# 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.

Interview with 7 Ordinary Professionals: After AI Arrived, How Are You Doing?

This article interviews seven professionals from diverse fields like Web3, bulk chemical trading, digital agriculture, and traditional wholesale to examine the impact of AI on their work. Key themes emerge from the discussions. AI has become integral to their workflows, primarily for increasing efficiency in tasks such as coding, content creation, research, and data analysis. Individuals across roles, from developers to managers, report that AI tools like ChatGPT and Claude have significantly reduced workloads and accelerated learning, creating opportunities for "super individuals" or one-person teams. However, this efficiency comes with a double-edged sword. It intensifies competition, pushing professionals to constantly learn new tools and adapt, leading to widespread anxiety about job security and a heightened pressure to keep pace. Interviewees anticipate significant job reductions in roles like administrative support, finance, HR, customer service, and some creative fields. A recurring view is that AI acts as a "great equalizer," amplifying the capabilities of those who use it effectively while leaving others behind, potentially deepening polarization. Despite AI's capabilities, interviewees identify enduring human strengths. AI struggles with tasks requiring deep contextual understanding, complex judgment in areas like risk assessment and system stability (especially in finance/Web3), nuanced human communication, and handling exceptions in logistics and manufacturing. These areas remain firmly in the human domain. Consequently, many professionals are refocusing their career strategies. They plan to evolve from task executors into "complex system owners," "super coordinators" managing AI agents, or specialists in high-level areas like business context, risk control, product design, and personal branding. In summary, the article portrays AI not as an optional tool but as a transformative force reshaping job demands. While it automates routine work, it also creates new forms of pressure and competition. The future, as seen by these professionals, belongs to those who can strategically integrate AI to augment uniquely human skills like judgment, responsibility, and strategic oversight.

marsbit06/01 08:17

Interview with 7 Ordinary Professionals: After AI Arrived, How Are You Doing?

marsbit06/01 08:17

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

After burning tens of billions of dollars on AI tokens, major Silicon Valley firms are now restricting employee usage. Companies like Microsoft, Uber, and Salesforce, which heavily promoted AI for "efficiency," are facing a cost crisis. The practice of "tokenmaxxing"—pushing employees to maximize AI tool usage—led to wasteful spending on trivial tasks like checking the weather or writing birthday messages, with studies showing significant hidden costs for bug fixes and code rewrites. The core issue is a misalignment between individual productivity gains and actual business value. While employees use AI to automate tasks they dislike, such as writing reports, this often doesn't translate to increased company revenue or improved core business outcomes. For instance, AI-generated code speeds up development but also sees an 800% increase in "code churn" (code being discarded or rewritten). As a result, only 14% of CFOs report seeing a clear, measurable return on AI investments. Firms are now shifting strategies. Microsoft has revoked most internal licenses for Claude Code, while others are implementing monitoring and cost controls. New tools from companies like Harness and CloudZero aim to track AI spending and tie costs to business results. Some AI vendors, like HubSpot, are moving from token-based pricing to charging based on outcomes, such as "resolved conversations" or "leads generated." This represents a necessary correction in the AI adoption cycle. The challenge now is for companies to move beyond using AI merely to speed up old tasks and instead rethink their workflows and business models fundamentally. The future of enterprise AI depends on proving its value, not just its usage.

marsbit06/01 04:06

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

marsbit06/01 04:06

Solo Company Craze: Some Earn Millions Annually, Others See Incomes Shrink by 90%

The Rise of the "One-Person Company" (OPC): AI Fuels a Solo Entrepreneurship Wave The concept of the "One-Person Company" (OPC)—where an individual leverages AI tools to start and run a business—is gaining significant traction, hailed by some as ushering in a "golden age" for solo entrepreneurship. While success stories abound, the reality is a mixed picture of high earnings and significant struggles. The article profiles several OPC founders across different industries: * A game developer created 6 bullet-chat (danmaku) games in a year using an AI-powered workflow, earning approximately 1 million RMB. AI handled around 70% of art and 99% of coding tasks, slashing development cycles from months to about 15 days per game. * A materials researcher in Japan, using AI for tasks from translation to legal advice, earns roughly triple the salary of a local white-collar worker. * A biotech entrepreneur uses AI Agents to automate 80% of repetitive work like data analysis, doubling their previous income while gaining time freedom. * Conversely, a former tech executive turned cross-border e-commerce founder in Latin America reports a 90% drop in income compared to their previous corporate job, cautioning against blindly following the trend. Key insights from these cases include: AI dramatically lowers barriers to entry and operational costs, but does not guarantee success. It excels at automating repetitive tasks but cannot replace core human skills like creativity, project management, judgment, and client acquisition. Industry experience and existing client/resources remain critical advantages. The model suits self-starters with specific expertise but poses challenges in areas like sales, compliance, and scaling. Ultimately, while AI empowers solo ventures, entrepreneurship's inherent risks and demands persist.

marsbit06/01 02:48

Solo Company Craze: Some Earn Millions Annually, Others See Incomes Shrink by 90%

marsbit06/01 02:48

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbit05/31 12:33

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbit05/31 12:33

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

Cognition AI, the company behind the AI programmer "Devin," has raised over $1 billion in new funding at a valuation of $26 billion, just eight months after reaching a $10.2 billion valuation. The round was led by Lux Capital, General Catalyst, and 8VC. Founded by three young Chinese entrepreneurs with strong competitive programming backgrounds, Cognition initially gained fame with Devin, marketed as the world's first AI software engineer capable of handling tasks from start to finish. While its early demos were impressive, real-world usage revealed reliability and cost-effectiveness issues, leading to a significant price cut for Devin in 2025. A pivotal moment came when Cognition acquired the assets of AI IDE company Windsurf after a failed acquisition by OpenAI. This move gave Cognition a crucial developer-facing tool, allowing it to pursue a two-pronged strategy: Devin for autonomous task execution and Windsurf for integrated, collaborative coding within an IDE. This shift helped the company move away from the controversial "AI replacement" narrative towards a model of augmenting human engineers, particularly for repetitive or maintenance tasks. This strategic pivot is backed by strong commercial metrics. The company reports a 10x increase in enterprise usage this year, with an annual revenue run-rate of $492 million and a 50% month-over-month growth in enterprise Devin usage over the past six months. Its client list now includes major corporations like Goldman Sachs and Mercedes-Benz, as well as government agencies like NASA and the U.S. Army. Investors are betting on Cognition becoming a foundational piece of next-generation software engineering infrastructure, positioning it at the center of a hybrid future where AI agents and human developers work in tandem.

marsbit05/31 10:22

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

marsbit05/31 10:22

In the Era of Agent Users, Where Does Crypto Value Flow?

Title: Who Makes Money from Agents? The rise of AI Agents as potential blockchain users raises a crucial question: if they become the next billion users, who will capture the value? Traditional crypto value capture theories—like "fat protocols" (where value accrues to the base layer) and "fat applications" (where value accrues to user-facing apps)—assume human users who value UX, brand, and convenience. Agents, however, operate differently: they interact via APIs, have no brand loyalty, and can switch services with near-zero cost. This shift could disrupt existing value flows. Applications might become "headless," offering their routing and infrastructure as APIs to Agents. Alternatively, Agents might bypass intermediaries entirely, allowing protocols to regain value capture ("fat protocols" reborn). A more extreme scenario is that Agents, being purely rational and cost-sensitive, could commoditize the entire stack, compressing margins toward marginal cost and turning crypto into a low-margin utility. However, Agents may not just amplify existing activities; they could enable entirely new ones—like continuous, sub-penny portfolio rebalancing, machine-to-machine commerce, and new market types only viable at automated speeds. This expands the economic pie rather than just redistributing it. Ultimately, the key question for builders is: what will make an Agent return to your service instead of a cheaper alternative? The answer may not be UX but factors like liquidity, latency, settlement guarantees, or a yet-unnamed business model. As humans and Agents will coexist as users, value capture may split: "fat apps" for human-facing services, and a new, evolving model for the Agent-dominated layer.

marsbit05/28 08:31

In the Era of Agent Users, Where Does Crypto Value Flow?

marsbit05/28 08:31

The AI Industrial Revolution: Where Are We Now?

This article explores the current stage of the AI industrial revolution, arguing we are still merely attaching new tools to old workflows rather than fundamentally redesigning production. The author compares this to the early Industrial Revolution, where factories simply replaced waterwheels with steam engines without changing their core structure. Similarly, today we embed AI chat windows into existing software but leave organizational processes unchanged. While massive investment floods into AI infrastructure (data centers, chips), akin to railway manias of the past, the real transformation lies in "dismantling the old workshop"—reorganizing companies around AI. Examples include Notion's use of hundreds of AI Agents and Y Combinator's experiments with self-improving AI systems that operate autonomously. The author notes a critical gap: while China has vast AI user growth, few companies have rebuilt core workflows. AI is beginning to impact entry-level jobs, and early adopters are gaining a compounding advantage. The conclusion is that the pivotal moment will not be the invention of better models, but when organizations decide to tear down old structures and rebuild around AI, shifting the bottleneck from human coordination to computing power. The future workplace and job titles are yet to be defined, but the imperative is to move away from legacy processes and position oneself where the new "railway" is being built.

marsbit05/27 01:32

The AI Industrial Revolution: Where Are We Now?

marsbit05/27 01:32

Goldman Sachs CEO: The 'AI Employment Apocalypse' Is Overstated

Goldman Sachs CEO David M. Solomon argues that fears of an AI-driven "job apocalypse" are overblown. While AI will significantly impact the labor market—automating up to 25% of current work hours, particularly routine tasks in white-collar professions like accounting, banking, and law—it is more likely to transform jobs than eliminate them en masse. Historical trends show that technological disruptions, from electrification to the digital revolution, have ultimately led to job creation and economic adaptation. Solomon notes that AI is already generating new roles in areas like data center construction and AI workflow management. The key challenge is not mass unemployment, but ensuring that social, educational, and corporate systems help workers transition into more complex, judgment-based roles. He calls for a combined public-private effort to support reskilling and the development of AI that augments rather than replaces human labor.

marsbit05/26 07:01

Goldman Sachs CEO: The 'AI Employment Apocalypse' Is Overstated

marsbit05/26 07:01

CoinQuant Introduces Trading Infrastructure for the Agent Economy

CoinQuant, an AI-powered no-code trading platform with over 15,000 users, is expanding its infrastructure to serve the emerging "agent economy." The company is introducing a unified trading intelligence architecture designed for both human traders and autonomous AI agents. This system acts as a trust layer, ensuring all trading strategies—whether human-created or AI-generated—undergo rigorous validation, backtesting, and risk analysis before live deployment. The core of the platform is an intelligence engine that combines institutional-grade backtesting, structured market data, AI optimization, and a proprietary Domain Expert system. Human traders interact via a natural language interface, while AI agents connect through APIs. Every validated strategy contributes to an anonymized aggregate intelligence layer. CoinQuant plans to launch an automated execution layer on HyperLiquid and is currently raising a $3 million Seed round to scale its product and infrastructure, including the development of HYDRA, a hierarchical multi-agent architecture. The company aims to become the foundational intelligence backbone for algorithmic trading in the agent-driven financial era.

TheNewsCrypto05/26 05:57

CoinQuant Introduces Trading Infrastructure for the Agent Economy

TheNewsCrypto05/26 05:57

The Paradox of Automation: The Stronger the AI, the Busier Humans Become

The Paradox of Automation: The more powerful AI becomes, the more work humans have to do. This article, based on observations from AI-heavy company Every, argues that while AI agents automate tasks like coding, writing, and customer service, they don't eliminate human jobs. Instead, they transform work and create *more* demand for human expertise. AI commoditizes "yesterday's human capabilities" by cheaply generating code, text, and images from past data. This leads to an abundance of similar, generic outputs. Consequently, what becomes scarce and valuable is human judgment in the present moment: knowing *what* is worth doing, *why*, and *how* to do it well. The article identifies two collaboration models: "Agent employees" for delegated tasks and "human-AI collaboration" within tools like Claude Code for complex work. In both cases, humans are essential to set direction, judge quality, and maintain systems. As AI makes execution cheap, human roles shift from executors to designers, reviewers, and meaning-makers. The author addresses "benchmark anxiety" by explaining that AI excels within specific, human-defined problem "frames." As AI masters one frame (e.g., code rewriting), new, more complex frames emerge (e.g., deciding *when* to rewrite). This creates an ongoing cycle where AI chases the frames, but humans remain the "framers." Even with advanced AGI, this dynamic may persist as long as AI lacks true human-like agency and self-directed purpose. The core paradox holds: automation amplifies the need for the very human judgment it seems to replace.

marsbit05/24 07:06

The Paradox of Automation: The Stronger the AI, the Busier Humans Become