内容导读：Forthepastyearandahalf,Mw2ps4crashingArtificialintelligenceinvestmentboom,SiliconValleystartupsobsessedwit...……
For the past year and a half,Mw2ps4crashingArtificial intelligence investment boom, Silicon Valley startups obsessed with the development of the ultimate AI model, as if the money was invested in the incinerator, instantly vanished.
However, when the enthusiasm of investors faded, people were dismayed to find that only the giants that monopolized AI infrastructure (such as Nvidia, Google, Microsoft) made money, while the downstream startups were caught in the predicament of "burning money" but not making money. Even the head AI startups known as the "four Little Dragons"-Anthropic, Stability AI, Inflection AI, OpenAI-- are struggling.
According to the latest media reports, Inflection AI, which is valued at $1.5 billion with almost no revenue, has closed its original business, Stability AI has laid off staff and parted ways with its CEO, Stability AI is eager to address a gap of up to $1.8 billion a year, and OpenAI faces challenges in terms of sales growth.
"the cruelty of reality is written on the wall," said Ali Ghodsi, CEO of Databricks, a data warehouse and analytics firm that works with artificial intelligence start-ups. "No matter how cool something you make, the question is whether it is commercially feasible?"
Unicorns, whose valuations soar, are still burning money!
Over the past three years, investors have injected $330 billion into about 26000 AI and machine learning startups, 2/3 more than they invested in 20350 AI companies from 2018 to 2020, according to PitchBook.
The cost of developing AI is huge, and as investors become lukewarm about AI, all startups are urgently looking for ways to cover current huge expenses and make a profit in order to win back investor confidence.
However, so far, they have not come out of the frenzied stage of spending money, and they are still a long way from making a profit.
The media quoted sources as saying that Anthropic, with the support of Amazon and Google, raised more than $7 billion and burned $2 billion a year, while the actual income was only 1.Mw2ps4crashing. 500-200 million US dollars, with an annual income and expenditure gap of 1.8 billion US dollars.
Anthropic, which is promoting partnerships with large technology companies to sell customized AI systems and chatbots to businesses and government agencies, recently announced that it was working with global consulting firm Accenture to create customized chatbots and AI systems for businesses and government organizations.
Sally Aldous, a spokesman for Anthropic, said thousands of companies are using the company's technology and millions of consumers are using its chat robot Claude.
The main image-generated Stability AI is expected to have sales of $60 million this year, while its image-generation system costs about $96 million, which will go on sale in 2022, the report said.
Due to the low cost of the image generation system, the financial position of Stability AI is better than that of other large model companies, but the payment demand is still uncertain. The founder of Stability AI was forced to resign under operational pressure, and then the company embarked on the road of layoffs and restructuring.
Notably, Stability AI has been operating without the support of tech giants, raising $101 million from venture capitalists in 2022. Stability AI needed more money last fall, but it was hard to prove to investors that it could sell technology to companies, the report said. It raised $50 million from Intel late last year, but it is still under considerable financial pressure.
Inflection AI, one of Silicon Valley's most highly valued AI startups with a valuation of more than $1.5 billion, has also been poached by Microsoft for a lack of business returns, forcing the founder team to return to the big factory system to survive.
One investor told the media that Inflection AI generated almost no income a year after launching AI's personal assistant. It also means that unless it continues to raise huge amounts of money, it cannot continue to upgrade its technology and compete with chatbots from companies such as Google and OpenAI.
Mustafa Suleyman, CEO of Inflection AI, rose to fame as one of the founders of DeepMind, and Suleyman co-founded Inflection AI with Karl é n Simonyan, a former DeepMind researcher, and Reid Hoffman, a well-known Silicon Valley venture capital.
Microsoft, once a financier of Inflection AI, announced in March that Suleyman and Simonyan would leave the company to go to Microsoft to form a new team called Microsoft AI. Most of Inflection AI's other employees have also been acquired by Microsoft.
It will cost Microsoft more than $650 million, but unlike Inflection AI, it can afford to play a protracted "money-burning" game, the report said.
OpenAI has made a lot of money with ChatGPT, but like many other AI startups, it is struggling to find a way to make money.
Two people familiar with the company's business told the media that OpenAI had earned about $1.6 billion in the past year, but it was not clear how much the company had spent.
OpenAI is said to face challenges in expanding sales, and corporate customers worry that GPT may output inaccurate answers. In addition, OpenAI has been questioned as to whether the data on which the model relies infringes copyright.
Compared with less than $2 billion in annual revenue, OpenAI is valued at a staggering $100 billion. In addition, the "2024 Global Unicorn list" released by Hurun Research Institute shows that OpenAI is valued at 710 billion yuan, making it the enterprise with the fastest growth in value, with an increase of nearly 570 billion yuan.
Microsoft has invested $13 billion in OpenAI and owns 49 per cent of the company. In February, OpenAI allowed some employees to sell their shares at a valuation of $86 billion through a takeover offer.
How do tech giants make money from AI? Integrate AI into every product
Unlike startups that focus on developing cutting-edge AI "breakthrough" technology, tech giants use AI as a "paving stone" to build huge infrastructure to deeply embed AI into every aspect of their products and services, creating a powerful new engine for profit growth.
Microsoft, Google, Nvidia and other giants monopolize AI productivity factors, including computing power, chips, data and so on, and build AI technical barriers to lay a solid foundation for their continued profitability.
As one of the first technology giants to enter the generative AI market, Microsoft is the best example of AI realization. Brad Reback, an analyst at Stifel, an investment bank, said Microsoft's cloud computing AI services contributed about $1 billion in sales in the first quarter, up from almost zero a year ago.
On the other hand, the giants are still adding to their AI investments. During the first quarter earnings call, Meta, Google and Microsoft all highlighted their investment expectations for AI this year:
Meta raised its capital expenditure forecast for this year by $10 billion, to $35 billion to $40 billion for the whole year.
Google says it will spend about $12 billion or more on capital expenditure each quarter this year.
Microsoft said capital expenditure was $14 billion in the most recent quarter and expects to continue to increase "significantly".
Judging from the first-quarter results and forecasts published by large technology companies, a company will have to spend $10 billion a quarter to have a place in AI.
In the new era of big models, small companies stand aside?
Morgan Stanley has previously pointed out that the world is entering a new era of rapid growth of large models driven by hardware and software, which will significantly improve their creativity, strategic thinking and ability to handle complex multi-dimensional tasks.
However, the high cost of supercomputers needed to train the next generation of big models is a huge challenge even for tech giants, let alone small ones.
In addition to high capital expenditure, barriers to chip power supply and artificial intelligence technology are also increasing. Together, these factors constitute a major obstacle to entering the field of large models, which may make it difficult for small companies to compete with powerful giants.
This article is from Wall Street, by Bu Shuqing.