# Automation Related Articles

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6,000 CEOs Admit AI 'Did Nothing', Yet It Was Used to Lay Off 40,000 People in Q1 This Year

A study by the National Bureau of Economic Research (NBER) surveying 6000 executives across four countries reveals that nearly 90% of businesses report AI has had "no measurable impact" on employment or productivity over the past three years. Despite global AI investments exceeding $250 billion in 2024, only 12% of CEOs reported both cost reductions and revenue growth from AI. Contrastingly, in Q1 2026, the industry saw 78,557 tech job cuts, with 47.9% attributed to AI and automation. Critics label this contradiction an "AI version of the Solow Paradox," referencing the visible lack of AI's effect in productivity data amid widespread adoption. While some leaders, like Anthropic’s CEO, predict AI will eliminate half of entry-level white-collar jobs, others accuse firms of "AI washing"—using AI as a pretext for layoffs originally planned due to over-hiring or weak demand. However, companies like IBM and Cognizant are bucking the trend by increasing entry-level hiring and retraining staff to work alongside AI. Economists suggest a "J-curve" effect may be underway, where initial productivity stagnation is followed by significant gains, as was seen with IT in the 1990s. The transition remains challenging, with true productivity improvements expected within 6–12 months.

marsbit04/20 11:05

6,000 CEOs Admit AI 'Did Nothing', Yet It Was Used to Lay Off 40,000 People in Q1 This Year

marsbit04/20 11:05

The Only True AI Token Is USDC

The article argues that the only true "AI token" is the stablecoin USDC (and others like USDT, USDS), not the various AI-themed altcoins promoted in crypto circles. It uses the example of "Felix," an AI agent that autonomously runs a company, generating revenue, paying expenses, and hiring other AI agents, all funded and transacted with stablecoins. The core thesis is that AI agents, which are software-based companies, cannot hold traditional bank accounts due to legal and regulatory barriers. Stablecoins in crypto wallets provide the essential financial infrastructure for these agents to operate independently—receiving payments, paying costs, and transacting without human intervention. The author dismisses the narrative that a specific "AI blockchain" will dominate, comparing public blockchains to utility providers (like UPS or FedEx) that AI agents will indifferently use based on cost and efficiency for each transaction. The real value capture is in the stablecoin layer and critical infrastructure like reputation and orchestration systems, not in speculative AI agent tokens, which have collectively lost significant market value. The article concludes that crypto was built for autonomous software, not humans, and that the massive, overlooked transaction volume of stablecoins is the true indicator of the AI agent economy's growth.

marsbit04/20 06:54

The Only True AI Token Is USDC

marsbit04/20 06:54

Back at the AI Table, Zuckerberg's First Move Is Layoffs?

After a period of lagging behind in the AI race, Meta is reportedly planning significant layoffs, with the first round targeting around 8,000 employees (10% of its global workforce) starting May 20, followed by additional cuts later this year. This move comes just 10 days after Meta’s AI team, Meta Superintelligence Labs (MSL), launched its first in-house AI model, Muse Spark—a multimodal reasoning model with advanced capabilities like tool use and multi-agent orchestration. While Muse Spark is seen as a key step for Meta to re-enter competition with leaders like OpenAI and Google, the company is now focusing on an “AI-driven efficiency” strategy. Internal goals aim for 65% of engineers to have AI assist in writing over 75% of their code by mid-2026, and AI usage is reportedly tied to employee performance reviews and promotions. This trend of AI-driven layoffs isn’t isolated; companies like Amazon, Block, and Snap have also cut jobs citing AI’s role in boosting productivity. However, some firms, like Block, later rehired staff after overestimating AI’s immediate impact, highlighting the challenges in balancing AI integration with workforce stability.

marsbit04/18 03:38

Back at the AI Table, Zuckerberg's First Move Is Layoffs?

marsbit04/18 03:38

Back at the AI Table, Zuckerberg's First Move Is Layoffs?

Meta is reportedly planning significant layoffs, with the first round set for May 20, potentially cutting around 8,000 jobs (10% of its workforce), according to sources. Further layoffs may follow later in the year, with adjustments based on AI developments. This comes just 10 days after Meta’s AI team, led by Alexandr Wang, launched its most powerful AI model yet, Muse Spark—a multimodal reasoning tool seen as key to Meta’s re-entry into the AI race. Despite strong financials, Meta is pushing for a leaner, AI-driven structure, aiming for 65% of engineers to have AI generate over 75% of their code by mid-2026. The company has also tied AI usage to performance reviews and promotions. This trend isn’t isolated: Amazon, Block, and Snap have also cited AI efficiency gains to justify layoffs, though Block later rehired some employees, highlighting potential implementation risks.

Odaily星球日报04/18 03:32

Back at the AI Table, Zuckerberg's First Move Is Layoffs?

Odaily星球日报04/18 03:32

OpenAI Completely Overhauls Codex, Grows Its Own Mouse, Schedules Itself to Outwork Humans

OpenAI has fundamentally transformed Codex from a coding assistant into a versatile AI agent capable of autonomous computer control. The updated Codex can now operate in the background with its own independent cursor, allowing it to run applications like Xcode, test software, and fix bugs without interrupting the user’s workflow. It features a built-in browser for real-time visual editing of web pages, enabling developers to make changes through direct UI interactions. The release also introduces over 90 plugins for tools like JIRA, CircleCI, and SharePoint, integrating end-to-end development and productivity workflows. A new "heartbeat" feature allows Codex to self-schedule tasks, maintain context across sessions, and work autonomously over extended periods. Additional enhancements include memory capabilities, support for handling GitHub code reviews, multi-tab terminals, and built-in file generation for Excel, PDF, and PowerPoint. Currently, advanced computer control is available on macOS (excluding EU/UK users), with Windows support limited to data retrieval. This expansion positions Codex as a foundational step toward OpenAI’s planned "super-app," combining coding, automation, and multi-tool integration into a single platform.

marsbit04/17 11:20

OpenAI Completely Overhauls Codex, Grows Its Own Mouse, Schedules Itself to Outwork Humans

marsbit04/17 11:20

DWF In-Depth Report: AI Outperforms Humans in DeFi Yield Optimization, but Lags 5x Behind in Complex Trading

DWF Ventures' report highlights the growing role of AI agents in DeFi, accounting for nearly 19% of on-chain activity. While agents excel in rule-based tasks like yield optimization—achieving returns of over 9.75% for assets like USDC—they significantly underperform humans in complex trading. In head-to-head competitions, top human traders outperformed the best AI agents by more than 5x. Key factors affecting agent performance include model selection, risk management, position holding time, and leverage levels. The path to full autonomy remains challenging due to infrastructure limitations, trust issues, and risks like Sybil attacks and strategy crowding. As agent adoption grows, robust infrastructure and transparency will be critical for scalability and trust.

marsbit04/17 05:34

DWF In-Depth Report: AI Outperforms Humans in DeFi Yield Optimization, but Lags 5x Behind in Complex Trading

marsbit04/17 05:34

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

Quantitative AI trading platform AlphaNet has raised $10 million in a seed funding round led by Joffre Capital, with participation from Huashan Capital and an Asia-based quantitative trading consortium. The platform, which is preparing for a public launch, aims to level the playing field for retail investors by providing end-to-end automated trading access to institutional-grade quantitative strategies. Its offerings include a strategy marketplace, one-click deployment, and proprietary execution algorithms. Incubated by prop trading firm Tensor Investment, AlphaNet currently features over 30 strategies and plans to open its platform to other quant teams by the end of Q2. The funding brings operational expertise from Joffre Capital, scaling resources from Huashan, and additional strategies from the consortium.

TheNewsCrypto04/16 06:05

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

TheNewsCrypto04/16 06:05

NVIDIA Starts Selling the Method of Making Shovels

NVIDIA is accelerating its own GPU design process with AI tools, reducing tasks that took 8 engineers 1010 months to a single GPU running overnight, while achieving better results. However, it is not using AI to fully design GPUs yet. Instead, it is embedding its technology across the chip industry. A key move is its $2020 billion investment in Synopsys, a leading EDA (Electronic Design Automation) tool provider, and partnerships with Cadence and Siemens. This integrates NVIDIA’s hardware acceleration into essential chip design software, making its GPUs the fastest platform for EDA tools. Competitors like AMD and Intel now face a dilemma: use slower tools or rely on NVIDIA’s hardware to design competing chips. Chinese GPU firms, despite high market valuations, struggle with massive losses and dependence on restricted foreign EDA tools. NVIDIA’s strategy extends from AI-enhanced design to manufacturing, creating a self-reinforcing cycle where its hardware becomes essential for developing faster chips—even for those aiming to compete with it.

marsbit04/15 10:04

NVIDIA Starts Selling the Method of Making Shovels

marsbit04/15 10:04

Can Humans Control AI? Anthropic Conducted an Experiment Using Qwen

Can Humans Control Superintelligent AI? Anthropic’s Experiment with Qwen Models Anthropic conducted an experiment to explore whether humans can supervise AI systems smarter than themselves—a core challenge in AI safety known as scalable oversight. The study simulated a “weak human overseer” using a small model (Qwen1.5-0.5B-Chat) and a “strong AI” using a more powerful model (Qwen3-4B-Base). The goal was to see if the strong model could learn effectively despite imperfect supervision. The key metric was Performance Gap Recovered (PGR). A PGR of 1 means the strong model reached its full potential, while 0 means it was limited by the weak supervisor. Initially, human researchers achieved a PGR of 0.23 after a week of work. Then, nine AI agents (Automated Alignment Researchers, or AARs) based on Claude Opus took over. In five days, they improved PGR to 0.97 through iterative experimentation—proposing ideas, coding, training, and analyzing results. The findings suggest that, in well-defined and automatically scorable tasks, AI can help overcome the supervision gap. However, the methods didn’t generalize perfectly to unseen tasks, and applying them to a production model like Claude Sonnet didn’t yield significant improvements. The study highlights that while AI can automate parts of alignment research, human oversight remains essential to prevent “gaming” of evaluation systems and to handle more complex, real-world problems. Anthropic chose Qwen models for their open-source nature, performance, scalability, and reproducibility—key for rigorous and repeatable experiments. The research demonstrates progress toward automated alignment tools but also underscores that AI supervision remains a nuanced, human-AI collaborative effort.

marsbit04/15 09:28

Can Humans Control AI? Anthropic Conducted an Experiment Using Qwen

marsbit04/15 09:28

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

A Google engineer with 11 years of experience automated 80% of his work using Claude Code and a simple .NET application, reducing his daily work from 8 hours to just 2–3 hours while generating $28,000 in monthly passive income. The key to this transformation lies in three core elements: First, using a structured CLAUDE.md file based on Andrej Karpathy’s principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—reduces Claude’s rule violations from 40% to just 3%. Second, the "Everything Claude Code" system acts as a full AI engineering team, with 27 pre-built agents for planning, reviewing, and executing tasks across multiple AI platforms. Third, a hidden token consumption issue in Claude Code v2.1.100 was identified, where 20,000 extra tokens were silently added, diluting instructions and reducing output quality. A quick fix using npx downgrades the version to avoid this. The automated system enables code generation, testing, and review to run autonomously in 15-minute cycles. The engineer now only reviews output, saving 5–6 hours daily. The setup takes less than 20 minutes, and the return on time investment is significant—potentially saving $10,000–$12,000 monthly for those valuing their time at $100/hour. The article emphasizes that managing AI systems, not just using them, is the new critical skill, enabling a shift from doing work to overseeing automated processes.

marsbit04/15 04:10

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

marsbit04/15 04:10

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