The artificial intelligence industry is so amazing that every time a wave of wind blows, it always makes people think that the industry is still promising.

Big language models are a typical example. Before Sam Altman and his Open AI did not become famous, not only domestic, the whole AI circle also just as a new type of tool, investors began to become cautious, so much so that some of them ran to look at the new consumer, the reason is simple, compared to the complex technology, consumer goods category to better understand some.

We all know the story later. The marriage between Open AI and Microsoft forcibly renewed the fire of the originally cold AI industry, and the heat spread to the country, with unprecedented discussions inside and outside the circle.

Especially in the first half of this year, following the release of Baidu Wenxin Yiyin, all kinds of new and old companies have emerged, in addition to hearing every now and then that a certain company has released its own big model, the most delightful, there are new group chats born every day, among which there are those selling tutorials, those peddling private councils under the name of AI, and of course, there are also some serious talk about the current situation and future of big models.

The most paradoxical thing is that the four little dragons of AI, which had a certain fame in the AI circle in the past, are actually not the top stream this time.

The company has also launched other big models, unlike Open AI, which continues to strengthen the LLM, and is in a hurry to prove itself to the outside world.

But things are not as they should be, since 2022, Shang Tang has been repeatedly reduced by major shareholders, including Softbank Group and Alibaba, the former has reduced its holdings 4 times, with cash in excess of HK$326 million, and the latter has reduced its holdings 3 times. Some industry insiders believe that the reduction of major shareholders' holdings, for Shang Tang, whose revenue is currently declining and not yet profitable, is tantamount to sending an unpromising signal to the market.

This is also the point that this article wants to talk clearly: whether the big model windfall can bring new life to Shang Tang?

01 The dilemma of artificial intelligence, the big model can not change

On the other hand, Ali itself is also adjusting its business lines, so it needs to cut the side investment projects.

But these reasons still seem a bit far-fetched, because from the point of view of value investment, the reduction of holdings can only explain a problem: the probability is not a high-quality asset in the eyes of others.

Let's take Shang Tang Technology as an example, a company that has been holding the golden key since the day it started. If you put a label on it, there is no doubt that it is a scientist himself. Shang Tang's co-founder, Xiaogu Tang, is a professor at the Chinese University of Hong Kong and is considered by outsiders to be the pioneer and pathfinder of global face recognition technology.

According to the new eye incomplete statistics, the establishment of four years of Shang Tang financing has been more than 1.7 billion U.S. dollars, is then the world's largest financing, the highest valuation of artificial intelligence unicorn company. But the good times have not lasted long, since Shang Tang went public at the end of 2021, its market value has sunk all the way down, and now Shang Tang's market value hovers around HK$70 billion.

So here's the question, why did Shang Tang, once the rage, change its flavor after the IPO?

On this issue, there is a high praise answer on Zhihu: "The biggest problem of AI four small dragons is not the lack of clear business, but the initial development route is not clear, resulting in incoherent business, the previous technology failed to form effective precipitation, can not well help the newly proposed main business strategy. In other words, the AI four dragons are only now finding a clear development direction, and the sunk costs have not been transformed into nutrients, most of which have been wasted."

This comment was published two years ago, but even now, it is still not outdated.

Translated into words we can all understand, it means that AI is cold and capital is no longer obsessed with AI myth. Since the previous billions of dollars and nearly ten rounds of investment have not been able to make it hold to a larger market, now even if you have a new strategy and release a new product, in the eyes of the market, it is still necessary to put some discount.

From another perspective, this is actually the main reason why artificial intelligence has been lukewarm over the years.

The company's business perspective alone, if a technology does not find the right landing scene, then the technology is likely to be only a lonely appreciation. For a long time in the past, a number of star technology companies, including Shang Tang, over-obsessed with technical beliefs, ignoring the scene landing, performance in the business, that is, their tentacles are very long, whether it is the C-side, or B-side and G-side, as long as there is a suitable job, it will do.

After all, between the ideal and reality, in order to continue to tell the story of AI, these traditional software companies can do the work, they will probably continue to do.

02 Business soup problem is a typical AI industry problem

On the matter of AI gold content, there are three main judgment factors in the industry: R&D investment, revenue scale and growth rate, and net profit margin of main business, but people tend to pay attention to the first two and ignore the most critical last one.

Because about the first two, almost every company doing software can do not bad, the key is that many outsourcing, integrators neck point, is unable to form a scale effect and technical barriers, here is a technical misunderstanding, many people mistakenly believe that the technical barriers represent the number of patents owned by the enterprise, but the most practical measure should be, this technology can be applied to social scenarios, whether the lack of it is not.

In this regard, Open AI is a typical example. To this day, what the industry cares more about than the big models that have been launched, is actually how it actually builds the models and how it conducts model training.

This is also the most lacking competitiveness of some domestic AI companies.

In other words, the problem of Shangtang is not only a problem of Shangtang itself, but also an industry-wide problem.

This also explains why artificial intelligence is difficult to form an absolute barrier. According to IT Orange data, as of 2020, 30% of growing AI companies have not yet been invested, and many of these uninvested companies have not found a segmented value segment, and the competitive advantage of product differentiation is not obvious, and there is even serious homogeneous competition.

So the question arises, can the big model actually solve the current AI dilemma?

The answer is no, in essence, most of the big models now can not be called the real AGI. some industry insiders told the New Eye, "the measure of success of the big model is not just how many parameters, but what kind of scenario problem it can solve, and this scenario problem, with AI to solve lower cost, higher security. "

According to this logic, the current big model is still far from being able to support a main business, but in turn has exacerbated the black box of AI, originally everyone for artificial intelligence is already very puzzling, and now steeply launched a variety of new programs, its reliability and commercial value is even more questionable.

AI status quo is also roughly the same, in recent years bursts of fire and silence in the middle of the table is typical. As far as domestic players are concerned, you will find that the ones who are living well are basically stuck in vertical areas, such as KDDI in the field of AI voice, FanSoft in the field of intelligent BI, etc., but currently there is no run out of a giant similar to Microsoft or Snowflake-type.

03 Seemingly but not the wind mouth, is making the situation more and more confusing

After the big model exploded, many people thought it would be a super windfall.

But the fact is that Open AI next door has worked closely with Microsoft to try to integrate AI capabilities into the original Microsoft product system, and its Azure cloud computing business, office 365, and even its search business, Bing, have all undergone major upgrades.

However, the domestic AI environment is different.

Basically the giants, including Ali and Tencent, are more willing to develop their own big models rather than cooperate with other vendors, which is determined by the domestic Internet development path. China is a super market, both typical AI companies, or Internet companies with some R&D capabilities, they are more willing to close their doors to do it themselves, as for the ecology, most still remain in the sales perspective and verbal.

In this case, often exacerbate the degree of industry involution, so much so that there is a strange phenomenon, the big model more and more fire, while the positioning of artificial intelligence companies in turn more and more ambiguous. This is also another issue that needs to be thought about, according to Keynesian economic logic, the key to the domestic AI cold is that the supply far exceeds the real demand, and want to cultivate this market, still need time to settle and multiple efforts.

According to this logic, we really should let AI cool down, back to the rational track.

The artificial intelligence industry is so amazing that every time a wave of wind blows, it always makes people think that the industry is still promising.

Big language models are a typical example. Before Sam Altman and his Open AI did not become famous, not only domestic, the whole AI circle also just as a new type of tool, investors began to become cautious, so much so that some of them ran to look at the new consumer, the reason is simple, compared to the complex technology, consumer goods category to better understand some.

We all know the story later. The marriage between Open AI and Microsoft forcibly renewed the fire of the originally cold AI industry, and the heat spread to the country, with unprecedented discussions inside and outside the circle.

Especially in the first half of this year, following the release of Baidu Wenxin Yiyin, all kinds of new and old companies have emerged, in addition to hearing every now and then that a certain company has released its own big model, the most delightful, there are new group chats born every day, among which there are those selling tutorials, those peddling private councils under the name of AI, and of course, there are also some serious talk about the current situation and future of big models.

The most paradoxical thing is that the four little dragons of AI, which had a certain fame in the AI circle in the past, are actually not the top stream this time.

The company has also launched other big models, unlike Open AI, which continues to strengthen the LLM, and is in a hurry to prove itself to the outside world.

But things are not as they should be, since 2022, Shang Tang has been repeatedly reduced by major shareholders, including Softbank Group and Alibaba, the former has reduced its holdings 4 times, with cash in excess of HK$326 million, and the latter has reduced its holdings 3 times. Some industry insiders believe that the reduction of major shareholders' holdings, for Shang Tang, whose revenue is currently declining and not yet profitable, is tantamount to sending an unpromising signal to the market.

This is also the point that this article wants to talk clearly: whether the big model windfall can bring new life to Shang Tang?

01 The dilemma of artificial intelligence, the big model can not change

On the other hand, Ali itself is also adjusting its business lines, so it needs to cut the side investment projects.

But these reasons still seem a bit far-fetched, because from the point of view of value investment, the reduction of holdings can only explain a problem: the probability is not a high-quality asset in the eyes of others.

Let's take Shang Tang Technology as an example, a company that has been holding the golden key since the day it started. If you put a label on it, there is no doubt that it is a scientist himself. Shang Tang's co-founder, Xiaogu Tang, is a professor at the Chinese University of Hong Kong and is considered by outsiders to be the pioneer and pathfinder of global face recognition technology.

According to the new eye incomplete statistics, the establishment of four years of Shang Tang financing has been more than 1.7 billion U.S. dollars, is then the world's largest financing, the highest valuation of artificial intelligence unicorn company. But the good times have not lasted long, since Shang Tang went public at the end of 2021, its market value has sunk all the way down, and now Shang Tang's market value hovers around HK$70 billion.

So here's the question, why did Shang Tang, once the rage, change its flavor after the IPO?

On this issue, there is a high praise answer on Zhihu: "The biggest problem of AI four small dragons is not the lack of clear business, but the initial development route is not clear, resulting in incoherent business, the previous technology failed to form effective precipitation, can not well help the newly proposed main business strategy. In other words, the AI four dragons are only now finding a clear development direction, and the sunk costs have not been transformed into nutrients, most of which have been wasted."

This comment was published two years ago, but even now, it is still not outdated.

Translated into words we can all understand, it means that AI is cold and capital is no longer obsessed with AI myth. Since the previous billions of dollars and nearly ten rounds of investment have not been able to make it hold to a larger market, now even if you have a new strategy and release a new product, in the eyes of the market, it is still necessary to put some discount.

From another perspective, this is actually the main reason why artificial intelligence has been lukewarm over the years.

The company's business perspective alone, if a technology does not find the right landing scene, then the technology is likely to be only a lonely appreciation. For a long time in the past, a number of star technology companies, including Shang Tang, over-obsessed with technical beliefs, ignoring the scene landing, performance in the business, that is, their tentacles are very long, whether it is the C-side, or B-side and G-side, as long as there is a suitable job, it will do.

After all, between the ideal and reality, in order to continue to tell the story of AI, these traditional software companies can do the work, they will probably continue to do.

02 Business soup problem is a typical AI industry problem

On the matter of AI gold content, there are three main judgment factors in the industry: R&D investment, revenue scale and growth rate, and net profit margin of main business, but people tend to pay attention to the first two and ignore the most critical last one.

Because about the first two, almost every company doing software can do not bad, the key is that many outsourcing, integrators neck point, is unable to form a scale effect and technical barriers, here is a technical misunderstanding, many people mistakenly believe that the technical barriers represent the number of patents owned by the enterprise, but the most practical measure should be, this technology can be applied to social scenarios, whether the lack of it is not.

In this regard, Open AI is a typical example. To this day, what the industry cares more about than the big models that have been launched, is actually how it actually builds the models and how it conducts model training.

This is also the most lacking competitiveness of some domestic AI companies.

In other words, the problem of Shangtang is not only a problem of Shangtang itself, but also an industry-wide problem.

This also explains why artificial intelligence is difficult to form an absolute barrier. According to IT Orange data, as of 2020, 30% of growing AI companies have not yet been invested, and many of these uninvested companies have not found a segmented value segment, and the competitive advantage of product differentiation is not obvious, and there is even serious homogeneous competition.

So the question arises, can the big model actually solve the current AI dilemma?

The answer is no, in essence, most of the big models now can not be called the real AGI. some industry insiders told the New Eye, "the measure of success of the big model is not just how many parameters, but what kind of scenario problem it can solve, and this scenario problem, with AI to solve lower cost, higher security. "

According to this logic, the current big model is still far from being able to support a main business, but in turn has exacerbated the black box of AI, originally everyone for artificial intelligence is already very puzzling, and now steeply launched a variety of new programs, its reliability and commercial value is even more questionable.

AI status quo is also roughly the same, in recent years bursts of fire and silence in the middle of the table is typical. As far as domestic players are concerned, you will find that the ones who are living well are basically stuck in vertical areas, such as KDDI in the field of AI voice, FanSoft in the field of intelligent BI, etc., but currently there is no run out of a giant similar to Microsoft or Snowflake-type.

03 Seemingly but not the wind mouth, is making the situation more and more confusing

After the big model exploded, many people thought it would be a super windfall.

But the fact is that Open AI next door has worked closely with Microsoft to try to integrate AI capabilities into the original Microsoft product system, and its Azure cloud computing business, office 365, and even its search business, Bing, have all undergone major upgrades.

However, the domestic AI environment is different.

Basically the giants, including Ali and Tencent, are more willing to develop their own big models rather than cooperate with other vendors, which is determined by the domestic Internet development path. China is a super market, both typical AI companies, or Internet companies with some R&D capabilities, they are more willing to close their doors to do it themselves, as for the ecology, most still remain in the sales perspective and verbal.

In this case, often exacerbate the degree of industry involution, so much so that there is a strange phenomenon, the big model more and more fire, while the positioning of artificial intelligence companies in turn more and more ambiguous. This is also another issue that needs to be thought about, according to Keynesian economic logic, the key to the domestic AI cold is that the supply far exceeds the real demand, and want to cultivate this market, still need time to settle and multiple efforts.

According to this logic, we really should let AI cool down, back to the rational track.