era of Inference Optimization)
Companies providing AI services have also entered a technical diet to survive. They are fiercely researching how to drastically reduce the time it takes to provide an answer to a user’s question (latency) and make them use less electricity and computing resources. This is called ‘Inference Optimization.’ Technologies that lighten AI models by removing fluff and efficiently organizing memory structures to dramatically lower server operating costs have now become a core challenge determining a company’s survival as much as simply increasing the AI’s intelligence [What is inference optimization? | Google Cloud](https://cloud.google.com/discover/inference-optimization).
3. Stricter Evaluation Standards
In the past, a single marketing phrase like “Our AI is the smartest in the world!” would attract investors’ money. But not anymore. To deploy these nascent technologies—which still make frequent mistakes and provide biased answers (the hallucination phenomenon)—into actual corporate customer service or medical environments, rigorous evaluation is essential. Beyond just the speed of asking and answering, evaluation systems that continuously monitor and strictly grade the reliability of answers, ethical issues, and the efficiency of server operations are becoming essential infrastructure [Best Practices and Methods for LLM Evaluation | Databricks Blog](https://www.databricks.com/blog/best-practices-and-methods-llm-evaluation).
In conclusion, the cloud gold rush of infinite competition, where tens of trillions of won were poured in to mindlessly create even more massive intelligence, is nearing its end. Instead, that space is being filled by a true technological maturity phase where competition is based on who is more ‘efficient,’ who keeps my personal information more ‘secure,’ and who creates more ‘practical’ tools. The real vein of gold revealed once the bubble has faded is hidden not in the flashy magic shows that once captivated people, but in the practical software that sits quietly on our desks, helping us with our daily work.
AI’s Take
The curtain is falling on the flashy artificial intelligence magic show that once enthralled the public. The magician (cloud-based giant AI) who pulled pigeons out of a hat on stage was certainly wondrous, but that alone could not change the world. Now, the quiet and intense ‘era of engineering’ has begun—an era of thoroughly analyzing and dismantling the principles of that amazing magic and refining it into everyday household appliances like the refrigerators or washing machines we use every day. This is because a true revolution is only completed when technology no longer seems wondrous and naturally melts into our daily lives like the air we breathe.
References
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[The end of the cloud-based LLM gold rush Hacker News](https://news.ycombinator.com/item?id=48527817) -
[The End of the LLMs Gold Rush, The Start of the AI Product… Medium](https://medium.com/@bytestobusiness/the-end-of-the-llm-gold-rush-the-start-of-the-ai-product-era-baf5441f3547) - The Unattainable Economics of LLMs: Why the AI Race May Collapse…
- “$NVDA will not stop at selling picks & shovels for the LLM gold……
- How 3 Breakthrough LLM Technologies Are… - Peter’s Pick
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[Best Uncensored Local LLMs (And Why You Might Want…) InsiderLLM](https://insiderllm.com/guides/best-uncensored-local-llms/) - Audio Is the New Dataset: Inside the LLM Gold Rush for Podcasts…
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[China’s OpenClaw Boom Is a Gold Rush for AI Companies WIRED](https://www.wired.com/story/china-is-going-all-in-on-openclaw/) -
[Best Practices and Methods for LLM Evaluation Databricks Blog](https://www.databricks.com/blog/best-practices-and-methods-llm-evaluation) -
[What is inference optimization? Google Cloud](https://cloud.google.com/discover/inference-optimization) -
[Large Language Models (LLMs) with Google AI Google Cloud](https://cloud.google.com/ai/llms) - What is Large Language Models (LLM) - Top Use Cases, Datasets, Future
- What LLMs and the Gold Rush Have in Common
- Ollama vs vLLM vs LM Studio: Best Way to Run LLMs Locally in 2026?
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[25x Growth in LLM Traffic in 3 Months daydream](https://www.withdaydream.com/library/case-studies/openart-llm) - Local LLMs vs. Cloud AI: Which Should You Choose?
- It is escalating into an infinite race to build even larger models.
- The focus is shifting from giant model development to underlying infrastructure and practical product development.
- All companies are going all-in on cloud server investments.
- Even after development is complete, massive computing and maintenance costs occur every time a user asks a question.
- There are no initial development costs at all.
- Server maintenance costs decrease exponentially as the number of users increases.
- Paper encyclopedias
- On-premise quantum computers
- Local LLMs