The launch of ChatGPT-5 was a big deal. It was promoted as an AI revolution. However, creating such significant hype may have been a mistake, as the atmosphere of enthusiasm has already faded weeks later. Why has ChatGPT-5 disappointed, and why is this not only its problem but a broader issue for AI technology?
ChatGPT-5 was launched on August 7. The next day, it was all over the internet. However, Google Trends today shows that the popularity of the “ChatGPT-5” term is roughly 10% of the initial interest it sparked. Meanwhile, social media and blogs are filled with reviews expressing disappointment. “Inconsistent, stubborn, hallucinatory, toneless, uncreative, disjointed, simplistic, robotic, artificial” – these are some of the words Reddit users often use to describe ChatGPT-5.
Current AI Models Are Facing Limitations
Is it really so disappointing, and why? Some critics argue that GPT-5’s rollout was as much a business maneuver –focused on cost optimization and maximizing efficiency – as it was a technological step forward. This highlights OpenAI’s challenge: balancing sustainable infrastructure, competitive edge, and user satisfaction simultaneously.
This issue reflects a broader problem in the AI industry, affecting all leading AI technologies. The current philosophy of improvement is reaching its limits. The rapid growth of language models is slowing down, as traditional growth drivers – more data and compute – are no longer yielding transformative gains. The internet has been largely consumed by previous training efforts, and building bigger models is now more expensive, data-hungry, and resource-intensive than ever.
While some limitations can be addressed by improving AI models’ ability to learn from sources beyond text (like videos and audio), a fundamental problem persists, particularly in business intelligence: AI can only learn what is already known.
No Unique Data, No Unique Value
The most valuable data and information a company may need – whether it’s a CFD broker or a technology provider – cannot be found online, so AI models cannot access it. This is why, while AI can generate “reports” or “research,” their value is limited. They merely compile widely available information faster than a junior analyst could do manually.
Experts in intelligence and research fields know that data is the most valuable asset. The more unique the data, the better. This means it cannot be found online. For example, Finance Magnates Intelligence collects data directly from the market participants through mutual connections and agreements. This type of data and information is inaccessible to popular AI agents, as it requires direct access to numerous small participants in specific markets and industries.
Not Skeptical About AI – Just Realistic
So, do we need AI at all? Of course. It’s impossible to operate today without AI’s help. However, it’s crucial to understand how AI models work and where they can be applied. AI agents excel at simplifying long, manual tasks, but they don’t add value beyond what’s widely available on the internet.
For this reason, after working in business intelligence, research, and analysis for over a decade, I am convinced that despite the growing use of AI technology, it will never replace human expertise in critical sectors. Just as the “dot-com bubble” didn’t replace physical stores – despite some experts’ warnings in the late nineties. AI won’t fully replace human insight.
There’s no turning back from AI. We must use “artificial intelligence” in today’s world, but let’s use it intelligently.
The launch of ChatGPT-5 was a big deal. It was promoted as an AI revolution. However, creating such significant hype may have been a mistake, as the atmosphere of enthusiasm has already faded weeks later. Why has ChatGPT-5 disappointed, and why is this not only its problem but a broader issue for AI technology?
ChatGPT-5 was launched on August 7. The next day, it was all over the internet. However, Google Trends today shows that the popularity of the “ChatGPT-5” term is roughly 10% of the initial interest it sparked. Meanwhile, social media and blogs are filled with reviews expressing disappointment. “Inconsistent, stubborn, hallucinatory, toneless, uncreative, disjointed, simplistic, robotic, artificial” – these are some of the words Reddit users often use to describe ChatGPT-5.
Current AI Models Are Facing Limitations
Is it really so disappointing, and why? Some critics argue that GPT-5’s rollout was as much a business maneuver –focused on cost optimization and maximizing efficiency – as it was a technological step forward. This highlights OpenAI’s challenge: balancing sustainable infrastructure, competitive edge, and user satisfaction simultaneously.
This issue reflects a broader problem in the AI industry, affecting all leading AI technologies. The current philosophy of improvement is reaching its limits. The rapid growth of language models is slowing down, as traditional growth drivers – more data and compute – are no longer yielding transformative gains. The internet has been largely consumed by previous training efforts, and building bigger models is now more expensive, data-hungry, and resource-intensive than ever.
While some limitations can be addressed by improving AI models’ ability to learn from sources beyond text (like videos and audio), a fundamental problem persists, particularly in business intelligence: AI can only learn what is already known.
No Unique Data, No Unique Value
The most valuable data and information a company may need – whether it’s a CFD broker or a technology provider – cannot be found online, so AI models cannot access it. This is why, while AI can generate “reports” or “research,” their value is limited. They merely compile widely available information faster than a junior analyst could do manually.
Experts in intelligence and research fields know that data is the most valuable asset. The more unique the data, the better. This means it cannot be found online. For example, Finance Magnates Intelligence collects data directly from the market participants through mutual connections and agreements. This type of data and information is inaccessible to popular AI agents, as it requires direct access to numerous small participants in specific markets and industries.
Not Skeptical About AI – Just Realistic
So, do we need AI at all? Of course. It’s impossible to operate today without AI’s help. However, it’s crucial to understand how AI models work and where they can be applied. AI agents excel at simplifying long, manual tasks, but they don’t add value beyond what’s widely available on the internet.
For this reason, after working in business intelligence, research, and analysis for over a decade, I am convinced that despite the growing use of AI technology, it will never replace human expertise in critical sectors. Just as the “dot-com bubble” didn’t replace physical stores – despite some experts’ warnings in the late nineties. AI won’t fully replace human insight.
There’s no turning back from AI. We must use “artificial intelligence” in today’s world, but let’s use it intelligently.
