# Automation Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Automation", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

The AI employee Viktor, developed by a team with DeepMind background, has achieved $20 million in annual revenue without a traditional sales team, serving over 30,000 companies. Its core innovation lies in positioning itself as a "Tier 3 AI Coworker" capable of "end-to-end execution and delivery of results," moving beyond the "draft and wait for human completion" model of typical AI assistants. Users can simply mention Viktor in Slack or Microsoft Teams using natural language commands, and it autonomously performs tasks like pulling sales data from a CRM, generating reports, or even cross-tool operations like creating board meeting PPTs by aggregating data from six different sources. Key to its growth is a pure Product-Led Growth (PLG) model, eliminating complex implementation cycles and per-seat licensing. Instead, it charges based on task credits or consumption, lowering the trial barrier with a $100 free credit offer and no credit card required. This enabled viral, bottom-up adoption within organizations. Viktor's interaction paradigm removes the barrier of prompt engineering, allowing non-technical employees to delegate complex workflows seamlessly. It also features proactive, automated task execution (e.g., overnight bookkeeping, scheduled reports) based on triggers, effectively embedding AI as an automated "process layer" within business operations. However, its expansion into Microsoft Teams—a platform with 320 million users—highlights challenges. Large enterprises require stringent IT compliance, security reviews (e.g., SOC 2), and governance, potentially hindering the frictionless, user-driven adoption that succeeded in Slack. Additionally, the "black box" nature of its autonomous decision-making raises concerns about operational risks, data integrity, and the need for robust audit logs and permission controls. Balancing efficiency gains with security and trust remains a critical hurdle for Viktor and similar AI agents aiming to become core enterprise infrastructure.

marsbit06/19 10:55

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

marsbit06/19 10:55

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

marsbit06/18 12:04

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

marsbit06/18 12:04

NVIDIA Team Enables Programming Agent to Take Over Real Robot Experiments, Achieves 99% Success Rate

NVIDIA's ENPIRE project demonstrates fully automated robotic research where AI agents, given only high-level goals, autonomously manage the entire loop—from literature search and code development to training, deployment, and hardware iteration—on a fleet of physical robots. The system achieves 99% success rates on dexterous real-world tasks like cable tying and peg sorting. Key insights include the discovery of a "physical scaling law" where more parallel robots speed up task resolution, and the observation that resetting an environment is often easier than the main task. The framework introduces metrics like Mean Robot Utilization (MRU) to measure efficiency, with robots often idle half the time, waiting for agent decisions. The long-term vision is a lab that runs autonomously, even without human oversight. The project will be open-sourced, allowing developers to build similar systems.

marsbit06/18 00:35

NVIDIA Team Enables Programming Agent to Take Over Real Robot Experiments, Achieves 99% Success Rate

marsbit06/18 00:35

Do Robots Also Need Encrypted Wallets? Stablecoin Giant Tether Bets on German Company NEURA Robotics

Do Robots Need Crypto Wallets? Stablecoin Giant Tether Bets on German Firm NEURA Robotics German robotics company NEURA Robotics has secured up to $1.4 billion in what is claimed to be the largest-ever funding round in the full-stack robotics industry, valuing the company at $7 billion. The Series C round attracted major investors like Tether, Qualcomm, Amazon, NVIDIA, Bosch, and the European Investment Bank. NEURA, founded in 2019, initially focused on AI-powered collaborative robots (cobots) for industrial automation, later expanding to autonomous mobile robots, service robots, and humanoid robots. Its core strategy is evolving from a hardware manufacturer to the operator of "Neuraverse," a platform designed to enable different robots to share learned experiences and data, creating network effects. A key, crypto-focused aspect of this investment is Tether's involvement. Tether plans to integrate its open-source Wallet Development Kit (WDK) into NEURA's robot platforms. This would embed self-custody wallet functionality, allowing robots to autonomously handle payments and settlements for tasks under pre-set rules—envisioning use cases in logistics or Robotics-as-a-Service (RaaS) models. This move could position stablecoins and crypto wallets as potential "machine payment infrastructure." Additionally, the partnership will see Tether's QVAC (QuantumVerse Automatic Computer) edge-AI framework tested and deployed within Neuraverse. This aims to enable low-latency, offline-capable AI decision-making directly on robots, reducing reliance on cloud computing for critical, time-sensitive operations. The investment underscores Tether's broader ambition to expand beyond being just a stablecoin issuer into AI, energy, and digital infrastructure, with NEURA's robotics network serving as a testbed for merging crypto-based financial layers with edge-based intelligence for the future of automation.

marsbit06/16 09:14

Do Robots Also Need Encrypted Wallets? Stablecoin Giant Tether Bets on German Company NEURA Robotics

marsbit06/16 09:14

Supply Chain, Energy, and Bloc Formation: Untangling the Core Threads of 2026 AI Investment

The core thesis for 2026 investment is a shift from traditional growth-inflation analysis towards a geopolitical framework of strategic blocs, supply chain reconfiguration, and capital expenditure (capex) direction. The US is moving from a global guarantor role towards a more bounded "camp system," reshaping trade, security, and technology flows. Key investment themes emerge from this realignment: production-capable US-aligned economies (e.g., Japan, South Korea), regional security focusing on Latin America, and the hard constraint of energy/grid capacity enabling reshoring. Europe's strength lies not in macro growth but in its high-quality "pick-and-shovel" industrial, electrical, and automation exporters serving the global capex cycle. AI remains the central US-China strategic battleground, driving massive investment in compute, power, and manufacturing stacks, with robotics gaining prominence. The investment implication is a rotation away from crowded US mega-cap tech momentum toward global beneficiaries of this restructuring: electrification equipment, industrial automation, energy storage, grid infrastructure, and select non-US markets. 2026 will be defined by investment intensity driven by geopolitical repositioning, not near-term commercial maturity.

marsbit06/15 11:20

Supply Chain, Energy, and Bloc Formation: Untangling the Core Threads of 2026 AI Investment

marsbit06/15 11:20

$9.4 Billion: The Largest Robotics Funding This Year Has Emerged

Munich-based humanoid robotics company Neura has completed a $1.4 billion (approximately RMB 94.9 billion) Series C funding round, valuing the company at around $7 billion and positioning it among the global leaders in the sector. The investment round is notable not just for its size—reportedly the largest in robotics this year—but also for its strategic backers, which include tech giants like NVIDIA and Amazon, alongside established industrial players such as German engineering firms Bosch and Schaeffler. This mix of investors signals a significant shift in the industry's focus from technological demonstrations and general-purpose narratives toward practical, industrial deployment and commercialization. Neura's approach centers on developing humanoid robots for defined, high-value industrial tasks rather than pursuing a general-purpose model. Its early validation comes from a partnership with BMW, where its robots are being tested on actual production lines. The involvement of Bosch and Schaeffler, companies deeply embedded in global manufacturing, underscores a growing belief that humanoid robots are transitioning from labs to viable factory-floor solutions. The article highlights two converging trends driving investment: advancements in AI and large language models, which enhance robots' perception and decision-making in unstructured environments, and mounting pressure from labor shortages and rising costs in major manufacturing regions. The funding landscape is now bifurcating between companies like Figure AI, focusing on versatile general-purpose robots, and firms like Neura, targeting specific vertical industrial applications with clearer, shorter paths to ROI. While technical hurdles remain, the core challenges for widespread adoption are increasingly seen as engineering and commercial in nature: managing the high integration and customization costs for different factory environments and establishing robust, localized maintenance and service networks. The record investment in Neura, particularly from industrial capital, indicates the industry's growing confidence in moving from proving feasibility to solving the practical problems of scalability, reliability, and building sustainable business models around humanoid robots in real-world settings like automotive manufacturing and hazardous labor environments.

marsbit06/14 02:54

$9.4 Billion: The Largest Robotics Funding This Year Has Emerged

marsbit06/14 02:54

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit06/13 03:32

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit06/13 03:32

The Recursive AI Anthropic Warned About: Tian Yuandong's New Company Has Just Taken the "First Step"

Anthropic recently highlighted the rapid progress toward "recursive self-improvement," where AI systems autonomously design and train their successors. In response, Recursive Superintelligence, a new company co-founded by former Meta researcher Tian Yuan Dong, has publicly demonstrated its first step toward automating AI research. The company released a system designed to autonomously execute the full AI research cycle: generating ideas, implementing code, running experiments, and learning from results. It validated this approach by achieving state-of-the-art results on three diverse benchmarks: 1. **NanoChat Autoresearch:** Optimizing a small language model's validation loss under a fixed 5-minute GPU budget, improving upon the community's best result. 2. **NanoGPT Speedrun:** Reducing the time to train a GPT model to a specific loss on 8 H100 GPUs from 79.7 seconds to 77.5 seconds, beating a highly optimized, human-driven community effort. 3. **SOL-ExecBench:** Improving the overall score on NVIDIA's suite of 235 GPU kernel optimization tasks by 18%, closing the gap to the hardware limit. The system discovered novel optimizations in this highly specialized domain without direct human expertise. Recursive's system operates as a general framework, capable of parallel exploration and cross-task knowledge transfer while incorporating safeguards against reward hacking. The company, backed by $650M in funding and a star-studded team including Richard Socher and Alexey Dosovitskiy, aims to create AI that recursively enhances its own research capabilities. This development represents an early but concrete move toward a new paradigm where AI accelerates its own advancement. It occurs alongside Anthropic's warnings about the need for industry coordination and potential pauses when recursive self-improvement thresholds are reached, highlighting the dual trajectory of rapid technical progress and growing calls for careful stewardship.

marsbit06/12 04:12

The Recursive AI Anthropic Warned About: Tian Yuandong's New Company Has Just Taken the "First Step"

marsbit06/12 04:12

Public Version of Mythos Officially Launched: Analyzing the Advantages and Limitations of AI Smart Contract Auditing

Publicly available Mythos, Anthropic's AI model, has officially launched, demonstrating both significant potential and limitations in smart contract security auditing. The article analyzes its capabilities through real-world cases. AI excels in identifying subtle, low-level vulnerabilities through pattern recognition and large-scale code screening. A key example is detecting a storage slot collision between a custom rewards mapping and a third-party library's ReentrancyGuard, a vulnerability easily missed in manual audits. In the recent Zcash incident, AI also rapidly discovered a critical soundness bug that had remained hidden for years. However, AI currently struggles with complex, interconnected scenarios. When tested on the Curve LlamaLend sDOLA exploit, which involved manipulating prices across multiple protocols (Curve pools, lending markets) to trigger liquidations, Fable 5 failed to identify the core cross-protocol attack vector. These scenarios require a deep understanding of DeFi economic models and multi-contract interactions. In conclusion, while AI tools like Mythos significantly boost efficiency in finding standardized, syntactic vulnerabilities, they cannot yet replace expert analysis for complex, business-logic, and cross-protocol attacks. An effective audit workflow combines AI's speed for initial screening with human expertise for in-depth, holistic analysis.

marsbit06/11 08:06

Public Version of Mythos Officially Launched: Analyzing the Advantages and Limitations of AI Smart Contract Auditing

marsbit06/11 08:06

AI Investors' 2026 Anxiety: When Models Devour Everything, What Moat Is Left for Startups?

In 2026, a wave of investor anxiety questions the defensibility of AI startups as models improve, fearing that most companies are just "thin wrappers" destined to be absorbed by foundation models or chipmakers. The author argues against this despair, positing that true moats lie not in benchmark performance but in areas models cannot easily reach. The logic of despair is that if models excel at all measurable tasks, only compute and cutting-edge model weights hold lasting value. However, the essay contends that the most valuable work is inherently "untrainable." Benchmarks measure what can be measured and thus optimized for, but real-world correctness often resides in private, complex systems. Examples include legacy codebases, intricate legal transactions, or hospital workflows. This kind of correctness is proprietary, costly to establish, and cannot be validated quickly—it requires time and trust within an organization. As models commodify visible, measurable tasks from both above (labs absorbing scaffolding) and below (saturation by cheaper models), value shifts to "untrainable ground." This encompasses work where correctness is a private truth, locked behind integration barriers, licenses, liability frameworks, and entrenched user habits. Trust and adoption are slow, human-centric processes that smarter models cannot accelerate. Successful companies defend their position by embedding deeply into client operations, owning the definition of "good" within a specific domain (e.g., Harvey in law, OpenEvidence in medicine), and pricing on outcomes rather than tokens. While labs compete fiercely, they are incentivized to keep the application layer vibrant. The future belongs not to those competing on generic benchmarks but to those navigating unscoreable terrain, doing the "unsexy work" of translation between models and messy human realities. The most cited benchmark scores are thus maps of territory about to become worthless, signaling who will lose the right to define what counts as good.

marsbit06/11 03:34

AI Investors' 2026 Anxiety: When Models Devour Everything, What Moat Is Left for Startups?