Netflix released its first “What We Watched” report Tuesday, which ranks almost all of its shows and movies by amount of hours viewed over the past six months. Netflix will release updated reports every six months, the company said.

Netflix has long had a reputation for lack of transparency about the popularity of its shows and movies. This has led to some distrust in the creator community, co-CEO Ted Sarandos acknowledged during a conference call with reporters Tuesday. Netflix kept its viewership data private as it built its business so it could experiment while not giving away data to potential competitors, Sarandos said.

“This is the actual data that we use to run the business,” Sarandos said. “I’m the co-CEO of a public company, so sharing bad information has consequences.”

Netflix now has almost 250 million global subscribers, far outpacing any other streaming service. That has given Sarandos confidence he can be open with viewership statistics. Hollywood actors and writers both mentioned heightened transparency during their strikes earlier this year as they campaigned to be paid in line with how audiences consumed their content. Netflix has also launched an advertising tier that demands more transparency as brands want information about how frequently certain shows and movies are watched.

“This is probably more information than you need, but I think it creates a better environment for the guilds, for us, for the producers, for creators and for the press,” Sarandos said.

Season one of “The Night Agent,” a Netflix original action thriller, was the service’s most-viewed show during the past six months, garnering 812 million viewing hours. “The Mother,” starring Jennifer Lopez, was the streaming service’s top movie. Between January and June, 55% of Netflix viewing came from original films and series and 45% from licensed titles, Sarandos said.

Netflix revealed viewing information for more than 18,000 titles, accounting for 99% of all its viewing and all titles watched more than 50,000 hours.

In May 2021, at the Google I/O conference, a demo of chatbot LaMDA playing the role of Pluto talking to humans was demonstrated, and immediately won the applause of the audience. At this time, six years had passed since Google set the goal of "AI First", and nearly 18 months had passed since the release of OpenAI's world-shaking ChatGPT.

At this point, Google is still a pioneer in the AI field. But two of the key engineers behind the project, De Freitas and Shazeer, are frustrated.

They wanted to be able to show the public the case for LaMDA's entry into Google's assistant, but over the years, the chatbot project has gone through many reviews and has been barred from releasing a broader version for a variety of reasons.

A year earlier, OpenAI had released GPT3 with 175 billion parameters and opened the API for testing. Google, however, was hesitant to make the product of the conversation model available to the public because of various risks of 'technical political correctness'.

De Freitas, Shazeer therefore want to go, despite CEO Pichai personally to retain, but ultimately, the two left Google at the end of 2021, founded Character AI - currently one of the unicorns in the field of AI big model.

Google thus lost its first-mover advantage to lead the change.

Later, the story became more widely publicized: at the end of 2022, ChatGPT came out, which not only made OpenAI famous, but also caused its investor Microsoft to kill in four directions, and with the support of GPT-4, Microsoft launched the search product New Bing, which was aimed at Google. Not only Google, but also the whole Silicon Valley and even the world were shaken.

Eight months later, on the other side of the ocean, the amazement brought by OpenAI has passed, Silicon Valley giants have passed the panic period, and have found their positions in the brand new battlefield, and startups are going forward and backward, while in China, the 100-mode war is a different scene.

In the past six months of rapid technological and commercial changes, the industry's knowledge and consensus on big models have been constantly updated. After exchanging ideas with nearly 100 entrepreneurs, investors and practitioners in Silicon Valley and China, Geek Park summarized 5 status quo situations of big model startups, and attempted to present a yet-to-be-validated "big model business worldview".

Google's missed opportunity and OpenAI's stunning debut remind us that lagging behind and having the first opportunity are alternating. The current technological and business evolution is far from over, and the real change has not even begun. All one needs to remember is that innovation can happen anywhere, anytime.

Note: The full article consists of 14,573 words and will take about 30 minutes to read. We recommend following and bookmarking it.

Schrödinger's OpenAI: Everyone's Hero, Could Be Everyone's Enemy

While China's startup community is still considering OpenAI as the new god of Silicon Valley, Silicon Valley has quietly started to get rid of OpenAI.

While OpenAI has achieved technological breakthroughs and remains a magnet for AI talent - with many companies claiming to snipe talent from OpenAI - the truth is that, to this day, OpenAI's top tech talent remains a "net inflow. "A net inflow of top technical talent to OpenAI today. Its belief in AGI and the vision-driven technological breakthroughs of the past eight years have made the company a hero to many.

But heroes have to live their lives, too. The next step in technological breakthroughs is to create value to create a business cycle. The debate in Silicon Valley is, can OpenAI really stay ahead?

Several Silicon Valley entrepreneurs, practitioners, and investors coincidentally expressed negative judgment to Geek Park. What people question is that, as of now, the business model presented by OpenAI has hidden worries - in the non-consensus stage of the "former ChatGPT", OpenAI could still win resources with the beliefs of a few people, but now that AGI has become a consensus and there are a lot of competitors, the challenge and difficulty of maintaining the lead will skyrocket.

If we categorize business profit models into toB and toC, OpenAI does not have the toB gene, and it has strong rivals - in terms of enterprise services, Microsoft, OpenAI's investor, is the king of the field, with over 65% of the market share of Microsoft's enterprise chat apps, and its Teams has gradually eaten up the market of the star company Slack in recent years. Its Teams has gradually eaten into the market of the star company Slack in recent years. In the toB space, Microsoft, which has been around for 48 years and has been through several technology cycles, has undoubtedly accumulated more experience than startup OpenAI.

But OpenAI also faces questions about the risks of centralization if it wants to do business in the toB space. At present, OpenAI's open API model for enterprises has attracted a number of customers to use it - especially small and medium-sized developers, who can't afford to train a large model independently, and access to the GPT series of APIs has become an excellent choice. Jasper.AI, which has just become a unicorn, is one of the best examples. By accessing GPT3, Jsaper.AI was valued at $1.5 billion in only 18 months after its establishment.

AI is valued at $1.5 billion after only 18 months of existence. "But people don't think highly of Jasper.AI because of this," an investor from a mainstream Silicon Valley fund told Geek Park. Private data is an enterprise's most important asset, and at this point, accessing it to a centralized model is first and foremost a compliance and security issue - although Sam Altman promised in May that OpenAI would not train on data from customers using APIs - but that's neither a disincentive nor a reason for enterprises to use the APIs to train. -but this neither assuages the enterprise's concerns nor gains its trust.

"Some enterprise customers in the U.S. are generally concerned about using OpenAI," the investor told us. The investor told us that, in the eyes of enterprises, OpenAI is the closest thing they have to AWS in the cloud, but they don't face it with the same logic they face AWS. "Customers are generally reluctant to hand over their data and key competencies to OpenAI because of the risks involved. Even if the GPT series can help train big models in the vertical domain with centralized capabilities, the competitiveness built with it is dangerous for customers - it will be a kind of "star-sucking": if their own data and experience can eventually be called by others, it will make the competitive barriers of the industry leaders fall.

And what about doing toC?

It looks like OpenAI has a user advantage in the C-suite. Since the release of its super product ChatGPT, its monthly activity has climbed all the way to 1.5 billion, compared to Instagram's 2 billion. However, the huge monthly activity may not necessarily bring OpenAI a data flywheel effect - "(through) users continue to ask questions to do (big model) training, the data value is not great," one entrepreneur pointed out. A startup founder pointed out.

It's worth noting that ChatGPT's monthly activity has declined for the first time since June. Speculations on this include:

Because the novelty of technology has waned - in fact, ChatGPT's visit growth rate has been slipping, ringing in at just 2.8 percent in May; Student vacations have led to lower student usage; As well as a more serious speculation, ChatGPT's declining answer quality has led to a drop in usage - when GPT-4 was first launched, it was slower and the quality of the answers was higher, whereas an update a few weeks ago saw user feedback that the speed of the answers had increased but the quality of the answers had declined perceptibly. What's more, giants including Google, Meta, Apple and others will also center their efforts around toC products. For example, Google has reintegrated two internal teams, Brain and Deepmind, in order to suppress OpenAI's advantage in technology. "With the former's massive user base, OpenAI's existing subscription revenues could be jeopardized if the giant launches a free product." OpenAI needs to maintain its technological barriers, and if they are broken, it could easily be attacked by giants with a price advantage.

OpenAI today is like Schrödinger's cat, it is hard to tell if its future is clear, but what is certain is that it will be under the close scrutiny of all the giants.

The top companies have their pick of the litter, but they're all on the same page in terms of their goals.

So what are the giants of Silicon Valley doing?

Geek Park talked to a large number of Silicon Valley practitioners and found that the panic period for Silicon Valley's giant companies is basically over compared to when ChatGPT first debuted. These mall veterans have quickly established their own mountains and accelerated their technology push to defend the quadrants they excel in and ensure they are not disrupted.

Their consistent approach is to expand the layout along their existing strengths, looking for big models that can help them, and even the direction of possible disruptive innovation, on the one hand, to reinforce their business advantages, to prevent rivals from sudden attacks; on the other hand, but also for the possible emergence of a new battlefield to lay a head start.

Zhang Peng, the founder and CEO of Geek Park, once explained such an observation in his speech at the AGI Playground conference, which attracted a lot of attention not long ago.

ChatGPT came out of nowhere, Microsoft immediately joined hands with ChatGPT to launch New Bing - because of the fear that GPT4-enhanced New Bing will shake the foundation of Google's search engine, Google chose to respond to the war in February in haste - - release Bard, which gave the outside world the impression of a big mess, and also affected the confidence of the capital market. -Released Bard, leaving the outside world the impression of a big mess, but also affected the outflow of talent and the confidence of the capital market.

However, the latest second-quarter earnings report showed more than expected growth, coupled with the comprehensive technology layout shown at the previous I/O conference, and successfully let the outside world to regain confidence in it.

This also seems to confirm that it is not so easy to use a paradigm revolution to disrupt the current giants in an already existing market.

As early as 2015, Google set the goal of AI First. However, due to internal reasons, it missed the opportunity to lead generative AI.After Bard, Google changed the support model behind it from LamDA, a lightweight dialog model, to Google's self-developed PaLM model. In May this year, Google released an upgraded version of the PaLM2 model, and added new features brought by generative AI to many products at once, including Gmail, Google Maps and other products, and was very active in C-end standardized products. Among them, the two that attracted the most attention from the outside world are the lightweight version of PaLM2, Gecko, which can run on the end side, and the Gemini multimodal model under development.

Of course, Google's strong genes in the C-side, this advantage is also considered to be a possible constraint on its exploration of the B-side - in fact, Google is also going to make an effort in the B-side: in addition to the Google TPU, the addition of A3 AI supercomputer based on the Nvidia H100, as well as the enterprise-oriented AI platform vertex AI.

For Google, the current situation is undoubtedly dangerous. In the era of big models, the field of search will be many rivals involved, as a search giant, Google may be sniped at any time, good defense is its dead door.

Geek Park learned that after the panic at the beginning of the year, the giant has calmed down and started its own actions. in April, Google merged Deep Mind and Google Brain and reorganized it into Google DeepMind, which is firmly believed that AGI's DeepMind co-founder Demis Hassabis served as the departmental leader, and the former Google In April, Google reorganized Deep Mind and Google Brain into Google DeepMind, with Demis Hassabis, co-founder of DeepMind, as department leader, and Jeff Dean, former head of Google Brain, as Google's Chief Scientist - a structural adjustment that not only gathers further resources, but also shows Google's determination to catch up.

After the merger of Google DeepMind and Google Research, the goal is to overcome a number of key AI projects, the first of which is the multimodal model. There are rumors that Google is using Youtube video data to train Gemini, which increases speculation about whether Google will take the lead, given that the next key technology for big models will be multimodal.

After all, Google has 3 billion users and a strong technical capability on top of tens of billions of dollars a year in revenue - which allows it to use its scale advantage to hold its security citadel even if it doesn't react fast enough, as long as it doesn't fall behind technologically.

Looking at the commercial layout of the giant, Geek Park summarized several conclusions after extensive exchanges:

The period of panic among the giants has ended, and they have re-targeted their own goals, the core of which is to maintain their highest position in the industry, and at the same time, if there is an opportunity to attack their competitors, they naturally will not miss it. Before the paradigm revolution of the big model, theoretically, any mature company that utilizes the big model well may have the ability to launch a blitzkrieg against the giants, while any giant that fails to act fast enough to incorporate the big model into its products risks being downgraded to sneak up on them - such as what Microsoft Search is to Google Search, and what Microsoft Cloud Services is to Amazon Cloud Services. The possibilities presented by big models are vast and unknown, and previously established business boundaries will blur anew. The purpose of giants training their own centralized big models is not the same as OpenAI's oft-talked-about "reaching AGI," but is more strategic and defensive. Except for Google, whose business is strongly related to Chatbox (chatbot), each company may not force to train a world-class ChatGPT-type product, but is more concerned about using large models to defend their business and have the ability to counter blitzkrieg. But because the technology is still in development, want to use the big model to launch a blitzkrieg to subvert rivals, or use the big model itself to benefit from scale, but also far from easy to imagine: Microsoft since the launch of the New Bing in February this year, was once thought to be more than the traffic growth of Google, but since April, there are reports showing that Bing's share of the search does not rise, but rather decline, as of July, Google's search position has not been shaken! Trend. And an entrepreneur who plans to use big models to serve the toB field told Geek Park that giants wanting to use big models to provide standardized services will also be caught up in fierce competition to a certain extent: his SaaS company, for example, has backend access to a number of big models (language models, translation models, etc.)-OpenAI, Google OpenAI, Google, open source models, etc. "Let them roll with price and performance," the entrepreneur said. In addition, Silicon Valley in the era of big models, "Brain Drain (brain drain) is very real". Multiple practitioners told Geek Park. Both historically and currently, any giant that fails to build a competitive business using big models will quickly lose top AI engineers. As early as 2022, Meta's focus on meta-universe concepts led to a number of senior AI experts jumping ship and the collapse of its London branch, while OpenAI poached more than a hundred people from Google in its early days to expand its business. The top AI programmers who leave a company are basically impossible to return in the short term. Finally, cloud computing in the AGI era is still an absolute giant's race - cloud computing itself is a giant's business, and training big models requires huge computing power. Just as under the gold rush, the one who earns money will be the one who sells shovels - at the moment when there is high uncertainty in both the underlying layer of the big model and the application, the cloud vendors will surely make profits from it, and in the present, how to provide 'better cloud services', such as optimizing the calculation results with lower arithmetic power and meeting the needs and scenarios of model training, will be a huge advantage.

Recently, Microsoft, Google, and Amazon have released their second-quarter earnings reports for the natural year 2023, and AI-related revenues have become the object of focused attention in each of their earnings reports.

However, with the outside world on the high expectations of AI is completely different, AI is currently on the three tech giants revenue impact "minimal". Amazon and Google did not directly disclose AI revenue figures, Microsoft is the only company that clearly disclosed the figures, which Azure AI revenue accounted for only about 2% of Azure revenue, compared with 1% in the previous quarter.

However, all three vendors are coincidentally treating AI as a core strategy for the future and increasing capital investment in AI.

Amazon CEO Andrew R. Jassy said that every business within Amazon is planning to implement generative AI applications, especially in customer service and cost cutting. Amazon plans to invest more than $50 billion in cash by 2023, with a focus on technology investment in AWS.

Google CEO Sundar Pichai said that with generative AI, Google is taking the next step to refactor all of its core products, including search. Google expects the level of investment in technology infrastructure to rise, continuing to grow from the second half of 2023 through 2024, including investments in GPUs and proprietary TPUs, as well as data center capacity expansion.

Microsoft CEO Satya Nadella said AI Copilot will be Microsoft's third growth curve in the long run. To support the growth of Microsoft's cloud and the demand for AI platforms, Microsoft will accelerate its investment in cloud infrastructure, with capital expenditures increasing in each successive quarter throughout the year.

1. Amazon: generative AI is at a very early stage

Amazon shares soared nearly 10% after hours following the release of its Q2 2023 earnings report. Amazon's overall stock price is also up more than 50% so far this year, making it one of the few tech stocks that can continue to move higher.

The investment and application of artificial intelligence is mainly focused on Amazon cloud AWS. the earnings report showed that AWS revenue in the second quarter of this year amounted to $22.14 billion, an increase of 12% year-on-year, and the operating profit amounted to $5.37 billion, accounting for nearly 70% of the total operating profit.

However, the 12% growth rate is the lowest level since 2015. Starting in the first quarter of 2022, its revenue growth rate has fallen to 37% from 40% in the fourth quarter of 2021, and has continued to dip since then, with its revenue growth rate having been declining for six consecutive quarters.

Meanwhile, AWS realized an operating profit of $5.37 billion, a small decline from $5.72 billion in the same period last year, with an operating margin of 24.23%, lower than the operating margin levels of 29.8% in 2021 and 28.7% in 2022.

According to Amazon CEO Andy Jassy, the core reason behind these changes is cost optimization for customers, but they are more optimistic about the future: "New technologies are attracting more new customers, and we've been providing our customers with all of the great solutions in generative AI and big language models and machine learning solutions. We are optimistic that these are continuing to attract new customers."

Generative AI is becoming an important part of AWS's current efforts to solidify its position. According to Andy Jassy, "AWS is continuing to solidify its leadership position in the cloud with a series of generative AI releases, competing with the likes of Google and Microsoft."

At the compute layer, AWS started developing its own custom AI chips for training and inference a few years ago, and according to Andy Jassy, "We're optimistic that in the future, training and inference for large-scale language models will run on AWS' TrAInium and Inferentia chips."

AWS launched Amazon Bedrock, a fully hosted generative AI service, and Amazon Titan, its own basic large-scale model, in April of this year. among them, the Amazon Titan family of models is divided into two kinds, one is a text model similar to the GPT-4 content generation, and the other is an embedding model that can efficiently create vector embeddings, which are used to create search functions and so on.

Amazon Bedrock is a new platform that integrates the industry's most mainstream basic big models, developers can integrate with AWS services to simplify the development process, AWS also customized chips for model training and inference, which can reduce the cost of developers.

Currently, Amazon Bedrock has access to mainstream basic models such as AI21 Labs, Anthropic, Stability AI, and Amazon Titan, which can be accessed by enterprises through the form of APIs, and has accumulated hundreds of customer cases.In June, AWS announced that it has partnered with the UAE's Institute of Science, Technology and Innovation (TII) to help its training of the open source big language model Falcon40B.

Andy Jassy revealed that organizations such as Bridgewater Associates, Coda, Lonely Planet, Omnicom, 3M, RyanAIr, Showpad, and Travelers are currently using Amazon Bedrock to create generative AI apps.Bedrock has also introduced new features, including new models for Cohere, Anthropic's Claude 2, and Stability AI's Stable Diffusion XL 1.0, as well as an agent for Amazon Bedrock, in addition to AWS offering the code assistant Amazon CodeWhisperer.

Meanwhile, AWS is accelerating its recruiting efforts. In March this year, the company announced that it would invest $ 100 million to set up a generative AI innovation center, and also launched the "Amazon Cloud Technology Startup Accelerator" focusing on startups in the field of AI.

According to Andy Jassy, "Generative AI will undoubtedly change the experience of almost all customers. But it's still early days, most companies are still thinking about how to get there, and I think we're at a very early stage, it's a marathon."

He also mentioned that the continued investment in the direction of the big language model is important for what may affect the company's overall cost investment: "The more customers demand, the more capital AWS invests up front in building data centers and hardware, and then it takes a long period of time after that to make money. We're hoping for that, and have made a reasonable estimate of capital expenditures."

2.AI drives high growth rate of Google's cloud revenue but does not disclose specific figures

Microsoft and Google's earnings reports are a week ahead of Amazon's.

The results show that Alphabet's second quarter total revenue of $74.604 billion, compared with $69.685 billion in the same period a year ago, an increase of 7%, excluding the impact of exchange rate changes for a year-on-year increase of 9%; second quarter net profit of $18.368 billion, compared with $16.002 billion in the same period a year ago, an increase of 15%. The overall revenue performance exceeded Wall Street analysts' expectations, leading to a nearly 6% rise in its after-hours stock price.

Google said, "We are very pleased with our second quarter results. We have been emphasizing for some time now that the company's focus will be on revenue growth rather than expense growth, and this quarter was the first time in some time that we achieved those goals."

Looking at the specific performance of the results, the rebound of the advertising business, which had declined in the previous two quarters, the growth of the YouTube business, and the rapid growth of Google's cloud business were the core drivers that supported this positive performance. Among them, search remained the biggest contributor to revenue growth, totaling $31.9 billion, up 6%.

Generative AI is becoming Google's center of gravity at the moment. At this year's 2023 Google I/O developer conference, Google CEO Sundar PichAI said, "With generative AI, we're taking the next step in refactoring all of our core products, including search."

On the integration of generative AI with search products, Pichai mentioned that progress has been ahead of pre-existing expectations. Since the launch of the relevant products, search efficiency has improved significantly, and search latency has also improved significantly. Relevant metrics have been seen to be met in internal testing. "AI search will also become the new way to search. We will continue to be prudent, thoughtful, and proactive in embracing and building another major change in search."

In addition, he mentioned that the current data related to Bard is very encouraging. "Some users code with Bard, exploring new application scenarios for Bard; we have also integrated Google Lens (Google Smart Lens) with Bard. We're all well aware that Google Lens has huge potential for growth, and visual search technology has grown tremendously over the past two years or so."

Google Cloud's second-quarter revenue was $8.031 billion, up about 28 percent year-over-year, making it the highest-growth business segment. Google Cloud reported an operating profit of $395 million for the quarter, about double the $191 million profit in the first quarter, with an operating margin of 5 percent. This is also Google Cloud from the first quarter of 2023, once again realized operating profit from negative to positive. According to Pichai, new AI-based services and products were the biggest contributors to Google Cloud's growth momentum.

Google Cloud consists of three main businesses: the Google Cloud Platform (GCP) service, Google Workspace communication and collaboration tools, and other enterprise services. GCP's growth in the quarter outpaced Google Cloud's overall growth and grew at a faster rate than Google Workspace, whose strong revenue growth was driven by growth in the number of users and average revenue per user.

By business, for Google Cloud Platform (GCP) services, more than 70 percent of next-generation AI unicorn companies are Google Cloud customers, including Cohere, Jasper, Typeface and others.

Google Workspace currently has a customer base of 9 million, which provides a potential opportunity for AI product penetration. More than 750,000 Workspaces use new product features such as Duet AI.

In addition, Google offers the widest range of AI supercomputer options through Google TPUs and advanced NVIDIA GPUs, and recently launched the new A3 AI supercomputer powered by the NVIDIA H100. This enables customers such as AppLovin to realize a price/performance ratio that is nearly 2x better than industry alternatives.

According to Pichai, the new generative AI offering is expanding the overall target market and winning new customers. "Google has now introduced more than 80 models behind vertexAI, Enterprise Search, and Conversational AI, and customers have demonstrated strong demand for these models, growing more than 15x from April to June, with Priceline, Carrefour, Capgemini, HSBC, Cerevel, and other Organizations across all industries are incorporating new AI technologies for industrial practices. ai provides 'up-sell' and 'cross-sell' opportunities for our base product installations."

In addition, Google is expanding its partner ecosystem to train more than 150,000 people on Google Cloud generative AI in collaboration with hundreds of ISVs and SaaS providers such as Box, Salesforce, Snorkel, and the world's largest consulting firms such as Accenture and Deloitte.

"All of the investments the company has made around AI, including DeepMind, the search business, Gemini and more, will be applied to the Google Cloud business in the future as well. We're very excited and excited about what's possible in the future." Pichai said.

1. for the first time, Azure accounted for more than 50 percent of cloud revenue, with Azure AI revenue accounting for 2 percent of that total

This time, the most expected Microsoft cloud in this wave of generative AI opportunities instead did not give too many stunning results.

The earnings report showed that in the second quarter of 2023, Microsoft's revenue was $56.189 billion, an increase of 8% year-on-year; net profit was $20.081 billion, an increase of 20% year-on-year. Revenue performance was lower than Wall Street analysts predicted, and the stock price fell 4% after the plate.

Specifically, the productivity and business process segment, which covers Office Office software, realized revenue of $18.291 billion in the quarter, compared with $16.600 billion in the same period last year, an increase of 10.2%.

Among them, Office commercial products and cloud services revenue grew 12% year-on-year, Office personal products and cloud services revenue grew 3% year-on-year, the number of subscribers to the personal version of 365 increased to 67 million, and Ahold DelhAIze, Deutsche Bank, Novartis, Siemens, and Wells Fargo are using 365 products. Microsoft has rolled out Microsoft 365 Copilot to 600 paying customers following the release of 365 Copilot, including Emirates, General Motors, Goodyear, Lumen and others.

Although, Microsoft introduced multi-modal visual search in Bing Chat, the personal computing business, which encompasses Surface, Xbox hardware, gaming, search and other business segments, continues to decline, with revenues of $13.905 in the quarter, compared to $14.461 billion in the same period last year, a decrease of 3.8% year-on-year. The latest figures are that Bing users have participated in more than 1 billion chats and created more than 750 million images using the Bing Image Builder. Microsoft Edge had its ninth consecutive quarter of market share gains.

Intelligent Cloud is Microsoft's cash cow business, with $23.99 billion in revenue in the second quarter of 2023, up 15 percent year-over-year. Operating profit was $10.526 billion, up 19.5% year-over-year. Intelligent Cloud mainly consists of business segments such as Azure, Enterprise Services, GitHub and Server Products. Among them, the Azure business saw a 27% year-over-year revenue growth within the quarter, a slowdown compared to the 31% growth rate in the previous quarter.

Microsoft Chairman and CEO Satya Nadella mentioned that sales of Azure cloud computing services accounted for more than half of Microsoft's full-year revenues for fiscal year 2023, the first time Azure has accounted for more than half of cloud computing revenues.

Among them, Azure Arc now has 18,000 customers, up 150% year-over-year, with customers such as Carnival Corp.

Azure AI offers the best selection of cutting-edge and open models, including Meta's recently announced support for Llama on Azure and Windows.

Azure AI services revenue grew by about 50 percent and accounted for about 2 percent of Azure's 27 percent revenue - Microsoft was the only vendor to announce AI-specific revenue.Azure OpenAI now has 11,000 enterprise customers, a significant increase from the 4,509 revealed in mid-May. It added an average of 100 customers per day during the quarter.

Microsoft says Azure AI Studio is becoming the tool of choice for this new era of AI development, helping organizations build, fine-tune, evaluate and deploy models. Nearly 90 percent of registered GitHub Copilot users are self-service users, and more than 27,000 organizations (up 2x quarter-over-quarter) have chosen GitHub Copilot for Business to improve developer productivity, including Airbnb, Dell and Scandinavian Airlines. In addition, Copilot in PowerBI combines the power of large-scale language models with an organization's data, and Copilot in PowerPages makes it easier to create secure, low-code business websites.

Satya Nadella mentioned that Microsoft remains focused on three key priorities: helping customers leverage the breadth and depth of the Microsoft Cloud to get the most value out of their investments, continuing to lead the AI platform posture by infusing AI at every layer of the technology stack, and increasing operational leverage.

Satya Nadella emphasized Microsoft's first-mover advantage in generative AI: "Azure didn't lead the industry in terms of scale, but we're leading strongly as number one right now when it comes to these new work facilities of AI. We're seeing customers who use other clouds to do most of their work start using Azure for the first time for some of these new AI workloads."

He also mentioned that the new generative AI technology will likely bring about a change in gross margins: "I expect gross margins to change over time, similar to the evolutionary cycle of cloud computing before, but we think gross margins will improve faster than cloud computing."

The investment, which includes cash and cloud services backing, values the company, which is only a year old, at $4 billion, a person familiar with the matter told media.

01 OpenAI's arch rival

Inflection AI is headquartered in Palo Alto, California, and has about 35 employees. The company raised $225 million in its first funding round in early 2022 from Greylock, Microsoft and Reid Hoffman.

Inflection, which launched its chatbot Pi last month, was founded by Google DeepMind co-founder Mustafa Suleyman and Collage co-founder Reid Hoffman and is focused on developing consumer-facing artificial intelligence products and is considered OpenAI's number one competitor.

Pi uses generative AI technology similar to ChatGPT to interact with users through conversations, allowing people to ask questions and share feedback. inflection says it wants to build a personal AI tool that helps users plan, schedule, gather information and perform other tasks.

Last week, Inflection released a report on its large language model, Inflection-1, which powers Pi and claims that the model outperforms most existing models on the market.

Inflection CEO Suleyman said most of the funding will be used to enhance the computing power to develop a more powerful base model.

"We will build a cluster of about 22,000 H100 chips. That's about three times the amount of computation used to train all of GPT4. The speed and scale will really allow us to build a differentiated product," Suleyman said Thursday at a technology conference.

02 AI investment boom

After OpenAI's ChatGPT, a chatbot, caused a stir late last year, the AI field was hailed as the next technological frontier, sparking a wave of investment.

Microsoft is not only an investor in Inflection, but has also invested in its competitor OpenAI.

Inflection said that NVIDIA has recently increased its investment in the AI field. In addition, Bill Gates and former Google CEO Eric Schmidt, among others, have also participated in the latest round of investments.

They said that in the era of AI, all products are worth re-upgrading with big models; AI will empower thousands of industries; AI will work for you and become your assistant, humans just need to do interesting things; the meta-universe is closer, virtual and reality will no longer have borders ......

The "ideal" world has triggered the climax of the capital market, doubling the rate of increase is not high, the share price of Hongbo shares, China Science and Technology, Wanxing Technology have risen about four times.

But now, for the changes brought about by AI, the general public has no disruptive feelings, the application and value in industry is not yet deep, AI track shareholders of listed companies began to retreat, some of the reduction also has "carefully designed" traces.

This phenomenon has undoubtedly cast a shadow on the boom.

The decision of the major shareholders has triggered the market to think: Is it that there is excessive hype in AI listed companies? What is the hype logic of AI concept stocks? What should we pay attention to in each stage of the hype? This article will answer the above questions.

AI "reduction wind" is blowing at home and abroad

Recently, in the A-share market, AI stock reduction has become a hot topic, and some people tease that they are embarrassed to say they are an "investor" if they do not reduce their holdings of AI stocks.

The reduction of major shareholders is like a sharp sword, piercing the confidence of the market, AI track companies, share prices have plummeted.

The biggest share price drop belongs to Kunlun Wanwei, June 20, the announcement that the company's ex-wife Li Qiong, the actual controller plans to reduce the company's 3% of the shares, the next day the company's share price fell 20%, the third day fell 12.77%, as of June 28, four trading days fell by 30%, the market value evaporated more than 26 billion yuan.

On June 14, before the release of the holding plan, Kunlun Wanwei announced that it would acquire an AI track company, Singularity AI Technology, at a price of $160 million (about RMB 1.1 billion), with the image of increasing the code of AI and vigorously developing AI jumping off the paper. Kunlun Wanwei said that after the completion of the acquisition, it will make every effort to build the world's leading AGI platform.

The announcement also said that half of the funds from Li Qiong's reduction will be loaned to Kunlun Wanwei at a relatively low interest rate to support the development of the company's AGI and AIGC business.

With good news on the front foot and bad news on the back, the exchange also issued a letter of concern, asking Kunlun Wanwei to explain whether there is any manipulation of share prices with the help of market hotspots and cooperation with Li Qiong's shareholding reduction.

Regardless of whether or not this is a bureau for holding reduction, Singularity AI Technology assets of less than 5 million yuan, liabilities of up to 39 million yuan, is worth 1.1 billion yuan, can not support the ambition to build the world's leading AGI platform, are big questions.

According to media statistics, there are at least 9 AI companies in A-share divorced this year, such as Fubon, three six zero, Tongcheng new material, science and technology, Tiandi digital, Tonghe technology, back to the new material, Saiteng shares, Jusheng micro. Among them, the ex-wife of Tiandi Digital's beneficial owner finished reducing her holdings by 56,800 shares in May and June.

In addition, Longchamp Information, one of the leading companies in artificial intelligence computing power, also recently issued a reduction announcement that three executives of the company will reduce their holdings of the company by a total of 656,700 shares.

Wande data shows that so far this year (as of June 28), including artificial intelligence, AI computing power, AI robotics, AI applications, AIGC, ChatGpt concept stocks, the number of completed and ongoing holdings is 510, has exceeded the number of holdings of 469 for the whole of last year.

The moon in foreign countries is no more round than in China, and the U.S. stock market has also seen a wave of holdings of AI listed companies.

Nvidia, the leading computing company, was significantly reduced by a custodian owned by the Rothschild family, an old European financial family, and in addition, two board members reduced their holdings by a total of more than $100 million.

Not long ago, Oracle, a company in the spotlight for AI, saw founder Larry Ellison reduce his holdings by $640 million in stock by way of option exercises after its stock price hit a record high.

The reasons for the AI cutback trend and stock price decline are multifaceted.

Some market participants interpreted the phenomenon as a wave of AI industry regulation being planned around the world, which made many investors nervous; others began to worry that AI technology could pose a threat to their national security, so they stepped up regulation; and some interpreters read it as concerns about a slowing economic recovery and the impact of the Federal Reserve's interest rate hike on market sentiment.

In short, regardless of the reason for the decline in the share prices of global AI companies, one of the main reasons is that these companies are over-valued and over-hyped by the market.

As Benjamin Melman, global chief investment officer at Rothschild Family Capital Management, said: Given that AI valuations are too high, we are becoming less and less sure ...... if (valuations) continue to grow, we will even become more cautious.

Emotional highs and over-optimism

Concept stock hype is common in the stock market. These stocks are usually based on a popular concept or innovative technology that skyrockets in a short period of time and generates a lot of attention. However, this hype also often brings bubbles and risks.

The launch of ChatGPT in November 2022 attracted countless media and users: it gained 1 million active users within 5 days of launch, 10 million users in 1 month, and 100 million users in 2 months, making it one of the fastest growing consumer apps in history. As a result, ChatGPT concept stocks started to rise sharply in January 2023.

To date, Nvidia shares have accumulated over 200% year-to-date, with a market cap of over $1 trillion. Others are BigBear.ai up 247%, C3.ai up nearly 198%, Meta up 140%, Amazon and Oracle up 54% and 46% respectively, Microsoft and Google up 40% and 39% respectively.

Domestic listed companies also followed the U.S. together with the rise. Since the beginning of the year, like a hang-up, Hongbo shares, in the information, Wanxing Technology shares have risen about four times, Jinqiao information, Cambium rose more than three times, Kunlun Wanwei is up nearly 250%, Shenzhou Taiyue and Foxit software shares also rose more than twice.

The logic of concept stock speculation, the basic principle lies in the expectations and scarcity.

First, the concept of stock speculation is expected to be an important factor.

Investors usually judge the future prospects of a company based on market trends, industry prospects, company news reports, etc.

According to SimilarWeb, a web traffic data site, ChatGPT reached another record high of 1.76 billion visits worldwide in April, surpassing other international search engines, such as Bing and DuckDuckGo, reaching 2% of Google and 60% of Baidu.

After the success of ChatGPT, Google announced that it is developing Gemini big model, and Amazon Cloud also released Bedrock tool for training AI models. Domestic enterprises and startups of all sizes are following the pace. Among the head enterprises, Alibaba's Tongyi Qianqian, Baidu's Wenxin Yiyin, Tencent's Hybrid Big Model, Huawei's Pangu Big Model, and industry enterprises are Shang Tang and Ke Da Xunfei.

According to statistics, at least 79 basic big models have been released by Chinese companies in three months.

Domestic and foreign companies have released AI-related technology products and big models, and their original technology accumulation overlaid with AI concepts has given investors reason to be bullish on their future profitability and growth potential.

Secondly, scarcity is also an important factor for concept stock hype.

Concept stocks usually refer to companies with unique business models, advanced technologies or scarce resources, whose products or services have uniqueness and competitive advantages in the market.

In the AI field, scarcity is mainly manifested in resource barriers and capability barriers.

The AI field requires advanced technology research and development capabilities and talent reserves, as well as the advantage of occupying certain data resources, so relatively few companies are able to venture into the AI field.

For example, ChatGPT has scarcity in language resources, pre-training data, algorithm optimization, talent pool and scene application, which makes it take the leading position in the NLP field.

One basis for the over-hyped stock price also lies in the fact that there is no consensus on the way to value new models and technologies.

New topics, especially technological breakthroughs that have not yet been able to fall to the commercialization and revenue level, its valuation does not apply to the traditional price-to-earnings ratio, price-to-net ratio and other ways, how to value them, investors are groping, and it takes time to form a consensus.

For example, when the burning Internet companies have gone public in the United States, it created a valuation method with the number of users, revenue growth rate as the core factor. AI stocks, there are institutions to PS for valuation, and is therefore, investors will think that Nvidia close to 30 times the P/S ratio is too high, there are other ways of valuation.

The acquisition of Kunlun Wanwei mentioned above mentions a valuation method - the market research rate, which means that regardless of whether there are results in R&D, regardless of whether the R&D results can be landed, and regardless of whether they can bring commercial benefits, as long as the money is spent on R&D, then the more money spent, the higher the valuation given.

According to the market research rate, Singularity AI Technology can be valued at 1.7 billion yuan.

The difference in the way the valuation is perceived is also the reason why AI stocks tend to "rise too much".

Although AI stocks are a hot topic in the market right now, investors need to be very cautious when pursuing them. Today many companies are rubbing shoulders with the AI concept, but there are only a handful of companies with real strength.

Since 2018, rumors about this mysterious headset device have never ceased. Every few months, news broke that Apple was seeking solutions in the upstream supply chain to carry out product validation, suggesting that the product was on the horizon, but each time it ended up 'skipping' until today.

This is the first time since the Apple Watch was released in 2014 that Apple has introduced a new computing platform. Everyone was expecting it. Users, developers, and even competitors in the industry were all the same. After all, in Apple's nearly half-century of entrepreneurial history, every time it has built a new hardware paradigm, it has opened up a huge incremental market.

But this time, things are getting a little delicate.

On the one hand, Apple Vision Pro has the industry's top hardware specifications and parameters, which will probably raise the headset experience to a whole new level; on the other hand, the pricing of $3499 (about RMB 25,000) also predestines it not to be like the iPhone, which will quickly spread the goods and become popular. As Apple's first product in the headset category, the Apple Vision Pro is much more symbolic than the actual revenue generated from sales. It anchors users' perceived expectations, raises a flag for developers, and is an industry-wide benchmark for experience. Regardless of the ultimate success or failure, Apple will bring an earthquake to the VR and AR industry.

From today onwards, the history of VR and AR has turned a new page.

01 $3499 "next-generation headset

Apple Vision Pro is equipped with a set of hardware configuration beyond the norm.

The first is the resolution, as previously announced, Apple Vision Pro is equipped with a monocular super 4K display module, plus a three-piece Pancake lens solution, and the total number of pixels in the entire display system is up to 23 million.

This display system has not yet reached the "monocular 8K" retina level, but it is also quite close, surpassing all the headset products currently on the market.

Apple has also spared no expense in sensor configuration. In addition to four built-in cameras for eye-tracking, Apple has also equipped the front panel of the headset with as many as 13 sensors, including high-definition cameras and laser sensors that capture and sense the user's surroundings, providing Apple Vision Pro with complete AR capabilities.

For many years, Apple has insisted on developing AR (augmented reality) technology, believing that computing devices cannot be completely divorced from reality. Including the previous "meta-universe" concept due to the Meta renaming and a big hit, Cook has also publicly expressed that he does not think VR is the direction of technological evolution.

But the technology associated with VR headsets is now mainstream in the industry. Even the Apple Vision Pro had to adopt the VR solution, and through the external camera, it was magically transformed into an AR platform.

The end result is that the Apple Vision Pro costs a lot, and the pricing is quite impressive. A media report previously compiled a supply chain quote for the Apple Vision Pro and found that the cost of the components alone was over $1,500, and the final price was $3,499.

It can be said that, despite the official release, within 1-2 years, Apple Vision Pro will still not immediately enter the lives of most ordinary people. Most consumers, especially domestic users, will not buy this generation of products.

Previously, it was revealed that Apple's sales target for the first year of Apple Vision Pro launch was set at 1 million units. This sales target is very conservative for Apple. For reference, when the first generation of Apple Watch was released, the number of reservations on the first day alone was more than 1 million.

Apple Vision Pro's high pricing means the product won't roll out quickly｜Apple

But given the size of the VR/AR market as a whole, that 1 million sales target is ambitious. After all, the total number of VR/AR headsets shipped worldwide in 2022 will be just 8.8 million, so if Apple manages to sell 1 million Apple Vision Pro units, it could take about 10 percent of the market. Considering that Apple will almost certainly launch lower-priced, next-generation products for a wider audience in the future, a first-year sales of 1 million would be a very good start.

What is certain is that the Apple Vision Pro will have a longer development blueprint than any previous Apple product. This is a true "next-generation" headset, and most ordinary users will probably have to wait a long time before they can actually use it.

02 "Raising the flag" for developers

Apple Vision Pro will not be officially available to the public until next spring, and initially only in the United States.

But Apple still chose to release Vision Pro half a year earlier at WWDC. The reason behind this is, on the one hand, to release it earlier to prevent the product from leaking in the process of mass production, and on the other hand, another important consideration is the "development ecology", which Apple needs to lay out in advance to raise a flag for developers.

At present, most of the applications in the Apple Vision Pro demo are "Vision versions" of iPhone, iPad, and Mac applications, just projecting flat apps into space and hanging them up, and the actual problems they solve are no different from native iOS and macOS apps.

The Vision App, which is native to visionOS and built with a spatial model, still requires the imagination of third-party developers to create.

Apple has not defined the specific use scenarios of Apple Vision Pro, but rather broadly demonstrated that it can run iPhone, iPad, Mac apps, display 3D photos and videos, and cover many scenarios such as home entertainment, productivity office, communication and collaboration.

In Apple's demo, Vision Pro can be everything from a phone, tablet, computer, TV and other computing devices at the same time. But how to go beyond these traditional computing devices and provide a unique experience is still something that developers need to explore.

It's fair to say that this time around, Apple Vision Pro smells like a "development kit" inside and out. It's not just that it was released at WWDC, it's not just that it's priced high, it's that it doesn't have a built-in battery, it's powered by an external battery, and it only lasts 2 hours without power.

After the event, some iOS developers argued that the Apple Vision Pro was almost a development version of the hardware for development teams to debug at their desks, which is why there was no built-in battery. The Vision App developed by developers on this generation of products will run on future next-generation products. It is likely that subsequent products will optimize the lightweight design, eventually achieving a built-in battery and lowering the price for a broader consumer market.

No one knows exactly what Apple's intentions are, but the only thing that's certain is that the entire XR development ecosystem will be given a shot in the arm with Apple's impetus.

03 VR, AR industry's "era of great sailing

With Apple's huge ship in the water, the entire VR and AR industry will usher in the "Grand Voyage Era".

In the past few years, the VR and AR industry is facing a major development dilemma is that few companies can come up with enough resources to break through the ceiling of VR and AR technology, products and development ecology. That's why Qualcomm Snapdragon XR chips are slow to iterate, Meta Quest updates are slowing down, and the global VR and AR headset shipments are declining in 2022.

Now Apple's entry means competition, but also brings more opportunities.

Over the past few decades, each time Apple has entered a new product category, it has almost always resulted in a huge incremental market gain. From iPhone opening up smartphones, iPad opening up tablets, to Apple Watch opening up watch bracelets, AirPods opening up TWS headphones ...... Apple has opened the door to many opportunities while leading the industry.

Including the way and logic of product definition, Apple Vision Pro also with many companies in the industry, formed some tacit understanding and consensus. For example, Apple has adopted the Pancake design used in Quest Pro and PICO 4 in the design of the lens.

As well as the industry's mainstream product definition is to do VR headset, but now both PICO 4 series products, or Meta just released Quest 3, have adopted a similar scheme with Apple, that is, in the front panel configuration camera, to explore the MR "virtual reality" product form.

For all companies doing VR and AR, especially those with relatively small startups, Apple brings market benefits that far outweigh competitive pressures. The curtain of the times has just opened.

On top of that, it's also those who are pioneers in the industry that offer some lower-priced options for users who want to try VR and AR.

For example, for domestic users, considering that Apple Vision Pro will not be available in China for at least a year, you can still choose PICO 4 series and experience a headset that is quite similar to Apple Vision Pro in terms of product definition at a lower price.

Whether it's the binocular 4K+ resolution, Pancake lenses, or the unibody, lightweight design, PICO 4 represents the current industry's first-tier level, while the content ecology is also relatively mature. It has also achieved the first domestic market share and the second global market share.

Many people regard the release of Apple Vision Pro as the "iPhone moment" in the field of VR and AR. However, the technology evolution cycle of the headset will be longer than that of the smartphone.

From this perspective, the most exciting thing about Apple Vision Pro is perhaps not the product itself, but the next era that it will open up.

After the launch of ChatGPT, the same type of products based on big language model technology are still emerging at an accelerated pace. Since April, from Internet majors, to A-share listed companies, and a host of startups, plus universities and research institutes, have released their own big models, totaling more than 30.

In just a few months, several models have emerged, what is the strength of each model? Is there a surplus of big models in China? Is the final game of the industry a blossoming one or a winner takes all? In the discussion of this technical change, which are the most critical and most likely to bring long-term impact among the new capabilities brought by big models?

AI big models are mushrooming, and there is no "perfect answer" for task performance testing.

According to the statistics of Minsheng Securities, there are more than 30 big models in China, and the industry is like a scene of "100 model war".

According to the feedback from AI industry players interviewed by Science and Technology Board Daily, the industry has not yet given any direct definite judgment on specific models, but they have provided some dimensions for the outside world to make reference.

In an interview with Science and Technology Board Daily, Yu Kai, co-founder and chief scientist of Spectrum and professor of Shanghai Jiaotong University, said that a fact that must be acknowledged is that only ChatGPT has passed the generality test (users broke 100 million) of the current big models, and there are still gaps between the domestic big models and them.

Kai Yu told reporters that the first way to measure the strength of a big model is to test the performance based on tasks, that is, by defining the task set to compare the completion degree of all big models on each task. This performance test is aligned with human abilities, including comprehension, reasoning, judgment, etc. According to the current feedback, none of the big models, including ChatGPT, can meet all the standards in the task performance test.

Second, judging from the security perspective, this is more reflected in the degree of coupling between large models and human values.

The third is the model operation perspective, which is judged from the engineering characteristics. "This is a particularly important capability." Yu Kai stressed, such as how much text the big model can receive, the speed of answer response, and the performance of operation.

Focusing on technical metrics, as Yu Kai puts it. Of course, there are also those who make judgments from the level of resource endowment.

Wang Jun (a pseudonym), a senior industry expert in the field of big model, told reporters that the requirements for the team to do big model are very high, and there should be no shortcomings in many aspects such as funding, technology, engineering, product, commercialization, etc. The final test is: whether the core members have really thought clearly about the general direction and rhythm, whether they can get enough resources and support, whether they can attract key talents from various aspects to join, and whether they can grind a group of different backgrounds after attracting After attracting a group of cattle people with different backgrounds, can they grind well together.

"One of the most scarce is the core algorithm research and platform engineering technical talents, and there are not many of them in the whole Chinese circle." Wang Jun stressed.

The battle for talent, which has been very distinctly portrayed in the take-off phase of the big model market.

The "pre-emptive" Baidu, sent to the helm is CTO Wang Haifeng, the team of entrepreneurs, Lanzhou Technology's Zhou Ming, Zhou Bowen, etc., their influence in the artificial intelligence industry has been needless to say. Previously, the high-profile official announcement of the artificial intelligence venture of Wang Huiwen, the first step into the game is to send out hero posts in its personal social media platform, heavy (75% of the shares of the new company) to recruit top R & D talent.

"The criteria for judging whether it's done well or not can't depend on each company's own propaganda, and some industry-recognized evaluation benchmarks can certainly be used as a reference, but the most important thing is user recognition, and the users, especially those with the most high-frequency or paid users, are the best." Wang Jun stated.

Approaching the core of AGI, "generalization" of industrial applications is the key

Due to various commercial reasons, it is difficult for the outside world to know all the actual data, test feedback indicators, resources invested, and even user data of each company's large model, so it is difficult to make a scientific judgment on its strength.

However, the reporter noticed that the interviewees all mentioned an obvious evaluation angle, that is, "user feedback", such as the response speed, accuracy, usability, contextual logic, etc. This is why every time there is a test, the user feedback is not only the most important factor, but also the most important factor. That's why, whenever a new big model is launched, users will first pay attention to whether the answer will "flop" or not.

In terms of several representative big models in China, the reporters of Science and Technology Board Daily have had practical experience with them, and combined with the feedback from many users, the overall characteristics of the big models are as follows:

ChatGPT-4 is a multimodal large language model, which supports image and text input, and outputs in the form of text.

In contrast, domestic large models are diverse and have different capabilities, and are currently more focused on exploring industrial applications and solving industrial technology barriers.

In terms of Chinese semantics, domestic models, including Wenxin Yiyin and QianYiTongQin, have high and low comprehension capabilities, and do not significantly distance themselves from each other. In terms of understanding tricky Chinese phrases, the training data of domestic big models mainly comes from Chinese corpus, compared with ChatGPT which mainly comes from English corpus, so the domestic big models are better.

However, there are some cases. Professor Qiu Xipeng's team at Fudan University released the first ChatGPT-like model in China, MOSS, which has a higher level of English responses than Chinese because English, as the mainstream language for scientific research, is widely used in academia and industry and has accumulated a large amount of high-quality corpus data, and the English data is highly open source compared to the Chinese data.

In addition, MOSS is designed with human ethical guidelines in mind and will not produce biased or potentially harmful responses, which will avoid some potential legal risks and business ethics issues to a certain extent. This point is not explicitly addressed by ChatGPT.

The sound of big model measurement is still on the rise. However, Yu Kai admits that it is not the right time to judge the ability of each model and how good or bad it is.

In his opinion, the big models that have been released now, the change is that they have basically realized the emergence of the thinking chain, approaching the core part of AGI (General Artificial Intelligence), and the industry is now more concerned about whether the big models have enough "generalization", that is, widespread use, but from the industrial point of view, the domestic users of big models have not yet reached the level of But from the industry's point of view, the volume of users of domestic large models has not yet reached ubiquity.

But from the industry's point of view, the volume of users of domestic large models has not yet reached ubiquity. "Before reaching widespread ubiquity, it is still necessary to be cautious in judging by the standard of ubiquity." Yu Kai emphasized.

The first mover is not necessarily the "king" of large models

With multiple models flooding the market at the same time in the short term, there are also views that there is a need for so many big models now. In other words, is there a surplus of big models now?

The industry is generally of the opinion that although there are so many big models now, it is far from a surplus.

Wang Jun believes that the technology, capital and strength requirements of large models for manufacturers can only be said to be just usable for the current products.

Yu Kai said that the future industry AI application paradigm will change from a generic model to a cluster of generic models, and the big model will be differentiated, such as differentiated by domain, differentiated by function, and combined with specific scenarios of specific industries. Now, the very professional and profound big models are not yet out, and these will keep emerging in the future.

Chen Yunwen, founder and CEO of Dagan Data, also said in an interview with Science and Technology Board Daily that the big model track is currently in the exploration catch-up stage in China, and the technology itself is not yet mature, so there is still a lot of room for growth in the future, "just like the beverage brands now, if we stretch the timeline to see, the number of big models today is actually not much. "

So in the future, the endgame of the industry, whether it is a hundred flowers, or winner takes all?

Yu Kai and Chen Yunwen both said that the future will be a blossoming state, the reason is that it is now in the initial stage, the future of the big model in each industry will grow out of the product form will be different, and for different industries, there will also be vertical industry application model.

Wang Jun said that the high threshold of the big model determines that this is something that only a few players can do, and the future will not be a hundred flowers, but whether it will be like a search engine, or only two or three operating systems, or the pattern of the development of cloud computing, "it is impossible to judge, or some variables."

A number of senior industry insiders told Science and Technology Board Daily that OpenAI is only a temporary "first mover" in the big model of artificial intelligence. It is still extremely difficult to tell which company will be able to achieve a global market position similar to Apple's today with its big AI model in the future. There are too many cases where the first mover is not the last king. Take China's Internet development as an example, the first ones to start are Sina, Sohu and Netease, but the ones who really cut the big cake later are Tencent, Ali and Byte.

To pay more attention to and think deeply about the application scenarios of AI

Whether you are amazed at the amazing performance of ChatGPT or thinking about the "Hundred Model War", the essence of all the discussions about big models today is nothing but the opportunities and challenges brought to human beings by this technological revolution.

However, in the opinion of Zhou Feng, CEO of NetEase Youdao and doctor of computer science, there is still one issue that has not been fully discussed in this technology trend, that is, which are the most critical and most likely to bring long-term impact among the new capabilities brought by big models.

According to Zhou Feng, compared with many previous natural language processing technologies, big language models have at least three fundamental new capabilities: emergent capabilities, the ability to support multiple applications as a base model, and the ability to support dialogue as a unified portal.

Feng Zhou says that emergent capabilities are important not only because they are new capabilities that have emerged since the emergence of big models, but also because most of the capabilities that emerge from big models are very important ones. For example, the ability of common sense reasoning has been a major challenge in AI, and the emergence of big models has led to significant progress in common sense reasoning. For example, once 'reasoning' capabilities emerge, 'thought chain hinting' strategies can be used to solve multi-step reasoning challenges. "Thus, the emergence of emergent capabilities is a fundamental change brought about by the Big Model.

In terms of base models, Zhou Feng said that large models can not only shorten the development cycle of each specific application and reduce the required human investment, but also get better application results based on the reasoning, common sense and writing capabilities of large models. Therefore, large models can become a grand unified base model for AI application development, which is a one-two punch and a new paradigm that deserves to be strongly promoted.

The opportunity to make the large language model really explode this round is ChatGPT, which is based on conversational chat. Zhou Feng said that although there were various problems with previous chatbots, the emergence of the large language model once again allows chatbots as an interaction model to be re-imagined. The future may see the emergence of many similar projects that allow assistants to perform various specific tasks in a conversational format.

"These three capabilities have been widely discussed in academia and are even considered common sense, but lack sufficient attention in industry and product teams." According to Zhou Feng, "The characteristics of these big model technologies have changed the way we think about business and product planning, and will change the economic model of many products. Therefore, product managers and business leaders need to pay more attention to and think deeply about the application scenarios of these new capabilities."

Yu Kai also believes that in this technical change, in addition to the technical level of the parameter magnitude, the resource level of algorithms, computing power, data, talent funding and other discussions, more attention needs to be paid to, in addition to the large language model, other things related to the artificial intelligence system.

These would include the understanding of language, the understanding of conversational AI, and the understanding of multimodal AI. These are not just questions of algorithms alone, but also about business, training strategies, and understanding of the technical journey of AI.

The Importance of Understanding Child Growth and Development

Child growth and development refer to the physical, mental, and emotional changes that occur in children as they age. These changes are a natural part of the human life cycle and are influenced by a range of factors, including genetics, environment, and nutrition. Understanding child growth and development is crucial for several reasons.

Firstly, it enables parents, caregivers, and educators to identify any developmental delays or issues early on, allowing for early intervention and support. For example, if a child is struggling with language development, identifying the issue early can help them access the resources they need to overcome this challenge.

Secondly, understanding child growth and development can help parents and caregivers create an environment that supports healthy growth and development. For example, providing age-appropriate toys, activities, and social interactions can help children develop their skills and abilities in a supportive environment.

Finally, understanding child growth and development can help us as a society prioritize policies and programs that support healthy growth and development for all children. For example, by investing in early childhood education and care, we can help ensure that all children have access to the resources they need to thrive.

Stages of Child Growth and Development

Child growth and development occur in a series of stages, each with its unique characteristics and milestones. Here are the key stages of child growth and development:

Infancy: Infancy is the period from birth to 18 months. During this time, babies develop rapidly, both physically and mentally. They learn to communicate through crying, cooing, and babbling and develop their motor skills, including crawling, sitting, and standing.

Toddlerhood: Toddlerhood is the period from 18 months to three years. During this time, toddlers become more mobile and independent, developing their language and social skills. They also begin to form their own personalities and preferences.

Preschool: Preschool is the period from three to five years. During this time, children continue to develop their language and social skills, as well as their cognitive and motor skills. They also begin to develop a sense of self and an understanding of their place in the world.

Middle Childhood: Middle childhood is the period from six to 11 years. During this time, children continue to develop their cognitive, social, and emotional skills. They also begin to develop a greater sense of independence and responsibility.

Adolescence: Adolescence is the period from 12 to 18 years. During this time, teenagers undergo significant physical, emotional, and social changes as they transition into adulthood. They begin to develop their own identity and explore their place in the world.

How to Support Child Growth and Development

Supporting child growth and development requires a multi-faceted approach that involves parents, caregivers, educators, and policymakers. Here are some ways you can support child growth and development:

Provide a safe and nurturing environment: Children need a safe and nurturing environment to thrive. This includes a stable home, access to healthy food and medical care, and positive social interactions with adults and peers.

Encourage play and exploration: Play is essential for children's development, allowing them to explore the world around them and develop their creativity, imagination, and problem-solving skills. Encourage children to engage in unstructured play and exploration, both indoors and outdoors.

Provide age-appropriate toys and activities: Children's toys and activities should be age-appropriate, providing them with opportunities to develop their skills and abilities. For example, infants benefit from toys that promote sensory exploration, while preschoolers benefit from toys that promote imaginative play.

Support language development: Language development is crucial for children's cognitive and social development. Encourage children to talk, read, and sing, and provide them with opportunities to engage in conversations and interactions with others.

Encourage physical activity: Physical activity is crucial for children's physical and mental health. Encourage children to engage in regular physical activity, such as outdoor play, sports, or dance.

Invest in early childhood education and care: Early childhood education and care can provide children with the resources they need to thrive. Invest in high-quality programs that prioritize children's physical, emotional, and cognitive development.

The Benefits of Minting a NFT

As a token of appreciation for our subscribers' commitment to educating themselves and others about child growth and development, we are offering a unique opportunity to mint a free NFT. An NFT, or non-fungible token, is a unique digital asset that is stored on a blockchain. By minting an NFT, subscribers can commemorate their dedication to this vital cause and contribute to the growing movement to prioritize children's healthy growth and development.

Minting an NFT is simple and easy. Subscribers can simply follow the instructions provided on our social media account and use the provided platform to mint their own unique NFT. The NFT can then be stored in a digital wallet, displayed on social media or personal websites, or even sold to other collectors.

https://opensea.io/assets/ethereum/0x28FB2A75566eA514C5d1BE64679647AF84A8d17e/0

Conclusion

Understanding child growth and development is essential for creating a bright future for our children and society as a whole. By prioritizing education and support for children's physical, mental, and emotional development, we can help them reach their full potential and create a better world for everyone. Subscribers to our social media account can take part in this movement and commemorate their commitment by minting a free NFT. Together, we can build a better future for all children.

They put the horoscope, eight characters, I Ching and these esoteric things to study the head is right, they obviously say they only want to engage in money, is a firm materialist, the results of each quietly search a week horoscope, zodiac horoscope ranking, and even face palmistry, home feng shui all do not fall, electronic metaphysics has spread in them.

With the approach of the Web3 era, when the devout Buddhist believers are still chanting sutras and fasting and meditation, the Internet trendy play has begun to knock the electronic wooden fish to accumulate merit.

The seemingly simple application has gained the favor of many young people, and with it is the overwhelming Buddha, wooden fish emoji package. So far, the electronic wooden fish user volume has long exceeded the 5 million mark, electronic wooden fish a month was knocked more than 1 million times.

Whether it is work or life, this generation of young people are entrusted with the hope of topping the full range of pressure, resulting in their always looking for an emotional outlet, and this simple, easy to use or a trendy entertainment activity, is the most convenient stress relief sanctuary.

Electronic wooden fish has undoubtedly become a cyber world co-created by young people, when science can not solve the problems of life, metaphysics may be the optimal solution to these problems.

Recently, a Japanese website called HOTOKE AI (https://HOTOKE.ai/) has rapidly become popular due to the use of ChatGPT to create an AI Buddha puissance.

HOTOKE is translated as Buddha in Japanese, and the HOTOKE AI website has set up an AI Buddha where users can tell the troubles they encounter in a dialogue window, and HOTOKE AI will then offer Buddhist advice to help all beings. The website was equipped with OpenAI's GPT-3.5 on March 3 and has already answered more than 13,000 troubles in less than 5 days since its launch.

Compared to the electronic wooden fish, the AI Buddha is a figurative image of a cyber metaphysical chatbot that has converted all cyber believers to the sect. So, can HOTOKE AI really do relief to the living beings? Why can it transform ChatGPT into a Buddhist chatbot?

Defeating magic with magic

HOTOKE AI is made by a Japanese developer named Kazuma Ieiri.

Although it is a Japanese website, you can ask questions in Chinese, only that the answers will still be presented in Japanese. At this point, you can use the browser's full page translation function to know what the content is writing, which is not bad and can be read, but compared to the ChatGPT translation results, HOTOKE AI lacks colloquialism and is no different from machine translation.

After entering the HOTOKE AI website, we could see the eye-catching slogan "AI is always with you".

Then we put the question we want to ask into the corresponding input box and press "Ask Buddha". One thing to note is that you should not enter any personal information, as your questions and answers may be made public. The developer also adds the following sentence "Buddhist views may be incorrect, please accept them with a generous heart."

If we ask a question, it will answer it immediately, and it will do so in a lively, good-natured way.

For example, how to get along with colleagues?

Roughly speaking, this AI Buddha is really answering your questions from the heart, and when you read it carefully, you will find that it is really Buddhist, with its language skills pulling all the stops and giving you a feeling of "Buddha's light".

First, it will choose to get closer to you and understand your troubles, a feeling it can empathize with. Then it will tell you about the relationship between the team and the individual in the workplace.

In other words, the AI Buddha's answer will slowly pull you out of anxiety, almost to "it's not all your problem, don't blame yourself too much" the phrase full-screen play, full of thoughtful answers is really very decompressed.

Finally it then releases the ultimate trick - to give you some practical advice. For example, do not give yourself too much pressure, to seek other help and so on, this one and two, to comfort you at the same time also directly to the problem.

Today HOTOKE AI has already answered the questions of nearly 140,000 people, and the number is growing.

First, it will make a main point: there is no absolute answer to this question, and both AI and Buddha have a point of existence.

It will then analyze the question from the perspective of psychology and sociology, telling you that you must listen to your heart and that both options are correct.

Finally it will take you to extend the question to do deep thinking from the perspective of Buddha, to understand the value of Buddha and AI and return to self, to do self-improvement and self-affirmation.

Thus, it seems that HOTOKE AI's answers do serve as comfort and advice to a certain extent, however, when we look back carefully, we will find that it is just a healing chatbot whose language style is set to Buddha, extracting keywords based on the questions you input, so as to sort out the answers.

Although it can only give each other some comfort and guidance from the psychological point of view and play a role in soothing and relieving stress, this is enough for the young people of today.

To solve the troubles of life, either struggle to climb upward, or meditate to harvest spiritual wealth, everyone wants to work hard and struggle for their lives, but there are always many uncontrollable variables in the outside world, at least, we can still try to change their own hearts first, to find the spiritual strength from within.

A little relief in the mind, but also allows us to quietly reflect on many life choices and directions, and perhaps a lot of different gains. In this way, maybe HOTOKE AI has really done it, it has entered the world and transformed into a "Cyber Buddha" to help the people.

Why people need "Cyber Buddha"

When most people start to believe that "the end of science is metaphysics", do you worry that you will gradually fall into the vortex of metaphysics? But there may be some unspeakable reasons behind the seemingly absurd acts of electronic superstitions and rituals such as "electronic moksha" and "cyberbuddhas" to achieve self-redemption.

◉ A delicious addition to the meal after a sandwich

Faced with the accelerating pace of life nowadays, stress and anxiety are infinitely magnified in the Internet.

People in the "pendulum" and "inside the volume" in the limit of pull, but also can not help but sigh self-deprecation "pendulum and can not pendulum, volume and can not roll, lie and lie not win, a bowl of sandwich rice, I swallowed and swallowed. "

Although most people in the complex society, adhere to the "atheist" basic literacy, but when the uncertainty and insecurity of real life value explosion, a large number of network lurkers also began to step into the so-called "superstition" and The "crooked way".

From the beginning of the forwarding of Yang beyond the Koi to the network worship saints, and now the knocking electronic wooden fish worship Saibo Buddha, this nonsensical blessing orgy, seemingly funny and comical, is not a hot and innocent.

When we are in the reality of obedience, cautious words, in order to live a life of subsistence, those who have accumulated a lot of time and can not release the emotions and anxiety, through the 5G surfing with a variety of electronic superstition to hit hard, wantonly vent.

The seemingly empty spiritual solace and psychological implication, for each of us, is not lacking in real life, a delicious meal and a kind of insignificant but invaluable spiritual motivation.

◉ The temple of Cyber without leaving home

Religious beliefs emerge when there is a very strong vision for something or something.

People attempt to accumulate merit and seek good fortune for themselves through a series of acts of blessing, and so they begin to burn incense and worship Buddha, striving to be a devout believer.

Once the traditional ritual of Buddha was solemn, but under the wave of cyber, a tap on the electronic screen and a twist can achieve "merit +1"; a snap with your fingertips and a question can get the "answer" that Buddha wants to tell you.

In May 2014, at the New Museum's annual Seven on Seven conference, Kevin McCoy and Annelle Desch presented their business concept "Money Map" (a metaphor for monetizing graphics). The conference aims to pair artists with technologists. McCoy and Desh's Money Map is a prototype for NFT, which is a combination of a purchase agreement and a tokenized certificate of authenticity (COA). Back in October 2013, McCoy posted on a message board that he was interested in "developing a system where contractually defined ownership tokens or messages could be embedded in blockchain transactions," and after the Seven on Seven conference, he founded Money Map. Around the same time, German startup Ascribe developed a mechanism to protect blockchain digital art and released a protocol, backend and application in 2014. Despite gaining some traction, Money Map and Ascribe ultimately failed. At the time, neither the technology nor the public's understanding of blockchain was sufficient to commercialize these companies' tools and unlock their potential.

These early efforts to authenticate artwork on the blockchain gave us a glimpse of the potential of NFT. They take the functionality of crypto tokens and see it as a combination of more improved options for advanced digital versions of authentication certificates, such as setting licenses for selling and collecting royalties. To support artists who adopt the medium for their work, Moneygram focuses on digital art; it is still struggling to find its place in the art market, despite the acquisition of some works by institutions and collectors. Looking at today's NFT landscape, one can speculate that the crypto entrepreneurs who created today's NFT market were not motivated by supporting digital artists, but rather by the eagerness of creators looking to make big money to join the digital image and collection markets, creating ease for them to expand into these markets. The use of crypto tokens as a sales mechanism obscures its potential to become a premium, scalable authentication certificate. To ensure that NFT endures, this potential must be realized by providing greater utility to artists, museums, and serious collectors.

The potential of crypto tokens as advanced, expansive authentication credentials is overshadowed by their use as a sales mechanism.

The term "NFT art" is often used to describe digital fine art from memes and collectibles, giving the impression that such art uses a new medium. However, it obscures the fact that NFT is not inherently related to digital art. (It only applies to artists using blockchain and tokens as a medium, which represent a very small fraction of so-called NFT art.) NFTs reside on the blockchain, while the artworks authenticated by them usually do not. Smart contracts, as a combination of an authentication certificate and a procurement protocol, contain a link with metadata that points to a centralized server and, in turn, to the actual artwork, usually stored in an IPFS (Interplanetary File System) used to establish peer-to-peer network protocols.

Because there is no intrinsic link to the artwork, any type of art can be cast as NFT, even non-reproducible non-digital forms, such as paintings and installations. In fact, most digital art forms, from installation art and software art to virtual reality and even most pure art, are not easily reproducible. In the past, the authenticity of digital artworks did not need to be ensured by NFT. For decades, they were authenticated through paper certificates and purchased by collectors and institutions as single pieces or as different editions of works. Only certain types of digital art, such as digital images that can be easily reproduced, and short films that "exist" and are distributed on the Internet, can benefit from the public record of digital ownership provided by the NFT.

In the field of fine art, most of the works referred to as digital art are much more complex than JPEG or GIF animations. Artists and galleries entering the NFT field, in order to accommodate the constraints of the market, now often produce complex interactive digital artworks as stills or short films that are recast as NFTs. because of the booming NFT market, these works themselves are priced lower than the abridged versions sold as NFTs. As a result, the works themselves are much more interesting to museums or devoted collectors of digital art than the NFTs from which they are derived.

The monetary value of art has always been built around the scarcity and authenticity of the work, while the scarcity of reproducible media, such as photography and video, is guaranteed by the number of editions. Artists such as Andy Warhol have even managed to turn reproductions of mass-produced goods such as Brillo Boxes into highly valuable works of art. For galleries and dealers, certificates of authenticity are key to determining monetary value; for museums not engaged in buying and selling, the provenance and research value of the work are key. nft has brought about a subtle shift in pricing perceptions. Traditionally, artworks purchased by collectors and institutions had to be accompanied by a certificate of authenticity. In the case of the NFT, the certificate they purchase is a codified title and often includes the location of the artwork. By "purchasing" an NFT, one does not mean acquiring a specific artwork through an NFT. The mechanism of sale itself has been commodified - a milestone of capitalism. One of the ultimate disruptors of these market mechanisms was the artist Tino Segal, who sold his shows for five figures by mere verbal agreement, without any documentation (whether a sales contract or a certificate of authenticity).

The impact of NFT art has been mixed. On the plus side, some in the crypto world have discovered the breadth and historical depth of digital art forms and are beginning to collect them. On the downside, the "NFT Art" label conflates blockchain-mediated works with sales mechanisms that sell ordinary digital images through tokenized certificates of authenticity. Some artists have used the generative potential of the blockchain to make a statement about the commodification of such sales mechanisms. Artist Jonas Lund's MVP (Most Valuable Painting) 2022, is a participatory artwork consisting of 512 digital images that have undergone a process of transformation. When one MVP is cast and sold, its properties determine the evolution of the remaining MVPs: an appropriate algorithm tracks each MVP's social media engagement and, based on that data, "optimizes" the composition of subsequent images to drive up attention. As a result, the final image sold captures all of the aesthetic value given to it by viewer and collector preferences over the course of the project.

The current focus on the NFT as a sales mechanism diminishes its potential to be used as an extended version of a certificate of authenticity. By combining ownership and provenance records with flexible copyright details, sales and preservation strategies, the NFT can become a more sophisticated authentication tool. Paper certificates of authenticity often exist separately from purchase agreements and from the information collected by institutions and arts organizations for future artwork display and preservation. A rare exception is Sol LeWitt's conceptual work, where his wall paintings including the work itself (execution notes) are on the certificate of authenticity. Of all the analog (non-digital) works, LeWitt's certificates of authenticity come closest to NFTs that cast the work's code on a blockchain, such as Rafaël Rozendaal's generative abstract NFT.

While NFTs do hold great promise for much-needed upgrades to primary authentication certificates, they are still a long way from realizing their potential. Most NFTs are cast to serve buyers of newly created images or GIF animations, meaning that the works have no past history, unlike those that already exist and are much older, whose past ownership can be encoded in detail. In addition, the depth of information that museums gather in the process of acquiring artworks may not be easily embedded in smart contracts or stored on the chain. For all forms of digital artworks - pure art, software art, installation art, virtual and augmented reality - this information includes the history of the work and its creation; the materials, techniques and expertise used; technical specifications; installation instructions and preservation-related information, from file formats and their structure, organization, meaning and function, to electrical wiring, and even wall colors and finishes. All this information may be stored down the chain, which requires an ideal repository capable of replacing the museum server.

The mechanism of sale itself has been commoditized:a milestone in capitalism.

Left Gallery (2015-2022), co-founded by artist Harm van den Dorpel, has developed more complex smart contracts using an extended language, pointing to publicly displayed copies of works (stored on IPFS) and providing collectors with complete archive packages containing information such as preservation advice and the artist's creative intent. left Gallery's contract also creates a digital twin of the metadata file on the artist's server, linked to the smart contract for verification, so the artist can validate his or her work and retain his or her sovereign identity. Some artists are addressing the inherent flaws of smart contracts. nancy Baker Cahill's Contract Killer (2021) is a series of site-specific augmented reality works that show scenes of handshakes in the process of dissolving. By casting them as NFTs, she places smart contracts in the context of other types of contracts in society, justice, finance, etc., emphasizing the instability of all such contracts. baker Cahill has been working with attorney Sarah Odenkirk to create off-chain contracts for owners and creators of digital artwork.

Another area that doesn't match the promise is the issue of transparency in blockchain ownership records. While the provenance and history of a work (including changes in ownership) is transparent to the public, many NFTs are purchased through anonymous wallets, obscuring the identity of the owner. Not every buyer wants transparency, and anonymity has long been valuable to high-end collectors who want to keep their collections private. At the same time, no system can be completely secure, and anonymity can be broken.

The NFT field is still in its infancy and there are many systemic issues that need to be addressed, from environmentally sustainable models associated with casting and trading markets, to a range of security issues. However, NFT is realizing the potential to become an extended authentication certificate for a variety of artworks, which will be a key factor in establishing a connection with the art market. "

Christiane Paul is Adjunct Curator of Digital Art at the Whitney Museum of American Art and Professor of Media Studies at The New School in New York.

With so many gaming projects, users often have to sift through them. A project should be evaluated not only by its popularity in the industry, but also by its team background, financing status, economic model, user data, etc.

The following is an analysis of the top GameFi projects in terms of daily active users and their popularity, to see if they are P2E games to watch.

5 important indicators for evaluating P2E games The number of GameFi game projects is growing rapidly, but not all of them have positive data performance. Therefore, users should analyze and do their due diligence before screening projects, which can be assessed by these 5 important indicators.

Team Background

Whether the team has a long-term growth plan and financial backing.

If there is support from a well-known venture capital investment, it can add a lot of credibility to the project.

Whether the founders and team technicians have experience in blockchain is also important, especially in the game field.

Financing

A good lead investor can bring traffic and visibility to the project, and can even participate in the design and iteration of the project's economic model. Also check to see if the team has past experience in successfully building and managing projects.

Pay attention to the amount and timing of the financing. For example, in a bull or bear market, the amount of funding varies from project to project.

Tokenomics

The design of the token economy is crucial in determining whether the game's mechanics will be stable over time and carry a large number of users for token transactions.

The liquidity as well as stability of the project's in-game funds can be examined through the single or dual coin model adopted by the project.

Project business model

How the project is profitable is directly reflected in the token price and token market value of the project.

Number of users

The number of users is the key to a healthy GameFi ecosystem. It reflects the popularity of a game project among its users.

Apply the 5 metrics to a real-world example

DeFi Kingdoms

Launched on August 23, 2021, DeFi Kingdoms is a Harmony chain-based metaverse project that incorporates more gamification into the DeFi revenue farming campaign.

Team Background

According to the official website, it is broken down into 9 groups of team members, with functions such as back-end development, front-end development, game design and lore artists, model design, community, operations, and commerce. Since the team members are basically anonymous, it is hard and difficult to judge their professional experience.

On the other hand, the white paper roadmap of the project is very clearly designed in 6 phases. Less than a year after launch, the team has already reached phase 4 and gone live with DFK Chain, the DeFi Kingdoms known for building kingdoms containing games, DEX, popularity pools, and NFT markets. At one point the game surpassed Axie Infinity in terms of trading volume.

As you can see, in a short period of time, the team has managed to move forward with development and operations as planned.

Financing Status

As of April 28th, there is no public funding information for this project. In comparison, Axie Infinity has received 5 rounds of funding totaling $9 million. Therefore, DeFi Kingdoms is a little behind in terms of funding.

However, on March 8, the Avalanche Foundation launched a $290 million incentive program to boost the growth of its subnet game program. DeFi Kingdoms is the first project to receive a $15 million incentive through the Multiverse program. It became the first multi-chain game.

Tokenomics

Many GameFi games currently have a multi-token model, although a single-token model is more convenient for players and less difficult for developers. However, a multi-token model minimizes the impact of token price fluctuations in the secondary market on the game.

DeFi Kingdoms uses a multi-token model to promote liquidity and stability of in-game funds.

$JEWEL is the primary value token in the game. Users can purchase game $JEWEL in the Marketplace and place it in Jeweler for a single coin pledge to receive equity tokens and governance tokens of $xJEWEL in the project to participate in eco-governance voting.

DeFi Kingdom's largest liquidity pool is $JEWEL with Harmony's native token ONE in the Marketplace to form an LP, which has seen approximately 300% to 400% APY over the past few months, generating high revenue generation for users.

To ensure the long-term stability of $JEWEL, most of the $JEWEL obtained from the LP pledge is temporarily locked by system settings. Locked tokens are gradually unlocked and can also be accelerated by mining tasks.

DeFi Kingdoms' Tokenomics and high revenue generation have attracted many users to the game.

Notably, since its release on Avalanche, the game also includes $CRYSTAL token, which functions similarly to $JEWEL on the chain.

The project's business model

The project's business model is directly reflected in its token price and token market value. As of April 27, DeFi Kingdom's market cap of $241 million exceeds that of Alien Worlds, the top game on the BSC chain, and the overall market cap trend for DeFi Kingdom is highly correlated with the number of users participating in the game.

Summary As you can see from the 5 dimensions above, DeFi Kingdoms is currently in its early stages and relies heavily on LPs to incentivize users or retail investors to enter in order to maintain a robust economic system. However, due to the fact that its team members are anonymous and unfunded, players or investors will need to be vetted for security when entering Play to Earn and investing.