Announcement Center
Core Summary
Chutes (SN64) is a decentralized Serverless AI computing platform built on the Bittensor network. In the AI computing race of Web3, its core positioning is similar to that of a ride-hailing platform and model PaaS (Platform as a Service). The platform integrates globally dispersed idle GPU computing power and combines advanced containerization scheduling technology to provide developers with out-of-the-box, pay-as-you-go AI inference APIs.
On the underlying architecture, Chutes adopts a classic dual-role game mechanism: miners provide the underlying hardware to respond to external requests at any time, and validators evaluate the quality and assign weights in real time, thus forming an industrial-grade inference network with both low cost and high concurrency.Currently, Chutes is leading the way in the field of decentralized computing by running a real business closed-loop. It has processed over 9.1 trillion Tokens, has over 400,000 active users, and has become the first self-reported sub-network in the Bittensor ecosystem to exceed $100 million in valuation. By feeding back real business revenue into the value of the token, Chutes has the potential to become a unicorn-level infrastructure in the decentralized AI track in the long run.
Industry Background: The Rise of AI Reasoning and the Dilemma of the Web2 Model
2.1 What is model inference? What is the essential difference between it and pre-training?
Before delving into computing platforms, we need to clarify two core stages in the AI model lifecycle: pre-training (Training) and inference (Inference) .
Model pre-training: This is the "learning phase" of AI models. Researchers need to input massive amounts of data (such as the text corpus of the entire Internet) into neural networks, and continuously adjust billions or even trillions of parameters within the model through large-scale matrix multiplication. This process is extremely time-consuming and requires high bandwidth of cluster computing power (such as NVLink), which is a heavy asset investment.
Model Inference: This is the "application phase" of the AI model. After the Model Training is completed, the parameters are fixed. At this time, the user inputs a prompt word (Prompt), and the model generates the next word with the highest probability through forward propagation calculation (Forward Pass). Compared with training, the single computing power required for inference is small, but it requires extremely high concurrent processing power, extremely low latency response (Latency), and 24/7 system stability.
2.2 The development logic of the computing power track and the shift of the industry's focus
Looking back at the development of the entire computing power track, we can clearly see a line of evolution: from early CPU general computing, to the rise of GPU parallel computing (the establishment of the CUDA ecosystem), and now to the flourishing of TPU and ASIC chips specifically designed for AI.
In the past few years, the focus of capital and technology has been almost entirely on "how to train smarter models". However, with the leap in the capabilities of open source large models such as the Llama series and DeepSeek, the intelligence gap between open source models and closed source giants (such as GPT-4) has been quickly bridged. The focus of value capture in the AI industry is irreversibly shifting from "model pre-training" to "model inference". The reason is that for large models to achieve true large-scale monetization and empowerment across various industries, they must have high availability and low latency response capabilities 24/7. At this point, "how to run models cheaply, stably, and quickly" becomes the biggest pain point in the industry.
2.3 Participants and Core Limitations of AI Reasoning in the Web2 Era
The current Web2 reasoning track is mainly dominated by the following types of participants:
Closed-source model API providers: such as OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini). They provide extremely easy-to-use APIs, but black box operation, expensive and there is a strong ecosystem binding.
Traditional Cloud as a Service giants: such as AWS (Amazon Web Services), Microsoft Azure, and Google Cloud. They provide low-level virtual machines or GPU bare-metal leasing, with high flexibility but extremely high operation and maintenance costs.
Vertical Reasoning as a Service (MaaS) platforms: such as Together AI, Anyscale, HuggingFace Inference Endpoints. They specialize in providing reasoning management services for open-source models.
However, when developers use the services of these Web2 giants such as OpenAI API, AWS, or Together AI, they still face three insurmountable challenges:
High "computing power tax" and rough settlement granularity: The software and hardware depreciation (venue rent, cooling system, expensive server procurement) and maintenance costs of centralized data centers are extremely high, resulting in high API call costs ultimately passed on to developers. In addition, traditional Cloud Services are often billed by "hour" or "whole machine", which is not friendly to large-scale applications with massive instantaneous concurrent demands. There is often serious waste of idle resources during off-peak periods.
Complex "infrastructure pitfalls": For start-up teams trying to bypass cloud vendor APIs and lease their own machines to deploy open-source large models, they must face an extremely steep learning curve. They need to solve complex issues such as GPU selection, low-level driver configuration, optimization of inference acceleration frameworks (such as vLLM and TensorRT), node maintenance, and containerized cluster orchestration, with a very high engineering threshold.
Vendor lock-in and data privacy risks: Once a company deeply integrates with a specific cloud vendor's API services, its future technical roadmap expansion and cost structure will be completely controlled by others. More critically, for highly sensitive industries such as healthcare, finance, and law, transmitting private data of core businesses to centralized API servers for processing poses a high risk of data leakage and compliance risks.
Solution: Chutes uses "network" to reconstruct AI reasoning
3.1 Core Positioning: "Ride-Hailing Platform" and Decentralized PaaS in the AI Ecosystem
In the large and well-defined Bittensor ecosystem, each subnet has its own role. For example, Templar (SN3) plays the role of a "car factory", its core task is to start from scratch and aggregate computing power to train top-notch open-source models; while Chutes (SN64) has a completely different positioning, focusing on "operational services", playing the role of a "ride-hailing platform" in the Web3 era.
Chutes itself is not responsible for producing models, but through its network protocol, it efficiently integrates globally distributed "vehicles" (i.e., idle GPU computing power scattered around the world), allowing ready-made and top-notch open-source models to run efficiently on these nodes, thus providing seamless inference services for external developers. Essentially, Chutes builds a decentralized, open-source-friendly, and highly standardized underlying PaaS (Platform as a Service) facility on top of the blockchain.
3.2 True Serverless Experience and Ultimate Cost Advantage
The core change that Chutes brings to developers is the realization of a true Serverless (serverless) experience. When using Chutes, developers don't have to worry about the underlying hardware selection, environment configuration, and cluster maintenance. They only need to modify a few lines of code to smoothly integrate into the network through fully compatible OpenAI-format APIs.
In terms of cost control, Chutes relies on the native encryption micropayment (Micropayments) mechanism of blockchain to achieve a super fine grain settlement that is rare in the industry. This disruptive settlement method completely eliminates the resource waste caused by the traditional cloud host's hourly billing. In practical applications, this advantage makes its price about 85% cheaper than traditional Cloud as a Service (such as AWS) and saves at least 40% of the cost compared to most centralized API platforms on the market.
3.3 Privacy Upgrade: TEE Architecture Builds a Trust Wheel
In decentralized networks, the biggest challenge has been how to protect the prompts and business data that users input to anonymous nodes. In response to the deep privacy concerns of enterprise users, Chutes is currently fully deploying TEE (Trusted Execution Environments) in its network.
TEE technology uses hardware-level encryption to isolate a strictly protected memory area within the CPU/GPU. This means that decentralized nodes can process reasoning requests in an encrypted "black box", and even the miners who provide computing power cannot spy on the user's sensitive input data during the entire calculation process. The introduction of this underlying technology fundamentally solves the compliance and privacy pain points of decentralized networks for enterprise-level commercial deployment, clearing the way for large-scale adoption of Web2 enterprises.
Core Architecture: How AI Inference is Completed in the Network
In the bottom-layer distributed architecture of Chutes, the system distributes massive inference tasks to the global network through complex routing and load balancing mechanisms. The core participants are clearly divided into two categories, and the final service quality is guaranteed through ingenious cryptography and economic market games
Miner (Service Provider): After the computing nodes around the world join the system through staking, they must load the "Permanently Hot Models" specified by the system according to the network instructions. "Hot Models" means that the massive parameters of the model have been pre-loaded into the GPU's video memory (VRAM). Based on advanced containerization scheduling technology, these computing nodes must maintain high system availability at all times to handle high concurrent API requests with minimal cold start latency.
Validators (Quality Inspectors): In a decentralized network, there is no central authority for supervision, so it is necessary to rely on validators. Validators are responsible for continuously sending randomly generated test requests to miners and routing real business requests. They strictly score miners' services from multiple core dimensions such as response latency (first word generation time TTFT), throughput (number of tokens generated per second), and output accuracy.Miners who perform well will receive generous rewards in the form of network tokens, while those who perform poorly or attempt malicious behavior will be ruthlessly eliminated by the system and even have their staking deposits confiscated.
This decentralized game architecture based on Bittensor's underlying consensus cleverly transforms interest-driven into a guarantee of service quality, ensuring that even a loosely distributed network can continuously output industrial-grade system stability comparable to that of a centralized top-tier data center.
Economic engine: Transitioning from "inflation-driven" to "real blood-making"
In the past cycles of the crypto world, many early Web3 computing power projects fell into a death spiral: they relied excessively on the malicious inflation of tokens to subsidize the attraction of computing power (i.e., so-called "mining"), and once the secondary market performed poorly, computing power would quickly be lost. In contrast, the core competitiveness of Chutes lies in its successful operation of a virtuous decentralized business loop.
Currently, the Chutes network can handle a large volume of real B-side (enterprise) and C-side (end point consumer) API requests every day. Through a token system, the network charges these users for real services. More importantly, relying on the system's built-in auto-staking and settlement mechanism, these business revenues from the external real world (which may start with fiat currency payments) will eventually be directly converted into strong buying for the network's ecosystem assets (tokens).This mechanism continuously feeds back to the token holders and all parties involved in maintaining the network, truly realizing the leap from the Ponzi model of "burning money to buy computing power" to a sustainable economic model of "real business blood production".
The current status of the ecosystem and its impressive data performance
According to recent tracking of on-chain and business data, the Chutes network has demonstrated an extremely strong throughput limit and deep market penetration rate in actual high-concurrency business scenarios
Breakthrough in core business volume: Chutes Network has processed over 9.1 trillion Tokens, a significant figure in both Web3 and many medium-sized Web2 platforms. Its peak daily processing capacity can reach 50 billion times, serving over 400,000 end point and developer users.
Absolute leading market position: With solid business data, Chutes became the first network in the Bittensor ecosystem to self-report a valuation exceeding $100 million milestone.
Deep ecosystem integration and "water, electricity, and coal" attributes: Externally, Chutes has successfully served many break into other demographic applications. Internally, Chutes has gradually become the core computing power provider for other subnets within the Bittensor ecosystem, such as subnets focusing on various vertical applications and data processing, playing a key role as the "water, electricity, and coal" of the entire decentralized AI ecosystem.
Healthy token economic indicators: As of April 7, 2026, the price of Chutes' subnet token Alpha (alpha token) was about 0.085 TAO. The network not only attracted about 13,666 coin holders, but also had 244 active miner nodes and 12 validator nodes. Its network emission was 8.77%. Meanwhile, in its DEX liquidity pool, the base TAO accounted for 7.88% and Alpha accounted for 92.12%. Regardless of the scale of computing power or capital, Chutes is an absolute top project in the TAO ecosystem. These data clearly reflect its actual market heat.
(Data source: https://bittensormarketcap.com/subnets/64 )
Competitive landscape, potential challenges, and final prospects
7.1 Core Advantage and Track Competitive Edge Barrier
The current decentralized computing (DePIN + AI) track has completely bid farewell to the barbaric era of "talking about concepts and writing white papers" and entered the deep water area of "competing in delivery, cost, and stability". Compared with platforms that only provide bare metal leasing, Chutes' most powerful Competitive Edge lies in its commercial-grade inference delivery capability that has been rigorously verified by massive business data and absolute cost crushing advantage over traditional Web2 giants .In combination with the TEE privacy encryption architecture that will be fully launched in the future, Chutes has successfully provided developers who fear the monopoly and data hegemony of Silicon Valley giants with an ideal infrastructure that is completely permissionless and cost-effective.
7.2 Potential Challenges and Breaking Bottlenecks
Although the current business data and model circulation are impressive, Chutes still needs to overcome some hardcore challenges if it wants to move from Web3 to the broader mainstream world in the future:
Testing redundancy and resilience under extreme concurrency: When a truly "killer" million-level DAU AI application appears in the future and suddenly accesses the network in a very short period of time, whether the decentralized network can maintain a low latency response of milliseconds without downtime even under a surge in computing power demand is the ultimate test of the scheduling algorithm.
Breaking the mindset of the enterprise market: Despite the support of TEE technology, it still takes a long and continuous market education and cultivation to break the stereotype of traditional Web2 companies and make more compliance enterprises trust and adopt decentralized API protocols on a large scale.
7.3 Terminal Simulation
In summary, with the advent of the era of high-frequency and autonomous interaction between large models of MultiModal Machine Learning and AI agents, there will be an exponential increase in the demand for reasoning between machines. At this time, a low-cost, unrestricted, decentralized reasoning layer that supports on-demand micropayment will become an indispensable infrastructure for the next generation of the Internet.
Chutes not only represents the decentralization of the bottom-layer computing quota method, but also the universal distribution of open-source intellectual resources in human society. If Chutes can successfully overcome the high barrier of traffic handling and the trust gap of traditional enterprise terminals, it has the potential to become a super base and unicorn platform with long-term value capture ability in the decentralized AI track in the next few years.
Core Summary
Chutes (SN64) is a decentralized Serverless AI computing platform built on the Bittensor network. In the AI computing race of Web3, its core positioning is similar to that of a ride-hailing platform and model PaaS (Platform as a Service). The platform integrates globally dispersed idle GPU computing power and combines advanced containerization scheduling technology to provide developers with out-of-the-box, pay-as-you-go AI inference APIs.
On the underlying architecture, Chutes adopts a classic dual-role game mechanism: miners provide the underlying hardware to respond to external requests at any time, and validators evaluate the quality and assign weights in real time, thus forming an industrial-grade inference network with both low cost and high concurrency.Currently, Chutes is leading the way in the field of decentralized computing by running a real business closed-loop. It has processed over 9.1 trillion Tokens, has over 400,000 active users, and has become the first self-reported sub-network in the Bittensor ecosystem to exceed $100 million in valuation. By feeding back real business revenue into the value of the token, Chutes has the potential to become a unicorn-level infrastructure in the decentralized AI track in the long run.
Industry Background: The Rise of AI Reasoning and the Dilemma of the Web2 Model
2.1 What is model inference? What is the essential difference between it and pre-training?
Before delving into computing platforms, we need to clarify two core stages in the AI model lifecycle: pre-training (Training) and inference (Inference) .
Model pre-training: This is the "learning phase" of AI models. Researchers need to input massive amounts of data (such as the text corpus of the entire Internet) into neural networks, and continuously adjust billions or even trillions of parameters within the model through large-scale matrix multiplication. This process is extremely time-consuming and requires high bandwidth of cluster computing power (such as NVLink), which is a heavy asset investment.
Model Inference: This is the "application phase" of the AI model. After the Model Training is completed, the parameters are fixed. At this time, the user inputs a prompt word (Prompt), and the model generates the next word with the highest probability through forward propagation calculation (Forward Pass). Compared with training, the single computing power required for inference is small, but it requires extremely high concurrent processing power, extremely low latency response (Latency), and 24/7 system stability.
2.2 The development logic of the computing power track and the shift of the industry's focus
Looking back at the development of the entire computing power track, we can clearly see a line of evolution: from early CPU general computing, to the rise of GPU parallel computing (the establishment of the CUDA ecosystem), and now to the flourishing of TPU and ASIC chips specifically designed for AI.
In the past few years, the focus of capital and technology has been almost entirely on "how to train smarter models". However, with the leap in the capabilities of open source large models such as the Llama series and DeepSeek, the intelligence gap between open source models and closed source giants (such as GPT-4) has been quickly bridged. The focus of value capture in the AI industry is irreversibly shifting from "model pre-training" to "model inference". The reason is that for large models to achieve true large-scale monetization and empowerment across various industries, they must have high availability and low latency response capabilities 24/7. At this point, "how to run models cheaply, stably, and quickly" becomes the biggest pain point in the industry.
2.3 Participants and Core Limitations of AI Reasoning in the Web2 Era
The current Web2 reasoning track is mainly dominated by the following types of participants:
Closed-source model API providers: such as OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini). They provide extremely easy-to-use APIs, but black box operation, expensive and there is a strong ecosystem binding.
Traditional Cloud as a Service giants: such as AWS (Amazon Web Services), Microsoft Azure, and Google Cloud. They provide low-level virtual machines or GPU bare-metal leasing, with high flexibility but extremely high operation and maintenance costs.
Vertical Reasoning as a Service (MaaS) platforms: such as Together AI, Anyscale, HuggingFace Inference Endpoints. They specialize in providing reasoning management services for open-source models.
However, when developers use the services of these Web2 giants such as OpenAI API, AWS, or Together AI, they still face three insurmountable challenges:
High "computing power tax" and rough settlement granularity: The software and hardware depreciation (venue rent, cooling system, expensive server procurement) and maintenance costs of centralized data centers are extremely high, resulting in high API call costs ultimately passed on to developers. In addition, traditional Cloud Services are often billed by "hour" or "whole machine", which is not friendly to large-scale applications with massive instantaneous concurrent demands. There is often serious waste of idle resources during off-peak periods.
Complex "infrastructure pitfalls": For start-up teams trying to bypass cloud vendor APIs and lease their own machines to deploy open-source large models, they must face an extremely steep learning curve. They need to solve complex issues such as GPU selection, low-level driver configuration, optimization of inference acceleration frameworks (such as vLLM and TensorRT), node maintenance, and containerized cluster orchestration, with a very high engineering threshold.
Vendor lock-in and data privacy risks: Once a company deeply integrates with a specific cloud vendor's API services, its future technical roadmap expansion and cost structure will be completely controlled by others. More critically, for highly sensitive industries such as healthcare, finance, and law, transmitting private data of core businesses to centralized API servers for processing poses a high risk of data leakage and compliance risks.
Solution: Chutes uses "network" to reconstruct AI reasoning
3.1 Core Positioning: "Ride-Hailing Platform" and Decentralized PaaS in the AI Ecosystem
In the large and well-defined Bittensor ecosystem, each subnet has its own role. For example, Templar (SN3) plays the role of a "car factory", its core task is to start from scratch and aggregate computing power to train top-notch open-source models; while Chutes (SN64) has a completely different positioning, focusing on "operational services", playing the role of a "ride-hailing platform" in the Web3 era.
Chutes itself is not responsible for producing models, but through its network protocol, it efficiently integrates globally distributed "vehicles" (i.e., idle GPU computing power scattered around the world), allowing ready-made and top-notch open-source models to run efficiently on these nodes, thus providing seamless inference services for external developers. Essentially, Chutes builds a decentralized, open-source-friendly, and highly standardized underlying PaaS (Platform as a Service) facility on top of the blockchain.
3.2 True Serverless Experience and Ultimate Cost Advantage
The core change that Chutes brings to developers is the realization of a true Serverless (serverless) experience. When using Chutes, developers don't have to worry about the underlying hardware selection, environment configuration, and cluster maintenance. They only need to modify a few lines of code to smoothly integrate into the network through fully compatible OpenAI-format APIs.
In terms of cost control, Chutes relies on the native encryption micropayment (Micropayments) mechanism of blockchain to achieve a super fine grain settlement that is rare in the industry. This disruptive settlement method completely eliminates the resource waste caused by the traditional cloud host's hourly billing. In practical applications, this advantage makes its price about 85% cheaper than traditional Cloud as a Service (such as AWS) and saves at least 40% of the cost compared to most centralized API platforms on the market.
3.3 Privacy Upgrade: TEE Architecture Builds a Trust Wheel
In decentralized networks, the biggest challenge has been how to protect the prompts and business data that users input to anonymous nodes. In response to the deep privacy concerns of enterprise users, Chutes is currently fully deploying TEE (Trusted Execution Environments) in its network.
TEE technology uses hardware-level encryption to isolate a strictly protected memory area within the CPU/GPU. This means that decentralized nodes can process reasoning requests in an encrypted "black box", and even the miners who provide computing power cannot spy on the user's sensitive input data during the entire calculation process. The introduction of this underlying technology fundamentally solves the compliance and privacy pain points of decentralized networks for enterprise-level commercial deployment, clearing the way for large-scale adoption of Web2 enterprises.
Core Architecture: How AI Inference is Completed in the Network
In the bottom-layer distributed architecture of Chutes, the system distributes massive inference tasks to the global network through complex routing and load balancing mechanisms. The core participants are clearly divided into two categories, and the final service quality is guaranteed through ingenious cryptography and economic market games
Miner (Service Provider): After the computing nodes around the world join the system through staking, they must load the "Permanently Hot Models" specified by the system according to the network instructions. "Hot Models" means that the massive parameters of the model have been pre-loaded into the GPU's video memory (VRAM). Based on advanced containerization scheduling technology, these computing nodes must maintain high system availability at all times to handle high concurrent API requests with minimal cold start latency.
Validators (Quality Inspectors): In a decentralized network, there is no central authority for supervision, so it is necessary to rely on validators. Validators are responsible for continuously sending randomly generated test requests to miners and routing real business requests. They strictly score miners' services from multiple core dimensions such as response latency (first word generation time TTFT), throughput (number of tokens generated per second), and output accuracy.Miners who perform well will receive generous rewards in the form of network tokens, while those who perform poorly or attempt malicious behavior will be ruthlessly eliminated by the system and even have their staking deposits confiscated.
This decentralized game architecture based on Bittensor's underlying consensus cleverly transforms interest-driven into a guarantee of service quality, ensuring that even a loosely distributed network can continuously output industrial-grade system stability comparable to that of a centralized top-tier data center.
Economic engine: Transitioning from "inflation-driven" to "real blood-making"
In the past cycles of the crypto world, many early Web3 computing power projects fell into a death spiral: they relied excessively on the malicious inflation of tokens to subsidize the attraction of computing power (i.e., so-called "mining"), and once the secondary market performed poorly, computing power would quickly be lost. In contrast, the core competitiveness of Chutes lies in its successful operation of a virtuous decentralized business loop.
Currently, the Chutes network can handle a large volume of real B-side (enterprise) and C-side (end point consumer) API requests every day. Through a token system, the network charges these users for real services. More importantly, relying on the system's built-in auto-staking and settlement mechanism, these business revenues from the external real world (which may start with fiat currency payments) will eventually be directly converted into strong buying for the network's ecosystem assets (tokens).This mechanism continuously feeds back to the token holders and all parties involved in maintaining the network, truly realizing the leap from the Ponzi model of "burning money to buy computing power" to a sustainable economic model of "real business blood production".
The current status of the ecosystem and its impressive data performance
According to recent tracking of on-chain and business data, the Chutes network has demonstrated an extremely strong throughput limit and deep market penetration rate in actual high-concurrency business scenarios
Breakthrough in core business volume: Chutes Network has processed over 9.1 trillion Tokens, a significant figure in both Web3 and many medium-sized Web2 platforms. Its peak daily processing capacity can reach 50 billion times, serving over 400,000 end point and developer users.
Absolute leading market position: With solid business data, Chutes became the first network in the Bittensor ecosystem to self-report a valuation exceeding $100 million milestone.
Deep ecosystem integration and "water, electricity, and coal" attributes: Externally, Chutes has successfully served many break into other demographic applications. Internally, Chutes has gradually become the core computing power provider for other subnets within the Bittensor ecosystem, such as subnets focusing on various vertical applications and data processing, playing a key role as the "water, electricity, and coal" of the entire decentralized AI ecosystem.
Healthy token economic indicators: As of April 7, 2026, the price of Chutes' subnet token Alpha (alpha token) was about 0.085 TAO. The network not only attracted about 13,666 coin holders, but also had 244 active miner nodes and 12 validator nodes. Its network emission was 8.77%. Meanwhile, in its DEX liquidity pool, the base TAO accounted for 7.88% and Alpha accounted for 92.12%. Regardless of the scale of computing power or capital, Chutes is an absolute top project in the TAO ecosystem. These data clearly reflect its actual market heat.
(Data source: https://bittensormarketcap.com/subnets/64 )
Competitive landscape, potential challenges, and final prospects
7.1 Core Advantage and Track Competitive Edge Barrier
The current decentralized computing (DePIN + AI) track has completely bid farewell to the barbaric era of "talking about concepts and writing white papers" and entered the deep water area of "competing in delivery, cost, and stability". Compared with platforms that only provide bare metal leasing, Chutes' most powerful Competitive Edge lies in its commercial-grade inference delivery capability that has been rigorously verified by massive business data and absolute cost crushing advantage over traditional Web2 giants .In combination with the TEE privacy encryption architecture that will be fully launched in the future, Chutes has successfully provided developers who fear the monopoly and data hegemony of Silicon Valley giants with an ideal infrastructure that is completely permissionless and cost-effective.
7.2 Potential Challenges and Breaking Bottlenecks
Although the current business data and model circulation are impressive, Chutes still needs to overcome some hardcore challenges if it wants to move from Web3 to the broader mainstream world in the future:
Testing redundancy and resilience under extreme concurrency: When a truly "killer" million-level DAU AI application appears in the future and suddenly accesses the network in a very short period of time, whether the decentralized network can maintain a low latency response of milliseconds without downtime even under a surge in computing power demand is the ultimate test of the scheduling algorithm.
Breaking the mindset of the enterprise market: Despite the support of TEE technology, it still takes a long and continuous market education and cultivation to break the stereotype of traditional Web2 companies and make more compliance enterprises trust and adopt decentralized API protocols on a large scale.
7.3 Terminal Simulation
In summary, with the advent of the era of high-frequency and autonomous interaction between large models of MultiModal Machine Learning and AI agents, there will be an exponential increase in the demand for reasoning between machines. At this time, a low-cost, unrestricted, decentralized reasoning layer that supports on-demand micropayment will become an indispensable infrastructure for the next generation of the Internet.
Chutes not only represents the decentralization of the bottom-layer computing quota method, but also the universal distribution of open-source intellectual resources in human society. If Chutes can successfully overcome the high barrier of traffic handling and the trust gap of traditional enterprise terminals, it has the potential to become a super base and unicorn platform with long-term value capture ability in the decentralized AI track in the next few years.