Historical source note: This edition translates the complete published trend synthesis. Headlines, funding figures, dates and predictions are retained as historical source claims, not independently reconfirmed announcements or proven forecasts. Expressions such as “AGI has arrived” and “replace Salesforce” describe the cited narrative, not an established conclusion of this translation.
When to read: when looking for the broad AI trends of 2025–2026, emerging categories, or predictions from major investors and companies. The published synthesis organizes 84+ Shensi trend articles into five groups: AGI progress, new categories, replacement of legacy SaaS, infrastructure, and leading figures' forecasts.
Its central narrative: Sequoia's 2026 claim that AGI had arrived; a16z's forecast that computer-use agents would surpass human efficiency in 18 months and that AI would reshape Moore's law around a 2028 inflection point; categories such as the agent economy, communication as a service, AI-native design and agent operating systems; and the transformation of Salesforce, ERP and Intercom.
0. Five Main Trend Areas
| Area | Key narrative | Representative material |
|---|---|---|
| AGI progress | Sequoia's 2026 “already here” framing; a16z on Moore's law | Sequoia annual presentation; a16z roundtable |
| Emerging categories | Agent economy, communication as a service, agent OS | YC discussion; a16z Top 50; a $20 million category bet |
| Legacy SaaS replacement | Transformation of Salesforce, ERP and Intercom | Peter Thiel's $35 million bet; Sequoia's $25 million bet |
| AI infrastructure | Memory, agent Git, insurance and email | Four new infrastructure categories |
| Leading forecasts | Sam Altman, Karpathy and Claude Code's creator | Separate forecast articles |
1. AGI Progress
1.1 Sequoia US: The 2026 Annual Presentation
The cited headline says: “Sequoia US's 2026 annual presentation: AGI has already arrived; redefining the AI revolution.” The synthesis interprets this as a sign that an “AGI has arrived” judgment had entered mainstream leading-VC discussion rather than remaining marginal. That interpretation does not settle the definition or attainment of AGI.
1.2 a16z: Surpassing Humans in 18 Months Through Computer Use
The preserved headline predicts that computer use lets AI agents work like humans and surpass human efficiency within 18 months. This remains a forecast attributed to that article.
1.3 a16z: Reshaping Moore's Law, With a 2028 Inflection Point
The source headline predicts superlinear AI development and a critical point in 2028, framed as AI overturning Moore's law.
1.4 A New Stage in the AI Paradigm
A related headline asks whether AGI is approaching and describes new stages and trends in the AI paradigm.
1.5 Aschenbrenner's Situational Awareness: Overlap With Wang Kai's Reading
The related notes on frontier papers and scaling laws cover AGI by 2027, trillion-dollar clusters, and “The Project,” meaning US national-scale AI investment. These are the scenario and forecast topics in those sources.
2. Emerging Categories
2.1 The Agent Economy: YC
A cited YC discussion is framed by the headline “The agent economy is really coming.”
2.2 Communication as a Service: A $20 Million Category Bet
The article asks why investors believe in communication as a service, describing a $20 million bet and asking whether it could become the next billion-dollar market.
2.3 An Operating System for Browser Agents
A cited company reportedly raised $22 million led by Accel, attracted YC founders' interest, and set out to build an operating system for browser AI agents.
2.4 AI Video Empires: a16z
An a16z partner's analysis is presented as AI video consuming the world and enabling future IP empires worth billions of dollars.
2.5 AI Comics for Global Markets
An interview with Miao Zang traces a move from a leading design group to entrepreneurship, financing of more than RMB100 million, and a new venture with Chushou AI, asking what international expansion in AI comics could look like.
2.6 AI Memory Systems
A 2025 comparison of AI memory systems examines the shift from plug-ins toward operating systems and asks who will define the next generation of agent infrastructure.
2.7 New AI-Social Products
A source describes an AI-social experiment, evokes a Westworld-like future, and reports Coatue participation in a seed round.
3. The Wave of Legacy SaaS Replacement
3.1 A Salesforce Challenger: Peter Thiel's $35 Million Bet
The related vertical-B2B note covers a company described as a Claude Code for sales, reportedly backed with $35 million by Peter Thiel and claiming to end the Salesforce era. The replacement claim belongs to the source's positioning.
3.2 ERP Replacement: Sequoia US's $25 Million Bet
A cited $25 million investment is framed around AI-native ERP seeking to displace traditional financial software.
3.3 Intercom Rebuilds Itself
A detailed article examines how the established company Intercom undertook an AI-first transformation as AI customer support rose.
3.4 Salesforce Removes Its Interface
The headline reports Salesforce removing an interface and quotes an a16z partner's view that the moat had never been in that interface.
3.5 Rebuilding a Law Firm Instead of Selling Lawyers Tools
A Sequoia-backed legal company is described as using AI to rebuild a law firm itself, rather than selling another tool to lawyers.
3.6 2024 as the Opening Year for Enterprise AI
A 2024 source presents 16 emerging B2B trends and non-consensus views, calling that year the beginning of enterprise AI's development.
4. Four New AI Infrastructure Categories
| Infrastructure | Representative company or reported figure | Role |
|---|---|---|
| AI memory systems | A broad 2025 comparison | A key gap in agent infrastructure |
| Agent Git | A former GitHub founder; $17 million from a16z | Agent version control |
| Agent insurance | $15 million | Paying for agent mistakes |
| Agent email registration | YC; $6 million | Agent identity infrastructure |
4.1 Another $30 Million AI Infrastructure Round
A source reports $30 million in financing and explores how combining data processing with AI might redefine multimodal data infrastructure.
4.2 The Reinforcement-Learning Training Stack Revealed by Cursor
The related coding-company note covers microVM training environments, MXFP8 microscaling, and custom Blackwell kernels.
5. Forecasts From Leading Figures
5.1 Sam Altman
A cited article covers Sam Altman's statements about OpenAI's future plans.
5.2 Claude Code's Creator
A separate forecast asks: after coding is solved, what comes next?
5.3 Karpathy: Software 3.0
The related Karpathy note's Agentic Decade section emphasizes an agent decade rather than an agent year, 2025–2035, partial rather than full autonomy, and four principles for partially autonomous products.
5.4 a16z's Top 100 Apps
An a16z partner interprets trends behind the 2026 Top 100 application list.
5.5 Entrepreneurship in the AI Era
A related startup guide identifies four new trends.
6. Predictions for the Next Agent Breakout
A Shensi analysis asks which agent use cases might expand beyond coding and customer support. Its candidates are:
- Sales: partly established, with room to grow.
- Law: companies reaching unicorn status.
- Medical copilots.
- HR and recruiting: already highly competitive.
- Financial infrastructure.
- Communication as a service, linked to the reported $20 million category bet.
- AI memory and agent operating systems, at the infrastructure layer.
These are candidate opportunities from the source, not validated market outcomes.
7. A Three-Layer Framework for AI Investing
The synthesis connects to Wang Kai's industry-judgment framework and Newtype's business-trend notes.
| Layer | What to examine | Shensi examples |
|---|---|---|
| 1. Technology stack | Models, agent protocols and infrastructure | Karpathy; Cursor's technical details; AI memory |
| 2. Value chain | Which part contains a defensible barrier | The a16z platform-company playbook: Google, Microsoft, AWS and Palantir |
| 3. Competitive landscape | Leading companies' actions | OpenAI versus Anthropic and the race in rising valuations |
8. From RPA to AI Agent Automation 3.0
A headline uses $20 million of financing as a signal of enterprise automation entering a third era:
- RPA 1.0: screen recording and rules.
- AI 2.0: NLP and machine-learning assistance.
- AI Agent 3.0: autonomous reasoning, tool use and multiple steps.
9. UX and UI Design Trends
A 2024 UX/UI design-trends article explores possibilities for future technological experiences. It overlaps with the related note on democratizing aesthetic creation.
10. Turning Points in the Published 2024–2026 Timeline
| Date in the historical synthesis | Event or framing |
|---|---|
| January 2024 | Enterprise AI's opening year; 16 B2B trends |
| November 2024 | Computer Use / Operator release framing |
| Q1 2025 | Karpathy's vibe-coding post; Software 3.0 |
| June 2025 | Karpathy's YC AI School talk; the Agentic Decade |
| August 2025 | Cursor Composer; a reported $32 million open-source AI coding-tool round |
| November 2025 | Cursor's first account of training as product |
| January 2026 | a16z on a supply-side leap; Sequoia's “AGI has arrived” claim |
| May 2026 | A permissions-based account of Anthropic's moat |
Dates and grouping above follow the published synthesis; they are not a newly checked product-release chronology.
11. Cross-Author Connections
| Topic | This Shensi synthesis | Related notes |
|---|---|---|
| Overall AI industry judgment | Primary-market and case perspectives | General AI industry judgment; Wang Kai industry judgment; Newtype AI business trends |
| AGI and scaling laws | Sequoia 2026; a16z's 18-month forecast | Scaling laws and AGI; Wang Kai's frontier-paper notes |
| Anticipating opportunities | a16z's supply-led view; YC's agent economy | Opportunity prediction; Yitang's opportunity framework |
| Emerging categories | Communication as a service; agent OS | AI product, design and engineering collaboration |
| Infrastructure | The four new infrastructure categories | MCP ecosystem; RAG and knowledge engineering |
12. How to Use This Note
| Reader's question | Main material | Starting point |
|---|---|---|
| Assess an AGI timeline | Sequoia, a16z and Karpathy | “Already here” in 2026; 2027; the 2028 inflection point |
| Explore a new category | Agent economy, communication as a service, agent OS | The four emerging-category framing |
| Challenge legacy SaaS | Salesforce, ERP and Intercom cases | The “end the era of X” pattern |
| Build AI infrastructure | Memory, agent Git, insurance, email | Four infrastructure categories |
| Read leading forecasts | Altman, Karpathy, Claude Code's creator | Five separate forecast articles |
| Explore the next breakout | The “beyond code and support” analysis | Sales, law, medicine, HR and finance |
The original note summarizes six recurring propositions: Sequoia's 2026 AGI claim; a16z's 18-month computer-use forecast; multiple emerging categories; replacement pressure on legacy SaaS; 2024 as the opening year for enterprise AI; and opportunities beyond coding and support. Treat these as a checklist of claims to investigate, retaining their attribution and dates.
13. References and Related Reading
- Shensi author overview — the collection entry point.
- The underlying Shensi collection contains approximately 84+ articles tagged around trends, predictions, the future, AGI and new categories. This English page does not expose private source-file paths.
- AI industry judgment — the combined framework.
- Scaling laws and AGI.
- Opportunity prediction — methods for anticipating opportunities.
- Karpathy's system — the Agentic Decade.
- Shensi: Leading Companies — major-company developments.
- Shensi: Agent Companies — the agent-company case library.
- Shensi: AI B2B Verticals — categories challenging legacy SaaS.
October 9, 2026 addition: Shared experiences can extend a game character's companionship
A character profile explains who the character is; shared experiences explain why someone wants to see them again. SenseAI's account of Yoroll and Yyo suggests a product hypothesis: let players get to know a character through an interactive story, then carry the events and relationship into ongoing conversation. Another route is to build new shared experiences through games, co-play, or watching content together. Both provide a more concrete starting point than continually adding conversation topics, but their effect on long-term retention still requires testing.
Carry verifiable state from the story into companionship
If a character forgets a crucial choice after the player finishes the game, or remembers events from a different branch as shared experiences, adding chat may damage the relationship. At minimum, distinguish the character's fixed background, the story the player actually experienced, and their subsequent interactions. Important memories should be traceable to their source and correctable by the user. Starting a new save, switching characters, and deleting memories also need explicit rules. These are design checks derived from the article, not confirmed details of Yyo's implementation.
A shared experience needs to resurface at an appropriate moment. A game lost together might open the next challenge. Mechanical repetition of old events, or inserting them into unrelated conversations, can instead turn recognition into an interruption. Character voice, values, and memory continuity should be tested alongside multimodal presentation. Smoother video alone does not establish a more credible relationship.
Test relationship continuity before projecting revenue
Compare users with prior story experience against users who enter chat directly, using the same observation window for return visits, user-initiated interactions, memory corrections, and payments. Account for selection bias: the groups may already have different levels of interest. Moving from a one-time game purchase to a subscription is a business hypothesis. Generation, live audio and video, and content maintenance costs must also be considered. High message volume may reflect repeated misunderstandings by the model, so it cannot independently measure relationship quality.
Separate an experience report from technical proof
The article mentions a Hermes Agent foundation, video-model speed, deletion after screen recognition, and several market figures. These have not been independently verified in this review and do not establish privacy guarantees, technical superiority, or revenue potential. Screen-based companionship still needs clear user authorization, capture scope, retention periods, and a way to stop. A character avoiding discussion of a sensitive screen is not a substitute for verifying how the underlying data is handled.
The connection to consumer AI entertainment is how games introduce people to characters. An experience report should retain its own scope rather than being treated as equivalent to financing statistics.
Source: Article or interview used for this addition.
2026-10-17 Addition: Identify buyers and tasks behind compute demand before committing resources
The source is Shensiquan’s Chinese account of Jensen Huang’s views; the original program has not been independently checked. NVIDIA is a supplier with a commercial interest in growth. This section extracts a research method and does not treat the source’s investment totals or growth forecasts as verified conclusions.
One infrastructure narrative can contain several kinds of demand
Huang extends the demand discussion to migration of existing computing workloads, generative AI and agent tasks. For product decisions, investigate these separately. Avoid attributing all industry investment to the revenue of a few model companies; equally, several potential demand categories do not prove that investment will pay back.
| Demand category | Buyer and task to identify first | Evidence still needed |
|---|---|---|
| Migration of existing computing workloads | The current budget owner and the workloads being considered for a different computing approach | Migration cost, compatibility, total cost and outcomes for the same task |
| Generative AI | Organizations or users paying for generation or inference capabilities | Actual usage, payments and continued use, not registrations or demonstrations alone |
| Agent tasks | Business owners seeking to delegate complete workflows on an ongoing basis | Successfully completed tasks, retries, human takeovers and an affordable price |
This table is a research tool proposed by this knowledge base, not an established market breakdown. Record who pays, which budget pays and which task is served, then establish whether the budget is additional or shifted from existing spending. A single customer may span all three categories, so do not simply add them together to calculate market size.
Lower unit cost and lower total spending require separate verification
When inference becomes cheaper, users may delegate more tasks or use more computation per task. Whether total spending falls depends on task volume, consumption per task, retries and human handling. For your own project, first compare costs using a fixed set of tasks, then separately observe changes in usage. Do not convert a supplier’s long-term expansion forecast directly into your revenue plan.
When to use it. Use this approach to clarify the source of demand when discussing compute procurement, agent pricing or industry opportunities. Without an identifiable payer, real tasks and continued-use evidence, keep the claim as a hypothesis. Track delivery constraints such as land, power and construction separately: demand does not mean supply can arrive on schedule. See Startups and investing for related investment discussions; this section makes no buy or sell recommendation.
October 22, 2026: Protect Strategic Resource Commitments in Long-Term Compute Planning
When computing capacity must be planned years ahead, current allocation and future competitive position operate on different time horizons. Shensi’s account of Sundar Pichai offers a strategic example: establish the research capacity needed to remain at the AI frontier, then weigh cloud contracts and short-term revenue within that constraint. It describes Google’s tradeoff, not a universal rule that every company should prioritize research.
For an individual project, first specify the goal that must be protected, its minimum resource needs and the conditions for reconsideration. Then compare the opportunity costs of new orders, research and product usage. Demand signals, contractual commitments, actual internal use and expectations about model development should be recorded separately. None represents all demand. Lead times, component bottlenecks and external shocks also deserve separate treatment: a sound long-term direction can still produce the wrong purchase size or timing.
This complements the existing method of tracing demand to a payer and task. That method asks where demand comes from; this one asks which constraints govern allocation when resources are scarce. Teams must set their own budget thresholds and review dates. The account supplies no optimal allocation ratio to copy. Model rankings, token totals, listing timelines and market-size forecasts are not treated as validated conclusions, and this section provides no investment recommendation.
Source: Shensi’s Chinese account of an interview with Sundar Pichai.
Source Links and Related Public Notes
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October 23, 2026 Addition: Separate Funding Availability From Demand and Delivery
To assess whether an AI infrastructure project can keep delivering, check funding conditions alongside chips, power and materials. In Semafor’s interview, DBS Group CEO Tan Su Shan treats capital as another supply constraint: lenders seek risk-adjusted returns, while long-term spending may not turn into cash on the expected schedule. She discusses hyperscalers moving from cash generators to heavy cash users and return pressure after money concentrates in a category of growth assets. These are her risk assessments, not proof of a capital shortage across every market. Her reference to the next two years is a forecast rather than an industry clock.
Application in this knowledge base: separately record the real user and payer, when construction and operating costs become payable, when revenue and cash may arrive, and the term and continuity of funding. Strong demand, willingness to try and access to financing are distinct forms of evidence. Revenue growth does not itself establish sufficient cash for subsequent investment.
This article already separates demand by payer and task and discusses resource floors for long-term research. The additional check connects funding time to delivery time: an approved resource budget still requires money to arrive when needed and fresh evidence about returns from use. This explains dependencies and risks without giving trading instructions, selecting financing instruments or imposing a universal return threshold. The forum post is a discovery path; the judgments remain attributed to Tan Su Shan’s original interview, rather than presented as independently verified conclusions by Wang Kai.
Source: https://www.semafor.com/article/10/16/2026/the-ai-bet-is-running-straight-into-a-capital-crunch