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
Have questions about this article?
Ask AI for FreeRelated Posts
Lenny Opens 350+ Newsletter Dataset: How to Integrate It with Your AI Assistant Using MCP
You might have heard the name Lenny Rachitsky. This former Airbnb product lead started writing his Newsletter in 2019 and now boasts over 1.1 million subscribers, generating over $2 million in annual revenue, making it the #1 business Newsletter on Substack . His podcast also ranks among the top ten in tech, featuring guests from Silicon Valley's top product managers, growth experts, and entrepreneurs. On March 17, 2026, Lenny did something unprecedented: he made all his content assets available as an AI-readable Markdown dataset. With 350+ in-depth Newsletter articles, 300+ full podcast transcripts, a complementary MCP server, and a GitHub repository, anyone can now build AI applications using this data . This article will cover the complete contents of this dataset, how to integrate it into your AI tools via the MCP server, 50+ creative projects already built by the community, and how you can leverage this data to create your own AI knowledge assistant. This article is suitable for content creators, Newsletter authors, AI application developers, and knowledge management enthusiasts. This is not a simple "content transfer." Lenny's dataset is meticulously organized and specifically designed for AI consumption scenarios. In terms of data scale, free users can access a starter pack of 10 Newsletter articles and 50 podcast transcripts, and connect to a starter-level MCP server via . Paid subscribers, on the other hand, gain access to the complete 349 Newsletter articles and 289 podcast transcripts, plus full MCP access and a private GitHub repository . In terms of data format, all files are in pure Markdown format, ready for direct use with Claude Code, Cursor, and other AI tools. The index.json file in the repository contains structured metadata such as titles, publication dates, word counts, Newsletter subtitles, podcast guest information, and episode descriptions. It's worth noting that Newsletter articles published within the last 3 months are not included in the dataset. In terms of content quality, this data covers core areas such as product management, user growth, startup strategies, and career development. Podcast guests include executives and founders from companies like Airbnb, Figma, Notion, Stripe, and Duolingo. This is not randomly scraped web content, but a high-quality knowledge base accumulated over 7 years and validated by 1.1 million people. The global AI training dataset market reached $3.59 billion in 2025 and is projected to grow to $23.18 billion by 2034, with a compound annual growth rate of 22.9% . In this era where data is fuel, high-quality, niche content data has become extremely scarce. Lenny's approach represents a new creator economy model. Traditionally, Newsletter authors protect content value through paywalls. Lenny, however, does the opposite: he opens his content as "data assets," allowing the community to build new value layers on top of it. This has not only not diminished his paid subscriptions (in fact, the dataset's spread has attracted more attention) but has also created a developer ecosystem around his content. Compared to other content creators' practices, this "content as API" approach is almost unprecedented. As Lenny himself said, "I don't think anyone has done anything like this before." The core insight of this model is: when your content is good enough and your data structure is clear enough, the community will help you create value you never even imagined. Imagine this scenario: you're a product manager preparing a presentation on user growth strategies. Instead of spending hours sifting through Lenny's historical articles, you can directly ask an AI assistant to retrieve all discussions about "growth loops" from 300+ podcast episodes and automatically generate a summary with specific examples and data. This is the efficiency leap brought by structured datasets. Integrating Lenny's dataset into your AI workflow is not complicated. Here are the specific steps. Go to and enter your subscription email to get a login link. Free users can download the starter pack ZIP file or directly clone the public GitHub repository: ``plaintext git clone https://github.com/LennysNewsletter/lennys-newsletterpodcastdata.git `` Paid users can log in to get access to the private repository containing the full dataset. MCP (Model Context Protocol) is an open standard introduced by Anthropic, allowing AI models to access external data sources in a standardized way. Lenny's dataset provides an official MCP server, which you can configure directly in Claude Code or other MCP-supported clients. Free users can use the starter-level MCP, while paid users get MCP access to the full data. Once configured, you can directly search and reference all of Lenny's content in your AI conversations. For example, you can ask: "Among Lenny's podcast guests, who discussed PLG (Product-Led Growth) strategies? What were their core insights?" Once you have the data, you can choose different building paths based on your needs. If you are a developer, you can use Claude Code or Cursor to build applications directly based on the Markdown files. If you are more inclined towards knowledge management, you can import this content into your preferred knowledge base tool. For example, you can create a dedicated Board in and batch-save links to Lenny's Newsletter articles there. YouMind's AI will automatically organize this content, and you can ask questions, retrieve, and analyze the entire knowledge base at any time. This method is particularly suitable for creators and knowledge workers who don't code but want to efficiently digest large amounts of content with AI. A common misconception to note: do not try to dump all data into one AI chat window at once. A better approach is to process it in batches by topic, or let the AI retrieve it on demand via the MCP server. Lenny previously only released podcast transcript data, and the community has already built over 50 projects. Below are 5 categories of the most representative applications. Gamified Learning: LennyRPG. Product designer Ben Shih transformed 300+ podcast transcripts into a Pokémon-style RPG game, . Players encounter podcast guests in a pixelated world and "battle" and "capture" them by answering product management questions. Ben used the Phaser game framework, Claude Code, and the OpenAI API to complete the entire development, from concept to launch, in just a few weeks . Cross-Domain Knowledge Transfer: Tiny Stakeholders. , developed by Ondrej Machart, applies product management methodologies from the podcasts to parenting scenarios. This project demonstrates an interesting characteristic of high-quality content data: good frameworks and mental models can be transferred across domains. Structured Knowledge Extraction: Lenny Skills Database. The Refound AI team extracted from the podcast archives, each with specific context and source citations . They used Claude for preprocessing and ChromaDB for vector embeddings, making the entire process highly automated. Social Media AI Agent: Learn from Lenny. is an AI Agent running on X (Twitter) that answers users' product management questions based on the podcast archives, with each reply including the original source. Visual Content Re-creation: Lenny Gallery. transforms the core insights of each podcast episode into beautiful infographics, turning an hour-long podcast into a shareable visual summary. The common characteristic of these projects is that they are not simple "content transfers," but rather create new forms of value based on the original data. Facing a large-scale content dataset like Lenny's, different tools are suitable for different use cases. Below is a comparison of mainstream solutions: If you are a developer, Claude Code + MCP server is the most direct path, allowing real-time querying of the full data in conversations. If you are a content creator or knowledge worker who doesn't want to code but wishes to digest this content with AI, YouMind's Board feature is more suitable: you can batch import article links and then use AI to ask questions and analyze the entire knowledge base. YouMind is currently more suitable for "collect → organize → AI Q&A" knowledge management scenarios but does not yet support direct connection to external MCP servers. For projects requiring deep code development, Claude Code or Cursor is still recommended. Q: Is Lenny's dataset completely free? A: Not entirely. Free users can access a starter pack containing 10 Newsletters and 50 podcast transcripts, as well as starter-level MCP access. The complete 349 articles and 289 transcripts require a paid subscription to Lenny's Newsletter (approximately $150 annually). Articles published within the last 3 months are not included in the dataset. Q: What is an MCP server? Can regular users use it? A: MCP (Model Context Protocol) is an open standard introduced by Anthropic in late 2024, allowing AI models to access external data in a standardized way. It is currently primarily used through development tools like Claude Code and Cursor. If regular users are not familiar with the command line, they can first download the Markdown files and import them into knowledge management tools like YouMind to use AI Q&A features. Q: Can I use this data to train my own AI model? A: The use of the dataset is governed by the file. Currently, the data is primarily designed for contextual retrieval in AI tools (e.g., RAG), rather than direct use for model fine-tuning. It is recommended to carefully read the license agreement in the GitHub repository before use. Q: Besides Lenny, have other Newsletter authors released similar datasets? A: Currently, Lenny is the first leading Newsletter author to open up full content in such a systematic way (Markdown + MCP + GitHub). This approach is unprecedented in the creator economy but may inspire more creators to follow suit. Q: What is the deadline for the creation challenge? A: The deadline for the creation challenge launched by Lenny is April 15, 2025. Participants need to build projects based on the dataset and submit links in the Newsletter comment section. Winners will receive a free one-year Newsletter subscription. Lenny Rachitsky's release of 350+ Newsletter articles and 300+ podcast transcript datasets marks a significant turning point in the content creator economy: high-quality content is no longer just something to be read; it is becoming a programmable data asset. Through the MCP server and structured Markdown format, any developer and creator can integrate this knowledge into their AI workflow. The community has already demonstrated the immense potential of this model with over 50 projects. Whether you want to build an AI-powered knowledge assistant or more efficiently digest and organize Newsletter content, now is a great time to act. You can go to to get the data, or try using to import the Newsletter and podcast content you follow into your personal knowledge base, letting AI help you complete the entire closed loop from information gathering to knowledge creation. [1] [2] [3] [4] [5] [6] [7]
Grok Imagine Video Generation Review: Triple Crown Power vs. Five Model Comparison
In January 2026, xAI's generated 1.245 billion videos in a single month. This number was unimaginable just a year prior, when xAI didn't even have a video product. From zero to the top, Grok Imagine achieved this in just seven months. Even more noteworthy are the leaderboard statistics. In the video review operated by Arcada Labs, Grok Imagine secured three first-place rankings: Video Generation Arena Elo 1337 (leading the second-place model by 33 points), Image-to-Video Arena Elo 1298 (defeating Google Veo 3.1, Kling, and Sora), and Video Editing Arena Elo 1291. No other model has simultaneously topped all three categories. This article is suitable for creators, marketing teams, and independent developers who are currently choosing AI video generation tools. You will find a comprehensive cross-comparison of the five major models: Grok Imagine, Google Veo 3.1, Kling 3.0, Sora 2, and Seedance 2.0, including pricing, core features, pros and cons, and scenario recommendations. DesignArena uses an Elo rating system, where users anonymously blind-test and vote between the outputs of two models. This mechanism is consistent with LMArena (formerly LMSYS Chatbot Arena) for evaluating large language models and is considered by the industry to be the ranking method closest to actual user preferences. Grok Imagine's three Elo scores represent different capability dimensions. Video Generation Elo 1337 measures the quality of videos generated directly from text prompts; Image-to-Video Elo 1298 tests the ability to transform static images into dynamic videos; and Video Editing Elo 1291 assesses performance in style transfer, adding/removing elements, and other operations on existing videos. The combination of these three capabilities forms a complete video creation loop. For practical workflows, you not only need to "generate a good-looking video" but also need to quickly create advertising material from product images (image-to-video) and fine-tune generated results without starting from scratch (video editing). Grok Imagine is currently the only model that ranks first in all three of these stages. It's worth noting that Kling 3.0 has regained its leading position in the text-to-video category in some independent benchmark tests. AI video generation rankings change weekly, but Grok Imagine's advantage in the image-to-video and video editing categories remains solid for now. Below is a comparison of the core parameters of the five mainstream AI video generation models as of March 2026. Data is sourced from official platform pricing pages and third-party reviews. Core Features: Text-to-video, image-to-video, video editing, video extension (Extend from Frame), multi-aspect ratio support (1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3). Based on xAI's self-developed Aurora autoregressive engine, trained using 110,000 NVIDIA GB200 GPUs. Pricing Structure: Free users have basic quota limits; X Premium ($8/month) provides basic access; SuperGrok ($30/month) unlocks 720p and 10-second videos, with a daily limit of approximately 100 videos; SuperGrok Heavy ($300/month) has a daily limit of 500 videos. API pricing is $4.20/minute. Pros: Extremely fast generation speed, almost instantly returning image streams after inputting prompts, with one-click conversion of each image to video. Video editing capability is a unique selling point: you can use natural language instructions to perform style transfer, add or remove objects, and control motion paths on existing videos without having to regenerate them. Supports the most aspect ratios, suitable for producing horizontal, vertical, and square materials simultaneously. Cons: Maximum resolution is only 720p, which is a significant drawback for brand projects requiring high-definition delivery. Video editing input is capped at 8.7 seconds. Image quality noticeably degrades after multiple chained extensions. Content moderation policies are controversial, with "Spicy Mode" having attracted international attention. Core Features: Text-to-video, image-to-video, first/last frame control, video extension, native audio (dialogue, sound effects, background music generated synchronously). Supports 720p, 1080p, and 4K output. Available through Gemini API and Vertex AI. Pricing Structure: Google AI Plus $7.99/month (Veo 3.1 Fast), AI Pro $19.99/month, AI Ultra $249.99/month. API pricing for Veo 3.1 Fast is $0.15/second, Standard is $0.40/second, both including audio. Pros: Currently the only model that supports true native 4K output (via Vertex AI). Audio generation quality is industry-leading, with automatic lip-sync for dialogue and synchronized sound effects with on-screen actions. First/last frame control makes shot-by-shot workflows more manageable, suitable for narrative projects requiring shot continuity. Google Cloud infrastructure provides enterprise-grade SLA. Cons: Standard duration is only 4/6/8 seconds, significantly shorter than Grok Imagine and Kling 3.0's 15-second cap. Aspect ratios only support 16:9 and 9:16. Image-to-video functionality on Vertex AI is still in Preview. 4K output requires high-tier subscriptions or API access, making it difficult for average users to access. Core Features: Text-to-video, image-to-video, multi-shot narrative (generates 2-6 shots in a single pass), Universal Reference (supports up to 7 reference images/videos to lock character consistency), native audio, lip-sync. Developed by Kuaishou. Pricing Structure: Free tier offers 66 credits per day (approx. 1-2 720p videos), Standard $5.99/month, Pro $37/month (3000 credits, approx. 50 1080p videos), Ultra is higher. API price per second is $0.029, making it the cheapest among the five major models. Pros: Unbeatable value for money. The Pro plan costs approximately $0.74 per video, significantly lower than other models. Multi-shot narrative is a killer feature: you can describe the subject, duration, and camera movement for multiple shots in a structured prompt, and the model automatically handles transitions and cuts between shots. Supports native 4K output. Text rendering capability is the strongest among all models, suitable for e-commerce and marketing scenarios. Cons: The free tier has watermarks and cannot be used for commercial purposes. Peak-time queue times can exceed 30 minutes. Failed generations still consume credits. Compared to Grok Imagine, it lacks video editing features (can only generate, not modify existing videos). Core Features: Text-to-video, image-to-video, Storyboard shot editing, video extension, character consistency engine. Sora 1 was officially retired on March 13, 2026, making Sora 2 the sole version. Pricing Structure: Free tier discontinued as of January 2026. ChatGPT Plus $20/month (limited quota), ChatGPT Pro $200/month (priority access). API pricing: 720p $0.10/second, 1080p $0.30-$0.70/second. Pros: Physical simulation capabilities are the strongest among all models. Details such as gravity, fluids, and material reflections are extremely realistic, suitable for highly realistic scenarios. Supports video generation up to 60 seconds, far exceeding other models. Storyboard functionality allows frame-by-frame editing, giving creators precise control. Cons: The price barrier is the highest among the five major models. The $200/month Pro subscription deters individual creators. Service stability issues are frequent: in March 2026, there were multiple errors such as videos getting stuck at 99% completion and "server overload." No free tier means you cannot fully evaluate before paying. Core Features: Text-to-video, image-to-video, multimodal reference input (up to 12 files, covering text, images, videos, audio), native audio (sound effects + music + 8 languages lip-sync), native 2K resolution. Developed by ByteDance, released on February 12, 2026. Pricing Structure: Dreamina free tier (daily free credits, with watermark), Jiemeng Basic Membership 69 RMB/month (approx. $9.60), Dreamina international paid plans. API provided via BytePlus, priced at approx. $0.02-$0.05/second. Pros: 12-file multimodal input is an exclusive feature. You can simultaneously upload character reference images, scene photos, action video clips, and background music, and the model synthesizes all references to generate video. This level of creative control is completely absent in other models. Native 2K resolution is available to all users (unlike Veo 3.1's 4K which requires a high-tier subscription). The entry price of 69 RMB/month is one-twentieth of Sora 2 Pro. Cons: Access experience outside of China still has friction, with the international version of Dreamina only launching in late February 2026. Content moderation is relatively strict. The learning curve is relatively steep, and fully utilizing multimodal input requires time to explore. Maximum duration is 10 seconds, shorter than Grok Imagine and Kling 3.0's 15 seconds. The core question when choosing an AI video generation model is not "which is best," but "which workflow are you optimizing?" Here are recommendations based on practical scenarios: Batch production of social media short videos: Choose Grok Imagine or Kling 3.0. You need to quickly produce materials in various aspect ratios, iterate frequently, and don't have high resolution requirements. Grok Imagine's "generate → edit → publish" loop is the smoothest; Kling 3.0's free tier and low cost are suitable for individual creators with limited budgets. Brand advertisements and product promotional videos: Choose Veo 3.1. When clients demand 4K delivery, synchronized audio and video, and shot continuity, Veo 3.1's first/last frame control and native audio are irreplaceable. Google Cloud's enterprise-grade support also makes it more suitable for commercial projects with compliance requirements. E-commerce product videos and materials with text: Choose Kling 3.0. Text rendering capability is Kling's unique advantage. Product names, price tags, and promotional copy can appear clearly in the video, which other models struggle with consistently. The $0.029/second API price also makes large-scale production possible. Film-grade concept previews and physical simulations: Choose Sora 2. If your scene involves complex physical interactions (water reflections, cloth dynamics, collision effects), Sora 2's physics engine is still the industry standard. The maximum duration of 60 seconds is also suitable for full scene previews. But be prepared for a $200/month budget. Creative projects with multiple material references: Choose Seedance 2.0. When you have character design images, scene references, action video clips, and background music, and you want the model to synthesize all materials to generate video, Seedance 2.0's 12-file multimodal input is the only choice. Suitable for animation studios, music video production, and concept art teams. Regardless of the model you choose, prompt quality directly determines output quality. Grok Imagine's official advice is to "write prompts like you're briefing a director of photography," rather than simply stacking keywords. An effective video prompt usually contains five levels: scene description, subject action, camera movement, lighting and atmosphere, and style reference. For example, "a cat on a table" and "an orange cat lazily peering over the edge of a wooden dining table, warm side lighting, shallow depth of field, slow push-in shot, film grain texture" will produce completely different results. The latter provides the model with enough creative anchors. If you want to get started quickly instead of exploring from scratch, contains 400+ community-selected video prompts, covering cinematic, product advertising, animation, social content, and other styles, supporting one-click copy and direct use. These community-validated prompt templates can significantly shorten your learning curve. Q: Is Grok Imagine video generation free? A: There is a free quota, but it's very limited. Free users get about 10 image generations every 2 hours, and videos need to be converted from images. The full 720p/10-second video functionality requires a SuperGrok subscription ($30/month). X Premium ($8/month) provides basic access but with limited features. Q: Which is the cheapest AI video generation tool in 2026? A: Based on API cost per second, Kling 3.0 is the cheapest ($0.029/second). Based on subscription entry price, Seedance 2.0's Jiemeng Basic Membership at 69 RMB/month (approx. $9.60) offers the best value. Both provide free tiers for evaluation. Q: Which is better, Grok Imagine or Sora 2? A: It depends on your needs. Grok Imagine ranks higher in image-to-video and video editing, generates faster, and is cheaper (SuperGrok $30/month vs. ChatGPT Pro $200/month). Sora 2 is stronger in physical simulation and long videos (up to 60 seconds). If you need to quickly iterate short videos, choose Grok Imagine; if you need cinematic realism, choose Sora 2. Q: Are AI video generation model rankings reliable? A: Platforms like DesignArena and Artificial Analysis use anonymous blind testing + Elo rating systems, similar to chess ranking systems, which are statistically reliable. However, rankings change weekly, and results from different benchmark tests may vary. It's recommended to use rankings as a reference rather than the sole decision-making basis, and to make judgments based on your own actual testing. Q: Which AI video model supports native audio generation? A: As of March 2026, Grok Imagine, Veo 3.1, Kling 3.0, Sora 2, and Seedance 2.0 all support native audio generation. Among them, Veo 3.1's audio quality (dialogue lip-sync, environmental sound effects) is considered the best by multiple reviews. AI video generation entered a true multi-model competitive era in 2026. Grok Imagine's journey from zero to a DesignArena triple crown in seven months proves that newcomers can completely disrupt the landscape. However, "strongest" does not equal "best for you": Kling 3.0's $0.029/second makes batch production a reality, Veo 3.1's 4K native audio sets a new standard for brand projects, and Seedance 2.0's 12-file multimodal input opens up entirely new creative avenues. The key to choosing a model is to clarify your core needs: whether it's iteration speed, output quality, cost control, or creative flexibility. The most efficient workflow often doesn't involve betting on a single model, but rather flexibly combining them based on project type. Want to quickly get started with Grok Imagine video generation? Visit the for 400+ community-selected video prompts that can be copied with one click, covering cinematic, advertising, animation, and other styles, helping you skip the prompt exploration phase and directly produce high-quality videos. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]
AI Devours Software: Naval's Tweet Triggers Trillion-Dollar Market Collapse, What Should Creators Do?
On March 14, 2026, Silicon Valley legendary investor Naval Ravikant posted a six-word tweet on X: "Software was eaten by AI." Elon Musk replied with one word: "Yeah." The tweet garnered over 100 million impressions. It went viral not because of its eloquent phrasing, but because it precisely inverted one of Silicon Valley's most classic predictions. In 2011, Marc Andreessen wrote "Software is eating the world" in The Wall Street Journal, declaring that software would devour all traditional industries . Fifteen years later, Naval used the same phrasing to announce: the devourer itself has been devoured. This article is for content creators, knowledge workers, and anyone who relies on software tools for creation and research. You will understand the underlying logic of this transformation and 5 actionable strategies to adapt. To understand the weight of Naval's statement, we first need to grasp what happened during those fifteen years when "software ate the world." A deep analysis published by Forbes the day after Naval's tweet pointed out that the SaaS era was essentially a "distribution story" rather than a "capability story" . Salesforce didn't invent customer management; it just allowed you to manage customers without spending $500,000 to deploy Oracle. Slack didn't invent team communication; it just made communication faster and more searchable. Shopify didn't invent retail; it just removed the barriers of physical storefronts and payment terminals. The model for every SaaS winner was the same: identify a workflow with high barriers, and package it into a monthly subscription. Innovation was at the distribution layer; the underlying tasks remained unchanged. AI does something completely different. It's not making tasks cheaper; it's replacing the tasks themselves. A $20/month general AI subscription can draft contracts, perform competitive analysis, generate sales email sequences, and build financial models. At this point, why would a company still pay $200 per person per month for a SaaS subscription for the same output? As analyst David Cyrus said, this is "already happening at the margins of the market" . Data is already validating this assessment. In the first six weeks of 2026, the S&P 500 Software & Services Index lost nearly $1 trillion in market capitalization . Morgan Stanley's software analyst report noted a 33% decline in SaaS valuation multiples and introduced the "software triple threat": companies building their own software (vibe coding), AI models replacing traditional applications, and AI-driven layoffs mechanically reducing software seats . The term "SaaSpocalypse" was coined by Jefferies traders to describe the massive collapse of enterprise software stocks that began in early February 2026 . The trigger was a statement by Palantir CEO Alex Karp during an earnings call: AI has become powerful enough in writing and managing enterprise software to render many SaaS companies irrelevant. This statement directly led to a wave of sell-offs, with Microsoft, Salesforce, and ServiceNow collectively losing $300 billion in market value . Even more noteworthy is the stance of Microsoft CEO Satya Nadella. In a podcast, he admitted that business applications might "collapse" in the agent era . When the CEO of a three-trillion-dollar company publicly acknowledges that its own product category faces an existential threat, it's not alarmism; it's a signal. For content creators, what does this collapse mean? It means that the tools you've relied on are undergoing a fundamental repricing. The era of paying separately each month for writing tools, SEO tools, social media management tools, and design tools is coming to an end. Instead, a sufficiently powerful AI platform can accomplish all these tasks simultaneously. Stack Overflow's 2025 developer survey shows that 84% of developers are already using AI tools . And the data in content creation is even more aggressive: 83% of creators are already using AI in their workflows, with 38.7% having fully integrated it . Now that you understand the trend, the crucial question is: what should you do? Here are 5 actionable strategies. Most creators' information sources are fragmented: reading an article here, listening to a podcast there, with hundreds of links saved in bookmarks. The core competency in the AI era is not "consuming a lot," but "integrating well." Specific approach: Choose a tool that can unify various information sources, bringing web pages, PDFs, videos, podcasts, and tweets all into one place. For example, using 's Board feature, you can save Naval's tweet, Forbes' analysis, Morgan Stanley's research report, and related podcasts all into the same knowledge space. Then, you can directly ask these materials: "What are the core disagreements among these sources?" "Which data points support my article's argument?" This is ten times more efficient than switching back and forth between ten browser tabs. Google search gives you ten blue links. AI research gives you structured answers. The difference is: the former requires you to spend two hours reading and organizing, while the latter gives you a ready-to-use analytical framework in two minutes. Specific approach: Before starting any creative project, conduct a round of deep research using AI. Don't just ask "What is AI's impact on the software industry?" Instead, ask "What are the three core drivers of the SaaS market cap collapse in 2026? What data supports each factor? What are the counterarguments?" The more specific the question, the more valuable the answer AI provides. This is the most crucial step. Most creators treat AI as a "writing assistant," using it only in the final step (creation). The real leap in efficiency comes from embedding AI into the entire loop: using AI to organize and digest information during the learning phase, using AI for comparative analysis and logical validation during the thinking phase, and using AI to accelerate output during the creation phase. 's design philosophy embodies this loop. It's not just a writing tool or a note-taking tool, but an Integrated Creation Environment (ICE) that integrates the entire process of learning, thinking, and creating. You can do research in a Board, turn research materials into a podcast program to "learn by listening" with Audio Pod, and then create content directly based on these materials in the Craft editor. However, it's important to note that YouMind is currently best suited for scenarios requiring deep creation by integrating diverse information sources. If you only need to quickly post a social media update, a lightweight tool might be more appropriate. An analysis by Buffer puts it well: most creators only need 3 to 5 tools to solve specific bottlenecks; exceeding this number usually only adds complexity without adding value . Specific approach: Audit your current tool stack. List all your monthly paid SaaS subscriptions and ask yourself two questions: Can AI directly perform the core function of this tool? If so, do I still need to pay for its "packaging"? You might find that your productivity actually increases after cutting half of your subscriptions. The last and most easily overlooked strategy. AI's greatest value is not helping you write articles (though it can), but helping you think clearly. Use AI to challenge your arguments, find your logical flaws, and provide counterarguments you hadn't considered. This is AI's deepest value for creators. There are many AI creation tools on the market, but their positioning varies greatly. Below is a comparison for content creators' "learn → research → create" loop: The key to choosing a tool is not "which is the strongest," but "which best matches your workflow bottleneck." If your pain point is fragmented information and low research efficiency, prioritize tools that can integrate diverse sources. If your pain point is team collaboration, Notion might be more suitable. Q: Will AI really replace all software? A: No. Software with proprietary data moats (like Bloomberg Terminal's 40 years of financial data), compliance infrastructure (like Epic in healthcare), and system-level software deeply embedded in enterprise tech stacks (like Salesforce's 3000+ app ecosystem) still have strong moats. The primary targets for replacement are general-purpose SaaS tools in the middle layer. Q: Do content creators need to learn programming? A: No need to become a programmer, but you need to understand the logic of "AI workflows." The core skills are: clearly describing your needs (prompt engineering), effectively organizing information sources, and judging the quality of AI output. These skills are more important than writing code. Q: How long will the SaaSpocalypse last? A: There are disagreements between Morgan Stanley and a16z. Pessimists believe that mid-tier SaaS companies will be significantly compressed in the next 3 to 5 years. Optimists (like a16z's Steven Sinofsky) believe that AI will create more software demand, not less . Historically, Jevons' paradox (the cheaper a resource, the more it's consumed overall) supports the optimists, but this time AI is replacing the tasks themselves, so the mechanism is indeed different. Q: How can an average creator determine if an AI tool is worth paying for? A: Ask yourself three questions: Does it solve the most time-consuming part of my workflow? Can its core function be replaced by a free general AI (like the free version of ChatGPT)? Can it scale with my growing needs? If the answers are "yes, no, yes" respectively, then it's worth paying for. Q: Are there any counterarguments to Naval's "AI eats software" thesis? A: Yes. HSBC analyst Stephen Bersey published a report titled "Software Will Eat AI," arguing that software will absorb AI rather than be replaced by it, and that software is the vehicle for AI . Business Insider also published an article pointing out that the failure rate of companies building their own software is extremely high, and the moats of SaaS vendors are underestimated . The truth likely lies somewhere in between. Naval's six words reveal a structural shift that is currently underway: AI is not assisting software; it is replacing the tasks that software performs. The evaporation of a trillion dollars in market value is not panic, but the market's repricing of this reality. For content creators, this is the biggest opportunity window of the past decade. When the cost of tools required for creation approaches zero, the focus of competition shifts from "who can afford better tools" to "who can more efficiently integrate information, think more deeply, and more quickly output valuable content." Start acting now: audit your tool stack, cut redundant subscriptions, choose an AI platform that connects the entire "learn → research → create" process, and invest the saved time into what truly matters. Your unique perspective, deep thinking, and authentic experience are the moats that AI cannot replace. Start experiencing for free and turn your fragmented information into creative fuel. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
