#大佬观点

Vinod Khosla’s warning for India's BPO in the age AI: The traditional IT services and BPO business “will be gone” But India can still win if it shifts to deploying AI. ---- From "SparX by Mukesh Bansal" YouTube channel, (link in comment)

Reuters: India’s biggest private employer TCS's Chairman ‌ AI agents could become as numerous as TCS employees. The Chairman said: "Some of the work being done will go to AI agents. That will be the ​nature of the transition that we have to go through ​not only ⁠as a company, as an industry, and as a country" . TCS has already announced 12K job cuts, reported $2.3B in annualised AI revenue, and has an OpenAI data-centre agreement, so this is not just talk from management. TCS has about 600K workers, so Natarajan Chandrasekaran’s claim points to a future where software tasks are handled by hundreds of thousands of digital agents rather than only human engineers. The company expects hiring to fall as AI takes over more coding, testing, support, maintenance, and back-office work, although Chandrasekaran also said new AI-related roles will appear. This hits India’s $315B IT services model hard because firms like TCS and Infosys grew by supplying large teams of lower-cost engineers to global companies. AI weakens that model because one agent can perform repeatable software work at scale, reducing the need to keep expanding human headcount for every new contract. --- IMO, India’s whole $315B tech-services industry was built on sending work to lower-cost human teams, and even recent outsourcing guides still describe offshore BPO as attractive mainly because staff costs are lower than in Europe and the US. If AI agents can do that same repeatable coding, testing, IT support, finance processing, or customer-service work, the buyer no longer needs India as the labor location, because the “worker” can run inside a US or European cloud stack, under local data rules, closer to the company’s own systems. That means the BPO threat is not only job loss at TCS; it is a possible collapse of the old outsourcing bargain, where cost savings came from moving work to cheaper people, while the new bargain may come from replacing the location advantage with software automation, especially when TCS itself says AI agents could match its human workforce --- reuters .com/world/india/indias-tcs-chairman-expects-ai-agents-equal-employee-count-2026-06-09/

What can a neuron compute? Real biological neurons are complex, but how capable are they? Using a new method, we found that a single cortical neuron can classify cats vs dogs, recognize spoken words, and solve 10-bit parity, all tasks thought to require entire networks. (1/15)

Everyone says the latest AI agents will be "job-ready" soon, especially after the release of Fable 5 this week. But is that really the case? Over the past many months, my group and collaborators have been building Agents' Last Exam (ALE), a benchmark designed to test exactly that claim on real digital labor-market work. My group and collaborators previously have created many of the benchmarks the field runs on, including MMLU, MATH, CyberGym, and ExploitGym. Today, I'm excited to share Agents' Last Exam (ALE): a rolling benchmark that measures whether AI agents can actually perform economically valuable work across a broad range of real-world domains. With ALE, we evaluated Fable 5, GPT-5.5, Composer 2.5, and other frontier agent systems across more than 1,500 expert-sourced tasks spanning 55 occupations. The result is both impressive and sobering. Today's agents can solve a meaningful fraction of professional tasks. But when we look at the hardest tasks, the ones requiring sustained reasoning, deep domain expertise, and reliable execution over long horizons, they are still far from human-level performance. On ALE's hardest tier, every frontier agent we tested, including Fable 5, achieved a 0% success rate. The age of useful agents is here. The age of truly job-ready agents is not. We hope Agents' Last Exam (ALE) will serve as a new guidepost and north star for developing agents capable of reliably performing economically valuable work across a broad range of domains. 🧵

Dario Amodei just published a super long blog, calling for an urgent policy overhaul because he thinks frontier AI is moving faster than governments can regulate it. He wants: - Mandatory pre-release testing and independent auditing of frontier AI models, with government power to block deployment when models pose serious cyber, biological, autonomy, or automated-R&D risks. - Stronger security rules for AI companies, including protection of model weights, regular red-teaming, penetration testing, and rapid reporting of critical safety incidents. - He wants governments to prepare for AI-driven labor disruption through better measurement, pro-employment incentives, wage support, training, and possibly long-term income support funded by AI-driven growth. - Democracies should coordinate globally on AI safety, chip supply chains, export controls, shared benefits, mutual defense, and safeguards against AI-powered repression.

Dario Amodei just published a super long blog, calling for an urgent policy overhaul because he thinks frontier AI is moving faster than governments can regulate it. He wants: - Mandatory pre-release testing and independent auditing of frontier AI models, with government power to block deployment when models pose serious cyber, biological, autonomy, or automated-R&D risks. - Stronger security rules for AI companies, including protection of model weights, regular red-teaming, penetration testing, and rapid reporting of critical safety incidents. - He wants governments to prepare for AI-driven labor disruption through better measurement, pro-employment incentives, wage support, training, and possibly long-term income support funded by AI-driven growth. - Democracies should coordinate globally on AI safety, chip supply chains, export controls, shared benefits, mutual defense, and safeguards against AI-powered repression.

今天我发表了一篇新文章，《Policy on the AI Exponential》。AI 的进展极其迅速——远比政策流程原本设计所能应对的速度快得多。这篇文章阐述了我认为这项技术目前所处的位置，以及为弥合这一差距所需的行动：https://darioamodei.com/post/policy-on-the-ai-exponential

今天我发表了一篇新文章，《Policy on the AI Exponential》。AI 的进展极其迅速——远比政策流程原本设计所能应对的速度快得多。这篇文章阐述了我认为这项技术目前所处的位置，以及为弥合这一差距所需的行动：https://darioamodei.com/post/policy-on-the-ai-exponential