Eighty-one years and a moment: Reconstructing the commercial frontier in the "deflationary dividend" and "ladder effect" of AI

       Eighty years is a vast time span that can encompass the journey of a baby learning to speak to an elderly person in their eighties. However, in the world of artificial intelligence, within just over a year, the magnitude of the changes we have witnessed has been compared by economists and tech giants to the inventions of microprocessors, steam engines, and electricity. This is not just about algorithm optimization or the stacking of computing power; it is a fundamental shift in variables - it is reshaping social structures, economic models, and even the boundaries of human cognition with unprecedented penetration.

       Standing at the juncture of the Year of the Horse in the Chinese zodiac calendar in 2026, the air is filled with a mixture of anxiety and excitement. Grand narratives are everywhere, but when it comes down to every specific entrepreneur, brand operator, and channel agent, the issues become extremely sharp and realistic: In this epic AI wave, what is the real, accessible "golden opportunity" that ordinary people can grasp?

       Recently, in a deep-tech podcast, two astonishing concepts - "Ladder Effect" and "Ultra-Deflationary Logic" - have torn away the fog for us, perfectly confirming the underlying layout logic of the AI hardware supply chain platform we are building.

I. Ladder Effect: The Division of Labor Revolution from "God-creating" to "Empowering"

       For a long time, the greatest fear haunting small companies and entrepreneurs has been: When giants like OpenAI and Google create "God models" like GPT-5, do we still have a chance? Are all opportunities monopolized? The "Ladder Effect" gives a negative answer, and even more so, it suggests that opportunities have become greater.

       The AI industry is forming a strict hierarchical structure like a pyramid or a ladder. The top is the "God models" deployed in huge supercomputing centers, which possess astonishing general intelligence and are responsible for handling the most complex thinking tasks of humans. But as we go down the ladder, the situation is completely different. What we see are "small models" like Kimi and Llama that can run on ordinary laptops or even mobile devices. Their general intelligence may not be as good as GPT-5, but in specific vertical tasks - such as precise legal document summaries, efficient code completion, or smooth oral practice - their performance is already excellent and even more efficient.

       This reminds me of the astonishing sentence in the podcast: "Not every task requires Einstein. Sometimes, a competent person with an IQ of 120 is enough."

       This sentence has completely liberated the imagination of hardware entrepreneurship. It means that AI applications can become extremely "lightweight" and "vertical". We don't need to install a brain capable of winning the Fields Medal in a smart toy; it just needs to know how to make children happy. We don't need to equip a lobster sorting machine with philosophical thinking ability; it just needs to be able to distinguish between lobsters and crabs.

       This is precisely the reason we established the "AI Product Business Opportunity Website" as a vertical supply chain platform. We do not create "Gods", but we focus on encapsulating the appropriate AI capabilities like solidified chips into specific hardware products to solve the specific pain points of a vertical scenario. Connecting the best AI hardware solutions with the most knowledgeable market partners, allowing technology to be technology and the market to be the market.

II. Ultra-Deflationary Logic: The "Sinking" of Costs Triggering a "Big Explosion" of Scenarios

       If the "Ladder Effect" solved the problem of capacity distribution, then "Ultra-Deflationary Logic" solved the problem of the driving force of business.

       The second trend mentioned in the podcast is astonishing: The business model of AI is the "token model" of pay-per-use, and the rate at which its costs (including computing power, model training, and inference) are decreasing far exceeds the prediction of Moore's Law. To put it simply and straightforwardly, the price of AI services will "drop like a rock" in the future, just like mobile data charges did in the past, until it approaches zero.

       What does this mean for hardware products? It means that the cost of the "brain" of the hardware will plummet sharply, or in other words, hardware with the same price will have an exponentially increasing intelligence.

       Recalling a few years ago, when we designed an AI toy, we had to be very meticulous about whether the chip's computing power was sufficient and whether the model invocation cost would eat up all the profits. But in the not-too-distant future, the price of the same-performance chips may be halved, and the cost of invoking top-tier AI APIs may be negligible. This "super deflation" of costs will inevitably lead to a "big inflation" in application scenarios. AI will, like the microprocessor in the past, quietly be embedded in any device you can think of.

       From the tireless machine vision quality inspection devices in factories to the companion robots that can observe and understand in the family; from the intelligent try-on mirrors that can change outfits with one click in the mall to the autonomous crop protection drones flying over farmlands… The variety and quantity of AI hardware will witness an unprecedented explosion.

III. 2026 Horse Year Action Plan: Searching for Light in the shadows..

       Having understood the stratification of the "ladder" and the trend of "deflation"， if we look back at the current hotspots， we can see through the clouds and see the sun：
       Regarding the AI competition between China and the United States， the more intense the competition， the more prosperous the open-source ecosystem (such as the rise of models like DeepSeek)， and the more affordable and high-quality "weapon kits" we hardware developers have available.

Regarding the AI regulatory controversy， as the podcast said， excessive regulation is no different from "self-harm". Fortunately， the pragmatic global competition landscape has forced regulation to become more open， leaving valuable gray areas and growth space for innovation.

       Regarding public psychology， although many people say they are afraid of AI replacing humans， their bodies are very honest. From intelligent customer service to autonomous driving， the public has long been unable to do without AI. This shows that the market education cost is rapidly decreasing， and users' payment habits are rapidly forming.
       Based on this， for each of us in the AI product field， brand operator， and agent， the 2026 action plan should be extremely clear：
First， choose one layer of the "ladder". Give up fantasies and don't try to build "God models". You need to think： In education， health， retail， or intelligent manufacturing， which specific and vertical field can use AI hardware to solve a user's "pain point" or "itch"？ Dig deep and go through thoroughly.
       Second， embrace the trend of "cost deflation". In the product definition stage， boldly assume that the cost of AI computing power will drop by another order of magnitude in the next 1-2 years. Use a more forward-looking architecture to design products with the highest cost performance today and huge profit margins tomorrow. Don't be bound by the current costs.
       Third， utilize mature supply chains. This is exactly what the "AI Product Business Opportunity Website" is focusing on. We are screening and integrating those verified and excellent AI hardware solutions worldwide - from the underlying chips， modules to the ODM of the entire machine. We want brand owners and agents to be able to quickly combine competitive products like "putting together Lego"， without having to start from scratch and struggle to develop， thereby significantly reducing the startup threshold.
       Fourth， tell the "value" story. When technology becomes more accessible to the masses， the key to competition returns to brand， channel， and user operation. This is precisely the core capability we have accumulated in projects like Huayao Zhuzhu Liquor - how to create brand potential from 0 to 1， how to build a three-dimensional distribution network online and offline， and how to truly touch consumers through content. Technology is just a means， the heart is the business.

Conclusion： Humility is a boat， and the waves are the sea.

       The podcast finally mentioned an insight from the venture capital industry that resonated deeply with me: "What keeps you humble the most is when you turn down a company that will become extremely successful in the future."

       In the face of this epic wave of AI, which can be called the "81st year", each of us should maintain awe and humility. We are not the creators, we are just the carriers of the times. But at the same time, we should not be intimidated by grand narratives. Great changes will ultimately be carried by countless specific products that solve specific problems.
       In the Year of Horse of the 2026 Horse Year, may we all ride this fast horse and find our own territory in the map of the world reconstructed by AI.