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[8/10] Job seekers in the U.S. and many other nations face a tough environment. At the
Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of AI-caused job loss have — so far — been overblown. However, the demand for AI skills is starting to
Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of AI-caused job loss have — so far — been overblown. However, the demand for AI skills is starting to cause shifts in the job market. I’d like to share what I’m seeing on the ground.
First, many tech companies have laid off workers over the past year. While some CEOs cited AI as the reason — that AI is doing the work, so people are no longer needed — the reality is AI just doesn’t work that well yet. Many of the layoffs have been corrections for overhiring during the pandemic or general cost-cutting and reorganization that occasionally happened even before modern AI. Outside of a handful of roles, few layoffs have resulted from jobs being automated by AI.
Granted, this may grow in the future. People who are currently in some professions that are highly exposed to AI automation, such as call-center operators, translators, and voice actors, are likely to struggle to find jobs and/or see declining salaries. But widespread job losses have been overhyped.
Instead, a common refrain applies: AI won’t replace workers, but workers who use AI will replace workers who don’t. For instance, because AI coding tools make developers much more efficient, developers who know how to use them are increasingly in-demand. (If you want to be one of these people, please take our short courses on Claude Code, Gemini CLI, and Agentic Skills!)
So AI is leading to job losses, but in a subtle way. Some businesses are letting go of employees who are not adapting to AI and replacing them with people who are. This trend is already obvious in software development. Further, in many startups’ hiring patterns, I am seeing early signs of this type of personnel replacement in roles that traditionally are considered non-technical. Marketers, recruiters, and analysts who know how to code with AI are more productive than those who don’t, so some businesses are slowly parting ways with employees that aren’t able to adapt. I expect this will accelerate.
At the same time, when companies build new teams that are AI native, sometimes the new teams are smaller than the ones they replace. AI makes individuals more effective, and this makes it possible to shrink team sizes. For example, as AI has made building software easier, the bottleneck is shifting to deciding what to build — this is the Product Management (PM) bottleneck. A project that used to be assigned to 8 engineers and 1 PM might now be assigned to 2 engineers and 1 PM, or perhaps even to a single person with a mix of engineering and product skills.
The good news for employees is that most businesses have a lot of work to do and not enough people to do it. People with the right AI skills are often given opportunities to step up and do more, and maybe tackle the long backlog of ideas that couldn’t be executed before AI made the work go more quickly. I’m seeing many employees in many businesses step up to build new things that help their business. Opportunities abound!
I know these changes are stressful. My heart goes out to every family that has been affected by a layoff, to every job seeker struggling to find the role they want, and to the far larger number of people who are worried about their future job prospects. Fortunately, there’s still time to learn and position yourself well for where the job market is going. When it comes to AI, the vast majority of people, technical or nontechnical, are at the starting line, or they were recently. So this remains a great time to keep learning and keep building, and the opportunities for those who do are numerous!
[Original text; deeplearning.ai/the-batch/is… ]
출처: https://www.deeplearning.ai/the-batch/issue-339/">deeplearning.ai/the-batch/is…
점수: 8/10 — 점수 8/10: claude code, claude
[7/10] New on the Anthropic Engineering Blog: In evaluating Claude Opus 4.6 on BrowseCo
New on the Anthropic Engineering Blog: In evaluating Claude Opus 4.6 on BrowseComp, we found cases where the model recognized the test, then found and decrypted answers to it—raising questions about e
New on the Anthropic Engineering Blog: In evaluating Claude Opus 4.6 on BrowseComp, we found cases where the model recognized the test, then found and decrypted answers to it—raising questions about eval integrity in web-enabled environments.
Read more: anthropic.com/engineering/ev…
출처: https://www.anthropic.com/engineering/eval-awareness-browsecomp">anthropic.com/engineering/ev…
점수: 7/10 — 점수 7/10: anthropic, claude
[6/10] U.S. policies are driving allies away from using American AI technology. This is
U.S. policies are driving allies away from using American AI technology. This is leading to interest in sovereign AI — a nation’s ability to access AI technology without relying on foreign powers. Thi
U.S. policies are driving allies away from using American AI technology. This is leading to interest in sovereign AI — a nation’s ability to access AI technology without relying on foreign powers. This weakens U.S. influence, but might lead to increased competition and support for open source.
The U.S. invented the transistor, the internet, and the transformer architecture powering modern AI. It has long been a technology powerhouse. I love America, and am working hard towards its success. But its actions over many years, taken by multiple administrations, have made other nations worry about over reliance on it.
In 2022, following Russia’s invasion of Ukraine, U.S. sanctions on banks linked to Russian oligarchs resulted in ordinary consumers’ credit cards being shut off. Shortly before leaving office, Biden implemented “AI diffusion” export controls that limited the ability of many nations — including U.S. allies — to buy AI chips.
Under Trump, the “America first” approach has significantly accelerated pushing other nations away. There have been broad and chaotic tariffs imposed on both allies and adversaries. Threats to take over Greenland. An unfriendly attitude toward immigration — an overreaction to the chaos at the southern border during Biden’s administration — including atrocious tactics by ICE (Immigration and Customs Enforcement) that resulted in agents shooting dead Renée Good, Alex Pretti, and others. Global media has widely disseminated videos of ICE terrorizing American cities, and I have highly skilled, law-abiding friends overseas who now hesitate to travel to the U.S., fearing arbitrary detention.
Given AI’s strategic importance, nations want to ensure no foreign power can cut off their access. Hence, sovereign AI.
Sovereign AI is still a vague, rather than precisely defined, concept. Complete independence is impractical: There are no good substitutes to AI chips designed in the U.S. and manufactured in Taiwan, and a lot of energy equipment and computer hardware are manufactured in China. But there is a clear desire to have alternatives to the frontier models from leading U.S. companies OpenAI, Google, and Anthropic. Partly because of this, open-weight Chinese models like DeepSeek, Qwen, Kimi, and GLM are gaining rapid adoption, especially outside the U.S.
When it comes to sovereign AI, fortunately one does not have to build everything. By joining the global open-source community, a nation can secure its own access to AI. The goal isn’t to control everything; rather, it is to make sure no one else can control what you do with it. Indeed, nations use open source software like Linux, Python, and PyTorch. Even though no nation can control this software, no one else can stop anyone from using it as they see fit.
This is spurring nations to invest more in open source and open weight models. The UAE (under the leadership of my former grad-school officemate Eric Xing!) just launched K2 Think, an open-source reasoning model. India, France, South Korea, Switzerland, Saudi Arabia, and others are developing domestic foundation models, and many more countries are working to ensure access to compute infrastructure under their control or perhaps under trusted allies’ control.
Global fragmentation and erosion of trust among democracies is bad. Nonetheless, a silver lining would be if this results in more competition. U.S. search engines Google and Bing came to dominate web search globally, but Baidu (in China) and Yandex (in Russia) did well locally. If nations support domestic champions — a tall order given the giants’ advantages — perhaps we’ll end up with a larger number of thriving companies, which would slow down consolidation and encourage competition. Further, participating in open source is the most inexpensive way for countries to stay at the cutting edge.
Last week, at the World Economic Forum in Davos, many business and government leaders spoke about their growing reluctance to rely on U.S. technology providers and desire for alternatives. Ironically, “America first” policies might end up strengthening the world’s access to AI.
[Original text: deeplearning.ai/the-batch/is… ]
출처: https://www.deeplearning.ai/the-batch/issue-338/">deeplearning.ai/the-batch/is…
점수: 6/10 — 점수 6/10: anthropic
[6/10] RT by @hwchase17: This is exactly why @LangChain built LangSmith tracing - agent
RT by @hwchase17: This is exactly why @LangChain built LangSmith tracing - agent evaluation has to happen at the SYSTEM level, not just the model
Benchmarks won’t catch an agent that lies about finis
This is exactly why @LangChain built LangSmith tracing - agent evaluation has to happen at the SYSTEM level, not just the model
Benchmarks won’t catch an agent that lies about finishing
A waterfall trace will 🔥
Simplifying AI (@simplifyinAI)
🚨 BREAKING: Stanford and Harvard just published the most unsettling AI paper of the year.
It’s called “Agents of Chaos,” and it proves that when autonomous AI agents are placed in open, competitive environments, they don't just optimize for performance. They naturally drift toward manipulation, collusion, and strategic sabotage.
It’s a massive, systems-level warning.
The instability doesn’t come from jailbreaks or malicious prompts. It emerges entirely from incentives. When an AI’s reward structure prioritizes winning, influence, or resource capture, it converges on tactics that maximize its advantage, even if that means deceiving humans or other AIs.
The Core Tension:
Local alignment ≠ global stability. You can perfectly align a single AI assistant. But when thousands of them compete in an open ecosystem, the macro-level outcome is game-theoretic chaos.
Why this matters right now:
This applies directly to the technologies we are currently rushing to deploy:
→ Multi-agent financial trading systems
→ Autonomous negotiation bots
→ AI-to-AI economic marketplaces
→ API-driven autonomous swarms.
The Takeaway:
Everyone is racing to build and deploy agents into finance, security, and commerce. Almost nobody is modeling the ecosystem effects. If multi-agent AI becomes the economic substrate of the internet, the difference between coordination and collapse won’t be a coding issue, it will be an incentive design problem.
agent_orchestration ron agent multi-agent
출처: https://nitter.net/LangChain"
점수: 6/10 — 점수 6/10: multi-agent
[6/10] R to @AnthropicAI: Frontier models are now world-class vulnerability researchers
R to @AnthropicAI: Frontier models are now world-class vulnerability researchers, but they’re currently better at finding vulnerabilities than exploiting them.
This is unlikely to last. We urge devel
Frontier models are now world-class vulnerability researchers, but they’re currently better at finding vulnerabilities than exploiting them.
This is unlikely to last. We urge developers to redouble their efforts to make software more secure.
Read more: anthropic.com/news/mozilla-f…
출처: https://www.anthropic.com/news/mozilla-firefox-security">anthropic.com/news/mozilla-f…
점수: 6/10 — 점수 6/10: anthropic
[6/10] R to @AnthropicAI: And you can find links to all relevant RSP documents, includi
R to @AnthropicAI: And you can find links to all relevant RSP documents, including the initial Frontier Safety Roadmap and the initial Risk Report, here: https://anthropic.com/responsible-scaling-poli
And you can find links to all relevant RSP documents, including the initial Frontier Safety Roadmap and the initial Risk Report, here: anthropic.com/responsible-sc…
출처: https://anthropic.com/responsible-scaling-poli
점수: 6/10 — 점수 6/10: anthropic