Why Do AI Agents Always Forget Things? A Deep Dive into the MemOS Memory System
TL; DR Key Takeaways
- Current AI Agents face severe "memory loss" issues in long conversations, with 65% of enterprise AI failures directly related to context drift.
- MemOS extracts memory from the Prompt into a system-level independent component, reducing actual Token consumption by approximately 61% and improving temporal reasoning accuracy by 159%.
- The most core differentiation of MemOS lies in its "conversation → Task → Skill" memory evolution chain, enabling Agents to truly reuse experience.
- This article provides a horizontal comparison of four major Agent memory solutions: MemOS, Mem0, Zep, and Letta, to help developers quickly choose the right one.
Is Your AI Agent Also Repeatedly Asking the Same Question?
You've probably encountered this scenario: you spend half an hour teaching an AI Agent about a project's background, only to start a new session the next day, and it asks you from scratch, "What is your project about?" Or, even worse, a complex multi-step task is halfway through, and the Agent suddenly "forgets" the steps already completed, starting to repeat operations.
This is not an isolated case. According to Zylos Research's 2025 report, nearly 65% of enterprise AI application failures can be attributed to context drift or memory loss 1. The root of the problem is that most current Agent frameworks still rely on the Context Window to maintain state. The longer the session, the greater the Token overhead, and critical information gets buried in lengthy conversation histories.
This article is suitable for developers building AI Agents, engineers using frameworks like LangChain / CrewAI, and all technical professionals who have been shocked by Token bills. We will deeply analyze how the open-source project MemOS solves this problem with a "memory operating system" approach, and provide a horizontal comparison of mainstream memory solutions to help you make technology selection decisions.
Why Is Long-Term Memory So Difficult for AI Agents?
To understand what problem MemOS is solving, we first need to understand where the AI Agent's memory dilemma truly lies.
Context Window does not equal memory. Many people think that Gemini's 1M Token window or Claude's 200K window is "enough," but window size and memory capacity are two different things. A study by JetBrains Research at the end of 2025 clearly pointed out that as context length increases, LLMs' efficiency in utilizing information significantly decreases 2. Stuffing the entire conversation history into the Prompt not only makes it difficult for the Agent to find critical information but also causes the "Lost in the Middle" phenomenon, where content in the middle of the context is recalled the worst.
Token costs expand exponentially. A typical customer service Agent consumes approximately 3,500 Tokens per interaction 3. If the full conversation history and knowledge base context need to be reloaded every time, an application with 10,000 daily active users can easily exceed five figures in monthly Token costs. This doesn't even account for the additional consumption from multi-turn reasoning and tool calls.
Experience cannot be accumulated and reused. This is the most easily overlooked problem. If an Agent helps a user solve a complex data cleaning task today, it won't "remember" the solution next time it encounters a similar problem. Every interaction is a one-off, making it impossible to form reusable experience. As an analysis by Tencent News stated: "An Agent without memory is just an advanced chatbot" 4.
These three problems combined constitute the most intractable infrastructure bottleneck in current Agent development.
MemOS's Solution: Turning Memory into an Operating System
MemOS was developed by the Chinese startup MemTensor. It first released the Memory³ hierarchical large model at the World Artificial Intelligence Conference (WAIC) in July 2024, and officially open-sourced MemOS 1.0 in July 2025. It has now iterated to v2.0 "Stardust." The project uses the Apache 2.0 open-source license and is continuously active on GitHub.
The core concept of MemOS can be summarized in one sentence: Extract Memory from the Prompt and run it as an independent component at the system layer.
The traditional approach is to stuff all conversation history, user preferences, and task context into the Prompt, making the LLM "re-read" all information during each inference. MemOS takes a completely different approach. It inserts a "memory operating system" layer between the LLM and the application, responsible for memory storage, retrieval, updating, and scheduling. The Agent no longer needs to load the full history every time; instead, MemOS intelligently retrieves the most relevant memory fragments into the context based on the current task's semantics.
This architecture brings three direct benefits:
First, Token consumption significantly decreases. Official data from the LoCoMo benchmark shows that MemOS reduces Token consumption by approximately 60.95% compared to traditional full-load methods, with memory Token savings reaching 35.24% 5. A report from JiQiZhiXing mentioned that overall accuracy increased by 38.97% 6. In other words, better results are achieved with fewer Tokens.
Second, cross-session memory persistence. MemOS supports automatic extraction and persistent storage of key information from conversations. When a new session is started next time, the Agent can directly access previously accumulated memories, eliminating the need for the user to re-explain the background. Data is stored locally in SQLite, running 100% locally, ensuring data privacy.
Third, multi-Agent memory sharing. Multiple Agent instances can share memory through the same user_id, enabling automatic context handover. This is a critical capability for building multi-Agent collaborative systems.
The Most Interesting Feature: How Conversations Evolve into Reusable Skills
MemOS's most striking design is its "memory evolution chain."
Most memory systems focus on "storing" and "retrieving": saving conversation history and retrieving it when needed. MemOS adds another layer of abstraction. Conversation content doesn't accumulate verbatim but evolves through three stages:
Stage One: Conversation → Structured Memory. Raw conversations are automatically extracted into structured memory entries, including key facts, user preferences, timestamps, and other metadata. MemOS uses its self-developed MemReader model (available in 4B/1.7B/0.6B sizes) to perform this extraction process, which is more efficient and accurate than directly using GPT-4 for summarization.
Stage Two: Memory → Task. When the system identifies that certain memory entries are associated with specific task patterns, it automatically aggregates them into Task-level knowledge units. For example, if you repeatedly ask the Agent to perform "Python data cleaning," the relevant conversation memories will be categorized into a Task template.
Stage Three: Task → Skill. When a Task is repeatedly triggered and validated as effective, it further evolves into a reusable Skill. This means that problems the Agent has encountered before will likely not be asked a second time; instead, it will directly invoke the existing Skill to execute.
The brilliance of this design lies in its simulation of human learning: from specific experiences to abstract rules, and then to automated skills. The MemOS paper refers to this capability as "Memory-Augmented Generation" and has published two related papers on arXiv 7.
Actual data also confirms the effectiveness of this design. In the LongMemEval evaluation, MemOS's cross-session reasoning capability improved by 40.43% compared to the GPT-4o-mini baseline; in the PrefEval-10 personalized preference evaluation, the improvement was an astonishing 2568% 5.
How Developers Can Quickly Get Started with MemOS
If you want to integrate MemOS into your Agent project, here's a quick start guide:
Step One: Choose a deployment method. MemOS offers two modes. Cloud mode allows you to directly register for an API Key on the MemOS Dashboard, and integrate with a few lines of code. Local mode deploys via Docker, with all data stored locally in SQLite, suitable for scenarios with data privacy requirements.
Step Two: Initialize the memory system. The core concept is MemCube (Memory Cube), where each MemCube corresponds to a user's or an Agent's memory space. Multiple MemCubes can be uniformly managed through the MOS (Memory Operating System) layer. Here's a code example:
``python
from memos.mem_os.main import MOS
from memos.configs.mem_os import MOSConfig
# Initialize MOS
config = MOSConfig.from_json_file("config.json")
memory = MOS(config)
# Create a user and register a memory space
memory.create_user(user_id="your-user-id")
memory.register_mem_cube("path/to/mem_cube", user_id="your-user-id")
# Add conversation memory
memory.add(
messages=[
{"role": "user", "content": "My project uses Python for data analysis"},
{"role": "assistant", "content": "Understood, I will remember this background information"}
],
user_id="your-user-id"
)
# Retrieve relevant memories later
results = memory.search(query="What language does my project use?", user_id="your-user-id")
``
Step Three: Integrate the MCP protocol. MemOS v1.1.2 and later fully support the Model Context Protocol (MCP), meaning you can use MemOS as an MCP Server, allowing any MCP-enabled IDE or Agent framework to directly read and write external memories.
Common pitfalls reminder: MemOS's memory extraction relies on LLM inference. If the underlying model's capability is insufficient, memory quality will suffer. Developers in the Reddit community have reported that when using small-parameter local models, memory accuracy is not as good as calling the OpenAI API 8. It is recommended to use at least a GPT-4o-mini level model as the memory processing backend in production environments.
In daily work, Agent-level memory management solves the problem of "how machines remember," but for developers and knowledge workers, "how humans efficiently accumulate and retrieve information" is equally important. YouMind's Board feature offers a complementary approach: you can save research materials, technical documents, and web links uniformly into a knowledge space, and the AI assistant will automatically organize them and support cross-document Q&A. For example, when evaluating MemOS, you can clip GitHub READMEs, arXiv papers, and community discussions to the same Board with one click, then directly ask, "What are the benchmark differences between MemOS and Mem0?" The AI will retrieve answers from all the materials you've saved. This "human + AI collaborative accumulation" model complements MemOS's Agent memory management well.
Horizontal Comparison of Mainstream Agent Memory Solutions
Since 2025, several open-source projects have emerged in the Agent memory space. Here's a comparison of four of the most representative solutions:
Tool | Best Use Case | Open Source License | Core Advantages | Main Limitations |
|---|---|---|---|---|
Complex Agents requiring memory evolution and Skill reuse | Apache 2.0 | Memory evolution chain, SOTA benchmark, MCP support | Heavier architecture, potentially over-engineered for small projects | |
Quickly adding a memory layer to existing Agents | Apache 2.0 | One-line code integration, cloud-hosted, rich ecosystem | Coarser memory granularity, no Skill evolution support | |
Long-term memory for enterprise-grade conversational systems | Commercial + Open Source | Automatic summarization, entity extraction, enterprise-grade security | Limited features in open-source version, full features require payment | |
Letta (formerly MemGPT) | Research projects and custom memory architectures | Apache 2.0 | Highly customizable, strong academic background | High barrier to entry, smaller community size |
A Zhihu article from 2025, "AI Memory System Horizontal Review," performed a detailed benchmark reproduction of these solutions, concluding that MemOS performed most stably on evaluation sets like LoCoMo and LongMemEval, and was the "only Memory OS with consistent official evaluations, GitHub cross-tests, and community reproduction results" 9.
If your need is not Agent-level memory management, but rather personal or team knowledge accumulation and retrieval, YouMind offers another dimension of solutions. Its positioning is an integrated studio for "learning → thinking → creating," supporting saving various sources like web pages, PDFs, videos, and podcasts, with AI automatically organizing them and supporting cross-document Q&A. Compared to Agent memory systems which focus on "making machines remember," YouMind focuses more on "helping people manage knowledge efficiently." However, it should be noted that YouMind currently does not provide Agent memory APIs similar to MemOS; they address different levels of needs.
Selection Advice:
- If you are building complex Agents that require cross-session memory and experience reuse, MemOS is currently the strongest benchmarked choice.
- If you just need to quickly add a memory layer to an existing Agent, Mem0 has the lowest integration cost.
- If you are an enterprise customer and require compliance and security, Zep's enterprise version is worth considering.
- If you are a researcher looking to deeply customize memory architecture, Letta offers the highest flexibility.
FAQ
Q: What is the difference between MemOS and RAG (Retrieval-Augmented Generation)?
A: RAG focuses on retrieving information from external knowledge bases and injecting it into the Prompt, essentially still following a "look up every time, insert every time" pattern. MemOS, on the other hand, manages memory as a system-level component, supporting automatic extraction, evolution, and Skill-ification of memory. The two can be used complementarily, with MemOS handling conversational memory and experience accumulation, and RAG handling static knowledge base retrieval.
Q: Which LLMs does MemOS support? What are the hardware requirements for deployment?
A: MemOS supports calling mainstream models like OpenAI and Claude via API, and also supports integrating local models via Ollama. Cloud mode has no hardware requirements; Local mode recommends a Linux environment, and the built-in MemReader model has a minimum size of 0.6B parameters, which can run on a regular GPU. Docker deployment is out-of-the-box.
Q: How secure is MemOS's data? Where is memory data stored?
A: In Local mode, all data is stored in a local SQLite database, running 100% locally, and is not uploaded to any external servers. In Cloud mode, data is stored on MemOS's official servers. For enterprise users, Local mode or private deployment solutions are recommended.
Q: How high are the Token costs for AI Agents generally?
A: Taking a typical customer service Agent as an example, each interaction consumes approximately 3,150 input Tokens and 400 output Tokens. Based on GPT-4o pricing in 2026, an application with 10,000 daily active users and an average of 5 interactions per user per day would have monthly Token costs between $2,000 and $5,000. Using memory optimization solutions like MemOS can reduce this figure by over 50%.
Q: Besides MemOS, what other methods can reduce Agent Token costs?
A: Mainstream methods include Prompt compression (e.g., LLMLingua), semantic caching (e.g., Redis semantic cache), context summarization, and selective loading strategies. Redis's 2026 technical blog points out that semantic caching can completely bypass LLM inference calls in scenarios with highly repetitive queries, leading to significant cost savings 10. These methods can be used in conjunction with MemOS.
Summary
The AI Agent memory problem is essentially a system architecture problem, not merely a model capability problem. MemOS's answer is to free memory from the Prompt and run it as an independent operating system layer. Empirical data proves the feasibility of this path: Token consumption reduced by 61%, temporal reasoning improved by 159%, and SOTA achieved across four major evaluation sets.
For developers, the most noteworthy aspect is MemOS's "conversation → Task → Skill" evolution chain. It transforms the Agent from a tool that "starts from scratch every time" into a system capable of accumulating experience and continuously evolving. This may be the critical step for Agents to go from "usable" to "effective."
If you are interested in AI-driven knowledge management and information accumulation, you are welcome to try YouMind for free and experience the integrated workflow of "learning → thinking → creating."
References
[1] LLM Context Window Management and Long Context Strategies 2026
[2] Cutting Through the Noise: Smarter Context Management for LLM-Powered Agents
[3] Understanding LLM Cost Per Token: A Practical Guide for 2026
[4] Ranked First in Four Major Evaluation Sets, How MemOS Defines the New Infrastructure of the AI Era
[5] MemOS GitHub Repository: AI Memory OS for LLM and Agent Systems
[7] MemOS: A Memory Operating System for AI Systems
[8] Reddit LocalLLaMA Community: MemOS Discussion Thread
[10] LLM Token Optimization: Cutting Costs and Latency in 2026
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GPT-5.4 can write fluid articles, and AI Agents can automate complex workflows. However, there is a massive chasm between "performing well on specific tasks" and "possessing general intelligence." An AI that can beat a world champion at chess might not even be able to "hand me the cup on the table." Step 2: Focus on the qualifiers, not the headlines. Huang said "I think," not "We have proven." Altman said "spiritual," not "literal." These qualifiers aren't modesty; they are precise legal and PR strategies. When tens of billions of dollars in contract terms are at stake, every word is carefully weighed. Step 3: Look at actions, not declarations. At GTC 2026, NVIDIA released seven new chips and introduced DLSS 5, the OpenClaw platform, and the NemoClaw enterprise Agent stack. These are tangible technical advancements. However, Huang mentioned "inference" nearly 40 times in his speech, while "training" was mentioned only about 10 times. This indicates the industry's focus is shifting from "building smarter AI" to "making AI execute tasks more efficiently." This is engineering progress, not an intelligence breakthrough. Step 4: Build your own information tracking system. The information density in the AI industry is extremely high, with major releases and statements every week. Relying solely on clickbait news feeds makes it easy to be misled. It is recommended to develop a habit of reading primary sources (such as official company blogs, academic papers, and podcast transcripts) and using tools to systematically save and organize this data. For example, you can use the Board feature in to save key sources, and use AI to ask questions and cross-verify the data at any time, avoiding being misled by a single narrative. Q: Is the AGI Jensen Huang is talking about the same as the AGI defined by OpenAI? A: No. Huang answered based on the narrow definition proposed by Lex Fridman (AI being able to start a $1 billion company), whereas the AGI definition in OpenAI's charter is "highly autonomous systems that outperform humans at most economically valuable work." There is a massive gap between the two standards, with the latter requiring a scope of capability far beyond the former. Q: Can current AI really operate a company independently? A: Not currently. Huang himself admitted that while an AI Agent might create a short-lived viral app, "the odds of building NVIDIA is zero." Current AI excels at structured task execution but still relies heavily on human guidance in scenarios requiring long-term strategic judgment, cross-domain coordination, and handling unknown situations. Q: What impact will the achievement of AGI have on everyday jobs? A: Even by the most optimistic definitions, the impact of current AI is primarily seen in improving the efficiency of specific tasks rather than fully replacing human work. Sam Altman also admitted in late 2025 that AGI's "social impact is much smaller than expected." In the short term, AI is more likely to change the way we work as a powerful assistant tool rather than directly replacing roles. Q: Why are tech CEOs so eager to declare that AGI has been achieved? A: The reasons are multifaceted. NVIDIA's core business is selling AI compute chips; the AGI narrative maintains market enthusiasm for investment in AI infrastructure. OpenAI's contract with Microsoft includes AGI trigger clauses, where the definition of AGI directly affects the distribution of tens of billions of dollars. Furthermore, in capital markets, the "AGI is coming" narrative is a major pillar supporting the high valuations of AI companies. Q: How far is China's AI development from AGI? A: China has made significant progress in the AI field. As of June 2025, the number of generative AI users in China reached 515 million, and large models like DeepSeek and Qwen have performed excellently in various benchmarks. However, AGI is a global technical challenge, and currently, there is no AGI system widely recognized by the global academic community. The market size of China's AI industry is expected to have a compound annual growth rate of 30.6%–47.1% from 2025 to 2035, showing strong momentum. Jensen Huang's "AGI achieved" statement is essentially an optimistic expression based on an extremely narrow definition, rather than a verified technical milestone. He himself admitted that current AI Agents are worlds away from building truly complex enterprises. The phenomenon of repeatedly "moving the goalposts" for the definition of AGI reveals the delicate interplay between technical narrative and commercial interests in the tech industry. From OpenAI to NVIDIA, every "we achieved AGI" claim is accompanied by a quiet lowering of the standard. As information consumers, what we need is not to chase headlines but to build our own framework for judgment. AI technology is undoubtedly progressing rapidly. The new chips, Agent platforms, and inference optimization technologies released at GTC 2026 are real engineering breakthroughs. But packaging these advancements as "AGI achieved" is more of a market narrative strategy than a scientific conclusion. Staying curious, remaining critical, and continuously tracking primary sources is the best strategy to avoid being overwhelmed by the flood of information in this era of AI acceleration. Want to systematically track AI industry trends? Try to save key sources to your personal knowledge base and let AI help you organize, query, and cross-verify. [1] [2] [3] [4] [5] [6]
The Rise of AI Influencers: Essential Trends and Opportunities for Creators
TL; DR Key Takeaways On March 21, 2026, Elon Musk posted a tweet on X with only eight words: "AI bots will be more human than human." This tweet garnered over 62 million views and 580,000 likes within 72 hours. He wrote this in response to an AI-generated image of a "perfect influencer face." This isn't a sci-fi prophecy. If you are a content creator, blogger, or social media manager, you have likely already scrolled past those "too perfect" faces in your feed, unable to tell if they are human or AI. This article will take you through the reality of AI virtual influencers, the income data of top cases, and how you, as a human creator, should respond to this transformation. This article is suitable for content creators, social media operators, brand marketers, and anyone interested in AI trends. First, let's look at a set of numbers that will make you sit up. The global virtual influencer market size reached $6.06 billion in 2024 and is expected to grow to $8.3 billion in 2025, with an annual growth rate exceeding 37%. According to Straits Research, this figure is projected to soar to $111.78 billion by 2033. Meanwhile, the entire influencer marketing industry reached $32.55 billion in 2025 and is expected to break the $40 billion mark by 2026. Looking at specific individuals, two representative cases are worth a closer look. Lil Miquela is widely recognized as the "first-generation AI influencer." This virtual character, born in 2016, has over 2.4 million followers on Instagram and has collaborated with brands like Prada, Calvin Klein, and Samsung. Her team (part of Dapper Labs) charges tens of thousands of dollars per branded post. Her subscription income on the Fanvue platform alone reaches $40,000 per month, and combined with brand partnerships, her monthly income can exceed $100,000. It is estimated that her average annual income since 2016 is approximately $2 million. Aitana López represents the possibility that "individual entrepreneurs can also create AI influencers." This pink-haired virtual model, created by the Spanish creative agency The Clueless, has over 370,000 followers on Instagram and earns between €3,000 and €10,000 per month. The reason for her creation was practical: founder Rubén Cruz was tired of the uncontrollable factors of human models (being late, cancellations, schedule conflicts), so he decided to "create an influencer who would never flake." A prediction by PR giant Ogilvy in 2024 sent shockwaves through the industry: by 2026, AI virtual influencers will occupy 30% of influencer marketing budgets. A survey of 1,000 senior marketers in the UK and US showed that 79% of respondents said they are increasing investment in AI-generated content creators. To see the underlying drivers of this change, you must understand the logic of brands. Zero risk, total control. The biggest risk with human influencers is "scandal." A single inappropriate comment or a personal scandal can flush millions of brand investment down the drain. Virtual influencers don't have this problem. They don't get tired, they don't age, and they won't post a tweet at 3 AM that makes the PR team collapse. As Rubén Cruz, founder of The Clueless, said: "Many projects were put on hold or canceled due to issues with the influencers themselves; it wasn't a design flaw, but human unpredictability." 24/7 content output. Virtual influencers can post daily, follow trends in real-time, and "appear" in any setting at a cost far lower than a human shoot. According to estimates by BeyondGames, if Lil Miquela posts once a day on Instagram, her potential income in 2026 could reach £4.7 million. This level of output efficiency is unmatched by any human creator. Precise brand consistency. Prada's collaboration with Lil Miquela resulted in an engagement rate 30% higher than regular marketing campaigns. Every expression, every outfit, and every caption of a virtual influencer can be precisely designed to ensure a perfect fit with the brand's tone. However, there are two sides to every coin. A report by Business Insider in March 2026 pointed out that consumer backlash against AI accounts is rising, and some brands have already begun to retreat from AI influencer strategies. A YouGov survey showed that more than one-third of respondents expressed concern about AI technology. This means virtual influencers are not a panacea; authenticity remains an important factor for consumers. In the face of the impact of AI virtual influencers, panic is useless; action is valuable. Here are four proven strategies for responding. Strategy 1: Deepen authentic experiences; do what AI cannot. AI can generate a perfect face, but it cannot truly taste a cup of coffee or feel the exhaustion and satisfaction of a hike. In a discussion on Reddit's r/Futurology, a user's comment received high praise: "AI influencers can sell products, but people still crave real connections." Turn your real-life experiences, unique perspectives, and imperfect moments into a content moat. Strategy 2: Arm yourself with AI tools rather than fighting AI. Smart creators are already using AI to boost efficiency. Creators on Reddit have shared complete workflows: using ChatGPT for scripts, ElevenLabs for voiceovers, and HeyGen for video production. You don't need to become an AI influencer, but you need to make AI your creative assistant. Strategy 3: Systematically track industry trends to build an information advantage. The AI influencer field moves incredibly fast, with new tools, cases, and data appearing every week. Randomly scrolling through Twitter and Reddit is far from enough. You can use to systematically manage industry information scattered everywhere: save key articles, tweets, and research reports into a Board, use AI to automatically organize and retrieve them, and ask your asset library questions at any time, such as "What were the three largest funding rounds in the virtual influencer space in 2026?". When you need to write an industry analysis or film a video, the materials are already in place instead of starting from scratch. Strategy 4: Explore human-AI collaborative content models. The future is not a zero-sum game of "Human vs. AI," but a collaborative symbiosis of "Human + AI." You can use AI to generate visual materials but give them a soul with a human voice and perspective. Analysis from points out that AI influencers are suitable for experimental, boundary-pushing concepts, while human influencers remain irreplaceable in building deep audience connections and solidifying brand value. The biggest challenge in tracking AI virtual influencer trends is not too little information, but too much information that is too scattered. A typical scenario: You see a tweet from Musk on X, read a breakdown post on Reddit about an AI influencer earning $10,000 a month, find an in-depth report on Business Insider about brands retreating, and then scroll past a tutorial on YouTube. This information is scattered across four platforms and five browser tabs. Three days later, when you want to write an article, you can't find that key piece of data. This is exactly the problem solves. You can use the to clip any webpage, tweet, or YouTube video to your dedicated Board with one click. AI will automatically extract key information and build an index, allowing you to search and ask questions in natural language at any time. For example, create an "AI Virtual Influencer Research" Board to manage all relevant materials centrally. When you need to produce content, ask the Board directly: "What is Aitana López's business model?" or "Which brands have started to retreat from AI influencer strategies?", and the answers will be presented with links to the original sources. It should be noted that YouMind's strength lies in information integration and research assistance; it is not an AI influencer generation tool. If your need is to create virtual character images, you still need professional tools like Midjourney, Stable Diffusion, or HeyGen. However, in the core creator workflow of "Research Trends → Accumulate Materials → Produce Content," can significantly shorten the distance from inspiration to finished product. Q: Will AI virtual influencers completely replace human influencers? A: Not in the short term. Virtual influencers have advantages in brand controllability and content output efficiency, but the consumer demand for authenticity remains strong. Business Insider's 2026 report shows that some brands have begun to reduce AI influencer investment due to consumer backlash. The two are more likely to form a complementary relationship rather than a replacement one. Q: Can an average person create their own AI virtual influencer? A: Yes. Many creators on Reddit have shared their experiences of starting from scratch. Common tools include Midjourney or Stable Diffusion for generating consistent images, ChatGPT for writing copy, and ElevenLabs for generating voice. The initial investment can be very low, but it requires 3 to 6 months of consistent operation to see significant growth. Q: What are the income sources for AI virtual influencers? A: There are mainly three categories: brand-sponsored posts (top virtual influencers charge thousands to tens of thousands of dollars per post), subscription platform income (such as Fanvue), and derivatives and music royalties. Lil Miquela earns an average of $40,000 per month from subscription income alone, with brand collaboration income being even higher. Q: What is the current state of the AI virtual idol market in China? A: China is one of the most active markets for virtual idol development globally. According to industry forecasts, the Chinese virtual influencer market will reach 270 billion RMB by 2030. From Hatsune Miku and Luo Tianyi to hyper-realistic virtual idols, the Chinese market has gone through several development stages and is currently evolving toward AI-driven real-time interaction. Q: What should brands look for when choosing to collaborate with virtual influencers? A: It is crucial to evaluate three points: the target audience's acceptance of virtual personas, the platform's AI content disclosure policies (TikTok and Instagram are strengthening related requirements), and the fit between the virtual influencer and the brand's tone. It is recommended to test with a small budget first and then decide whether to increase investment based on data. The rise of AI virtual influencers is not a distant prophecy but a reality happening right now. Market data clearly shows that the commercial value of virtual influencers has been verified—from Lil Miquela's $2 million annual income to Aitana López's €10,000 monthly earnings, these numbers cannot be ignored. But for human creators, this is not a story of "being replaced," but an opportunity to "reposition." Your authentic experiences, unique perspectives, and emotional connection with your audience are core assets that AI cannot replicate. The key lies in using AI tools to improve efficiency, using systematic methods to track trends, and using authenticity to build an irreplaceable competitive moat. Want to systematically track AI influencer trends and accumulate creative materials? Try building your dedicated research space with and start for free. 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