There has been much discussion about the risk of an AI bubble and the challenges of running a startup in this space. However, I believe that even if the market undergoes a correction, AI itself will not be at risk. We are not on the brink of an “AI bubble,” but rather an LLM bubble. It is this specific bubble, not AI as a whole, that may burst in the near future. I don’t foresee any significant issues with AI as a field because the AI industry is already quite large and diversified. This means that even if a segment of the industry, such as LLMs, is overvalued, it is unlikely to have a massive impact on the overall AI field or its businesses.
Language models powering Gemini, ChatGPT, and many other products receive disproportionately high attention today. In the news, enormous budgets are being poured into these massive systems, and competition is increasing month by month. It cannot remain this way forever. Suppose we are now trying to solve everything with one universal model — in any business, in any industry, with any needs. But is that even possible?
LLMs are not the answer to everything. They are not the ideal tool for all tasks. Even now, living in an LLM bubble, we already know that the future belongs to smaller, more specialized models, because they are cheaper to build, more predictable in operation, and can be easily personalized for particular business cases.
For example, does your bank need a universal model capable of holding philosophical conversations? Of course not. Banks need tools for client care management, fraud detection, safe transaction handling, risk assessment, client verification, and internal processes. Following this logic, in the coming years, we will see what is already being called model differentiation — the emergence of thousands of personalized, narrowly focused systems.
The real threat is not the LLMs themselves but the economics of their scaling. Today, R&D is developing faster than businesses can actually deploy such models into production. This means that investments are growing faster than markets can absorb them, infrastructure is becoming too expensive, and more and more companies are building almost identical models, which leads to oversupply. As a result, the market overheats and needs a correction.
And this correction will be 100% inevitable. It won’t be the “end of AI,” but it will be the end of an overvalued chapter marked by excessive investment in large language models without assessing their real impact on business. The simple picture is this: AI as an industry is too large to be affected, because AI is not only about LLMs. It is a vast ecosystem that includes computer vision, recommendation systems, AI agents, process optimization, robotics models, and specialized domain systems. And this is why, even if the LLM segment experiences a strong correction, the AI industry as a whole will remain stable and continue to grow. What we should fear is not AI, but universal solutions positioned as “one-size-fits-all answers.”
Universality is a myth. Real solutions are personalized. Don’t believe in one corporate solution that fits everyone. Even with AI, this is impossible. When we chose AI as our expertise focus 10+ years ago, we understood that it is impossible to build a model that fits every business equally well, and this understanding helped us build a different philosophy:
Products are built not only by engineers, but also by industry experts who deeply analyze the business structure, study needs, risks, and processes, and provide recommendations on which models make sense for a specific company, and which do not.
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Because the future of AI is not in universal giants, but in personalization, domain expertise, and precise solutions that truly work in real business.
How Muteki Group Approaches AI Strategy
At Muteki Group, we intentionally moved away from the idea of “universal models” and built a strategy based on three core principles:
Diagnosis Before Technology
We start with a full business audit — not with choosing a model. Our experts analyze processes, costs, constraints, risks, and opportunities, and then build a clear AI roadmap with measurable ROI for every step. Result: no generic tools, only economically justified AI solutions.
Specialized Models, Not One-Size-Fits-All Systems
We focus on building highly targeted AI systems tailored to specific business functions:
fraud detection,
anomaly recognition,
document automation,
risk scoring,
logistics optimization,
sales intelligence,
computer vision,
HR automation, and more.
These models are trained on industry-specific and client-specific data — making them more accurate, more stable, and far more cost-efficient.
Full-Cycle Transformation, End-to-End
We cover the complete AI lifecycle: strategy, PoC, MVP, infrastructure, deployment, integration, team onboarding, and long-term model evolution. This ensures that solutions don’t remain “concepts” — they work in real production environments.
Security and Compliance at the Core
Our solutions follow strict data privacy rules, local regulations, transparent model behavior, and continuous monitoring — critical for finance, aviation, healthcare, retail, and other high-responsibility sectors.
Built for the Long Term
Our systems are designed to evolve: models can be retrained, scaled, and transferred to new infrastructures without vendor lock-in. This guarantees long-term value, not short-term hype.
If you’re looking for AI solutions that actually transform your business — not abstract “universal tools” — our team will be glad to help. Contact Muteki Group to discuss your goals and build a tailored AI strategy that delivers real results.
CEO and Founder at Muteki Group
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